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Ao, Kevin
Doctor of Philosophy - PhD
University of British Columbia
Plants possess a complex multi-layered immune system that enables them to defend themselves against many potential invaders. However, some virulent pathogens can overcome these defenses to cause plant disease. Plant pathogens are a major obstacle to efficient and sustainable global food production. Understanding the underlying molecular mechanisms behind plant immunity will allow precise science-based engineering and breeding of plant disease resistance. Nucleotide-binding leucine-rich repeat proteins (NLRs) are one type of immune receptor that mediate the recognition of pathogens and the activation of defense responses. Although many regulators contributing to NLR homeostasis and activation have been uncovered, our understanding of plant immune signaling is far from complete. One limitation that has hindered gene discovery is genetic redundancy, which can mask immune phenotypes in single mutant plants. Using ectopic overexpression and CRISPR/Cas9-mediated targeted deletion strategies for reverse genetic screens, these limitations can be overcome. In this study, two novel regulators involved in NLR immunity were characterized. The first gene, called SNIPER7 (SNC1-INFLUENCING PLANT E3 LIGASE REVERSE GENETIC 7), was isolated from a previous E3 ubiquitin ligase overexpression screen. SNIPER7 overexpression results in constitutive activation of NLR-mediated defense. SNIPER7 targets the conserved unfoldase CDC48A (CELL DIVISION CYCLE 48A) for degradation. Since CDC48A is involved in the homeostasis of the NLR SNC1 (SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1), SNIPER7 may contribute to immune receptor accumulation upon pathogen infection. The second gene, called TC1b (TRAF CANDIDATE 1b), was isolated from a new CRISPR/Cas9 screen targeting TRAF (TUMOR NECROSIS FACTOR RECEPTOR ASSOCIATED FACTOR) domain containing genes. TRAF domain genes are hypothesized to play a role in plant NLR immunity since many mammalian TRAF domain proteins are involved in innate immunity. TC1b is required for full SNC1 function, but TC1b does not seem to play a role in SNC1 protein homeostasis nor general NLR downstream signalling. The precise molecular function of TC1b is still unclear. Overall, the study of SNIPER7 and TC1b contributes to knowledge of how NLRs like SNC1 are regulated. These findings may be relevant for the precise engineering of NLRs to mediate disease resistance while avoiding growth/yield trade-off from NLR overexpression.
CHARACTERIZATION OF TWO REGULATORS IN PLANT IMMUNE RECEPTOR-MEDIATED DEFENSE by KEVIN AO B.Sc. (Honours), The University of British Columbia, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2022 © Kevin Ao, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Characterization of Two Regulators in Plant Immune Receptor-Mediated Defense submitted by Kevin Ao in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Botany Examining Committee: Dr. Xin Li, Professor, Botany, UBC Supervisor Dr. Marcel Wiermer, Professor, Biochemistry of Plant-Microbe Interactions, Georg-August-University Göttingen Supervisory Committee Member Dr. James Kronstad, Professor, Microbiology and Immunology, UBC University Examiner Dr. Susan Murch, Professor, Biochemistry and Molecular Biology, Chemistry, UBC-Okanagan University Examiner Dr. Ping He, Professor, Biochemistry & Biophysics, Texas A&M University External Examiner Additional Supervisory Committee Members: Dr. Ljerka Kunst, Professor, Botany, UBC Supervisory Committee Member Dr. Cara Haney, Associate Professor, Microbiology and Immunology, UBC Supervisory Committee Member iii Abstract Plants possess a complex multi-layered immune system that enables them to defend themselves against many potential invaders. However, some virulent pathogens can overcome these defenses to cause plant disease. Plant pathogens are a major obstacle to efficient and sustainable global food production. Understanding the underlying molecular mechanisms behind plant immunity will allow precise science-based engineering and breeding of plant disease resistance. Nucleotide-binding leucine-rich repeat proteins (NLRs) are one type of immune receptor that mediate the recognition of pathogens and the activation of defense responses. Although many regulators contributing to NLR homeostasis and activation have been uncovered, our understanding of plant immune signaling is far from complete. One limitation that has hindered gene discovery is genetic redundancy, which can mask immune phenotypes in single mutant plants. Using ectopic overexpression and CRISPR/Cas9-mediated targeted deletion strategies for reverse genetic screens, these limitations can be overcome. In this study, two novel regulators involved in NLR immunity were characterized. The first gene, called SNIPER7 (SNC1-INFLUENCING PLANT E3 LIGASE REVERSE GENETIC 7), was isolated from a previous E3 ubiquitin ligase overexpression screen. SNIPER7 overexpression results in constitutive activation of NLR-mediated defense. SNIPER7 targets the conserved unfoldase CDC48A (CELL DIVISION CYCLE 48A) for degradation. Since CDC48A is involved in the homeostasis of the NLR SNC1 (SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1), SNIPER7 may contribute to immune receptor accumulation upon pathogen infection. The second gene, called TC1b (TRAF CANDIDATE 1b), was isolated from a new CRISPR/Cas9 screen targeting TRAF (TUMOR NECROSIS FACTOR RECEPTOR ASSOCIATED FACTOR) domain containing genes. TRAF domain genes are iv hypothesized to play a role in plant NLR immunity since many mammalian TRAF domain proteins are involved in innate immunity. TC1b is required for full SNC1 function, but TC1b does not seem to play a role in SNC1 protein homeostasis nor general NLR downstream signalling. The precise molecular function of TC1b is still unclear. Overall, the study of SNIPER7 and TC1b contributes to knowledge of how NLRs like SNC1 are regulated. These findings may be relevant for the precise engineering of NLRs to mediate disease resistance while avoiding growth/yield trade-off from NLR overexpression. v Lay Summary Plants provide tremendous dietary and medicinal value for society but are under constant threat of infection by plant pathogens. Plants can defend themselves by using immune receptors that mediate the recognition of pathogens and the activation of anti-microbial defenses. The molecular pathways involved in the regulation of receptors is not fully understood. In this study, two genes that play a role in immune receptor function were identified and characterized. The first gene, called SNIPER7, contributes to the accumulation of immune receptors and may function to boost immune responses during infection. The second gene, called TC1b, is required for the full function of an immune receptor called SNC1. However, TC1b does not affect SNC1 accumulation or downstream signalling. These findings provide a better understanding of the molecular mechanisms underlying the precise control of immune receptors and may inform the engineering of receptors in crop plants. vi Preface The research presented in this thesis was performed between May 2015 and April 2022. Chapter 2 and portions of Chapter 1 and 4 have now been published. Chapter 3 has been submitted for publication. Specific details including personal contributions are provided below. Ao, K., Li, X. (2022). Indirect recognition of pathogen effectors by NLRs. Essays Biochem Advance Access published May 10, 2022, doi:10.1042/EBC20210097. Substantial text and figures were modified from this publication to be included in Chapter 1. Smaller excerpts from this text were used in Chapter 4. KA prepared figures and drafted the manuscript. XL organized and revised the manuscript. Both authors approved the final version of the manuscript. Liu, X.*, Ao, K.*, Yao, J., Zhang, Y., and Li, X. (2021). Engineering plant disease resistance against biotrophic pathogens. Curr Opin Plant Biol 60:101987 (*These authors contributed equally). Minor excerpts and the main figure were taken and modified from this publication and included in Chapter 1. XLiu and JY drafted the original manuscript. KA prepared figures and reorganized and substantially rewrote the final version of manuscript to fit journal themes in collaboration with XLiu. YZ and XLi revised the manuscript. All authors approved the final published version. vii Ao, K., Tong, M., Li, L., Lüdke, D., Lipka, V., Chen, S., Wiermer, M., and Li, X. (2021). SCFSNIPER7 controls protein turnover of unfoldase CDC48A to promote plant immunity. New Phytol 229:2795–2811. Text and figures were taken from this publication, modified slightly, and used in Chapter 2. MT carried out the original sniper genetic screen and performed preliminary characterization of the HA-SNIPER7 snc1 enhancer; LL performed the MS analysis; DL performed confocal microscopy and western blot experiments of fluorescent-protein tagged SNIPER7; KA performed the remaining experiments; MW, VL, SC, and XL provided tools, materials, expertise, and analyzed data; XL supervised the project and KA and XL wrote the manuscript with contributions from all authors. Ao, K., Li, L., Rohmann, P., Lipka, V., Chen, S., Wiermer, M., and Li, X. (under revision). TRAF domain protein TC1b is required for the defense responses mediated by plant NLR SNC1. Chapter 3 contains a modified version of this manuscript that was submitted for publication. KA carried out the tc CRISPR/Cas9 reverse genetic screen, characterized the mutants, and performed most experiments; LL performed the MS analysis; PR performed confocal microscopy; MW, VL, SC, and XL provided tools, materials, expertise, and analyzed data; XL supervised the project; KA wrote the manuscript draft and figures; XL revised the manuscript. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ................................................................................................................. xvi Acknowledgements .................................................................................................................. xxiv Dedication ................................................................................................................................. xxvi Chapter 1: Introduction ................................................................................................................1 1.1 Agriculture, plants, and food security ............................................................................... 1 1.2 Overview of the plant immune system .............................................................................. 2 1.3 Physical, structural, chemical, and antimicrobial defenses – constitutive and induced .... 3 1.4 Small RNA mediated defense ............................................................................................ 5 1.5 Systemic acquired resistance (SAR) and induced systemic resistance (ISR).................... 5 1.6 Defense mediated by pattern recognition receptors (PRRs) .............................................. 6 1.7 Defense mediated by nucleotide-binding leucine rich repeat receptors (NLRs) ............... 8 1.7.1 Different modes of NLR recognition ..................................................................... 9 1.7.2 NLR activation ..................................................................................................... 13 1.7.3 Immediate signalling events upon NLR activation .............................................. 18 1.7.4 NLR downstream signalling ................................................................................. 20 1.7.5 Regulation of NLR expression ............................................................................. 21 ix 1.7.6 Regulation of NLR protein homeostasis .............................................................. 24 1.7.7 Regulation of NLR nucleocytoplasmic trafficking .............................................. 27 1.8 Engineering of NLR-mediated defenses .......................................................................... 29 1.9 Identification of novel regulators of plant immunity through genetic screens ................ 34 1.9.1 Autoimmune mutants that activate NLR pathways .............................................. 34 1.9.2 The snc1 autoimmune mutant .............................................................................. 36 1.9.3 MOS and MUSE forward genetic screens ........................................................... 37 1.9.4 Reverse genetic screens ........................................................................................ 39 1.10 Thesis objectives .............................................................................................................. 40 1.10.1 Characterization of SNIPER7 function in immunity ........................................... 40 1.10.2 Identification and characterization of TRAF proteins involved in immunity ...... 41 Chapter 2: SCFSNIPER7 controls protein turnover of unfoldase CDC48A to promote plant immunity ..................................................................................................................................42 2.1 Summary .......................................................................................................................... 42 2.2 Introduction...................................................................................................................... 42 2.3 Materials and methods ..................................................................................................... 45 2.3.1 Plant materials and growth conditions ................................................................. 45 2.3.2 Plasmid construction and bacterial strains ........................................................... 45 2.3.3 Plant transformation and screening ...................................................................... 47 2.3.4 Gene expression analysis...................................................................................... 47 2.3.5 Pathogen assays .................................................................................................... 48 2.3.6 Total protein analysis ........................................................................................... 48 2.3.7 Co-IP and IP-MS analysis .................................................................................... 49 2.3.8 Mass Spectrometric Analysis ............................................................................... 49 x 2.3.9 Nicotiana benthamiana transient expression, in vitro degradation and MG132 experiments .......................................................................................................... 50 2.3.10 Split-luciferase complementation assay ............................................................... 51 2.3.11 Confocal microscopy ............................................................................................ 51 2.3.12 Subcellular fractionation ...................................................................................... 51 2.3.13 Domain alignments, 3D structures ....................................................................... 52 2.3.14 Phylogenetic analysis ........................................................................................... 52 2.3.15 Statistical analysis ................................................................................................ 53 2.4 Results ............................................................................................................................. 53 2.4.1 SNIPER7 overexpression enhances snc1 and triggers autoimmunity in WT plants .................................................................................................................... 53 2.4.2 SNIPER7 interacts with CDC48A ....................................................................... 57 2.4.3 SNIPER7 overexpression lines resemble Atcdc48a partial loss-of-function phenotypes ............................................................................................................ 60 2.4.4 SNIPER7 is involved in the turnover of CDC48A likely through ubiquitination and the 26S proteasome ................................................................ 63 2.4.5 SNIPER7 mainly co-localizes with a CDC48A subpool in the nucleus .............. 66 2.4.6 No obvious immune phenotypes were detected in sniper7 mutant or 35S:FLAG-SNIPER7DN lines .............................................................................. 69 2.5 Discussion ........................................................................................................................ 71 2.5.1 CDC48A is a multifunctional unfoldase .............................................................. 71 2.5.2 The FBK protein SNIPER7 is part of an SCF E3 ligase complex that targets CDC48A ............................................................................................................... 74 2.5.3 SCFSNIPER7 boosts immunity by promoting CDC48A degradation ...................... 77 Chapter 3: TRAF domain protein TC1b is required for the defense responses mediated by plant NLR SNC1 ................................................................................................................80 3.1 Summary .......................................................................................................................... 80 3.2 Introduction...................................................................................................................... 81 xi 3.3 Materials and methods ..................................................................................................... 84 3.3.1 Plant materials and growth conditions ................................................................. 84 3.3.2 Plasmid construction ............................................................................................ 85 3.3.3 Plant transformation, screening, and Sanger sequencing ..................................... 86 3.3.4 Gene expression analysis...................................................................................... 86 3.3.5 Infection assays .................................................................................................... 87 3.3.6 Total protein analysis ........................................................................................... 87 3.3.7 TurboID proximity labelling and co-IP assays..................................................... 88 3.3.8 Mass Spectrometric Analysis ............................................................................... 88 3.3.9 Confocal microscopy ............................................................................................ 89 3.3.10 Bioinformatics and generation of gene and protein diagrams .............................. 89 3.3.11 Phylogenetic analysis ........................................................................................... 90 3.3.12 Statistical analysis ................................................................................................ 91 3.4 Results ............................................................................................................................. 91 3.4.1 A reverse genetic screen to identify regulators of immunity ............................... 91 3.4.2 Mutations in TC1b suppress snc1 ......................................................................... 93 3.4.3 Overexpression of TC1b can enhance snc1 and cause autoimmunity in wild-type plants............................................................................................................. 97 3.4.4 TC1b does not play a general role in receptor-mediated immunity ..................... 98 3.4.5 TC1b acts genetically upstream of ADR1s, but does not physically interact with SNC1 .......................................................................................................... 100 3.4.6 TC1b does not obviously affect SNC1 protein levels ........................................ 101 3.4.7 TC1b localizes to punctate structures in the cytoplasm ..................................... 103 3.4.8 Identification of TC1b interactors ...................................................................... 106 3.4.9 TC1a is not conserved and TC1A overexpression leads to autoimmunity ........ 109 xii 3.5 Discussion ...................................................................................................................... 111 3.5.1 TC1b as a novel TRAF protein involved in plant immunity .............................. 111 3.5.2 Potential mechanism for the suppression of snc1 by loss of TC1b .................... 113 3.5.3 Implications of TC1b protein domains ............................................................... 115 3.5.4 Implications of TC1b localization to cytoplasmic puncta .................................. 116 3.5.5 TRAF proteins in plants and animals ................................................................. 117 Chapter 4: Summary, future directions, and research implications.....................................118 4.1 Summary ........................................................................................................................ 118 4.2 Future directions ............................................................................................................ 119 4.2.1 Further molecular characterization of SNIPER7 ................................................ 119 4.2.2 Additional research avenues for TC1b and TRAF proteins ............................... 121 4.3 Research implications .................................................................................................... 124 References ...................................................................................................................................125 Appendices ..................................................................................................................................166 Appendix A - Supplemental materials for Chapter 2 ............................................................. 166 Appendix B - Supplemental materials for Chapter 3 ............................................................. 176 xiii List of Tables Table 3.1 Potential TC1b interactors from IP-MS analysis. .................................................. 108 Table A.1 Primers used in Chapter 2. ..................................................................................... 166 Table A.2 Antibodies used in Chapter 2. ................................................................................ 167 Table A.3 List of CDC48A, CDC48B, and CDC48C peptides identified from IP-MS. ........ 168 Table B.1 Primers used in Chapter 3. ..................................................................................... 176 Table B.2 Potential target sites for CRISPR sgRNAs. ........................................................... 178 Table B.3 Antibodies and protein detection reagents used in Chapter 3. ............................... 179 Table B.4 Protein accessions and data sources for phylogenetic tree ..................................... 179 xiv List of Figures Figure 1.1 Different modes of NLR recognition ....................................................................... 12 Figure 1.2 Comparison of indirect recognition mediated by a single protein, multiple proteins, NLR partners, and NLR networks. ........................................................... 13 Figure 1.3 Different modes of NLR activation.......................................................................... 17 Figure 1.4 Layers of NLR regulation ........................................................................................ 24 Figure 1.5 Ubiquitin-proteasome system .................................................................................. 27 Figure 1.6 The plant immunity engineering toolbox. ................................................................ 31 Figure 1.7 A schematic for a potential decoy engineering pipeline. ......................................... 33 Figure 1.8 A schematic for genetic screens in the snc1 background ......................................... 37 Figure 2.1 SNIPER7 expression is induced by flg22 and HrpZ treatment and by infection with Pseudomonas syringae pv. tomato DC3000. ................................................... 54 Figure 2.2 SNIPER7 contains typical F-box protein domains. ................................................. 55 Figure 2.3 Overexpression of SNIPER7 enhances snc1 and causes autoimmunity in wild type Arabidopsis thaliana. ....................................................................................... 56 Figure 2.4 Overexpression of non-tagged SNIPER7 enhances snc1. ........................................ 57 Figure 2.5 SNIPER7DN interacts with CDC48A. .................................................................... 58 Figure 2.6 SNIPER7 peptides were identified by IP-MS in FLAG-SNIPER7DN immunoprecipitates. ................................................................................................. 60 Figure 2.7 SNIPER7 overexpression phenotypes resemble those of cdc48a-4 mutant. ............ 61 Figure 2.8 CDC48A protein levels are reduced when SNIPER7 is co-expressed in Nicotiana benthamiana. ........................................................................................... 64 Figure 2.9 CDC48A protein levels are reduced by SNIPER7 overexpression and stabilized by SNIPER7DN overexpression in Arabidopsis stable transgenic plants. .............. 65 Figure 2.10 GFP-SNIPER7 overexpression enhances snc1 dwarfism. ....................................... 67 xv Figure 2.11 Confocal analysis of GFP-SNIPER7 and co-localization analysis of CDC48A-GFP and tagRFP-SNIPER7. .................................................................................... 68 Figure 2.12 Loss of SNIPER7 does not suppress snc1, nor result in enhanced susceptibility. ... 70 Figure 2.13 Phylogenetic analysis of FBK proteins in Arabidopsis. .......................................... 72 Figure 2.14 A working model: SNIPER7 modulates CDC48A levels to promote immunity. .... 77 Figure 3.1 Chromosomal locations of genes encoding TRAF domain containing proteins. .... 92 Figure 3.2 CRISPR reverse genetic screen for TRAF candidates. ............................................ 93 Figure 3.3 TC1b, a TRAF-ARM-CC protein, is required for full snc1 autoimmunity. ............ 95 Figure 3.4 Morphology and deletion mutations in tc1ab-3, tc1ab-4 lines. ............................... 96 Figure 3.5 Overexpression of TC1b results in autoimmunity and enhances snc1 dwarfism. ... 97 Figure 3.6 TC1b does not contribute to autoimmunity mediated by snc2-1D, mekk1-5, chs3-2D, chs1-2, or chs2-1. .............................................................................................. 99 Figure 3.7 TC1b acts upstream of ADR1s/PAD4 and does not interact with SNC1. ............. 101 Figure 3.8 TC1b does not obviously affect SNC1 protein levels. ........................................... 102 Figure 3.9 TC1b-YFP and TC1b-2HA-GFP are functional and localize to cytoplasmic puncta. .................................................................................................................... 104 Figure 3.10 TC1b-YFP does not co-localize with peroxisomal marker px-rb. ......................... 105 Figure 3.11 The puncta localization of TC1b relies on the CC domains. ................................. 106 Figure 3.12 TC1b-FLAG can be immunoprecipitated. ............................................................. 107 Figure 3.13 Phylogenetic analysis of TC1a and TC1b in select plant species. ......................... 110 Figure 3.14 Overexpression of TC1a can enhance snc1 autoimmunity. ................................... 111 Figure A.1 CDC48A is ubiquitinated. ...................................................................................... 175 Figure B.1 Protein consequences of mutations in tc1ab-1 and tc1ab-2 lines. ......................... 180 xvi List of Abbreviations ½ MS ½ strength Murashige and Skoog medium 2’-3’cAMP/cGMP 2′,3′-cyclic adenosine monophosphate/2′,3′-cyclic guanosine monophosphate 26S 26 Svedberg unit 35S promoter Promoter driving expression of the 35 Svedberg unit RNA from Cauliflower mosaic virus 3D three-dimensional AAA+ ATPases associated with diverse cellular activities superfamily ABRC Arabidopsis Biological Resource Centre ADP adenosine diphosphate ADR1, ACTIVATED DISEASE RESISTANCE 1 ADR1-L1/2 ACTIVATED DISEASE RESISTANCE 1-LIKE 1/2 AGB1 Arabidopsis heterotrimeric G protein β 1 AGO Argonaute protein AIC Akaike information criterion AMP anti-microbial peptide ANOVA analysis of variance ARM ARMADILLO ASK1/2 ARABIDOPSIS SKP1-RELATED 1/2 Asp aspartic acid ATG6/13 AUTOPHAGY PROTEIN 6/13 ATP adenosine triphosphate AtPAM16 PRE-SEQUENCE TRANSLOCASE-ASSOCIATED PROTEIN IMPORT MOTOR 16 ATPase enzyme catalyzing hydrolysis of ATP to ADP ATR1 ARABIDOPSIS THALIANA RECOGNIZED 1 ATXR7 ARABIDOPSIS TRITHORAX-RELATED 7 Avr Avirulence AVR1-CO39 effector from Magnaporthe oryzae Avr4 effector from Cladosporium fulvum AvrAC effector from Xanthomonas campestris AvrB effector from Pseudomonas syringae AVR-Pia effector from Magnaporthe oryzae AvrPphB effector protease from Pseudomonas syringae AvrRpm1 effector from Pseudomonas syringae AvrRPS4 effector from Pseudomonas syringae pv. pisi AvrRpt2 effector from Pseudomonas syringae AvrSr35 effector from Puccinia graminis f sp. tritici BAK1 BRI1 ASSOCIATED KINASE 1 bal Arabidopsis variant in which the SNC1 region is duplicated bHLH84 BASIC HELIX-LOOP-HELIX 84 BIC Bayesian information criterion xvii BIK1 BOTRYTIS-INDUCED KINASE 1 bon1 bonzai 1 bp basepair BTB bric-a-brac-tramtrack-broad complex domain Ca2+ calcium ion Cas9 CRISPR associated protein 9 CBP60g CALMODULIN-BINDING PROTEIN 60G CC coiled-coil CCR RPW8-like CC domain CDC48A/B/C/D/E CELL DIVISION CYCLE 48A/B/C/D/E CDG1 CONSTITUTIVE DIFFERENTIAL GROWTH 1 cDNA complementary DNA CERK1 CHITIN ELICITOR RECEPTOR KINASE 1 cfu colony forming units Cf-4 tomato resistance gene to Cladosporium fulvum CHLORAD chloroplast associated protein degradation CHR5 CHROMATIN REMODELING 5 CHS1/2/3 CHILLING SENSITIVE 1/2/3 cm/mm/μm/nm centimeter/millimeter/micrometer/nanometer CNL CC-domain containing NLR Col-0 Columbia ecotype of Arabidopsis thaliana CPK CALCIUM-DEPENDENT PROTEIN KINASE CPR1/5 CONSTITUTIVE EXPRESSER OF PR GENES 1/5 CRCK3 CALMODULIN‐BINDING RLCK 3 CREATE Collaborative Research and Training Experience program CRISPR clustered regularly interspaced short palindromic repeats CRL CULLIN-RING E3 ligases cryo-EM cryogenic electron microscopy CUL1/3 CULLIN 1/3 DAMP damage-associated molecular patterns DCL Dicer-like enzyme DFG Deutsche Forschungsgemeinschaft DN dominant negative DNA deoxyribonucleic acid DPSS diode-pumped solid-state dsRNA double stranded RNA DTT dithiothreitol E1/2/3/4 enzymes involved in the process of ubiquitination EDS1/5 ENHANCED DISEASE SUSCEPTIBILITY 1/5 EDTA ethylenediaminetetraacetic acid EFR ELONGATION FACTOR-TU RECEPTOR EF-Tu bacterial elongation factor-Tu EMS ethyl methanesulfonate eNLR executor NLR ER endoplasmic reticulum xviii ERA1 ENHANCED RESPONSE TO ABSCISIC ACID 1 ERAD endoplasmic reticulum-associated degradation ESI Electrospray ionization ET ethylene ETI effector-triggered immunity EXA1 ESSENTIAL FOR POTEXVIRUS ACCUMULATION 1 FBA F-box associated FBK F-box Kelch F-box domain identified from cyclin F FLAG FLAG epitope tag flg22 bacterial flagellin peptide epitope FLS2 FLAGELLIN-SENSITIVE 2 FPA RNA-binding protein involved in flowering time g g-force g/mg gram/milligram GFP green fluorescent protein Glu glutamic acid H3(K4/K9/K36) Histone H3 (lysine 4/9/36) H2B Histone H2B HA Human influenza hemagglutinin tag HCD High-energy Collisional Dissociation HDA9 HISTONE DEACETYLASE 9 HECT HOMOLOGOUS TO THE E6-AP CARBOXYL TERMINUS hNLR helper NLR HopAI1 effector from Pseudomonas syringae HOS15 HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 15 HPLC high-performance liquid chromatography HR hypersensitive response hrcC HR and conserved C HRP horseradish peroxidase HrpZ harpin from Pseudomonas syringae HSD honestly significant difference HSP90 HEAT SHOCK PROTEIN 90 HUB1/2 HISTONE MONO-UBIQUITINATION 1/2 HvMYB6 member of the MYB family of transcription factors HvWRKY1/2 members of the WRKY family of transcription factors ID domain integrated domain/decoy IMP-α3 IMPORTINα-3 indels insertion/deletions INMAD inner nuclear membrane associated degradation IP immunoprecipitation IPI International Protein Index IQR inter-quartile range IRTG International Research Training Group ISR induced systemic resistance xix JA jasmonic acid KA120 member of importin/karyopherin-β superfamily kDa kiloDaltons KEAP1 Kelch-like ECH-associated protein 1 LAZ2/5 LAZARUS 2/5 LB lysogeny broth LIM Lin-11, Isl-1 and Mec-3 domain LRR leucine-rich repeat LUC luciferase Lys lysine LysM Lysin Motif MAB1/2 MATH-BTB 1/2 MAC MOS4-associated complex MAD mitochondrion-associated degradation MAMP microbe-associated molecular patterns MAP mitogen-activated protein MATH meprin and TRAF-C homology mCherry Variant of red fluorescent protein MEKK1 MAPK/ERK KINASE KINASE 1 MG132 proteasome inhibitor MHD methionine-histidine-aspartate motif MIR microRNA genes MIR1 MLA-INTERACTING RING-TYPE E3 UBIQUITIN LIGASE 1 miR482/miR2118 miRNA families miRNA microRNA MKK1/2/6 MAP KINASE KINASE 1/2/6 ML maximum likelihood mL/μL millilitre/microlitre MLA10/13 MILDEW LOCUS A 10/13 MLO Mildew Locus O mM/μM millimolar/micromolar MOS1-MOS14 MODIFIER OF SNC1 (1-14) MPK4 MAP KINASE 4 mRNA messenger RNA MS mass spectrometry MUSE MUTANT, SNC1-ENHANCING MW molecular weight MYB72 member of the MYB family of transcription factors MYC2 member of the MYC family of transcription factors N Nicotiana (N protein in tobacco) NAD+ nicotinamide adenine dinucleotide NADase NAD+ cleaving enzymatic activity NADPH reduced nicotinamide adenine dinucleotide phosphate NAM nicotinamide NatA N-TERMINAL ACETYLTRANSFERASE A xx NB nucleotide-binding NbDBCP DNA-BINDING BROMODOMAIN-CONTAINING PROTEIN NbGLK1 GOLDEN2-LIKE 1 NHP N-hydroxypipecolic acid NIa nuclear inclusion-a NLR nucleotide-binding leucine-rich repeat proteins NLS nuclear localization signal NMD nonsense mediated decay NP-40 nonyl phenoxypolyethoxylethanol NPC nuclear pore complex NPR1/3/4 NONEXPRESSOR OF PATHOGENESIS-RELATED PROTEINS 1/3/4 NRC NLR REQUIRED FOR CELL DEATH NRG1 N REQUIREMENT GENE 1 NRPC7 NUCLEAR RNA POLYMERASE C, SUBUNIT 7 NSERC The Natural Sciences and Engineering Research Council of Canada NTI NLR-triggered immunity NTR nuclear transport receptor NUP NUCLEOPORIN OD600 optical density at 600nm P.s.m. Pseudomonas syringae pv. maculicola P.s.t Pseudomonas syringae pv. tomato PAD4 PHYTOALEXIN DEFICIENT 4 PAGE polyacrylamide gel electrophoresis PAMP pathogen-associated molecular patterns PAS/LOV Per-Arnt-Sim/light-oxygen-voltage-sensing domain PBL/PBL2 PBS1-like/ PBS1-like 2 PBS1 AVRPPHB SUSCEPTIBLE 1 PCR polymerase chain reaction PEPC phosphoenolpyruvate carboxylase phasiRNA phased secondary small interfering RNA Pii rice NLR Pip pipecolic acid PMSF phenylmethylsulfonyl fluoride PMT photomultiplier tubes PopP2 effector from Ralstonia solanacearum PR/PR1 PATHOGENESIS-RELATED/ PATHOGENESIS-RELATED 1 Pro proline PRoTECT Plant Responses To Eliminate Critical Threats PRR pattern recognition receptors PTI PAMP/PRR triggered immunity PTM post-translational modification PTO PSEUDOMONAS SYRINGAE PV. TOMATO KINASE PTRE1 PROTEASOME REGULATOR 1 PUB43 PLANT U-BOX 43 PVPP polyvinylpolypyrrolidone xxi px-rb mCherry marker tagged with peroxisome signal peptide qPCR quantitative polymerase chain reaction R Resistance RAD ribosome associated degradation RAR1 REQUIRED FOR MLA12 RESISTANCE 1 RBOHD RESPIRATORY BURST OXIDASE HOMOLOGUE PROTEIN D RBX1 RING BOX PROTEIN 1 RDRP RNA-dependent RNA polymerase RGA4/5 R GENE ANALOG 4/5 RIG-I retinoic acid-inducible gene-I RIN4 RPM1-INTERACTING PROTEIN 4 RING REALLY INTERESTING NEW GENE RIPK RPM1-INDUCED PROTEIN KINASE RISC RNA-induced silencing complex RKS1 RESISTANCE RELATED KINASE 1 RLCK receptor-like cytoplasmic kinases RLK receptor-like kinase RLP receptor-like protein RLR RIG-I-like receptors RNA ribonucleic acid RNAi RNA interference ROQ1 RECOGNITION OF XOPQ 1 ROS reactive oxygen species RPM1 RESISTANCE TO PSEUDOMONAS SYRINGAE PV. MACULICOLA 1 RPP1/4/8 RECOGNITION OF PERONOSPORA PARASITICA 1/4/8 RPS2/5/6 RESISTANCE TO PSEUDOMONAS SYRINGAE 2/5/6 RPW8 RESISTANCE TO POWDERY MILDEW 8 RRS1 RESISTANCE TO RALSTONIA SOLANACEARUM 1 RTM3 RESTRICTED TEV MOVEMENT 3 Rx1 potato NLR conferring resistance against Potato Virus X SA salicylic acid SAG101 SENESCENCE-ASSOCIATED GENE 101 SAR systemic acquired resistance SARD1 SAR-DEFICIENT 1 SAUL1 SENESCENCE-ASSOCIATED E3 UBIQUITIN LIGASE 1 SCF SKP1-CUL1-F-box SD standard deviation SDS sodium dodecyl sulfate SEH1 SEC13 HOMOLOG 1 Ser serine SERK SOMATIC EMBRYOGENESIS RECEPTOR KINASE sgRNA single guide RNA SGT1b SUPPRESSOR OF THE G2 ALLELE OF SKP1 SIKIC1/2/3 SIDEKICK SNC1 1/2/3 SINA1/2/3/4/5/6 SEVEN IN ABSENTIA 1/2/3/4/5/6 xxii SINAT1/2/6 SEVEN IN ABSENTIA OF ARABIDOPSIS THALIANA 1/2/6 siRNA small-interfering RNA SIZ1 SAP AND MIZ1 DOMAIN- CONTAINING LIGASE1 SKP S-phase kinase-associated protein SlNAC1 NAM, ATAF1,2, CUC2 1 SMG7 Nonsense-mediated mRNA decay factor SNC1/2 SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1/2 SNIPER1/2/4/7 SNC1-INFLUENCING PLANT E3 LIGASE REVERSE GENETIC 1/2/4/7 sNLR sensor NLR SNP single nucleotide polymorphism SOBIR1 SUPPRESSOR OF BIR1-1 SOC3 SUPPRESSORS OF CHS1-2 3 SPL6 SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 6 SR serine/arginine-rich Sr35 stem rust resistance 35 SRFR1 SUPPRESSOR OF RPS4-RLD 1 sRNA small RNA SUMM2 SUPPRESSOR OF MKK1 MKK2 1 SUMO small ubiquitin-like modifier SYD SPLAYED TAF15b TATA-BINDING PROTEIN-ASSOCIATED FACTOR 15B TagRFP variant of red fluorescent protein TAIR The Arabidopsis Information Resource TALENS transcription activator-like effector nucleases TC1a/b TRAF CANDIDATE 1a/1b T-DNA transfer DNA TF transcription factor TFP thiocyanate-forming protein TGA2/5/6 TGACG-binding 2/5/6 Thr threonine TIR Toll and Interlukin-1 receptor TIR1 TRANSPORT INHIBITOR RESPONSE 1 TLR Toll-like receptors TN/TN2 TIR-NB/TIR-NBS 2 TNF-R tumor necrosis factor receptor TNL TIR-domain containing NLR TPL/TPR1/2/3 TOPLESS/TOPLESS RELATED 1/2/3 TRAF TUMOR NECROSIS FACTOR RECEPTOR ASSOCIATED FACTOR Tris-HCl tris(hydroxymethyl)aminomethane hydrochloride TurboID biotin ligase Ub ubiquitin UBA1 UBIQUITIN-ACTIVATING ENZYME 1 UBC The University of British Columbia U-box UFD2-homology domain UBP12/13 UBIQUITIN-SPECIFIC PROTEASE 12/13 xxiii UBR7 Ubiquitin Protein Ligase E3 Component N-Recognin7 U-clone sequence verified open reading frame clones UPF1/3 UP-FRAMESHIFT 1/3 UPLC ultra-performance liquid chromatography UTR untranslated region Val valine v-cADPR variant cyclic adenosine diphosphate ribose VCP Valosin-Containing Protein WAK WALL-ASSOCIATED KINASE WRKY domain found in WRKY family of transcription factors WT wild type XoqQ Xanthomonas effector XPO4 EXPORTIN 4 YFP yellow fluorescent protein ZAR1 HOPZ-ACTIVATED RESISTANCE 1 ZRK ZED1-RELATED KINASE xxiv Acknowledgements First, I owe tremendous gratitude to my supervisor, Dr. Xin Li, whose approachability, moral support, and scientific guidance have greatly contributed to my PhD journey. I appreciate the academic freedom she provided me that allowed me to pursue interesting side projects and have diverse experiences. Thanks to my committee members Dr. Cara Haney, Dr. Ljerka Kunst, and Dr. Marcel Wiermer for their critical discussions and valuable comments regarding my projects, and Dr. Yuelin Zhang, who provided me interesting insights and opportunities to develop and use my bioinformatics skills. Thanks to the communities of the UBC Botany Department and Michael Smith Laboratories for providing research support and a welcoming work environment. I am grateful to have many excellent collaborators, particularly Dr. Daniel Luedke and Philipp Rohmann, who generously helped me with confocal microscopy experiments. Thanks to Dr. Marcel Wiermer, Dr. Elena Petutschnig, and Dr. Volker Lipka for research materials and lab space during my exchange in Göttingen, and to the many friends and colleagues from Göttingen for their help and companionship. Thanks to Dr. Lin Li and Dr. She Chen from NIBS for assisting with IP-MS analysis, which was useful for both projects. I appreciate all past and present members of the Li and Zhang labs who have provided comradery and discussed exciting hypotheses, publications, and results with me. Thank you to my predecessors and mentors who have trained me in lab techniques: Dr. Shuai Huang, Dr. Meixuezi Tong, Dr. Charles Copeland, Dr. Oliver Dong, Dr. Kaeli Johnson, Dr. Jianhua Huang, Dr. Zhongshou Wu, and Dr. Wanwan Liang. I appreciate the efforts of Dr. Zhongshou Wu, Dr. Wanwan Liang, Yan Xu, Tina Tan, Lei Tian, and Karen Thulasi Devendrakumar who selflessly took on many lab manager duties, yet always had time for conversations with me. In addition, xxv thanks to others who have also helped me with experiments or discussed my research with me: Yujun Peng, Weijie Huang, Jiameng Lan, Dr. Hainan Tian, Dr. Tongjun Sun, Dr. Yanan Liu, Dr. Qiang Zhang, Dr. Yuli Ding, Dr. Paul Kapos, Dr. Junxing Lu, Xueru Liu, Solveig van Wersch, Nanbing Zhang, other graduate students, and our undergraduate and visiting students/scientists. I am grateful for financial support from multiple funding sources including: NSERC Master’s Graduate Scholarship, Alexander Graham Bell Canada Graduate Scholarship, NSERC Michael Smith Foreign Study Supplement, UBC Graduate Teaching Assistantship and UBC Four-Year Fellowship. I also appreciate the funding from NSERC-CREATE and the Deutsche Forschungsgemeinschaft (DFG) to the International Research Training Group (IRTG 2172, PRoTECT), which allowed me to do an exchange in Germany for collaborative research. Special thanks to Keith Turner, my instructor for an undergraduate plant physiology course, whose engaging classes and passion for plants ignited my interest in plant biology. To my friends both in and out of academia and my family, thank you for your support throughout this long journey and for reminding me to have work-life balance. I would not be where I am today without you. To my partner, Rico Misajon, your sense of humour is what got me through long days and tough periods. You are a shining beacon of light in my life. Thank you for your understanding, love, and support and for reminding me to take life less seriously. Finally, but most importantly, my deepest gratitude goes to my parents to whom I am forever indebted. Fearless immigrants with a tireless work ethic, their many sacrifices throughout my life are major contributors to my success. This accomplishment is theirs as much as it is mine. 我想对我的父母表达最诚挚的感谢。是你们的支持与激励，这篇论文才能得以完成。若没有你们的爱与照顾、 奉献与牺牲, 我无以至今日，所以这个成就也是你们的。谢谢你们为我付出的一切，寸草春晖，无以为报，唯有感谢！ xxvi Dedication To my parents 1 Chapter 1: Introduction 1.1 Agriculture, plants, and food security As the world population continues to grow, food insecurity remains a critical concern. Even in modern times, an estimated 768 million people were undernourished in 2020 (FAO et al., 2021). Plants are crucial for human survival on earth. Much of what we eat is derived from plants directly (for example, vegetable or grain crops) or indirectly (for example, livestock that are fed hay, maize, or soy). As much of the world depends on staple crops such as rice, wheat, and maize for nourishment, increasing the efficiency of crop production would monumentally improve food security. One major obstacle to efficient production, is the prevalence of plant pests and pathogens that can cause significant losses in crop yield (Savary et al., 2019; Ristaino et al., 2021). In the past, plant pathogens have contributed to devastating losses of human life due to famine. One example is the Irish Great Famine that was caused by the potato pathogen Phytophthora infestans and exacerbated by sociopolitical factors. A virulent strain from North America, HERB-1, was responsible for an epidemic that spread throughout Ireland and Great Britain in the 1840s and led to severe losses in potato crops – a staple of the Irish diet (Austin Bourke and M, 1964; Yoshida et al., 2013). Today, plant pathogens can still disrupt agricultural productivity and the livelihoods of farmers. The rice blast pathogen Magnaporthe oryzae remains one of the most significant rice pathogens (Wilson and Talbot, 2009). Furthermore, wheat rusts (Puccinia spp.) and fusarium head blight (Fusarium graminearum) continue to cause significant economic damage worldwide (Figueroa et al., 2018). Beyond staple crops, coffee leaf rust and banana fusarium wilt, caused by the fungal pathogens Hemileia vastatrix and Fusarium oxysporum f. sp. cubense, respectively, can 2 cause severe crop yield losses – affecting the livelihoods of small-scale farmers (Talhinhas et al., 2017; Dita et al., 2018). Pesticides and fungicides are frequently employed to control plant pathogens. However, there are concerns about their effect on the environment and human health. Furthermore, it is important to consider the detrimental effect of these chemicals on beneficial organisms (Damalas and Eleftherohorinos, 2011). An alternative strategy is the use of genes that confer resistance to pest or pathogen attack. Despite the presence of diverse microbes in the soil and air, most do not result in plant disease. Plants can resist most potential pathogens and remain healthy due to their robust multi-layered immune system (Jones and Dangl, 2006; Dangl et al., 2013). To combat plant pathogens, it is critical that we understand the plant immune system and utilize this understanding to generate resilient cultivars to maximize agricultural productivity and minimize waste. 1.2 Overview of the plant immune system Plants have multiple complex layers of defense, many tailored to the types of herbivores and pathogens they encounter. For example, chemical defense provided by secondary metabolites is an important defense mechanism against herbivores (Mithöfer and Boland, 2012). Additionally, mechanical defenses including thorns and spines can deter larger herbivores (Lucas et al., 2000; Hanley et al., 2007). Here, we focus on the various constitutive and induced defense mechanisms against microbial pathogens. Physical, structural, chemical, and antimicrobial defenses can provide constitutive defense but can also be modified and fortified upon pathogen induction. Additionally, small RNAs can mediate resistance against viruses and fungi through gene silencing. Plants also possess a form of adaptive immunity called systemic acquired resistance (SAR) that provides long-term broad-spectrum resistance upon secondary infections. 3 In order to provide comprehensive surveillance of pathogens and deploy appropriate countermeasures, plants employ both plasma membrane-localized and intracellular receptors (Jones et al., 2016; Kourelis and van der Hoorn, 2018; Zhou and Zhang, 2020; Saur et al., 2021; Ngou et al., 2022a; Bentham et al., 2020; Ngou et al., 2022b). Activation of either types of immune receptors results in the induction of various immune responses that impede pathogen growth. Although some aspects of the downstream signalling of these two pathways are different, it has recently become clear that these pathways are deeply interconnected and interdependent (Hatsugai et al., 2017; Lu and Tsuda, 2021; Ngou et al., 2021; Yuan et al., 2021a; Yuan et al., 2021b; Pruitt et al., 2021; Tian et al., 2021). Additionally, examples from diverse pathosystems have challenged the dichotomous categorization of pathogen-derived molecules and motivated a more holistic view of plant immunity (Thomma et al., 2011; Cook et al., 2015; van der Burgh and Joosten, 2019). Some of the many layers of plant immunity that protect against microbial pathogens are discussed in more detail in the following sections. 1.3 Physical, structural, chemical, and antimicrobial defenses – constitutive and induced Plants possess multiple formidable physical obstacles to prevent microbial infection including the waxy cuticle, the cell wall, and guard cells. The plant cuticle is a hydrophobic layer, composed of a matrix of cutin (a fatty acid polymer) and waxes, that protects plant cells from dehydration and pathogen invasion (Serrano et al., 2014; Ziv et al., 2018). The cell wall is a structurally complex and dynamic layer that is primarily composed of cellulose microfibrils in a complex network. The cell wall provides structure to the plant, but also has a limited pore size that physically exclude microbes (Underwood, 2012; Bellincampi et al., 2014; Malinovsky et al., 2014). Lastly, stomata are natural openings that enable gas exchange. Guard cells that border 4 stomata can regulate stomatal closure to limit pathogen entry (Melotto et al., 2008; Melotto et al., 2017). Plants also possess an arsenal of preformed or inducible chemicals with antimicrobial activity. Examples of preformed chemicals (termed phytoanticipins) include saponins and glucosinolates, whereas an example of inducible chemicals (termed phytoalexins) include camalexin (Pusztahelyi et al., 2015; Piasecka et al., 2015). Furthermore, plants have anti-microbial proteins such as inducible chitinases and PATHOGENESIS-RELATED (PR) proteins, and anti-microbial peptides (AMPs) to provide general defense against invaders (van Loon et al., 2006; Campos et al., 2018). Successful pathogens have strategies to overcome these defenses. For example, some fungi and bacteria use enzymes such as cutinases, lipases, and cell wall degrading enzymes to disrupt cuticle and cell wall integrity (Jha et al., 2005; Kubicek et al., 2014). Some fungi, like M. oryzae, are able to use specialized structures called appressoria to generate turgor pressure and forcibly puncture the cell wall for infiltration (Wilson and Talbot, 2009). Additionally, some pathogens carry phytotoxins (for example, coronatine) that can prevent stomata closure (Melotto et al., 2008; Melotto et al., 2017). Lastly, some pathogens possess virulence proteins (effectors) that can be used to alter or attenuate host defense responses to promote pathogen proliferation. Many bacterial effectors are injected into plant cells via a needle-like pilus structure called the type III secretion system (Alfano and Collmer, 2004; Büttner and He, 2009), whereas fungi can secrete effectors into the apoplast, the biotrophic interface or the plant cytoplasm or in order to modify host responses (de Jonge et al., 2011; Giraldo and Valent, 2013). Interestingly, many strategies used by microbes to subvert these defense processes can be detected through plant immune receptors (discussed further below). 5 1.4 Small RNA mediated defense Gene silencing (RNA interference, RNAi) is a defense mechanism plants use to fight viral infection. Virus-derived double stranded RNA (dsRNA) is processed by Dicer-like enzymes (DCLs) into short 21-24 nt (nucleotide) fragments called small-interfering RNAs (siRNAs) that are then loaded onto Argonaute proteins (AGOs) to form the RNA-induced silencing complex (RISC). RISC can target complementary viral genomes and transcripts for transcriptional or post-transcriptional silencing (Yang and Li, 2018; Guo et al., 2019; Jin et al., 2021). In turn, viruses encode viral suppressors that counteract RNAi (Csorba et al., 2015). Small RNAs are also involved in immunity against fungi. It was recently uncovered that plants can deliver extracellular vesicles containing small RNAs into fungal cells to inhibit fungal virulence in a phenomenon called cross-kingdom RNAi (Cai et al., 2018a; Cai et al., 2019). The mechanisms behind sRNA loading and translocation across cell walls is an active field of research. 1.5 Systemic acquired resistance (SAR) and induced systemic resistance (ISR) Plants possess a form of adaptive immunity called systemic acquired resistance (SAR), where local infection responses can induce enhanced systemic defense responses against secondary infections (Fu and Dong, 2013; Klessig et al., 2018). This form of immunity has been intensively studied in Arabidopsis thaliana (hereafter Arabidopsis). This phenomenon depends on key defense hormones SA (salicylic acid) and NHP (N-hydroxypipecolic acid) (Hartmann and Zeier, 2019; Huang et al., 2020a; Peng et al., 2021). Upon pathogen infection, the expression of SA biosynthesis genes is rapidly induced by transcription factors (TFs) SARD1 (SAR-DEFICIENT 1) and CBP60g (CALMODULIN-BINDING PROTEIN 60G). SA is perceived through SA receptors NPR1 and NPR3/4 (NONEXPRESSOR OF PATHOGENESIS-RELATED 6 PROTEINS 1/3/4) which play opposite roles in the regulation of immune genes through TFs TGA2, TGA5, and TGA6 (TGACG-binding 2/5/6). In low SA conditions, the NPR1 transcriptional activator is inactive, and the NPR3/4 transcriptional repressors block defense gene expression. In high SA conditions, NPR1 is activated and recruits transcriptional machinery, whereas defense gene expression is derepressed upon SA-binding by NPR3/4. NHP is a critical systemic signal for SAR. SA perception is important for the transcription of NHP biosynthesis genes. Treatment with NHP precursor Pip (pipecolic acid) also promotes expression of SA biosynthesis genes. Thus, Pip/NHP and SA work in a mutual amplification loop to promote defense in local and distal tissue. A different form of induced immunity includes induced systemic resistance (ISR), which is mediated by beneficial microorganisms in the soil and provides enhanced protection against diverse pathogens and insects (Pieterse et al., 2014; Vlot et al., 2021). In contrast with SA/NHP-mediated SAR, plant hormones jasmonic acid (JA) and ethylene (ET) play central roles in ISR (Pieterse et al., 1998). In addition, TFs MYB72 and MYC2 are important for the onset of ISR and for priming, respectively (Pozo et al., 2008; Van der Ent et al., 2008). 1.6 Defense mediated by pattern recognition receptors (PRRs) Plants have membrane localized receptors called PRRs (pattern recognition receptors) that can detect the presence of microbe-derived molecules (pathogen/microbe-associated molecular patterns or PAMPs/MAMPs) or damage-derived molecules (damage-associated molecular patterns or DAMPs) at the plant cell surface (Boller and Felix, 2009; Macho and Zipfel, 2014; Hou et al., 2019; DeFalco and Zipfel, 2021). A diverse set of PRRs from different species have been identified and characterized along with their ligands. Some well-known examples include: FLS2 7 (FLAGELLIN-SENSITIVE 2), which recognizes the flg22 peptide from bacterial flagellin; CERK1 (CHITIN ELICITOR RECEPTOR KINASE 1), which recognizes chitin from fungal cell walls; EFR (ELONGATION FACTOR-TU RECEPTOR), which recognizes bacterial elongation factor-Tu (EF-Tu); and WAKs (WALL-ASSOCIATED KINASEs), which recognizes pectin derived oligogalacturonic acid fragments (Gómez-Gómez and Boller, 2000; Chinchilla et al., 2006; Zipfel et al., 2006; Miya et al., 2007; Brutus et al., 2010; Liu et al., 2012; Albert et al., 2020). Although some PRRs recognize widely conserved patterns (PAMPs/DAMPs), other PRRs can recognize lineage-specific and quickly evolving extracellular ligands such as pathogen effectors. One commonly used example is the tomato PRR Cf-4 which recognizes Avr4, a chitin-binding effector used by fungal pathogen Cladosporium fulvum to avoid host antifungal chitinases (van den Burg et al., 2006; Thomma et al., 2011). PRRs are often separated into two groups depending on their domain structure. Receptor-like kinases (RLKs) are PRRs that contain an extracellular ligand-binding domain (which can be a leucine-rich repeat (LRR), a LysM domain, or other domains), a transmembrane domain and a cytoplasmic kinase domain. Receptor-like proteins (RLPs) are similar to RLKs, except that they lack the intracellular kinase domain. Both types of PRRs work with coreceptors to bind their ligand in a “sandwich”, with the ligand acting as a molecular “glue” (Sun et al., 2013; Couto and Zipfel, 2016). Different classes of PRRs use different coreceptors. Many PRRs of the LRR-RLK class have been shown to recruit coreceptors like the RLK BAK1 (BRI1 ASSOCIATED KINASE 1), or other members of the SERK (SOMATIC EMBRYOGENESIS RECEPTOR KINASE) family. Similarly, many LRR-RLPs constitutively interact with the RLK SOBIR1 (SUPPRESSOR OF BIR1-1) to recruit SERK members (Liebrand et al., 2014; Schwessinger and Rathjen, 2015; Couto 8 and Zipfel, 2016). Many LysM-RLK and LysM-RLPs, on the other hand, require the CERK1 (CHITIN ELICITOR RECEPTOR KINASE 1) RLK coreceptor (Couto and Zipfel, 2016). Heteromeric complex formation leads to a series of auto- and trans-phosphorylation events of the PRR cytoplasmic kinase domains and the phosphorylation of additional components including a family of receptor-like cytoplasmic kinases (RLCKs) (Liang and Zhou, 2018). Activated RLCKs can phosphorylate multiple targets including MAP (mitogen-activated protein) kinases, calcium channels, NADPH oxidase RBOHD (RESPIRATORY BURST OXIDASE HOMOLOGUE PROTEIN D), and TFs leading to early responses such as Ca2+ influx, calcium-dependent protein kinase (CPK) activation, MAP kinase cascade activation, reactive oxygen species (ROS) production, and transcriptional reprograming. Later responses include the synthesis of defense hormones such as SA and NHP, the release of anti-microbial proteins, and the deposition of callose. The downstream defense responses mediated by PRR recognition are called PAMP/PRR triggered immunity (PTI) (Boller and Felix, 2009; Dangl et al., 2013; Macho and Zipfel, 2014; Bigeard et al., 2015; DeFalco and Zipfel, 2021; Ngou et al., 2022a). 1.7 Defense mediated by nucleotide-binding leucine rich repeat receptors (NLRs) Many adapted pathogens that have evolved alongside their hosts are able to suppress host defense mechanisms (like PTI) by delivering effector proteins into the host cytoplasm. Nucleotide-binding leucine rich repeat proteins (NLRs) are intracellular receptors that can recognize these effectors and their immunity-subverting activities (Jones et al., 2016; Saur et al., 2021; El Kasmi, 2021). The activation of NLRs can often lead to localized cell death, a process referred to as the hypersensitive response (HR) (Balint-Kurti, 2019). The collective downstream responses of NLR-mediated immunity are referred to as effector/NLR-triggered immunity (ETI or NTI). 9 NLRs are multi-domain proteins, typically containing an N-terminal domain, a central nucleotide-binding (NB) domain and a C-terminal LRR. Classified by their N-termini, plant NLRs can be separated into three main phylogenetic groups: Toll and interlukin-1 receptor (TIR)-domain containing NLRs (TNLs); coiled-coil (CC)-domain containing NLRs (CNLs); and RPW8 (RESISTANCE TO POWDERY MILDEW 8)-like CC domain (CCR) containing ‘helper’ NLRs (CCR-hNLRs) (Shao et al., 2016). However, special subclades have been identified – such as the solanaceous NRC (NLR REQUIRED FOR CELL DEATH) CNL subclade, which contains the NRC class of ‘helper’ NLRs (NRC-hNLRs) (Wu et al., 2017b). In addition to these canonical architectures, many NLRs also possess integrated domains (NLR-IDs) (Cesari et al., 2014; Wu et al., 2015; Sarris et al., 2016; Kroj et al., 2016), while some have atypical or missing domains but retain functions in immunity (Meyers et al., 2002; Nandety et al., 2013). Examples of NLR-IDs include rice RGA5 (R GENE ANALOG 5) and Arabidopsis RRS1 (RESISTANCE TO RALSTONIA SOLANACEARUM 1) (Cesari et al., 2013; Le Roux et al., 2015). Examples of truncated NLRs include Arabidopsis CHS1 (CHILLING SENSITIVE 1) and TN2 (TIR-NBS 2), which are TN (TIR-NB) proteins (Wang et al., 2013; Zhao et al., 2015). Overall, NLR architecture is diverse (Van de Weyer et al., 2019; Barragan and Weigel, 2021; Kourelis et al., 2021). 1.7.1 Different modes of NLR recognition In addition to NLR architecture diversity, there are different modes for NLR recognition and function. Some NLRs directly bind effectors while others recognize effectors indirectly. Furthermore, some NLRs, including ZAR1 (HOPZ-ACTIVATED RESISTANCE 1), function alone without requiring other NLRs (Wang et al., 2015b). Others, including NLR-IDs and truncated NLRs, function in sensor-executor (sNLR-eNLR) heteromeric pairs (also referred to as 10 sensor-helper pairs) with other NLRs. In these sNLR-eNLR pairs, one partner provides effector recognition functionality, and the other activates downstream signalling (e.g. RGA4/5) (Cesari et al., 2013; Césari et al., 2014; Grund et al., 2019). Some effectors can be bound directly. The gene-for-gene hypothesis provided conceptual guidance early in the study of plant immunity; it proposed that plant disease resistance (R) genes correspond to pathogen avirulence (Avr) genes, and the degree of compatibility between pathogen and plant was dependent on the presence or absence of the R or Avr genes (Van der Biezen and Jones, 1998). This genetic model implies that R proteins directly bind Avr proteins in a receptor-ligand model. Indeed, most R genes encode NLRs, and some NLRs do bind directly with their cognate Avr protein (effector) (Figure 1.1A) (Cesari, 2017; Kourelis and van der Hoorn, 2018). In contrast to direct effector binding, studies of tomato kinase PTO (PSEUDOMONAS SYRINGAE PV. TOMATO KINASE) and Arabidopsis RIN4 (RPM1 (RESISTANCE TO PSEUDOMONAS SYRINGAE PV. MACULICOLA 1)-INTERACTING PROTEIN 4) revealed that some NLRs recognize their cognate effectors indirectly, through a host intermediate protein (Van der Biezen and Jones, 1998; Dangl and Jones, 2001; Belkhadir et al., 2004; Ntoukakis et al., 2014). In this guard model, NLRs “guard” host proteins (“guardees”) that are targeted by pathogen effectors and activate when they sense modifications to the guardee (Figure 1.1B-D). Some guardees have intrinsic roles in immunity independent of the guarding NLR, but additional studies identified host intermediates that seemed to have lost their host roles yet can still bind effectors/NLRs to mediate NLR-dependent defense. These “decoy” proteins are guardee mimics and effector traps that likely arose from gene duplication due to constraints on the guardee to balance immunity-promoting protein function, while maintaining recognition by the NLR (van der Hoorn and Kamoun, 2008). The distinction between guardees and decoys may not always be clear 11 since some host proteins that are thought to be decoys may still be biochemically functional. Thus, guards/decoys may exist on a spectrum, depending on host and pathogen genetics and evolution. Although some NLRs rely on a single guardee/decoy, others require additional adapter proteins (see RPM1 and ZAR1 discussion below) (Figure 1.1B-C, Figure 1.2A-B). In addition to direct and guardee/decoy models, NLR-IDs can recognize effectors through their ID to promote defense activation. A translationally fused ID domain allows these NLR-IDs (also called integrated decoys) to trap effectors in a similar way to decoys (Figure 1.1E). Most NLR-ID examples, including Arabidopsis RRS1 (RESISTANCE TO RALSTONIA SOLANACEARUM 1) (Sarris et al., 2015) and rice NLR RGA5 (Cesari et al., 2013), act as sNLRs that directly bind to effectors. Others, like rice NLR Pii (Fujisaki et al., 2017), seem to bind effectors through a guardee (an example of an NLR-ID using indirect recognition). Similar to the necessity of guardees for effector detection by guard NLRs, eNLRs require sNLRs for effector detection. Thus, sNLR-eNLR pairs can be considered a form of indirect recognition (Figure 1.2C). Expanded forms of this sNLR-eNLR system exists, where a group of hNLRs mediate the activation for a large repertoire of sNLRs in a large NLR network. The solanaceous NRC-hNLR clade members act as signal transducers of some sNLRs and PRRs (Gabriëls et al., 2007; Wu et al., 2016; Wu et al., 2017b). Furthermore, multi-level forms of the sensor-executor system exists. The CCR-NLR class of hNLRs, consisting of the ADR1 (ACTIVATED DISEASE RESISTANCE 1) and NRG1 (N REQUIREMENT GENE 1) families, work in parallel pathways to activate defense downstream of other NLRs (Figure 1.2D) (Jubic et al., 2019). Thus, hNLRs indirectly recognize effectors through sensor NLRs in a multi-level mode of recognition that is conceptually like the guardee/decoy model (Figure 1.2D) (Wu et al., 2018; Adachi et al., 2019a; Feehan et al., 2020). 12 Figure 1.1 Different modes of NLR recognition Schematic diagram of sensor NLR recognition and activation mechanisms. NLRs are either self-inhibited (A), inhibited by guardees/decoys (B-D), or inhibited by partner NLRs (E) prior to effector recognition. Some NLRs such as RPP1 and ROQ1 can directly interact with cognate effectors (A). Other NLRs, such as RPM1, RPS2, RPS5, and SUMM2 monitor guardees/decoys, and activate in response to effector modifications (B). ZAR1 works in a similar way, but also requires adapter proteins like RKS1 (C). SOC3 functions with truncated TNs that monitor SAUL protein levels (D). NLR-IDs like RRS1 and RGA5 directly recognize effectors with their IDs, and release inhibition of their partner executor NLRs (E). In one model of NLR activation, recognition of effectors or effector activity results in the activation of NLRs, which promotes NLR oligomerization and interaction of the N-terminal domains (F-G). Examples of resistosomes include RPP1 and ROQ1, which form tetrameric resistosomes, leading to the generation of TIR enzymatic products (F). TIR domains can also form TIR-DNA/RNA complexes which also lead to the formation of additional enzymatic products (F). Additionally, ZAR1 forms a pentameric resistosome upon PBL2 modification by the AvrAC effector, likely leading to the formation of a calcium channel (G). For sensor-executor pairs, it is unclear whether the executor NLRs homo-oligomerize, or whether both sensor and executor NLRs are involved in the resistosome. Helper NLRs are required for the function of some NLRs. ADR1s/NRG1s may monitor EDS1/PAD4/SAG101, lipase-like proteins which may be receptors for TIR enzymatic products. Upon activation, ADR1s/NRG1s may form a resistosome similar to ZAR1 that may serve as membrane cation channels. Dark/light blue - NLRs, Orange - guardee/decoy/NLR-ID, Olive - effector, Grey - adapter protein, question marks - unknown, unclear, or proposed mechanisms. 13 Figure 1.2 Comparison of indirect recognition mediated by a single protein, multiple proteins, NLR partners, and NLR networks. (A) In the classic indirect recognition model, an NLR recognizes changes to a guardee/decoy and activates. One example is RPM1/RIN4. (B) Some NLRs require additional adapter proteins to recognize effector modifications. An example is ZAR1/RKS1/PBL2. (C) Since sensor NLRs inhibit the activity of their corresponding executor NLRs, they function similarly to guardee/decoy-mediated indirect recognition. Examples like RGA4/RGA5/AVR-Pia can be considered an expanded version of indirect recognition. (D) Finally, some NLRs rely on downstream hNLRs for function. hNLRs, such as the ADR1s/NRG1s, along with lipase-like proteins (EDS1/PAD4/SAG101) indirectly recognize effectors through monitoring for signals from upstream sensor NLR activities. Examples include RPP1 and ROQ1, which can oligomerize upon ATR1 and XoqQ recognition, respectively. TIR domain enzymatic activity leads to the production of TIR signals, some of which have been shown to be recognized by downstream hNLRs through lipase-like receptors. TIR signal recognition results in hNLR activation. Thus, ADR1s/NRG1s can indirectly detect effectors through an NLR network. Dark blue - NLRs, Orange - guardee/decoy/NLR-ID, Olive circle – effector, Olive stars - TIR signals, Grey - adapter protein, question marks – unclear or proposed mechanisms. 1.7.2 NLR activation Early studies on NLR activation revealed that NLRs are molecular switches. Before recognition, NLRs are in an inhibited state. NLRs can be self-inhibited through inter-domain interactions, inhibited by guardees/decoys/adapters through inter-molecular interactions, or inhibited by interactions with a partner NLR (Figure 1.1A-E). Effector recognition (either through direct effector binding or indirect activation) results in a conformational change in the NLR, 14 converting it from an inhibited state to an activated state (Figure 1.1F-G) (Qi and Innes, 2013; Jones et al., 2016; Burdett et al., 2019; Saur et al., 2021; Maruta et al., 2022). LRR or ID-mediated recognition of an effector or modified guardee results in a structural change, the NB-mediated exchange of ADP for ATP, NLR oligomerization, and the release of the N-terminal domain (TIR/CC) for oligomerization and signalling (Bernoux et al., 2011; Maekawa et al., 2011a). The specific effector-triggered alteration that leads to NLR conformational change depends on the NLR-effector system. It is not fully understood how effectors that directly bind NLRs can release NLR self-inhibition. Likely, effector binding disrupts NLR inter-domain interactions and stabilizes the active NLR state to promote oligomerization (Bernoux et al., 2016). Some mechanistic details for this direct-binding model has been shown using cryo-EM structures of the Arabidopsis TNL RPP1 (RECOGNITION OF PERONOSPORA PARASITICA 1) bound to the Hyaloperonospora arabidopsidis effector ATR1 (ARABIDOPSIS THALIANA RECOGNIZED 1), of the Nicotiana benthamiana TNL ROQ1 (RECOGNITION OF XOPQ 1) with the Xanthomonas effector XopQ (Figure 1.1A,F), and of the wheat CNL Sr35 (stem rust resistance 35) bound to the Puccinia graminis f sp. tritici effector AvrSr35 (Ma et al., 2020; Martin et al., 2020; Förderer et al., 2022). Many mechanistic details for effector-mediated guardee/decoy modification and subsequent release of guard NLR inhibition are available. The Arabidopsis RIN4-RPM1/RIN4-RPS2 (RESISTANCE TO PSEUDOMONAS SYRINGAE 2) system is one of the best studied examples (Mackey et al., 2002; Axtell and Staskawicz, 2003; Mackey et al., 2003; Toruño et al., 2019). RIN4 is a conserved intrinsically disordered protein guarded by multiple NLRs. RIN4 acts as a molecular switch with a yet unclear role in PRR-mediated defense and is controlled through post-translational modifications. For example, RIN4Ser141 phosphorylation promotes defense, 15 whereas RIN4Thr166 phosphorylation or acetylation represses RINSer141 phosphorylation and inhibits defense (Chung et al., 2014; Lee et al., 2015). Numerous effectors target RIN4 for modification to interfere with host immunity. The P. syringae effector AvrB promotes RIN4Thr166 phosphorylation by recruiting host RLCKs, such as RIPK (RPM1-INDUCED PROTEIN KINASE) (Figure 1.1B, Figure 1.3A) (Liu et al., 2011; Chung et al., 2014; Xu et al., 2017b). Similarly, the P. syringae effector AvrRpm1 ADP-ribosylates RIN4Asp153 and promotes subsequent RIN4Thr166 phosphorylation, likely by recruiting RLCKs such as CDG1 (CONSTITUTIVE DIFFERENTIAL GROWTH 1) (Figure 1.1B, Figure 1.3B) (Redditt et al., 2019; Yang et al., 2021). AvrB or AvrRpm1-mediated phosphorylation of RIN4Thr166 and subsequent isomerization of RIN4Pro149 leads to activation of the CNL RPM1 (Mackey et al., 2002; Chung et al., 2011; Li et al., 2014). A different P. syringae effector, AvrRpt2, is a protease that can cleave RIN4 at two sites (Chisholm et al., 2005). Such cleavage releases RIN4 fragments from the plasma membrane, leading to its degradation through the proteasome (Kim et al., 2005; Takemoto and Jones, 2005). Cleavage of RIN4 may prevent the recognition of other effectors by RPM1, and results in released fragments that may suppress PRR-mediated responses (Kim et al., 2005; Afzal et al., 2011). The absence or cleavage of RIN4 can activate the CNL RPS2 (Figure 1.1B, Figure 1.3C) (Axtell and Staskawicz, 2003; Mackey et al., 2003) through the loss of RPS2 inhibition by the membrane-tethered C-terminal half of RIN4 (Day et al., 2005; Kim et al., 2005). Thus, the RIN4 node is targeted by multiple pathogen effectors with different enzymatic activities resulting in the activation of different NLRs through different mechanisms. Structural studies will be essential to fully understand the structural changes of RPM1 and RPS2 upon modification or loss of the RIN4 guardee. 16 Another example of effector triggered activation involves the CNL RPS5 (RESISTANCE TO PSEUDOMONAS SYRINGAE 5). Arabidopsis RLCK PBS1 (AVRPPHB SUSCEPTIBLE 1) and related PBS1-like (PBL) kinases (BIK1, PBL1, PBL2, RIPK, etc.) are involved in basal immunity and are targeted by the AvrPphB effector protease from P. syringae (Shao et al., 2003; Zhang et al., 2010a; Liang and Zhou, 2018; Russell et al., 2015). AvrPphB can cleave PBS1, and the resulting slight conformational change in PBS1 is recognized by RPS5, which exists in a pre-activation complex with the PBS1 guardee (Figure 1.3C) (Shao et al., 2003; Ade et al., 2007; DeYoung et al., 2012; Qi et al., 2014; Pottinger and Innes, 2020). The structural data of the CNL ZAR1 provides important additional insights into how NLRs are activated (Wang et al., 2019a; Wang et al., 2019b). ZAR1 is an ancient CNL that associates with members of the RLCK XII (ZRK) pseudokinase adapter family, including RKS1, in a preformed complex. This complex indirectly recognizes various effectors that target PBL RLCK family members (Wang et al., 2015; Bastedo et al., 2019). The Xanthomonas campestris effector AvrAC can uridylylate PBL2 (Wang et al., 2015b). The ZAR1-RKS1 complex can bind uridylylated PBL2 to form a ternary complex, causing a conformational change where the ZAR1 NB domain shifts and releases ADP (Figure 1.3D) (Wang et al., 2019b). The inhibition and activation mechanisms of NLR-ID sensor-executor pairs are an active area of research. Evidence from the study of RRS1/RPS4 (RESISTANCE TO PSEUDOMONAS SYRINGAE 4) suggests that similar to guardee/decoy NLR inhibition, this NLR pair exists as a preformed inactive complex. RPS4 is inhibited by the NLR-ID RRS1 which contains a WRKY domain at its C-terminus that acts as an effector decoy. Pseudomonas syringae pv. pisi effector AvrRps4 and Ralstonia solanacearum effector PopP2 disrupt the inter-molecular RPS4/RRS1 interactions using different mechanisms to derepress NLR inhibition (Figure 1.1E, Figure 1.2C). 17 Figure 1.3 Different modes of NLR activation. Some NLRs can indirectly recognize effectors through detecting changes to their guardee/decoy. Some well-characterized activation mechanisms are shown here. (A) Phosphorylation of RIN4 by AvrB leads RIN4 proline isomerization and RPM1 activation. (B) Using a similar mechanism, ADP-ribosylation of RIN4 by AvrRpm1 leads to RPM1 activation. (C) Cleavage of RIN4 or PBS1 by AvrRpt2 or AvrPphB leads to RPS2 or RPS5 activation, respectively. (D) Uridylylation of PBL2 by AvrAC promotes its association with the ZAR1-RKS1 complex, leading to ZAR1 activation. A variety of additional activation mechanisms exist. Dark blue - NLRs, Orange - guardee/decoy, Olive - effector, Grey - adapter protein, White – protein modification, P – phosphate group, ADP - adenosine diphosphate, UMP - uridine monophosphate. AvrRps4 binds to the RRS1WRKY ID, which interferes with its autoinhibitory interactions with an adjacent domain and enhances its interaction with the RRS1TIR domain. The release of RRS1TIR from RPS4TIR may result in RPS4TIR oligomerization and RPS4 activation. Phosphorylation of RRS1Thr1214 (in the WRKY ID) is important for inhibition of activation. O-acetylation of the same site by PopP2 prevents its phosphorylation. This modification, along with additional phosphorylated residues in the C-terminal tail of RRS1 likely results in PopP2-mediated 18 enhancement of RRS1TIR-RRS1WRKY interactions, prevention of RRS1TIR-RPS4TIR interactions, and thus activation of RPS4 (Ma et al., 2018; Guo et al., 2020). 1.7.3 Immediate signalling events upon NLR activation The immediate events that occur after NLR activation are intensively studied. In current models, exchange of ADP for ATP during NLR activation promotes oligomerization. However, there seems to be an equilibrium between inactivated and activated NLR states that is tipped towards the activated state upon ligand binding (Bernoux et al., 2016). In the ZAR1 example, binding of uridylylated PBL2 by ZAR1-RKS1 and subsequent ADP-ATP exchange promotes oligomerization of the complex into the pentameric ZAR1 wheel-like resistosome (Fig. 1G) (Wang et al., 2019a). Furthermore, RPP1/ATR1 and ROQ1/XopQ can form tetrameric resistosomes (Ma et al., 2020; Martin et al., 2020). Additional published NLR oligomers from both plants and animals, along with previous data on NLR oligomerization, strongly suggest that other plant NLRs form similar spiral resistosomes (Bentham et al., 2017; Förderer et al., 2022). The resistosome configurations of sensor-executor NLR pairs, and whether they form hetero- or homo-oligomeric resistosomes, have yet to be determined. Finally, some NLRs also seem to be capable of forming pre-activation oligomers – the function of which is unclear (Ade et al., 2007; Maekawa et al., 2011a; El Kasmi et al., 2017). NLR oligomerization seems to result in the assembly of the N-terminal domains for signalling (Wang et al., 2019a; Wang et al., 2019b; Duxbury et al., 2020). Indeed, the oligomerization of the N-terminal TIR domains or CC domains is critical for signalling, and ectopic expression of these domains is often sufficient to activate defense (Bernoux et al., 2011; Maekawa et al., 2011a; Williams et al., 2014; Cesari et al., 2016; Casey et al., 2016; Zhang et al., 19 2017c). Oligomerization of the N-terminal domains produces different outcomes depending on the domain. Activation of TNLs promotes induced proximity of the TIR domains which enables the TIR NAD+ cleaving (NADase) enzymatic activity (Horsefield et al., 2019; Wan et al., 2019; Ma et al., 2020; Martin et al., 2020). In addition, TIR domains also exhibit 2′,3′-cAMP synthase activities (Figure 1.1F) (Yu et al., 2022b). The function of these enzymatic products in signalling is unclear. TNL signalling requires lipase-like proteins EDS1 (ENHANCED DISEASE SUSCEPTIBILITY 1), PAD4 (PHYTOALEXIN DEFICIENT 4), and SAG101 (SENESCENCE-ASSOCIATED GENE 101) (Aarts et al., 1998; Feys et al., 2005; Zhu et al., 2011; Dongus and Parker, 2021), as well as CCR-hNLRs ADR1s and NRG1s (Peart et al., 2005; Collier et al., 2011; Bonardi et al., 2011; Qi et al., 2018; Wu et al., 2019; Castel et al., 2019). Intriguingly, ADR1s and NRG1s form distinct complexes with the lipase-like proteins. ADR1s exist in a complex with PAD4 and EDS1, whereas NRG1s exist in a complex with SAG101 and EDS1 (Wagner et al., 2013; Lapin et al., 2019; Sun et al., 2021; Wu et al., 2021). Recent evidence suggests that EDS1/PAD4 serves as a receptor for TIR-generated enzymatic products. Recognition of TIR-derived signals by the lipase-like receptors seems to promote their association with CCR-hNLRs and likely stimulates their activation and oligomerization (Figure 1.2D) (Huang et al., 2022). The oligomerized CC domains of some CNLs, like ZAR1, and CCR-hNLRs, like ADR1, appear to be plasma membrane localized calcium channels (Figure 1.1G) (Bi et al., 2021; Jacob et al., 2021). NRC-hNLRs likely also form resistosomes and membrane channels upon activation (Adachi et al., 2019b). It is thought that this membrane permeabilization leads to HR cell death. However, the relationship between HR cell death and pathogen resistance still needs to be clarified. 20 1.7.4 NLR downstream signalling Both TNL and CNL activation seem to culminate in membrane channel formation and calcium influx which presumably activates similar immune responses as calcium responses after PRR activation. Where this calcium influx occurs could be different between resistosomes depending on where they are localized. In particular, EDS1/PAD4/ADR1s and EDS/SAG101/NRG1s complexes seem to localize to different compartments and mediate different defense outputs (Lapin et al., 2020; Dongus and Parker, 2021; Lüdke et al., 2022). It will also be interesting to determine the effects of TIR produced signals that may be independent of hNLR activation. Various NLRs have been reported to be able to interact with TFs, to directly induce transcriptional changes (Cui et al., 2015; Tsuda and Somssich, 2015; Sun et al., 2020). For example, activated barley CNL MLA10 (MILDEW LOCUS A 10) can interact with transcription repressor HvWRKY1/2 to derepress TF HvMYB6 leading to defense gene transcription (Shen et al., 2007; Chang et al., 2013). Activation of tobacco TNL N can interact with TF SPL6 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 6) in the nucleus (Padmanabhan et al., 2013). Arabidopsis TNL SNC1 (SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1) can associate with transcriptional corepressor TPR1 (TOPLESS RELATED 1) and TF bHLH84 (BASIC HELIX-LOOP-HELIX 84) (Zhu et al., 2010b; Xu et al., 2014a). Finally, potato Rx1 can interact with TF NbGLK1 (GOLDEN2-LIKE 1) to inhibit its binding to DNA until Rx1 activation and can also interact with chromatin-interacting protein NbDBCP (DNA-BINDING BROMODOMAIN-CONTAINING PROTEIN) to interfere with its interaction with chromatin (Townsend et al., 2018; Sukarta et al., 2020). Consistent with these findings, nuclear accumulation of some NLRs (e.g. SNC1, RPS4, MLA10, N) has been shown to be important for signalling (Shen et al., 2007; Burch-21 Smith et al., 2007; Wirthmueller et al., 2007; Xu et al., 2014b). Thus, NLRs may play a direct role in transcription that may be dependent or independent on resistosome formation. It’s also possible that some NLRs require nuclear localization and TF interaction due to their function as guard NLRs that recognize nuclear guardees. 1.7.5 Regulation of NLR expression Inappropriate activation of NLRs can result in detrimental effects on plant growth (Huot et al., 2014; van Wersch et al., 2016; van Butselaar and Van den Ackerveken, 2020). Thus, the regulation of NLR transcript and protein levels is critical to maintaining balance. NLRs are regulated through multiple mechanisms both transcriptionally and post-transcriptionally (Figure 1.4) (Halter and Navarro, 2015; Lai and Eulgem, 2018; van Wersch et al., 2020; Lapin et al., 2022). NLR expression can be tissue specific and conditionally induced. Induction of NLR expression during immunity is thought to be achieved through a combination of de-repression of epigenetic/small RNA mediated regulation and activation/induction of TFs mediated by SA and potentially calcium (Tian et al., 2021; Lapin et al., 2022). Promoter elements in RPP8 and positive and negative regulatory promoter elements in SNC1 have been identified to be important for proper transcription, and may serve as TF binding sites (Mohr et al., 2010; Yu et al., 2022a). NLRs can be epigenetically regulated through histone or chromatin modification (Figure 1.4). Proper expression of TNL LAZ5 (LAZARUS 5) relies on trimethylation of H3K36 (Histone H3 lysine 36) at LAZ5 chromatin mediated by the histone lysine methyltransferase LAZ2 (LAZARUS 2) (Palma et al., 2010). Similarly, MOS9 (MODIFIER OF SNC1 9) and ATXR7 (ARABIDOPSIS TRITHORAX-RELATED 7) seem to function together to mediate H3K4 trimethylation in the promoters of SNC1 and RPP4 to contribute to their expression (Xia et al., 22 2013). Furthermore, HISTONE MONO-UBIQUITINATION 1/2 (HUB1/2) facilitates monoubiquitination of H2B in the SNC1 locus, positively contributing to SNC1 expression (Zou et al., 2014). On the other hand, deacetylation of H3K9 by HOS15 (HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 15) and HDA9 (HISTONE DEACETYLASE 9) limits the expression of SNC1 and other NLRs (Yang et al., 2020a). In addition to histone modification, chromatin remodeling proteins such as CHROMATIN REMODELING 5 (CHR5) and SPLAYED (SYD) play positive and negative roles in SNC1 expression, respectively (Johnson et al., 2015; Zou et al., 2017). Transposable elements, which can often be found in NLR encoding regions, can introduce epigenetic changes that limit NLR expression and/or alter splicing of NLR genes (Tsuchiya and Eulgem, 2013). There have been multiple reports on the importance of splicing for NLR regulation (Figure 1.4). Tobacco NLR N and Arabidopsis TNL RPS4 are both alternatively spliced upon pathogen treatment, and the transcript variants are required for full immunity (Dinesh-Kumar and Baker, 2000; Zhang and Gassmann, 2003; Zhang and Gassmann, 2007). Barley MLA13 contains upstream open reading frames that are alternatively spliced and that contribute to downregulation of MLA13 (Halterman et al., 2003; Halterman and Wise, 2006). Components of the MOS4-associated complex (MAC) are involved in RNA processing and miRNA biogenesis (Zhang et al., 2013; Zhang et al., 2014; Jia et al., 2017; Li et al., 2018a). Multiple MAC components and associated proteins were found to be necessary for full snc1 defense activation with most directly or indirectly affecting SNC1 splicing (Zhang et al., 2005; Palma et al., 2007; Monaghan et al., 2009; Monaghan et al., 2010; Xu et al., 2011; Xu et al., 2012; Wu et al., 2013; Copeland et al., 2013). Alternative splicing is often coupled to nonsense mediated decay (NMD), a process where transcripts with premature stop codons are targeted for degradation. NMD also contributes to NLR 23 regulation (Halter and Navarro, 2015). Loss of NMD components SMG7, UPF1, or UPF3 (UP-FRAMESHIFT 1/3) results in accumulation of NLR mRNAs and the aberrant activation of defense (Jeong et al., 2011; Rayson et al., 2012; Riehs-Kearnan et al., 2012; Gloggnitzer et al., 2014; Raxwal et al., 2020). Interestingly, pathogen infection results in host-programmed inhibition/degradation of NMD components and the stabilization of NLR transcript levels, suggesting NMD inhibition is a mechanism for amplifying defense activation (Gloggnitzer et al., 2014; Jung et al., 2020). Another RNA-processing component, FPA, is an RNA-binding protein that contributes to premature cleavage and alternative polyadenylation of NLR transcripts, thus limiting the accumulation of fully functional NLR transcripts (Parker et al., 2021). The regulation of NLRs by microRNAs (miRNAs) and phased, secondary small interfering RNAs (phasiRNAs) has been described in different plant species (Halter and Navarro, 2015; Fei et al., 2016; Zhang et al., 2016). Similar to the process for cross-kingdom RNAi, this process relies on RNA-dependent RNA polymerase (RDRP)-generated dsRNA that is processed by DCL enzymes into small RNAs and loaded onto AGO proteins to form the RNA-guided RISC complex. The source and target of many of these phasiRNAs are NLR transcripts that regulate their own expression in cis or by targeting complementary NLR transcripts (Fei et al., 2013). For example, the miR482/miR2118 superfamily directly and/or indirectly targets NLRs to limit the abundance of NLR and other transcripts. In tomato, miR482 directly targets NB domain-encoding regions of NLR mRNAs and mediates the generation of secondary phasiRNAs from NLR transcripts, whereas miR2118 targets a noncoding RNA to generate secondary phasiRNAs that likely also target NLR transcripts (Zhai et al., 2011; Shivaprasad et al., 2012; Fei et al., 2013; Canto-Pastor et al., 2019; Liu et al., 2020). This regulation is also dynamic; pathogen treatment can inhibit NLR silencing resulting in NLR derepression and the amplification of NLR-mediated responses 24 (Shivaprasad et al., 2012; Fei et al., 2016). In another example, nuclear SNC1 can inhibit phasiRNA biosynthesis (possibly through repressing MIR gene transcription) resulting in global derepression of NLR gene expression (Cai et al., 2018b). This regulation may serve as a defense amplification mechanism. Figure 1.4 Layers of NLR regulation A diagram of NLR regulation layers, with SNC1 as an example. NLR expression, protein homeostasis, and subcellular localization can be regulated. All layers contribute to ensuring NLRs are only activated when appropriate. 1.7.6 Regulation of NLR protein homeostasis NLRs are also regulated in different ways at the protein level (Figure 1.4). The inhibition of NLR activation through protein-protein interactions has been discussed previously (see section on NLR activation). The importance of chaperone proteins for proper NLR protein folding and accumulation has been well characterized (Shirasu, 2009; Kadota and Shirasu, 2012). SGT1b (SUPPRESSOR OF THE G2 ALLELE OF SKP1), HSP90 (HEAT SHOCK PROTEIN 90), and RAR1 (REQUIRED FOR MLA12 RESISTANCE) are members of the chaperone complex that are essential for proper NLR function. However, some components also seem to play negative SNC1Epigene�c regula�onTranscrip�onal regula�on Post-transcrip�onal (splicing) regula�on Transla�onal regula�on Post-transla�onal regula�onNucleocytoplasmictraffickingProtein degrada�onDownstream immune signallingADR1s-EDS1-PAD4NRG1s-EDS1-SAG10125 roles in immunity through NLR turnover. For example, some HSP90s contribute positively to SNC1 turnover (Huang et al., 2014b). One major mechanism of NLR protein regulation is ubiquitination and degradation through the 26S proteasome (Figure 1.4) (Mazzucotelli et al., 2006; Vierstra, 2009; Marino et al., 2012; Chen and Hellmann, 2013; Copeland and Li, 2019; Chen et al., 2022). Ubiquitination is a post-translational modification consisting of the covalent attachment of a small ubiquitin (Ub) protein onto a lysine residue of substrate proteins or onto a lysine residue of a previously attached Ub. Different linkage types and different numbers of Ub attachments can occur, which determine the fate of the substrate protein. For example, monoubiquitination often promotes/disrupts protein-protein interaction, affects protein activity, or alters protein localization. On the other hand, Lys48-linked polyubiquitin chains are a signal for substrate degradation through a proteolytic complex called the 26S proteasome (Komander and Rape, 2012; Bodnar and Rapoport, 2017; Cooney et al., 2019; Twomey et al., 2019). Proteasomal degradation of polyubiquitinated proteins is also facilitated by the protein unfoldase CDC48A (Copeland et al., 2016; Bodnar and Rapoport, 2017; Cooney et al., 2019; Twomey et al., 2019). Ubiquitination is mediated by a pathway of enzymes: E1 Ub-activating enzymes, E2 Ub-conjugating enzymes, E3 Ub-ligases, and E4 Ub-elongating ligases (Figure 1.5). Specificity is provided by the E3 Ub ligase which specifically binds to the substrate protein. There are multiple types of E3 ligases; some are multi-functional single proteins, while others are a multimeric complex (Figure 1.5). RING (REALLY INTERESTING NEW GENE), U-box and HECT (HOMOLOGOUS TO THE E6-AP CARBOXYL TERMINUS) type E3 ligases contain both E2-adapting domains and substrate recognition domains in the same protein. On the other hand, CULLIN-RING E3 ligases (CRLs) are multi-protein complexes. SCF (SKP1-CUL1-F-box)-type 26 E3 ligases are one type of CRLs that rely on CUL1 (CULLIN 1), RBX1 (RING BOX PROTEIN 1), ASKs (ARABIDOPSIS SKP1-RELATED), and an F-box protein. Target specificity is provided by the F-box protein, which binds to target substrates through a C-terminal domain and to the CUL1-RBX1-ASK complex through its F-box domain (Figure 1.5). BTB (bric-a-brac-tramtrack-broad complex)-type CRLs work similarly, relying on CUL3, RBX1 and a BTB protein. Target specificity is provided by the BTB protein, which binds to target substrates through additional domains, and to the CUL3-RBX1 complex through its BTB domain. Multiple E3 ligases have been characterized to be involved in NLR regulation (Cheng and Li, 2012; Chen et al., 2022). CPR1 (CONSTITUTIVE EXPRESSER OF PR GENES 1) is an F-box protein that works as part of a SCF complex to recognize and mediate the turnover of SNC1 and RPS2 through the proteasome (Cheng et al., 2011; Gou et al., 2012). Two TRAF (tumor necrosis factor receptor associated factor) domain proteins, MUSE13/14 (MUTANT, SNC1-ENHANCING 13/14) associate with SCFCPR1 and facilitate NLR turnover (Huang et al., 2016b). MUSE13/14 themselves are targeted by E3 ligase SNIPER4 (SNC1-INFLUENCING PLANT E3 LIGASE REVERSE GENETIC 4) for degradation (Huang et al., 2018). Tobacco putative E3 UBR7 contributes to the degradation of TNL N (Zhang et al., 2019), whereas barley RING type E3 ligase MIR1 (MLA-INTERACTING RING-TYPE E3 UBIQUITIN LIGASE 1) mediates the degradation of multiple MLA CNLs (Wang et al., 2016). Interestingly, some E3 ligases serve broad functions. SNIPER1/2 are RING type E3 ligases that can recognize the NB domain of NLRs and mediate their turnover through the proteasome. Thus, SNIPER1/2 may serve to globally regulate NLR levels to prevent growth defects from NLR over-accumulation (Wu et al., 2020). Additional protein modifications also contribute to NLR protein homeostasis. For example, differential N-terminal methionine acetylation can affect the turnover rate of SNC1 (Xu et al., 27 2015b). In addition, SUMO (small ubiquitin-like modifier) E3 ligase SIZ1 appears to sumoylate SNC1 and TPR1 to negatively regulate their function (Lee et al., 2007; Gou et al., 2017; Niu et al., 2019). Figure 1.5 Ubiquitin-proteasome system A simplified schematic of the E3 ligase mediated ubiquitin-proteasome pathway. E1, E2, E3 and E4 enzymes work together to mediate the polyubiquitination of target proteins. E3 ligases specifically recognize substrates and serves as an adapter for the rest of the ubiquitination machinery. Some E3 ligases work as single proteins (top left), whereas others, such as SCF-type E3 ligases consist of a complex of proteins (bottom left). Polyubiquitinated proteins can be degraded through the 26S proteasome. Small white circles – ubiquitin. Modified from (van Wersch et al., 2020) 1.7.7 Regulation of NLR nucleocytoplasmic trafficking Nuclear accumulation of some NLRs (e.g. SNC1, RPS4, MLA10, N) is required for their defense signalling (Figure 1.4) (Shen et al., 2007; Burch-Smith et al., 2007; Wirthmueller et al., 2007; Bai et al., 2012; Mang et al., 2012; Wirthmueller et al., 2013; Xu et al., 2014b). Forced MLA10 nuclear accumulation is sufficient for fungal resistance in barley, however, forced cytoplasmic accumulation of MLA10, but not nuclear MLA10 results in HR, suggesting that both E3E2 E4poly ubiquitination26SproteasomedegradationCUL1RBX1E2ASK1ubiquitinationSCF-complexF-boxE1 F-box adapter proteinE2 TargetubiquitinationE1E3E328 nuclear and cytoplasmic MLA10 pools are important for pathogen resistance (Bai et al., 2012). Additionally, disruption of SNC1 nuclear accumulation inhibits SNC1-induced immune activation (Palma et al., 2005; Cheng et al., 2009; Zhu et al., 2010a; Zhu et al., 2010b; Mang et al., 2012; Jia et al., 2021). One regulatory system that controls the NLR nucleocytoplasmic distribution is the nuclear transport receptor (NTR) system, in which NTRs facilitate cargo entry and exit from the nucleus (Wirthmueller et al., 2013; Lüdke et al., 2021a; Lüdke et al., 2022). NTR MOS6/IMP-α3 (IMPORTINα-3) is required for SNC1-triggered defense activation (Palma et al., 2005). Although additional importin family members exist with similar cargo binding sites, only IMP-α3 strongly associates with SNC1 and is required for response to pathogens (Wirthmueller et al., 2013; Lüdke et al., 2021b). Another NTR KA120, which belongs to the importin/karyopherin-β superfamily, limits SNC1 nuclear accumulation to prevent defense activation (Jia et al., 2021). In addition to NLR trafficking, NTRs can also mediate the trafficking of regulators necessary for NLR signalling and regulation. Exportin XPO4 (EXPORTIN 4) directly interacts with and limits the nuclear accumulation of transcriptional corepressors TOPLESS and TPR (Xu et al., 2021). In addition, importin-β superfamily member MOS14/TRANSPORTIN-SR contributes to proper splicing of SNC1 and RPS4 transcripts, likely through mediating nuclear import of serine/arginine-rich (SR) splicing factors (Xu et al., 2011). These NTRs seem to have some cargo specificity since only some are involved in the nucleocytoplasmic trafficking of NLRs or NLR regulators (Wirthmueller et al., 2015; Lüdke et al., 2021b; Jia et al., 2021; Lüdke et al., 2022). Another regulatory system is the nuclear pore complex (NPC), which mediates the selective entry and exit of cargo including NLRs and NLR regulators. MOS7/NUP88 (NUCLEOPORIN 88) influences the nuclear accumulation of SNC1 and EDS1 (Cheng et al., 29 2009). Multiple members of the NUP107-160 NPC sub-complex, MOS3/NUP96, NUP160, and SEH1, impact SNC1 defense activation (Zhang and Li, 2005; Wiermer et al., 2012). These components are positive regulators of nuclear mRNA export and NUP160 may contribute to EDS1 expression and mRNA export (Parry et al., 2006; Dong et al., 2006; Wiermer et al., 2012; Roth and Wiermer, 2012). Finally, transmembrane nucleoporin CPR5 (CONSTITUTIVE EXPRESSER OF PR GENES 5) is a regulator of NLR-mediated signalling. NLR activation seems to disrupt the CPR5 module in the NPC, and the CPR5 confirmational change allows the NPC to be more permissive for immune signalling cargo (Bowling et al., 1997; Wang et al., 2014; Gu et al., 2016). Downstream immune components of NLR signalling are compartmentalized and dynamic, which may serve as another layer of regulation. EDS1/PAD4 complexes are nucleocytoplasmic, whereas EDS1/SAG101 complexes seem predominantly nuclear in Arabidopsis (Feys et al., 2005; Rietz et al., 2011). Similarly, ADR1s and NRG1s seem have different subcellular localization patterns (Wu et al., 2019; Jacob et al., 2021; Saile et al., 2021). Consistently, the EDS1/PAD/ADR1s module and the EDS1/SAG101/NRG1s module seem to mediate largely overlapping but different immune responses, with the former predominantly mediating transcriptional/SA/pathogen resistance responses and the latter predominantly activating HR (Wu et al., 2019; Castel et al., 2019; Lapin et al., 2019; Lapin et al., 2020, 202; Saile et al., 2020; Dongus and Parker, 2021). It is unclear how the dynamic localization of different NLR resistosomes, lipase-like proteins, and hNLR complexes upon infection may lead to different immune responses. 1.8 Engineering of NLR-mediated defenses A major goal in plant immunity research is to translate research findings to agriculturally relevant improvements in crop yield and productivity. Conventional generation of resistant 30 varieties through intraspecies/interspecies introgression is a time-consuming process (Dangl et al., 2013). One alternative solution is the use of knowledge from plant immunity to genetically engineer disease resistance. There are various strategies that have been proposed (Dangl et al., 2013; Rodriguez-Moreno et al., 2017; Dong and Ronald, 2019; van Esse et al., 2020; Frailie and Innes, 2021; Schultink and Steinbrenner, 2021). PRRs, NLRs, decoys and other immune regulators can be engineered for expanded or novel functions. In addition, technologies like CRISPR-Cas, TALENS, and RNAi/small RNA can be used to modify or disrupt immune genes. Finally, the use of strategies like gene pyramiding and inducible transcriptional/translational controls will allow robust defense while minimizing growth trade-offs (Figure 1.6) (Xu et al., 2017a; Mundt, 2018). Here, we focus on the use of designer decoys and NLR-IDs for expanded effector recognition. Decoy and NLR-ID engineering has been a recent focus due to several intrinsic properties. Firstly, indirect recognition allows for the detection of a wider range of effectors, and the protection of a larger subset of host pathways compared to direct recognition NLRs (see ZAR1, SUMM2, RPS5 examples). Secondly, compared to the overexpression of NLRs, the expression of designer decoys is less likely to trigger inappropriate activation of defense (autoimmunity) (van Wersch et al., 2016; van Wersch et al., 2020; Wan et al., 2021; Li and Weigel, 2021). This is because decoys play an inhibitory role in indirect-recognition NLRs until effector recognition. Thus, the need for engineering strict NLR regulation can be circumvented. Finally, decoys can provide some safeguards against effector evolution since pathogens likely need to lose guardee-modifying effectors and associated virulence activity to avoid triggering activation of guard NLRs. The studies and breakthroughs in plant NLR research has made pathogen-targeted decoy engineering a possibility. One possible strategy is detailed here. To engineer useful decoys, it will be critical to understand which pathogens and effectors are agriculturally relevant. This can be 31 Figure 1.6 The plant immunity engineering toolbox. Three sets of tools have been developed to aid crop protection. First, a solid understanding of the genetics of plant immunity will provide genes and novel alleles that can be targeted or engineered. Second, technologies such as CRISPR-Cas and TALENs are quickly evolving. Many existing and developing variants will help provide novel ways to generate mutations or potentially insertions. Third, interesting bioengineering methods have been recently tested. Receptor chimeras and decoy/ID modifications have the potential to greatly expand pathogen detection possibilities. Gene pyramiding can provide long-term disease resistance that is difficult for pathogens to overcome. Inducible promoters or uORF inhibition can be used to provide strict control over transgenes. achieved through field monitoring and pathogen characterization/sequencing (Figure 1.7A-B). Using phylogenetic and bioinformatic comparisons, a core subset of effectors important for virulence can be chosen for biochemical characterization (Figure 1.7C-E). Due to the limited number of well-characterized guardee-NLR systems currently, it will be important to identify additional systems that can be repurposed for decoy engineering (Figure 1.7F) (Mukhtar et al., 2011; Jupe et al., 2013; Weßling et al., 2014; Sarris et al., 2016; Kroj et al., 2016; Witek et al., 32 2016; Van de Weyer et al., 2019; Barragan and Weigel, 2021; Kourelis et al., 2021). Upon selection of appropriate guardees, there are several strategies for engineering them for expanded effector recognition (Figure 1.7G-H). Targeted approaches to guardee/decoy modification have been used in the past, but rely on more in-depth knowledge of effector activities, structural interactions, etc. (Figure 1.7G). For example, PBS1 variants were generated where the AvrPphB cleavage sequence on PBS1 was replaced with sequences that can be cleaved by the P. syringae effector protease AvrRpt2, the turnip mosaic virus protease NIa, the tobacco etch virus protease NIa, or the soybean mosaic virus protease NIa. These PBS1 decoy variants could activate RPS5-mediated defense in response to the presence of the respective pathogen proteases when introduced into Arabidopsis, N. benthamiana, or soybean (Kim et al., 2016; Helm et al., 2019; Pottinger et al., 2020; Pottinger and Innes, 2020). With the available structural and biochemical data on ZAR1, a similar strategy could be used to engineer both the ZAR1 adapter pseudokinases and guardees (Adachi et al., 2022). In addition, the use of structures has allowed the engineering of various NLR-IDs for expanded effector recognition (De la Concepcion et al., 2019; Liu et al., 2021; Cesari et al., 2022). An alternative approach to engineering designer decoys is the random mutagenesis of existing guardees (Figure 1.7H). This approach has been used successfully to expand the effector repertoire of some direct-recognition NLRs (Harris et al., 2013; Segretin et al., 2014; Steinbrenner et al., 2015). To ensure successful application in the field, the functionality of the engineered decoy needs to be thoroughly tested during the design and refinement process in heterologous and host crop systems (Figure 1.7I). Once functional decoys have been made, they can be incorporated into multi-gene decoy stacks for deployment in crops for durable resistance (Figure 1.7J-K). Finally, resistance can be maintained through strategies like frequent updating of gene stacks, and planting individuals with gene stack variations in polyculture fields (Figure 1.7L). 33 Figure 1.7 A schematic for a potential decoy engineering pipeline. Prevalent or emerging virulent pathogen strains can be determined through crop field monitoring (A). The genomes of these strains can be fully sequenced using next-generation sequencing (B), and compared with other virulent, avirulent, or mutualistic strains using phylogenetic and bioinformatic analyses (C) to find candidate effectors important for virulence. These candidate effectors can then be tested to determine which ones are essential for virulence. From these, a core repertoire of effectors can be selected for decoy engineering (D). To understand how the targeted core effectors subvert host immunity, the biochemical/enzymatic activity of the effectors should be functionally characterized (E). Data gathered from NLR-omes, effector-omes, and guardee/decoy studies will be useful for choosing which NLRs/guardees/decoys to engineer. These databases can be searched for NLR-guardee/decoy systems in the same or different species that recognize similar effectors or effector activities as the target effectors (F). Different approaches can be taken to modify existing decoys to recognize new effectors. Firstly, guardees that recognize similar effector biochemical activities as the target effector can be used for the basis of structure- or knowledge-based directed engineering (G). Alternatively, random or directed mutagenesis could be used to modify an existing decoy to recognize a novel effector (H). Prior to deployment, decoys should be tested in heterologous systems and the original crop plant to ensure functionality (I). For example, N. benthamiana transient expression can be used to test for HR in the presence of the NLR, decoy and effector (I). Furthermore, the decoy can be introduced into the crop plant to test for enhanced resistance against the pathogen without growth defects (I). To ensure durable resistance, multiple decoys targeting different pathogen effectors should be stacked into a multi-gene cassette (J). Cognate NLRs can also be included in these stacks if the endogenous NLRs are not used for detection. Finally, decoy stacks can be introduced into crop species (K). Fields should be continuously monitored for resistance-breaking strains or novel pathogens, and the decoys should be updated accordingly (L). Effectors - olive, decoys - orange, NLRs - blue, Green - resistant plants, yellow - susceptible plants. 34 1.9 Identification of novel regulators of plant immunity through genetic screens 1.9.1 Autoimmune mutants that activate NLR pathways Many genetic screens have been done to identify components involved in different NLR immune pathways. One strategy is to use autoimmune mutants, which display constitutive activation of defence that can result in stunting and cell death (van Wersch et al., 2016; Wan et al., 2021; Li and Weigel, 2021; Freh et al., 2022). It is thought that autoimmune dwarfism is due to a trade-off between growth and defense – stronger defense activation suppresses plant growth and development (Huot et al., 2014; van Butselaar and Van den Ackerveken, 2020). In some cases, the underlying cause of autoimmunity is a gain-of-function mutation in an NLR gene that loosens NLR inhibition or increases NLR stability. It is not well understood how exactly these mutations alter the NLR and its inter- and intra-molecular interactions to contribute to inappropriate activation. In other cases, the cause of autoimmunity is the loss or modification of a guardee, leading to activation of NLR-mediated defense. Several examples exist of autoimmunity caused by mutations in NLRs. A screen looking for mutants sensitive to chilling resulted in the isolation of a gain-of-function allele of the typical TNL RPP4 (RESISTANCE TO PERONOSPORA PARASITICA 4) called chs2-1 (chilling sensitive 2-1) (Huang et al., 2010). This mutant has constitutively activated defense at chilling temperatures and exhibits dwarfism. RPP4 is a typical TNL that was first identified as a resistance gene against the oomycete pathogen Hyaloperonospora arabidopsidis races Emoy2 and Emwa1 (Holub et al., 1994; van der Biezen et al., 2002). Gain-of-function alleles of the atypical TNL CHS3 (CHILLING SENSITIVE 3) called chs3-1 and chs3-2d, exhibit autoimmune phenotypes and dwarfism (Yang et al., 2010; Bi et al., 2011). Intriguingly, CHS3 is an atypical TNL with a LIM (Lin-11, Isl-1 and Mec-3 domains) repressor domain at its carboxyl terminus. The gain-of-function mutations in 35 chs3-1 and chs3-2d are near or within the LIM domain, suggesting that interference with this domain results in activated CHS3 signalling (Yang et al., 2010; Bi et al., 2011). Additionally, there are examples of autoimmunity caused by loss of guardees. SAUL1 (SENESCENCE-ASSOCIATED E3 UBIQUITIN LIGASE 1) is an E3 ligase with overlapping functions with its close homolog PUB43 (PLANT U-BOX 43) as positive regulators of PRR-mediated immunity (Tong et al., 2017). Both the saul1-1 knockout mutant and SAUL1 overexpression plants exhibit (different) autoimmune phenotypes. Loss of SAUL1 results in the activation of the paired TNL SOC3 (SUPPRESSORS OF CHS1-2 3) and TIR-NB (TN) protein CHS1 (CHILLING SENSITIVE 1) (Figure 1.1D) (Zhang et al., 2017a). In contrast, overaccumulation of SAUL1 leads to activation of the paired TNL SOC3 with another TN protein TN2 (TIR–NBS 2) (Figure 1.1D) (Tong et al., 2017; Liang et al., 2019). Thus, SAUL1 levels are intricately guarded and SAUL1 and/or its targets may be targeted by yet to be identified effectors. It is unclear mechanistically how TN2 and CHS1 participate in effector recognition and SOC3 activation upon altered SAUL1 levels, especially since both proteins are missing LRR domains and both require SOC3. Another example is an Arabidopsis MAP kinase cascade guarded by the CNL SUMM2 (SUPPRESSOR OF MKK1 MKK2 1). MEKK1 (MAPK/ERK KINASE KINASE 1), MKK1/MKK2 (MAP KINASE KINASE 1/2), AND MPK4 (MAP KINASE 4) form a key MAP kinase cascade in plant immunity that positively contributes to immune signalling downstream of PAMP perception, and loss of these components results in autoimmunity (Gao et al., 2008). SUMM2 guards the MPK4 phosphorylation substrate CRCK3 (CALMODULIN‐BINDING RECEPTOR‐LIKE CYTOPLASMIC KINASE 3) and is activated when any cascade components are disturbed, resulting in autoimmunity (Figure 1.1B) (Zhang et al., 2012; Zhang et al., 2017b). 36 These findings suggest that SUMM2 guards the MEKK1-MKK1/MKK2-MPK4 kinase cascade through monitoring the phosphorylation levels of CRCK3. Similarly, the P. syringae phosphatase effector HopAI1 can inactivate MPK4, resulting in SUMM2 activation (Figure 1.1B) (Zhang et al., 2012). Interestingly, this MAPK cascade is also guarded in parallel by the TNL RPS6 (RESISTANCE TO PSEUDOMONAS SYRINGAE 6) (Figure 1.1B) (Takagi et al., 2019). 1.9.2 The snc1 autoimmune mutant One of the best studied autoimmune mutants is snc1, which carries a gain-of-function glutamate to lysine mutation in the typical TNL SNC1. The Glu to Lys mutation stabilizes SNC1 protein levels through an unknown mechanism and results in the activation of immune responses which are further amplified in a positive feedback loop (Yang and Hua, 2004; Cheng et al., 2011). snc1 plants exhibit dwarfism and have constitutive activation of defense responses including elevated SA levels, elevated expression of downstream defense gene PR1, and enhanced resistance to bacterial pathogens P. syringae pv. tomato DC3000 and P. syringae pv. maculicola ES4326 and to oomycete pathogen H. arabidopsidis (Li et al., 2001; Zhang et al., 2003). Since the plant size of snc1 is inversely correlated with defense activation, plant size can be used as a proxy for defense activation. Thus, the snc1 mutant is a useful genetic tool for genetic screens. Several forward and reverse screens have been completed in order to identify components affecting SNC1-mediated defense (Figure 1.8) (Johnson et al., 2012; Li et al., 2015a). These screens are discussed further below. Interestingly, multiple other autoimmune mutants have been found where the underlying cause of autoimmunity is SNC1 activation. Some of these include bon1-1, bal and srfr1-3 (Stokes et al., 2002; Yang and Hua, 2004; Kwon et al., 2004; Kwon et al., 2009; Yi and Richards, 2009; Li et al., 2010b). 37 1 Random mutagenesis (EMS) Look for enhancers or suppressors of snc1 2 Overexpression of candidate genes 3 Targeted mutagenesis of candidate genes (CRISPR-Cas9) snc1 suppressor Size enhancer Identify/characterize gene responsible for phenotype Defense Figure 1.8 A schematic for genetic screens in the snc1 background Different types of genetic screens can be done in snc1. (1) Random mutagenesis (using EMS or other methods) is a frequent method used for unbiased gene discovery. (2) Overexpression of candidate genes, such as E3 ligases, can reveal novel genes involved in immunity. (3) Targeted mutagenesis of candidate genes can be achieved by using CRISPR-Cas9 to selectively disrupt genes. Mutated or transformed plants can then be preliminarily screened for suppressors or enhancers of snc1 dwarfism. Finally, candidate genes can be identified and characterized further. 1.9.3 MOS and MUSE forward genetic screens Various forward genetic screens have been done using EMS (ethyl methanesulfonate), fast-neutron bombardment or T-DNA mutagenesis to identify enhancers or suppressors of snc1 autoimmunity (Figure 1.8). The mos (modifier of snc1) screen aimed to identify snc1 suppressors in the snc1 npr1-1 or snc1 background which may be positive regulators of defense (Johnson et al., 2012). In contrast, the muse (mutant, snc1-enhancing) screen aimed to identify snc1 enhancers in the mos2 snc1 npr1 and mos4 snc1 backgrounds which may be negative regulators of defense. The muse screen used mos2 or mos4 in the genetic backgrounds to enable better recovery of very dwarfed enhancers. From these suppressor and enhancer screens, genes involved in many different layers of NLR regulation were characterized (Figure 1.4) (Johnson et al., 2012; Li et al., 2015a). Many of these findings propelled the field of NLR regulation, and some of these genes were discussed in the NLR regulation section above and briefly mentioned here. 38 BAT2 domain containing protein MOS1, putative histone methylase component MOS9, and chromatin remodeler MUSE9/SYD contribute to epigenetic regulation (Li et al., 2010a; Li et al., 2011; Xia et al., 2013; Johnson et al., 2015). Nuclear pore complex component MOS3/NUP96 and putative mRNA-binding protein MOS11 are components that contribute to mRNA export (Zhang and Li, 2005; Germain et al., 2010). Putative RNA-binding protein MOS2, MAC component MOS4, MAC-associated cyclin L-like protein MOS12, putative nuclear import receptor MOS14, and RNA polymerase III subunit MUSE4/NRPC7 (NUCLEAR RNA POLYMERASE C, SUBUNIT 7) contribute to SNC1 splicing and/or miRNA biogenesis (Zhang et al., 2005; Palma et al., 2007; Xu et al., 2011; Xu et al., 2012; Wu et al., 2013; Copeland et al., 2013; Johnson et al., 2016; Jia et al., 2017). Ribosome-associated factor MUSE11/EXA1 (ESSENTIAL FOR POTEXVIRUS ACCUMULATION 1) is involved in translation (Wu et al., 2017a). Regulators involved in protein turnover include: E1 enzyme MOS5/UBA1 (UBIQUITIN-ACTIVATING ENZYME 1), E3 ligases MUSE1/2, E4 ligase MUSE3, unfoldase MUSE8/CDC48A (CELL DIVISION CYCLE 48), N-terminal acetyltransferase MUSE6/NatA (N-TERMINAL ACETYLTRANSFERASE A), putative kinase substrate MUSE7, SCF complex modulators MUSE13/14, and chaperone proteins MUSE10/HSP90.3 and MUSE12/HSP90.2 (Goritschnig et al., 2007; Huang et al., 2014a; Huang et al., 2014b; Xu et al., 2015b; Copeland et al., 2016; Huang et al., 2016b; Johnson et al., 2017; Dong et al., 2018). Nuclear transport receptor MOS6/IMPα-3 and nuclear pore component MOS7/NUP88 contribute to nucleocytoplasmic trafficking of multiple proteins and likely promotes SNC1 nuclear import (Palma et al., 2005; Cheng et al., 2009; Lüdke et al., 2021b). Some components important for downstream signalling include transcriptional corepressor MOS10/TPR (Zhu et al., 2010b) and hNLR MUSE15/ADR1-L2 (Dong et al., 2016b). The layer of regulation at which farnesyltransferase component 39 MOS8/ERA1 (ENHANCED RESPONSE TO ABSCISIC ACID 1) and mitochondrial import component MUSE5/AtPAM16 (PRE-SEQUENCE TRANSLOCASE-ASSOCIATED PROTEIN IMPORT MOTOR) function to modulate snc1 autoimmunity is unclear (Goritschnig et al., 2008; Huang et al., 2013). The finding that multiple layers of regulation control SNC1 homeostasis suggests that NLRs are tightly regulated to prevent autoimmunity. Interestingly, many regulators involved in protein turnover were uncovered. Additional regulation through E3 ligases is likely. Genes particularly relevant to this thesis include CDC48A, an unfoldase that mediates the turnover of polyubiquitinated proteins including SNC1 through the proteasome (Copeland et al., 2016), and MUSE13/14, TRAF domain proteins that contribute to SNC1 turnover through interacting with the SCFCPR1 E3 ligase complex. 1.9.4 Reverse genetic screens A problem that limits the effectiveness of random mutagenesis based forward genetic screens is genetic redundancy. Loss of single genes may not reveal phenotypic effects due to compensation from paralogs. Reverse genetic strategies can be used to circumvent this issue (Figure 1.8). One strategy to study new genes is overexpression analysis. For example, RNAi overexpression screens have been done to identify regulators of the MEKK1/SUMM2 pathway (Yang et al., 2020b). In addition, dominant-negative NLR overexpression screens have been done to identify guard NLR proteins of a potential guardee (Lolle et al., 2017). An alternative strategy is to directly mutate individual genes that are potentially interesting. Higher order mutants of paralogous genes could be generated to overcome redundancy, but this is a time-consuming process if done through traditional crossing. In addition, this strategy does not 40 overcome the problem of tandemly encoded genes. For example, this strategy could only be used to knock out three of the four bHLH84 family members (Xu et al., 2014a). In contrast, CRISPR-Cas9 technology can be used to quickly generate targeted deletion of gene families and is especially useful for tandemly encoded genes (Figure 1.8). Indeed, CRISPR-Cas9 has been used in the past to answer questions regarding hNLR paralogs and tandemly encoded paired NLRs (Wu et al., 2019; Liang et al., 2019). 1.10 Thesis objectives Although previous genetic screens completed in our lab have revealed the importance of SNC1 regulation, our understanding of the layers of regulation is incomplete. Furthermore, it is unclear whether there are additional components downstream of NLR activation. Thus, my objectives were to identify additional regulatory or signalling components of NLR-mediated defense. To address these objectives, two reverse genetic approaches were taken. 1.10.1 Characterization of SNIPER7 function in immunity In the first objective, I focused on the identification of E3 ligases that may recognize and degrade novel immune components. Many members of the very large plant E3 ligase family are uncharacterized, so I reasoned that I may identify additional interesting E3s and E3 targets involved in plant defense. A library of candidate E3 ligases was previously overexpressed in the sensitized snc1 background to identify enhancers or suppressors of snc1 dwarfism and immunity. Multiple E3 ligases were recovered from this screen including SNIPER1/2, SAUL1, and SNIPER4 that caused enhanced or suppressed immune phenotypes when overexpressed (Tong et al., 2017; Huang et al., 2018; Wu et al., 2020). SNIPER7 is another E3 ligase recovered from this screen that 41 had yet to be characterized. The primary objective of this first project was to identify the targets of SNIPER7 and characterize how SNIPER7 and these targets are involved in immunity. The results of this study are described in Chapter 2. 1.10.2 Identification and characterization of TRAF proteins involved in immunity In the second objective, I used CRISPR-Cas9 to delete members of a gene family suspected to be involved in immunity. TRAF domain proteins are involved in downstream signalling of mammalian immune receptors (Xie, 2013). In addition, MUSE13/14 are TRAF domain proteins that contribute to SNC1 turnover (Huang et al., 2016b). The plant TRAF family is largely uncharacterized, and many of the genes are encoded in tandem, which may have prevented their discovery and characterization in forward genetic screens. Thus, I hypothesized that some TRAF proteins may play roles in plant immunity. The objective of this project was to identify TRAF domain proteins that affect immunity and characterize how they contribute to immunity. The results of this study are described in Chapter 3. 42 Chapter 2: SCFSNIPER7 controls protein turnover of unfoldase CDC48A to promote plant immunity 2.1 Summary The unfoldase CDC48 (CELL DIVISION CYCLE 48) is highly conserved in eukaryotes, serving as an AAA+ ATPase to extract ubiquitinated proteins from large protein complexes and membranes. Although its biochemical properties have been extensively studied in yeast and animal systems, the biological roles and regulations of the plant CDC48s have only recently been explored. Here we describe the identification of a novel E3 ligase from the SNIPER (SNC1-INFLUENCING PLANT E3 LIGASE REVERSE GENETIC) screen, which contributes to plant defense regulation by targeting CDC48A for degradation. SNIPER7 encodes an F-box protein and its overexpression leads to autoimmunity. We identified CDC48s as interactors of SNIPER7 through immunoprecipitation followed by mass spectrometry proteomic analysis. SNIPER7 overexpression lines phenocopy the autoimmune mutant Atcdc48a-4. Furthermore, CDC48A protein levels are either reduced or stabilized when SNIPER7 is overexpressed or inhibited, respectively, suggesting that CDC48A is the ubiquitination substrate of SCFSNIPER7. Taken together, this study reveals a new mechanism by which a SCFSNIPER7 complex regulates CDC48 unfoldase levels and modulates immune output. 2.2 Introduction Higher plants employ cell-surface pattern recognition receptors (PRRs) and intracellular nucleotide-binding leucine-rich repeat receptors (NLRs) for pathogen detection (Jones and Dangl, 43 2006; Maekawa et al., 2011b; Cui et al., 2015; Jones et al., 2016). PRRs recognize conserved signatures of microbe/pathogen-derived molecules (termed microbe/pathogen-associated molecular patterns; M/PAMPs) such as flagellin, chitin, and lipopolysaccharides, and elicit responses termed PAMP-triggered immunity (PTI) (Boller and Felix, 2009; Bigeard et al., 2015). In contrast, NLRs recognize pathogen-secreted effectors through detection of effector-induced modifications of host targets, or through direct binding of the effector. NLR activation triggers an immune response often culminating in a cell death hypersensitive response (HR). Canonical plant NLRs can be classified into three subtypes depending on their N-terminal domains: Toll interleukin 1 receptor (TIR)-type NLRs (TNLs), coiled-coil (CC)-type NLRs (CNLs) and CCRPW8-type helper NLRs (hNLRs) (Peart et al., 2005; Collier et al., 2011; van Wersch et al., 2020). TNLs and CNLs act as sensor NLRs (sNLRs) to detect effectors and elicit downstream responses that often depend on hNLRs (Bonardi et al., 2011; Wu et al., 2019; Jubic et al., 2019). Reduced NLR levels can result in susceptibility to specific pathogens (Bent et al., 1994; Mindrinos et al., 1994). By contrast, NLR over-accumulations can lead to autoimmunity and compromised growth. An example in Arabidopsis is the snc1 gain-of-function mutant, which over-accumulates the TNL SNC1 (Zhang et al., 2003; Cheng et al., 2011). snc1 plants are resistant to virulent biotrophic pathogens but are dwarfed due to constitutive activation of defense. In previous suppressor and enhancer screens of snc1, we have identified multiple regulators involved in SNC1 gene expression, RNA processing, nucleocytoplasmic trafficking, protein folding, and post-translational modifications, demonstrating that NLR homeostasis is tightly regulated at multiple levels (Johnson et al., 2012; Li et al., 2015a; van Wersch et al., 2020). One major NLR regulation mechanism is ubiquitin-mediated proteasomal degradation (van Wersch et al., 2020). Ubiquitination is a common mechanism for protein regulation in eukaryotes 44 (Trujillo and Shirasu, 2010; Komander and Rape, 2012; Marino et al., 2012). It depends on an enzymatic cascade including an E1 Ub activating enzyme, an E2 Ub conjugating enzyme and an E3 Ub ligase. Substrate specificity is conferred mostly through the E3 ligase, which directly binds to the target and to the E1/E2 enzymes to mediate ubiquitination of the substrate. Upon successful polyubiquitination, substrates are unfolded by the CDC48 unfoldase for their degradation by the 26S proteasome (Bodnar and Rapoport, 2017; Cooney et al., 2019; Twomey et al., 2019). Thus, any disturbance of the activity of E3 ligases, CDC48, or the proteasome can lead to altered homeostasis of the target proteins. There are a few examples of ubiquitination-mediated regulation of TNLs. SCFCPR1 and MUSE1/MUSE2 are E3 ligases that regulate the turnover of the SNC1 and SIKIC1/2/3, respectively (Cheng et al., 2011; Dong et al., 2018). E3 ligase MIR1 regulates the levels of barley CNLs MLA1 and MLA10 (Wang et al., 2016). SCFSNIPER4 recognizes and targets the TRAF proteins MUSE13/14 for degradation, which may serve to restore SNC1 levels after infection (Huang et al., 2016b; Huang et al., 2018). Finally, SNC1 turnover is negatively regulated by CDC48A (Copeland et al., 2016). Despite the importance of E3 ligases in plant immunity and other biological processes, most members of the largely expanded plant E3 families are still uncharacterized (Mazzucotelli et al., 2006). We hypothesized that many of them may be regulators of key processes in plant immunity. In order to discover novel immune-related E3s, we conducted the snc1-influencing plant E3 ligase reverse genetic (SNIPER) screen (Tong et al., 2017; Huang et al., 2018). Candidate E3s were overexpressed in the snc1 mutant background to screen for enhancers or suppressors of the dwarfism and enhanced defense phenotypes of snc1. Here we report the identification and characterization of SNIPER7, a pathogen-induced F-box protein that acts as a positive regulator 45 of immunity. Our analysis reveals a novel mechanism by which an E3 ligase targets a major component of the proteasomal pathway to promote immunity. 2.3 Materials and methods 2.3.1 Plant materials and growth conditions Unless otherwise specified, all plants were grown at 22°C under long-day (16hr light/8hr dark) conditions. Light was provided at an intensity of ∼100 μmol/m2/s from fluorescent bulbs. Before planting, seeds were surfaced-sterilized using 15% bleach and 0.1% Tween-20, washed in ddH2O, suspended in 1% agar, and stratified for two days at 4°C. Seeds were sown on autoclaved Sunshine® Mix #4 soil, or on 1/2 Murashige and Skoog (MS) medium (PhytoTech Labs) supplemented with 0.5% Sucrose, 3g/L Phytogel™ (½ MSA), and the appropriate herbicide/antibiotic selection and adjusted to pH 5.6. All mutants and transgenic lines were generated in the Col-0 ecotype. The sniper7-1 (Salk_121520) mutant line was obtained from the Arabidopsis Biological Resource Centre (ABRC). The presence and homozygosity of the T-DNA insertion was determined by PCR using primers specific to the T-DNA and the gene (Table A.1). Other mutant lines, snc1, Atcdc48a-4, Atcdc48a-4 snc1, agb1-2, eds5-3 and Col-0 eds1-2 were previously described (Nawrath and Métraux, 1999; Ullah et al., 2003; Zhang et al., 2003; Bartsch et al., 2006; Copeland et al., 2016). 2.3.2 Plasmid construction and bacterial strains The SNIPER7 screen was previously described (Tong et al., 2017). The sequence-verified U-clone (Yamada et al., 2003) of AT4G03030 (pUNI51-AT4G03030) was obtained from the 46 ABRC and subcloned into the pGreen-ST1 (pGST1) vector using SfiI to generate pGST1-35S:HA-SNIPER7. The SNIPER7 fragment was digested from pUNI51-AT4G03030 using EcoRI and NotI and ligated into the digested backbone of pBS-Strep-FLAG to form pBS-Strep-FLAG-SNIPER7. KpnI and NotI were used to isolate the Strep-FLAG-SNIPER7 fragment which was then ligated into pCambia1300-35S:FLAG to form pCambia1300 35S:Strep-FLAG-SNIPER7. The EcoRI and Not1 digested SNIPER7 fragment was also ligated into pBS-GFP to form pBS-GFP-SNIPER7. KpnI and SacI were used to isolate the GFP-SNIPER7 fragment which was then ligated into pCambia1300-35S:FLAG to form pCambia1300-35S:GFP-SNIPER7. To generate pGreen0229-Han-35S:SNIPER7, the SNIPER7 fragment obtained from digesting pUNI51-AT4G03030 with KpnI and XbaI was ligated into pGreen0229-Han-35S. To generate TagRFP-tagged constructs, the TagRFP coding sequence was amplified from pHG40:p35S:TagRFP-Lti6b (Ghareeb et al., 2016) (Table A.1). The GFP fragment in pBS-GFP-SNIPER7 flanked by KpnI and EcoRI was replaced with the KpnI and EcoRI digested TagRFP PCR fragment to generate pBS-TagRFP-SNIPER7. TagRFP-SNIPER7 was amplified using specific primers (Table A.1), digested with KpnI and XbaI, and ligated into digested pBASTA-35S:3FLAG to produce pBasta-35S:TagRFP-SNIPER7. The F-box deletion constructs were made by overlap-extension site-directed mutagenesis using specific primers (Table A.1). The generated PCR product was digested with SfiI to replace the WT fragment bordered by SfiI sites in pBS-Strep-FLAG-SNIPER7, and pGST1-35S:HA-SNIPER7. pBS-Strep-FLAG-SNIPER7DN was subsequently digested with KpnI and NotI to isolate the tagged SNIPER7 fragment, and the fragment was ligated into KpnI and NotI-digested pCambia1300-35S:FLAG to form pCambia1300-35S:Strep-FLAG-SNIPER7DN. To generate 47 pCambia1300‐35S:cLUC-SNIPER7DN, the SfiI digested fragment from pGST-35S:HA-SNIPER7DN was ligated into the DraIII digested pCambia1300‐35S‐cLUC vector. To generate pBASTA-35S:CDC48A-FLAG and pCambia1300‐35S:CDC48A-HA-nLUC, CDC48A was PCR amplified (Table A.1), digested with KpnI and SalI, and ligated into KpnI and XhoI-digested pBASTA-35S:3FLAG and KpnI and SalI-digested pCambia1300‐35S‐HA‐nLUC, respectively. 2.3.3 Plant transformation and screening Agrobacterium tumefaciens cells carrying the binary vectors described above were used to generate transgenic lines through Agrobacterium-mediated floral-dip transformation (Clough and Bent, 1998). Transgenic plants carrying hygromycin resistance transgenes were screened on ½ MSA containing 100mg/L hygromycin B, whereas transgenic plants carrying phosphinothricin resistance transgenes were screened on soil by spraying 100mg/L Basta several times over several days on young seedlings. 2.3.4 Gene expression analysis RNA was extracted from 2-week-old plate-grown seedlings or from 4-week-old soil-grown plants using TRIzol/chloroform or a plant RNA extraction kit (Bio Basic; Cat#BS82314). The RNA was treated with DNase I (ThermoFisher Scientific) or RQ1 DNase (Promega), and 1μg was reverse-transcribed using RevertAid H Minus reverse transcriptase (ThermoFisher Scientific) or the OneScript™ cDNA Synthesis Kit (ABM). SsoFast EvaGreen Supermix mastermix or a SYBR premix (TaKaRa, Cat#RR82LR) was mixed with the cDNA according to recommendations by the manufacturer, and qPCR was completed using a CFX96 Real-Time PCR machine (Bio-Rad). 48 Melting curve analysis was completed to ensure single products were amplified. ACTIN8 or ACTIN1 was used to normalize the expression values (Table A.1). 2.3.5 Pathogen assays Infection assays were carried out as previously described. Briefly, 14-day-old soil-grown seedlings were sprayed with 105 Hyaloperonospora arabidopsidis Noco2 spores/mL H2O. Plants were transferred to a humid chamber (12h light/12h dark, 18°C) for 7 days. Spores were quantified 7d after inoculation using a hemocytometer. For bacterial infections, leaves of 4-week-old soil-grown plants were infiltrated with bacterial strains at indicated OD600 using a blunt-end syringe. Leaf disks from infected leaves were collected on 0- and 3-days post infiltration. Bacterial titres were quantified by plating serial dilutions on LB plates with appropriate antibiotics. 2.3.6 Total protein analysis 50-100 mg of plant tissue was harvested in tubes containing glass beads, frozen in liquid nitrogen and ground to a fine powder using a Precellys tissue homogenizer (Bertin Instruments). Total protein was extracted using extraction buffer (100mM Tris-HCl pH 8.0, 0.1% SDS, 2% β-mercaptoethanol), added at a 1:1 ratio. Cell debris was removed through centrifugation at 21,130 x g for 10 mins, and 4x SDS-PAGE sample buffer was added to the supernatant. Samples were boiled for 10 mins at 95°C and separated on 8-10% SDS-PAGE gels. Immunoblot analysis was carried out on nitrocellulose membranes using standard protocols. Antibody sources and dilutions can be found in Table A.2. Protein band intensity quantification was completed using ImageJ (Rueden et al., 2017). 49 2.3.7 Co-IP and IP-MS analysis IP-MS was performed following previously described protocols, with minor modifications (Xu et al., 2015c). Briefly, 2g of plate-grown transgenic Arabidopsis tissue was harvested per sample, frozen and ground using a mortar and pestle. Total protein was extracted using co-IP buffer (100mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 10% Glycerol, 2% polyvinylpolypyrrolidone (PVPP), 10mM DTT, 0.15% NP-40, 1x Protease inhibitor cocktail (#11836170001, Sigma-Aldrich), 1mM PMSF). Cell debris was removed through centrifugation at 21,130 x g for 10 mins. The supernatant (input) was mixed with Anti-FLAG M2 Affinity gel agarose beads (A2220, Sigma-Aldrich), and incubated at 4°C for 2 hours with gentle agitation. Beads were collected through centrifugation, washed 3 times with fresh co-IP buffer without PVPP and boiled in 2x SDS-PAGE sample buffer. Proteins eluted from beads were run briefly on an SDS-PAGE gel. The ProteoSilver™ Plus Silver Stain Kit (Sigma Aldrich), was used to visualize immunoprecipitated proteins. Highly abundant bands present in both samples were excised and discarded, and the remaining bands were sent for MS analysis. Co-IPs were performed similarly. N. benthamiana leaves were infiltrated with Agrobacterium containing the indicated constructs. 2g of leaf tissue per sample was collected 48h post-infiltration, frozen in liquid nitrogen and ground. Protein extraction was performed as described above. 2.3.8 Mass Spectrometric Analysis Protein bands on the SDS-PAGE gel were de-stained and in-gel digested with sequencing grade trypsin (10 ng/μL trypsin, 50 mM ammonium bicarbonate, pH 8.0) overnight at 37 °C. Peptides were sequentially extracted with 5% formic acid/50% acetonitrile and 0.1% formic acid/75% acetonitrile and then concentrated to ~ 20 μL. The extracted peptides were separated by 50 an analytical capillary column (50 μm × 10 cm) packed with 5 μm spherical C18 reversed phase material (YMC, Kyoto, Japan). A Waters nanoAcquity UPLC system (Waters, Milford, USA) was used to generate the following HPLC gradient: 0-30% B in 60 min, 30-70% B in 15 min (A = 0.1% formic acid in water, B = 0.1% formic acid in acetonitrile). The eluted peptides were sprayed into a Q Exactive mass spectrometer (ThermoFisher Scientific, San Jose, CA, USA) equipped with a nano-ESI ion source. The mass spectrometer was operated in data-dependent mode with one MS scan followed by ten HCD (High-energy Collisional Dissociation) MS/MS scans for each cycle. Database searches were performed on an in-house Mascot server (Matrix Science Ltd., London, UK) against IPI (International Protein Index) Arabidopsis thaliana protein database. The search parameters are: 8 ppm mass tolerance for precursor ions; 0.1 Da mass tolerance for product ions; two missed cleavage sites were allowed for trypsin digestion. The search results were filtered with both peptide significance threshold and expectation value to be below 0.05. 2.3.9 Nicotiana benthamiana transient expression, in vitro degradation and MG132 experiments Transient expression of constructs was carried out as previously described (Xu et al., 2015c). For MG132 experiments using Arabidopsis tissue, protein was extracted from CDC48A-GFP and CDC48A-GFP HA-SNIPER7 lines using extraction buffer (50mM Tris-HCl pH 7.5, 10mM NaCl, 10mM MgCl2, 5mM DTT, 10mM ATP, 1mM PMSF, 2% PVPP). Indicated concentrations of MG132 or DMSO was added and reactions were incubated for indicated times. 51 2.3.10 Split-luciferase complementation assay The split-luciferase reconstitution assay was previously described (Chen et al., 2008). Four-week-old N. benthamiana leaves were co-infiltrated with Agrobacterium carrying the indicated nLUC and cLUC constructs. 1mM luciferin was infiltrated 48 hours post infiltration and luminescence was imaged within 10 minutes. 2.3.11 Confocal microscopy A Leica TSC-SP5 confocal laser-scanning microscope controlled by the Leica LAS AF software was used to collect images of water-mounted leaf discs using a 20x /0.70 objective (PL APO, CS). GFP was excited using 488 nm of an argon laser line and TagRFP was excited using 561 nm of a diode-pumped solid-state (DPSS) laser. Emitted fluorescence was detected at 500-540 nm for GFP and 580-620 nm for TagRFP using HyD detectors. Chlorophyll autofluorescence was detected at 730-770 nm using a PMT detector. Images were sequentially scanned. ImageJ was used to generate maximum projections and to merge channels (Schindelin et al., 2012; Rueden et al., 2017). 2.3.12 Subcellular fractionation Subcellular fractionation was carried out using a previously described protocol (Xu and Copeland, 2012). Briefly, 12- to 14-day-old plate-grown Arabidopsis seedlings were collected, ground in liquid nitrogen, and mixed with extraction buffer. Total protein extract was obtained after sequential filtration and was centrifuged at 1500 x g for 10 minutes to obtain a supernatant (nuclei-depleted fraction) and pellet (nuclei-enriched fraction). The nuclei pellet was washed 5 52 times with wash buffer. 4x sample buffer was added to samples, and western blot analysis was completed as previously described (Table A.2). 2.3.13 Domain alignments, 3D structures F-box protein sequences and CDC48 protein sequences were aligned using MUSCLE version 3.8 (Edgar, 2004). Aligned sequences were colored using Boxshade 3.21. Homology modelling of Kelch domains was completed using SWISS-MODEL (Waterhouse et al., 2018), and 3D superimposition models were assembled using ChimaeraX (Goddard et al., 2018). Protein Data Bank IDs: 1U6D (KEAP1), 5A10.1A (TFP). 2.3.14 Phylogenetic analysis A. thaliana proteins containing Kelch domains (IPR006652, IPR011043, IPR011498, IPR015915) were obtained from Ensembl Plants through the R package biomaRt (Durinck et al., 2009). Of these, proteins further annotated as containing an F-box domain (IPR001810, IPR036047), or proteins that have an unannotated N-terminal domain that aligns well with the F-box domain, were considered FBKs. PAS/LOV domain and FBA domain containing FBKs were excluded from this analysis, since they are considered a different class of F-box proteins. Gene models for AT3G27910 and AT5G02990/AT5G02995 were likely misannotated, and their sequences were obtained from Uniprot (Q9LK86) and manually derived, respectively. One protein isoform per gene was manually chosen. Full length A. thaliana sequences, along with the single-copy FBKs from Chlamydomonas reinhardtii and Homo sapiens were aligned using PROMALS3D (Pei et al., 2008) guided by the KEAP1 structure (PDB ID: 1U6D). A phylogeny was inferred from the resulting alignment using IQ-TREE 1.6.11 (Trifinopoulos et al., 2016). 53 Branch support for the maximum-likelihood tree was assessed using ultrafast bootstrap approximation and the SH-like approximate likelihood ratio test (1000 replicates each). 2.3.15 Statistical analysis Graph data are presented as means with standard deviations represented as error bars. One-way analysis of variance (ANOVA) was used, followed by Tukey’s HSD test. Alternatively, unpaired Student’s T-test was used. Different letters are used to represent statistically significant differences. Means not sharing any letter are significantly different (at least p < 0.05). 2.4 Results 2.4.1 SNIPER7 overexpression enhances snc1 and triggers autoimmunity in WT plants We carried out a reverse genetic screen to identify novel E3s playing roles in plant immunity (Tong et al., 2017; Huang et al., 2018). In brief, we selected E3 candidate genes whose transcription was at least 1.7-fold induced after MAMP treatment. Candidate overexpression constructs were individually transformed into snc1, and transformants were screened for enhancement or suppression of the snc1 phenotypes. One candidate, SNIPER7, is highly expressed after flg22 or harpin HrpZ treatment, and after infection with the bacterial pathogen Pseudomonas syringae pv. tomato (P.s.t.) DC3000, suggesting that it may contribute to immune responses (Figure 2.1A-B) (AtGenExpress Consortium) (Waese et al., 2017). The SNIPER7 gene (AT4G03030) has a single exon encoding an F-box protein (Figure 2.2A). F-box proteins, named after the presence of an F-box domain, are E3 adaptors functioning together with SKP (ASK in plants) and CULLIN1s to form the SCF (SKP-CULLIN-F-BOX) E3 ligase complex. SNIPER7 has an N-terminal serine-rich region of unknown function, and an N- 54 Figure 2.1 SNIPER7 expression is induced by flg22 and HrpZ treatment and by infection with Pseudomonas syringae pv. tomato DC3000. (A) Comparison of SNIPER7 gene expression (microarray signal intensity) in Arabidopsis thaliana 1 and 4 hours after treatment with water, 1 μM flg22, or 10μM HrpZ. Bars represent means ± SD (n=3 biological replicates). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. Data obtained from publicly available gene expression databases. TAIR accession: ExpressionSet:1008080727. (B) Comparison of SNIPER7 microarray signal intensity 2, 6 and 24 hours after mock treatment with 10mM MgCl2 or infiltration with 108 cfu/mL Pseudomonas syringae pv. tomato (P.s.t.) DC3000. Bars represent means ± SD (n=3 biological replicates). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. Data obtained from publicly available gene expression databases. TAIR accession: ExpressionSet:1007966202. terminal F-box domain predicted to interact with the rest of the SCF complex (Figure 2.2A). Its C-terminal Kelch domain contains six Kelch repeats, which are predicted to be involved in protein-protein interactions with the E3 substrate (Figure 2.2A) (Schumann et al., 2011; Canning et al., 2015). The F-box of SNIPER7 is similar to that of other members of the F-box Kelch (FBK) protein family (Figure 2.2B). Although sequence identity is lowly conserved, homology modelling of the SNIPER7 Kelch domain revealed that it could form the beta-propeller tertiary structure seen in other Kelch-containing proteins (Figure 2.2C-D) (Li et al., 2004; Gumz et al., 2015). snc1 plants overexpressing 35S:HA-SNIPER7 exhibit enhanced dwarfism (Figure 2.3A). The HA-SNIPER7 fusion protein is well expressed, indicating that the observed phenotype is caused by HA-SNIPER7 over-accumulation rather than transgene-induced silencing of SNIPER7 55 Figure 2.2 SNIPER7 contains typical F-box protein domains. (A) Schematic diagram of the Arabidopsis SNIPER7 gene with predicted domains. Pale green – 5’ and 3’ UTRs, Dark green – Kelch Repeats, Red – F-box domain, Purple – Serine-rich region. Neon green – no predicted domains. The region removed in dominant negative F-box domain deletion constructs is indicated. Diagram is drawn to scale, with scale bar representing 100bp. (B) Alignment of the F-box domain of SNIPER7 with the consensus F-box domain from all Kelch-repeat type F-box proteins (Schumann et al., 2011). The F-box domain shares 35% identity and 46% similarity to the consensus sequence. (C) 3D models of the Kelch repeat domain of SNIPER7 (residues 93-442) based on homology modelling using Kelch domain templates from the human BTB-Kelch protein KEAP1. Grey – KEAP1 Kelch domain, Blue – SNIPER7 Kelch domain. (D) 3D models of the Kelch repeat domain of SNIPER7 (residues 93-442) based on homology modelling using Kelch domain templates from the Thiocyanate-forming protein (TFP) from the Brassicaceae plant Thlaspi arvense. Grey – TFP Kelch domain, Blue – SNIPER7 Kelch domain. 56 Figure 2.3 Overexpression of SNIPER7 enhances snc1 and causes autoimmunity in wild type Arabidopsis thaliana. (A) Morphology of 4-week-old soil grown plants of the indicated genotypes. Scale bar is 1 cm. (B) Western blot analysis of HA-SNIPER7 levels in 10-day old plate-grown plants of the indicated genotypes. Ponceau S staining serves as a loading control. Protein sizes in kDa are indicated to the right. (C) Morphology of 4-week-old soil grown plants of the indicated genotypes. Scale bar is 1cm. (D) Quantification of Pseudomonas syringae pv. maculicola (P.s.m.) ES4326 growth in leaves of 4-week-old plants of indicated genotypes. Plants were infiltrated with a bacterial inoculum of OD600 = 0.0001 and bacterial titre was measured at 0 and 3 days post infiltration. Bars represent means ± SD (n=5). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (E) Quantification of Hyaloperonospora arabidopsidis (H.a.) Noco2 sporulation on the indicated genotypes 7 days post inoculation with 105 spores/mL. Bars represent means ± SD (n=4). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (F) qPCR analysis of PR1 transcript levels in the indicated genotypes. Expression values were normalized with ACTIN1 expression. Bars represent means ± SD (n=3). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (Figure 2.3B). To determine whether wild-type (WT) plants overexpressing SNIPER7 exhibit immune phenotypes, 35S:HA-SNIPER7 was expressed in Col-0 (Figure 2.3B). Multiple independent transgenic lines are dwarfed with curly leaves, reminiscent of other autoimmune mutants (Figure 2.3C) (van Wersch et al., 2016). The dwarf morphology co-segregated perfectly with the transgene, indicating that HA-SNIPER7 overexpression is the cause for the phenotype. Consistently, HA-SNIPER7 overexpression lines displayed enhanced resistance against the virulent bacterial pathogen Pseudomonas syringae pv. maculicola (P.s.m.) ES4326 and the 57 oomycete pathogen Hyaloperonospora arabidopsidis (H.a.) Noco2 as compared to WT (Figure 2.3D-E). They also showed elevated defense marker gene Pathogenesis Related 1 (PR1) expression (Figure 2.3F). As a negative control to exclude the potential interference of the HA tag, a non-tagged WT SNIPER7 construct was generated and transformed into snc1. Transgenic snc1 lines overexpressing non-tagged SNIPER7 also exhibit enhanced dwarfism (Figure 2.4). Thus, the snc1-enhancement and autoimmune phenotypes of both the HA-tagged and non-tagged SNIPER7 overexpression lines are due to over-accumulation of the functional SNIPER7, and SNIPER7 acts as a positive regulator of immunity. Figure 2.4 Overexpression of non-tagged SNIPER7 enhances snc1. Morphology of representative 4-week-old soil grown Arabidopsis plants of the indicated genotypes. Scale bar is 1 cm. 2.4.2 SNIPER7 interacts with CDC48A Since SNIPER7 is likely part of an E3 complex, the autoimmunity caused by SNIPER7 overexpression is likely due to increased ubiquitination and degradation of its target(s). To identify potential substrates of SNIPER7, we generated FLAG-tagged SNIPER7 transgenic lines for immunoprecipitation coupled with mass spectrometry (IP-MS). Multiple independent transgenic lines expressing 35S:FLAG-SNIPER7 in snc1 exhibited enhanced dwarfism (Figure 2.5A), suggesting that the fusion protein is functional. E3 target proteins are quickly ubiquitinated and degraded upon interacting with the cognate E3 ligase, thereby impeding IP-MS analyses 58 Figure 2.5 SNIPER7DN interacts with CDC48A. (A) Morphology of 4-week-old soil grown Arabidopsis plants of the indicated genotypes. Scale bar is 1 cm. (B) Western blot analysis of immunoprecipitated FLAG-SNIPER7DN from an Arabidopsis stable transgenic line, compared with Col-0 control. Anti-FLAG agarose beads were used to immunoprecipitate (IP) FLAG-tagged protein from total protein extract. Protein sizes in kDa are indicated to the right. Input – total protein extract; IP: α-FLAG immunoprecipitates. Asterisk indicates predicted size of FLAG-SNIPER7DN. (C) Silver stained SDS-PAGE gel containing anti-FLAG protein immunoprecipitates. Protein sizes in kDa are indicated to the right. Asterisks indicate abundant non-specific bands excised from all samples prior to IP-MS analysis. (D) Top 10 interaction candidates of SNIPER7DN differentially identified by IP-MS analysis. Green – SNIPER7DN bait, red – identified CDC48 homologs, blue – SCF complex components, grey – others. Data from negative control Col-0 immunoprecipitates is included for reference. (E) Western blot analysis of a co-immunoprecipitation assay to test interaction between SNIPER7DN and CDC48A. Agrobacterium carrying FLAG-SNIPER7DN and CDC48A-GFP constructs were co-infiltrated into Nicotiana benthamiana leaves and samples were harvested after 48h. An empty vector construct was used as a negative control. Protein sizes in kDa are indicated to the right. (F) N. benthamiana split-luciferase reconstitution assay to test interaction between cLUC-SNIPER7DN and CDC48A-nLUC. Left panel: diagram showing the different combinations of gene constructs used, and the location on the leaf where they were infiltrated. Middle panel: image of the luminescence intensity detected after infiltration of the entire leaf with 1mM luciferin. Colour scale bar represents the luminescence intensity. Right panel: image of the leaf under white light, annotated with the areas where Agrobacteria carrying the construct combinations was infiltrated. MPK4-nLUC and cLUC-MKK6 were used as positive controls and PTRE1-nLUC and cLUC-SNIPER4DN were used as negative controls (Huang et al., 2018; Lian et al., 2018; Thulasi Devendrakumar et al., 2019). Agrobacterium strains were infiltrated at an OD600=0.2 each. 59 (Pierce et al., 2009). In order to circumvent such problem, we generated a dominant negative (DN) version of SNIPER7 by deleting the F-box domain (amino acids 37-84), which disables ubiquitination by preventing the protein from interacting with other SCF components (Figure 2.2A). Similar strategies have been successfully applied for other F-box proteins to stabilize their interactions with substrates rather than catalyzing their degradation, enabling the identification of interacting proteins via IP-MS (Lee et al., 2018). Transgenic FLAG-SNIPER7DN lines in snc1 were snc1-like (Figure 2.5A), confirming that the autoimmunity caused by SNIPER7 overexpression requires its F-box domain. Upon IP, a band corresponding to the predicted size of FLAG-SNIPER7DN was enriched from the transgenic line but not in Col-0 (asterisk in Figure 2.5B). Silver staining of the IP samples separated by SDS-PAGE did not reveal obvious differences between FLAG-SNIPER7DN and Col-0 control samples (Figure 2.5C). When MS analysis was carried out to identify differential peptides, SNIPER7 was among the top hits identified in the SNIPER7DN, but not in the Col-0 IP, suggesting that the IP-MS experiment was successful (Figure 2.5D, green; Figure 2.6). Components of the SCF complex, ASK1, ASK2, and CUL1, were also co-purified at high abundance (Figure 2.5D, blue). Strikingly, multiple isoforms of CDC48 (CDC48A, CDC48B, CDC48C) were uniquely detected (Figure 2.5D, red; Table A.3). In the control IP, no peptides of CDC48A and CDC48C were detectable, and only one matched peptide for CDC48B was detected. Due to the identification of the SCF components and SNIPER7 and the lack of CDC48s in our WT control samples, we reasoned that CDC48 is a specific interactor of SNIPER7. To test whether CDC48 indeed interacts with SNIPER7, we performed co-IP in Nicotiana benthamiana. CDC48A-GFP was efficiently immunoprecipitated by FLAG-SNIPER7DN, but not EV-FLAG, confirming that CDC48A and SNIPER7 interact in planta (Figure 2.5E). 60 Figure 2.6 SNIPER7 peptides were identified by IP-MS in FLAG-SNIPER7DN immunoprecipitates. SNIPER7 protein sequence with peptides identified during IP-MS underlined. Each line represents a different identified peptide. All identified peptides were unique to SNIPER7. Domains are colour coded as follows: blue – serine-rich region, orange – F-box domain, green – Kelch repeats. To test the SNIPER7-CDC48A interaction directly, we used a split-luciferase reconstitution assay (Chen et al., 2008). Agrobacteria carrying CDC48A-nLUC and cLUC-SNIPER7DN constructs were co-infiltrated in tobacco leaves. For negative controls, PTRE1, a proteasome regulator with a similar nucleocytoplasmic localization as CDC48A (Yang et al., 2016; Thulasi Devendrakumar et al., 2019), and a DN mutant of SNIPER4, a previously characterized F-box protein (Huang et al., 2018), were used. Strong luminescence was detected in leaf areas expressing CDC48A-nLUC and cLUC-SNIPER7DN, but not with the negative controls, further supporting that CDC48A and SNIPER7 directly and specifically interact in vivo (Figure 2.5F). 2.4.3 SNIPER7 overexpression lines resemble Atcdc48a partial loss-of-function phenotypes Loss-of-function mutations in CDC48B and CDC48C are WT-like and do not suppress or enhance snc1, whereas the partial loss-of-function mutant Atcdc48a-4 exhibits autoimmune phenotypes such as dwarfism and enhanced resistance to pathogens (Copeland et al., 2016). 61 Among the CDC48 family members in Arabidopsis, CDC48A is also the most abundant. Thus, we focused on CDC48A. Interestingly, Atcdc48a-4 plants highly resemble SNIPER7 overexpression lines (Figure 2.7A-B). In addition, Atcdc48a-4 snc1 double mutants are extremely dwarfed, similar to SNIPER7 overexpression lines in the snc1 background (Copeland et al., 2016) (Figure 2.7A-B). Figure 2.7 SNIPER7 overexpression phenotypes resemble those of cdc48a-4 mutant. (A) Morphology of 3-week-old soil grown Arabidopsis plants of the indicated genotypes. Scale bar is 1 cm. (B) Fresh weight of 4-week-old soil grown plants of the indicated genotypes. Bars represent means ± SD (n=6 biological replicates). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (C) Morphology of representative 4-week-old soil grown plants of the indicated genotypes. Scale bar is 1 cm. (D) Western blot analysis of SNC1 and SNIPER7-HA protein levels in 10-day old plate-grown plants of the indicated genotypes. Protein sizes in kDa are indicated on the right. Ponceau S staining serves as a loading control. 62 Since SNIPER7 overexpression phenocopies Atcdc48a-4, we tested if SNIPER7 functions in the same pathway as CDC48A. TNL activation depends on the key regulator ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) (Parker et al., 1996; Aarts et al., 1998). Previously, it was shown that Atcdc48a-4 autoimmune phenotypes can be largely suppressed by eds1-2 (Copeland et al., 2016). In comparison, the dwarf morphology of the HA-SNIPER7 overexpression line is also mostly suppressed by eds1-2 (Figure 2.7C). HA-SNIPER7 is still expressed in these lines (Figure 2.7D), suggesting that the suppression is not due to silencing of the HA-SNIPER7 transgene. In contrast, when the HA-SNIPER7 overexpression line was crossed with eds5-3, which is compromised in SA biosynthesis (Nawrath and Métraux, 1999; Rekhter et al., 2019), the dwarfism of HA-SNIPER7 overexpression is only mildly suppressed (Figure 2.7C). These results suggest that overexpression of SNIPER7 results in activation of mostly TNL-mediated immunity, similar to Atcdc48a-4. Atcdc48a-4 plants were previously found to accumulate higher SNC1 levels than WT. When SNC1 levels were examined, more SNC1 protein accumulated in SNIPER7 overexpression lines than WT (Figure 2.7D). To overcome the interference from positive transcriptional feedback, we also measured SNC1 in eds5-3 HA-SNIPER7 and eds1-2 HA-SNIPER7 plants, which block the transcriptional feedback upregulation from SA. In these backgrounds, SNC1 levels were still higher than WT, suggesting that SNIPER7 is a positive regulator of SNC1 accumulation. The similarity of SNIPER7 overexpression and Atcdc48a mutant phenotypes (Figure 2.7A-D), together with the direct interaction between SNIPER7DN and CDC48A (Figure 2.5E-F), suggest that SNIPER7 is likely directly involved in the turnover of CDC48A. 63 2.4.4 SNIPER7 is involved in the turnover of CDC48A likely through ubiquitination and the 26S proteasome As SNIPER7 and CDC48A interact (Figure 2.5), and SNIPER7 overexpression phenocopies Atcdc48a-4 (Figure 2.7), we hypothesized that CDC48A is the substrate of SCFSNIPER7. To test this, we first examined whether CDC48A protein levels are altered in the presence of SNIPER7. When FLAG-SNIPER7 and CDC48A-GFP were transiently co-expressed in N. benthamiana, CDC48A-GFP levels were consistently lower as compared to where CDC48A-GFP was expressed with empty vector (EV) (Figure 2.8A-B). In comparison, no significant changes to CDC48A-GFP levels were observed when FLAG-SNIPER7DN was co-expressed (Figure 2.8C-D), suggesting that the F-box domain is required for the reduction of CDC48A-GFP in the presence of FLAG-SNIPER7. To confirm our observations from N. benthamiana, we generated Arabidopsis double transgenic lines by transforming HA-SNIPER7 or HA-SNIPER7DN into a CDC48A-GFP line, and by crossing HA-SNIPER7 #13 with CDC48A-GFP. CDC48A-GFP levels were significantly lower in the presence of HA-SNIPER7 (Figure 2.9A-B). No difference in CDC48A-GFP transcript level was observed between CDC48A-GFP and HA-SNIPER7 CDC48A-GFP double transgenic plants (Figure 2.9C). Thus, the observed protein level differences of CDC48A-GFP in the presence of SNIPER7 are not due to transcriptional alterations. Since co-expression of the DN E3s might stabilize the substrate through interfering with the endogenous E3 or sequestering the substrate, we also tested the effect of HA-SNIPER7DN on CDC48A-GFP. Indeed CDC48A-GFP lines co-expressing HA-SNIPER7DN showed higher levels of CDC48A-GFP as compared to plants expressing CDC48A-GFP alone (Figure 2.9A). 64 Figure 2.8 CDC48A protein levels are reduced when SNIPER7 is co-expressed in Nicotiana benthamiana. (A) Western blot analysis of CDC48A-GFP protein levels when co-expressed with FLAG-SNIPER7 in N. benthamiana. Samples were collected 48h post infiltration of Agrobacteria containing the indicated constructs into N. benthamiana leaves. One representative replicate is shown. Protein sizes in kDa are indicated to the right. Values on bottom depict relative CDC48A-GFP band intensity as quantified by ImageJ and normalized to Ponceau S loading control. (B) Quantification of CDC48A-GFP protein levels when co-expressed with FLAG-SNIPER7 in N. benthamiana. Samples were collected 48h post infiltration with Agrobacterium containing the indicated constructs into N. benthamiana leaves. Bars represent the means ± SD of CDC48A-GFP band intensity as quantified by ImageJ and normalized to Ponceau S loading control (n = 4 biological replicates). Means not sharing any letter are significantly different (p < 0.01), determined using a paired Student’s T-test. (C) Western blot analysis of CDC48A-GFP protein levels when co-expressed with FLAG-SNIPER7DN in N. benthamiana. Samples collected 48h post infiltration of Agrobacteria containing the indicated constructs into N. benthamiana leaves. One representative replicate is shown. Protein sizes in kDa are indicated to the right. Values on bottom depict relative CDC48A-GFP band intensity as quantified by ImageJ and normalized to Ponceau S loading control. (D) Quantification of CDC48A-GFP protein levels when co-expressed with FLAG-SNIPER7DN in N. benthamiana. Samples were collected 48h post infiltration with Agrobacteria containing the indicated constructs into N. benthamiana leaves. Bars represent the means ± SD of CDC48A-GFP band intensity as quantified by ImageJ and normalized to Ponceau S loading control (n = 3 biological replicates). Means not sharing any letter are significantly different (p < 0.05), determined using a paired Student’s T-test. To determine if SNIPER7 promotes CDC48A turnover through the 26S proteasome, we first tested whether the proteasome inhibitor MG132 could protect CDC48A from degradation using a commonly used protocol (Osterlund et al., 2000; Wang et al., 2009; Liu et al., 2010; Li et al., 2019). Indeed, MG132 treatment consistently inhibited the degradation of CDC48A-GFP in 65 Figure 2.9 CDC48A protein levels are reduced by SNIPER7 overexpression and stabilized by SNIPER7DN overexpression in Arabidopsis stable transgenic plants. (A) Western blot analysis of CDC48A-GFP protein levels in CDC48A-GFP Arabidopsis lines independently transformed with HA-SNIPER7 or HA-SNIPER7DN. Total protein extracts of 10-day-old plate-grown seedlings were subject to immunoblot analysis using the indicated antibodies. Protein sizes in kDa are indicated to the right. Values on bottom depict relative CDC48A-GFP band intensity as quantified by ImageJ (see methods) and normalized to Ponceau S loading control. (B) Western blot analysis of CDC48A-GFP protein levels in F3 homozygous transgenic Arabidopsis plants co-expressing CDC48A-GFP and HA-SNIPER7. Total protein extracts of 10-day-old plate-grown seedlings was subject to immunoblot analysis using the indicated antibodies. Protein sizes in kDa are indicated to the right. Values on bottom depict relative CDC48A-GFP band intensity as quantified by ImageJ and normalized to Ponceau S loading control. (C) qPCR analysis of CDC48A-GFP transcript levels in the indicated genotypes. Expression values were normalized with ACTIN8 expression. Bars represent means ± SD. Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (D) Western blot analysis of CDC48A-GFP protein levels in 10-day old soil-grown plants of the indicated genotypes. Total protein was extracted and treated with 100µM MG132 or DMSO (-) for 2 hours at 4°C. Protein sizes in kDa are indicated to the right. Ponceau S was used as a loading control. Values beneath the anti-GFP blot depict relative CDC48A-GFP band intensity as quantified by Image J (see methods) and normalized to Ponceau S loading control. A summary of normalized relative CDC48A-GFP band intensity in 3 independent repeats is shown in the bottom graph. Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (E) Morphology of 4-week-old soil grown Arabidopsis plants of indicated genotypes. Scale bar is 1cm. (F) Fresh weight of 4-week-old soil grown plants of the indicated genotypes. Bars represent means ± SD (n=5). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. 66 the presence of HA-SNIPER7 (Figure 2.9D). This is consistent with a previous proteomics study showing that CDC48A was stabilized by MG132 in Arabidopsis (Kim et al., 2013). We further examined whether SNIPER7 expression results in increased ubiquitination. More ubiquitination was observed in the presence of SNIPER7 when compared with the parent line (Figure 2.9D). Lastly, we searched public large-scale Arabidopsis ubiquitome datasets for ubiquitinated CDC48A peptides. CDC48A was indeed found to be ubiquitinated in multiple studies (Maor et al., 2007; Saracco et al., 2009; Svozil et al., 2014), and several specific ubiquitination sites were identified (Figure A.1) (Kim et al., 2013; Walton et al., 2016). Interestingly, these sites exist on the predicted exterior, solvent-exposed surface of the hexamer, potentially providing accessibility to the E3. Together, these data suggest that CDC48A may be ubiquitinated and is likely degraded by the 26S proteasome upon SCFSNIPER7 recognition. These observations are consistent with the hypothesis that SNIPER7 targets CDC48A for ubiquitination and degradation. If CDC48A is the substrate of SNIPER7 and the autoimmunity of SNIPER7 overexpression lines is due to reduced CDC48A levels, introducing CDC48A into SNIPER7 overexpression plants might suppress their autoimmunity. The dwarfism of HA-SNIPER7 plants is indeed suppressed in the CDC48A-GFP HA-SNIPER7 double transgenic lines (Figure 2.9E-F) which still express HA-SNIPER7 (Figure 2.9B), supporting the notion that reduced CDC48A levels contribute to the autoimmunity of HA-SNIPER7 plants. 2.4.5 SNIPER7 mainly co-localizes with a CDC48A subpool in the nucleus If SNIPER7 is involved in CDC48A ubiquitination, the two proteins should co-localize to the same subcellular compartments. Previously, it was shown that CDC48A is localized in the cytosol and nucleus (Park et al., 2008). To determine the localization of SNIPER7, a GFP-67 SNIPER7 construct was generated. snc1 plants overexpressing GFP-SNIPER7 exhibit enhanced dwarfism, suggesting that this tagged protein is functional and should localize to its native subcellular location (Figure 2.10A-B). Figure 2.10 GFP-SNIPER7 overexpression enhances snc1 dwarfism. (A) Morphology of 3-week-old soil grown Arabidopsis plants of indicated genotypes. Scale bar is 1 cm. (B) Fresh weight of 3-week-old soil grown plants of the indicated genotypes. Bars represent means ± SD (n=6 biological replicates). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. In epidermal cells of stable transgenic Arabidopsis snc1, GFP-SNIPER7 resides mainly in the nucleus but could also be weakly detected in the cytoplasm (Figure 2.11A). To compare SNIPER7 and CDC48A localization, a TagRFP-SNIPER7 construct was generated for co-localization studies with CDC48A-GFP. When CDC48A-GFP and TagRFP-SNIPER7 were transiently co-expressed in N. benthamiana, TagRFP-SNIPER7 mainly co-localizes with the nuclear pool of CDC48A-GFP (Figure 2.11B-C). To further investigate the localization of SNIPER7 and CDC48A, we carried out subcellular fractionation on CDC48A-GFP HA-SNIPER7 double transgenic lines. Consistent with our microscopy results, CDC48A-GFP was present in both the nuclei-enriched as well as the nuclei-depleted fractions (Figure 2.11D). HA-SNIPER7 was enriched in the nuclear fraction, but 68 Figure 2.11 Confocal analysis of GFP-SNIPER7 and co-localization analysis of CDC48A-GFP and tagRFP-SNIPER7. (A) Confocal images of stable transgenic Arabidopsis snc1 leaves expressing 35S:GFP-SNIPER7 (line #3). Merge images are overlays of the previous three channels. Scale bars represent 10μm. Nuclei are labelled with asterisks. (B) Confocal images of Nicotiana benthamiana leaves transiently expressing CDC48A-GFP and TagRFP-SNIPER7 together (bottom panel) or alone (top and middle panel). Merge images are overlays of the GFP/RFP channels (CDC48A-GFP + TagRFP-SNIPER7) or of the GFP/RFP channel with the bright field channel (CDC48A-GFP, TagRFP-SNIPER7 alone). Scale bars represent 25μm. (C) Western blot analysis of CDC48A-GFP and TagRFP-SNIPER7 protein levels in N. benthamiana leaves used for confocal microscopy. Protein sizes in kDa are indicated to the right. Ponceau S staining is used as a loading control. Non-specific bands are marked with asterisks. (D) Cellular fractionation for nuclei enrichment in indicated Arabidopsis CDC48A-GFP lines expressing HA-SNIPER7. Total protein from 14-day-old plate-grown plants was extracted and fractionated by centrifugation. PEPC and H3 are used as cytosolic and nuclear markers, respectively. Protein sizes in kDa are indicated to the right. Ponceau S staining is used as a loading control. 69 was also detected in the nuclei-depleted fraction. Thus, CDC48A and SNIPER7 co-localize. As expected, with SNIPER7 presence, CDC48A-GFP levels were decreased in the nucleus. However, CDC48A-GFP levels in the nuclei-depleted fraction were also decreased, suggesting that cytosolic SNIPER7 likely still has significant impact on cytosolic CDC48A-GFP levels. 2.4.6 No obvious immune phenotypes were detected in sniper7 mutant or 35S:FLAG-SNIPER7DN lines To further investigate the role of SNIPER7 in immunity, we examined SNIPER7 loss-of-function phenotypes. A T-DNA knockout line sniper7-1 (Salk_121520) has significantly reduced SNIPER7 expression (Figure 2.12A) (Ikram et al., 2014), and the T-DNA is inserted in the exon in a region encoding the first Kelch repeat (Figure 2.2A), leading to a severe truncation. Since SNIPER7 overexpression enhances snc1 autoimmunity, we first investigated whether sniper7-1 could suppress snc1. However, sniper7-1 snc1 plants resembled snc1 morphologically and pathologically with regard to resistance against H.a. Noco2 (Figure 2.12B-C). An unknown E3 gene may be genetically redundant with SNIPER7, thereby compensating for the loss of SNIPER7 and masking the possible snc1 suppression phenotypes. To exclude this possibility, we examined the phenotypes of FLAG-SNIPER7DN overexpression since the DN form may lead to interference with the endogenous E3 and its redundant partners (Lee et al., 2018). snc1 plants overexpressing 35S:FLAG-SNIPER7DN remained snc1-like (Figure 2.5A). Thus, interference with SNIPER7 does not impair snc1 autoimmunity. As loss of SNIPER7 should lead to accumulation of its target, we also investigated if CDC48A overexpression lines could suppress snc1 autoimmunity. No suppression was observed when CDC48A-FLAG was overexpressed in snc1 (Figure 2.12D-E). This is consistent with the 70 Figure 2.12 Loss of SNIPER7 does not suppress snc1, nor result in enhanced susceptibility. (A) qPCR analysis of SNIPER7 transcript levels in the indicated genotypes. Expression values were normalized with ACTIN1 expression. Bars represent means ± SD (n=3). Means not sharing any letter are significantly different (p < 0.05), determined using an unpaired Student’s T-test. (B) Morphology of 4-week-old soil grown plants of the indicated genotypes. Scale bar is 1 cm. (C) Quantification of Hyaloperonospora arabidopsidis (H.a.) Noco2 sporulation on the indicated genotypes 7 days post inoculation with 105 spores/mL. Bars represent means ± SD (n=4 biological replicates). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (D) Morphology of 4-week-old soil grown Arabidopsis plants of the indicated genotypes. Scale bar is 1 cm. (E) Western blot analysis of CDC48A-FLAG levels in 10-day old plate-grown plants of the indicated genotypes. Ponceau S staining serves as a loading control. Protein sizes in kDa are indicated to the right. (F) Western blot analysis of SNC1 protein levels in 3-week-old soil-grown Arabidopsis plants of indicated genotypes. Protein sizes in kDa are indicated on the right. Ponceau S staining serves as a loading control. (G) Quantification of Pseudomonas syringae pv. tomato (P.s.t.) DC3000 growth in leaves of 4-week-old plants of indicated genotypes. Plants were infiltrated with a bacterial inoculum of OD600 = 0.0001 and bacterial titre was measured at 0 and 3 days post infiltration. Bars represent means ± SD (n=4). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (H) Quantification of P.s.t. DC3000 hrcC- growth in leaves of 4-week-old plants of indicated genotypes. Plants were infiltrated with a bacterial inoculum of OD600 = 0.02 and bacterial titre was measured at 0 and 3 days post infiltration. Bars represent means ± SD (n=6). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. (I) Quantification of P.s.t. DC3000 AvrRPS4 growth in leaves of 4-week-old plants of indicated genotypes. Plants were infiltrated with a bacterial inoculum of OD600 = 0.0001 and bacterial titre was measured at 0 and 3 days post infiltration. Bars represent means ± SD (n=4). Means not sharing any letter are significantly different (p < 0.01), determined using one-way ANOVA with post-hoc Tukey HSD test. 71 hypothesis that CDC48A is the substrate of SNIPER7 since snc1 lines overexpressing CDC48A should resemble sniper7-1 snc1 lines. Since loss of SNIPER7 may lead to increased CDC48A and therefore decreased SNC1 levels, we measured SNC1 in the sniper7-1 background. There was slight reduction of SNC1 levels in sniper7-1 (Figure 2.12F). We further tested whether sniper7-1 has other immune defects by performing infection experiments using virulent or avirulent pathogens. Compared to WT, sniper7-1 plants did not exhibit altered susceptibility to virulent P.s.t. DC3000, a type-III secretion system mutant strain P.s.t. DC3000 hrcC-, or an avirulent strain P.s.t. DC3000 AvrRPS4 (Figure 2.12G-I). Thus, loss of SNIPER7 does not seem to confer any obvious immune defects in basal immunity, PTI, or ETI. To further examine SNIPER7 redundancy, we reconstructed a FBK phylogeny in Arabidopsis. SNIPER7 does not seem to have a close homolog in its clade (Figure 2.13). Although the SNIPER7 branch could not be placed with confidence, previous phylogenies also suggest that SNIPER7 grouped in its own branch (Schumann et al., 2011). However, we cannot exclude the possibility that immune phenotypes in sniper7-1 are masked by an unrelated E3 with similar function. 2.5 Discussion 2.5.1 CDC48A is a multifunctional unfoldase CDC48, also referred to as VCP (Valosin-Containing Protein) and p97 in mammals, is a hexameric AAA-type ATPase highly conserved in eukaryotes. Each CDC48 subunit contains flexible N- and C-terminal domains that interact with cofactors, and two stacked ATPase domains, which generate energy through the binding and hydrolysis of ATP. The six subunits are organized 72 Figure 2.13 Phylogenetic analysis of FBK proteins in Arabidopsis. Maximum-likelihood tree of full-length Arabidopsis thaliana FBK proteins inferred using IQ-TREE with the VT+F+R6 substitution model. SNIPER7 is highlighted in green. Two branch support values, derived from 1000 replicates each, are annotated as SH-aLRT support (%)/UFBoot support (%). Tree was rooted using the single human FBK. Scale bar represents substitutions per site. 73 in a ring with a central pore where the ubiquitinated proteins are processed (Cooney et al., 2019; Twomey et al., 2019). CDC48 plays a critical role in the unfolding of ubiquitinated proteins and the extraction of proteins from membranes. In particular, the involvement of CDC48 in processes like endoplasmic reticulum-associated degradation (ERAD), mitochondrion-associated degradation (MAD), the ribosome associated degradation (RAD), chloroplast associated protein degradation (CHLORAD), inner nuclear membrane associated degradation (INMAD), and membrane protein extraction from lipid droplets has been previously characterized (Hirsch et al., 2009; Karbowski and Youle, 2011; Verma et al., 2013; Deruyffelaere et al., 2018; Kretzschmar et al., 2018; Ling et al., 2019; Huang et al., 2020b). Interestingly, CDC48 has also been implicated in both mammalian and plant immunity (Yen et al., 2000; Hao et al., 2015; Bègue et al., 2019). For example, mammalian p97 promotes proteasome-mediated degradation of the anti-viral PRR RIG-I (retinoic acid-inducible gene-I) to dampen anti-viral signalling in the absence of infection (Hao et al., 2015). In plants, CDC48A is involved in viral protein translocation, NLR degradation, signalling in response to the elicitin cryptogein, and MLO and SERK1 regulation/signalling (Aker and de Vries, 2008; Copeland et al., 2016; Bègue et al., 2019). In rice, mutations in OsCDC48/OsCDC48E confer early senescence and cell death and overexpression of an inducible CDC48A-DN construct in Arabidopsis can result in a chlorotic leaf phenotype (Park et al., 2008; Huang et al., 2016a; Ling et al., 2019; Shi et al., 2019), reminiscent of the Atcdc48a-4 mutant and SNIPER7 overexpression autoimmune phenotypes (Figure 2.3, Figure 2.7). The Arabidopsis genome encodes additional paralogs of CDC48A. CDC48B and CDC48C are phylogenetically closer and share a similar gene structure. CDC48D and CDC48E are more distantly related (Copeland et al., 2016). Both CDC48B and CDC48C were also uniquely 74 identified by SNIPER7 IP-MS (Table A.3), suggesting that they may work together with CDC48A, or share partial redundancy. As even more peptides for CDC48A and CDC48B were identified than the bait SNIPER7DN, we speculate that the SCF probably targets the hexameric ring of the CDC48 complex, and these CDC48 isoforms were likely co-purified due to their partial genetic redundancy. As CDC48D and CDC48E were not identified in the IP-MS data, they may function independently of CDC48A as was previously suggested (Copeland et al., 2016). Post-translational modifications (PTMs) of CDC48 play major roles in regulating CDC48 function (Hänzelmann and Schindelin, 2017). Several sites for lysine acetylation, palmitoylation, phosphorylation, SUMOylation, methylation, succinylation, S-glutathionylation, and ubiquitination on mammalian p97 have been identified through high-throughput proteomics (Hornbeck et al., 2015; Fang et al., 2016; Hänzelmann and Schindelin, 2017). Many of them have biochemical consequences, such as conformational changes that activate/deactivate p97 or interfere/promote interactions with its cofactors. Less is known regarding PTMs of plant CDC48s. The effects of phosphorylation and S-nitrosylation of CDC48 have been previously characterized (Rienties et al., 2005; Astier et al., 2012). Our findings suggest another level of regulation where CDC48A is ubiquitinated by SNIPER7 to promote its degradation. 2.5.2 The FBK protein SNIPER7 is part of an SCF E3 ligase complex that targets CDC48A The FBK protein family is characterized by a common protein architecture: an N-terminal F-box domain and C-terminal Kelch repeats (Schumann et al., 2011). Compared to mammals, it is extensively expanded in plants and its members play diverse roles in circadian rhythm, development, secondary metabolism, and immunity (Jin et al., 2004; Schumann et al., 2011; Naeem ul Hassan et al., 2015). SNIPER7 is conserved in the plant kingdom. Orthologs of 75 SNIPER7 were identified in eudicots (P. trichocarpa), monocots (O. sativa, S. bicolor), and in more ancient lineages (S. moellendorffii, P. patens) (Schumann et al., 2011). It would be interesting to test whether SNIPER7 orthologs regulate CDC48A orthologs in these species. SNIPER7 is likely a bona fide F-box adaptor since it interacts with ASK1/2 and CULLIN1 according to our IP-MS dataset, albeit a DN form of SNIPER7 lacking its F-box that mediates interaction with ASK1 was used in the IP experiment (Figure 2.5). As F-box proteins frequently form dimers (Skaar et al., 2013), the identification of SCF components could be explained by hetero-dimerization between the endogenous SNIPER7 and FLAG-SNIPER7DN. Consistently, peptides mapping to the deleted F-box domain were detected by MS (Figure 2.6). One example of F-box protein dimerization in plants is TIR1, which can homo-oligomerize to enhance binding with auxin to promote auxin signalling (Dezfulian et al., 2016). Alternatively, the subunits of the CDC48A hexamer may have simultaneously but independently bound both FLAG-SNIPER7DN and endogenous SNIPER7, leading to their indirect association and co-IP in our experiment. Although SNIPER7 can localize to the nucleus, we could not confidently predict any NLS site(s). It could be trafficked to the nucleus by associating with other proteins, such as the SCF complex. Interestingly CDC48A-GFP levels are lower in both the cytosol and nucleus in the presence of SNIPER7, despite the mainly nuclear accumulation of SNIPER7 (Figure 2.11). One possibility is that the amount of SNIPER7 in the cytosol is sufficient to reduce CDC48A-GFP. Alternatively, CDC48A may be shuttled between cytosol and nucleus, resulting in lower CDC48A levels in both compartments. Indeed, CDC48A can localize to the nucleus (Park et al., 2008) (Figure 2.11B). CDC48A was also recently shown to be involved in inner nuclear membrane mediated degradation (Huang et al., 2020b). Furthermore, yeast Cdc48 was shown to have a functional NLS (Lucena et al., 2015), which is somewhat conserved in Arabidopsis (Figure A.1). 76 Since CDC48A is a proteasome regulator that recognizes and unfolds ubiquitinated proteins, it is possible that instead of being the target of SNIPER7, CDC48A works with SNIPER7 to promote the turnover of an unknown SNIPER7 substrate (Ling et al., 2019; Huang et al., 2020b). However, several pieces of evidence argue against such hypotheses that CDC48A is merely an interactor and not the substrate of SNIPER7. First, the DN form of SNIPER7 could interact with CDC48A through IP-MS and co-IP (Figure 2.5). The DN form of SNIPER7 lacks the F-box domain required for interaction with the SCF complex components, and therefore cannot facilitate ubiquitination of its substrate. Thus, the interaction observed is more likely due to the recognition of CDC48A by the C-terminal substrate-specific Kelch repeats of SNIPER7. Secondly, other proteasome components were not specifically identified with CDC48A, suggesting that the interaction between CDC48A and SNIPER7DN is not due to a proteasomal process. Thirdly, SNIPER7DN, but not SNIPER4DN, was found to interact with CDC48A in split-luciferase experiments, suggesting that the interaction between SNIPER7 and CDC48A is specific (Figure 2.5). In addition, the genetic evidence is consistent with a substrate-E3 ligase relationship. Both the overexpression and knock-out phenotypes of SNIPER7 resemble CDC48A partial loss-of-function and overexpression phenotypes, respectively (Figure 2.7, Figure 2.12B-E), and overexpression of CDC48A can compensate the autoimmunity effect of SNIPER7 overexpression (Figure 2.9E-F). Finally, SNIPER7 can reduce the levels of CDC48A-GFP, which is dependent on a functional 26S proteasome (Figure 2.8, Figure 2.9). Altogether, our data strongly support that SNIPER7 targets CDC48A to promote its turnover. 77 2.5.3 SCFSNIPER7 boosts immunity by promoting CDC48A degradation Plants must maintain tight control over their immune signalling pathways to prevent their improper, resource-consuming activation. However, to provide sufficient protection in response to pathogen stress, these controls need to be alleviated in a timely and coordinated manner. Our current study suggests a mechanism by which immune responses are activated through promoting SNIPER7 activity (Figure 2.14): Upon infection, SNIPER7 transcription is induced (Figure 2.1). Figure 2.14 A working model: SNIPER7 modulates CDC48A levels to promote immunity. A working model of CDC48A regulation by SCFSNIPER7. (1) SNIPER7 is normally lowly expressed but can be induced upon pathogen infection and other stimuli. SNIPER7 protein mainly residues in the nucleus, but also the cytoplasm. (2) SNIPER7 interacts with ASK proteins through its F-box domain and together with RBX1 and CUL1, assembles into the SCFSNIPER7 E3 ligase complex. (3) SNIPER7 interacts with its substrate, CDC48A, likely through its Kelch repeat domain. (4) SCFSNIPER7 targets CDC48A for degradation through the 26S proteasome. (5) CDC48A levels decrease in both the nucleus and cytosol, leading to enhanced immunity, likely due to accumulation of NLRs like SNC1. 78 SNIPER7 resides mainly in the nucleus, but also in the cytosol (Figure 2.11). As part of the SCFSNIPER7 complex, SNIPER7 recognizes CDC48A and targets it for subsequent degradation via the 26S proteasome (Figure 2.8, Figure 2.9). SNIPER7 accumulation and CDC48A reduction results in SNC1 accumulation (and likely other NLRs), which may serve to enhance defense. Previous studies have shown that nuclear trafficking and accumulation of some NLRs is essential for full activation of defense responses (Wirthmueller et al., 2013). A nuclear pool of SNC1 is required for activation of SNC1-mediated defense (Cheng et al., 2009; Zhu et al., 2010a; Xu et al., 2014b). Besides SNC1, the accumulation of the Arabidopsis TNL RPS4 in the nucleus is required for resistance against Pseudomonas syringae AvrRps4 (Wirthmueller et al., 2007). Since nuclear accumulation of some NLRs promotes immune signalling, SNIPER7 overexpression results in SNC1 accumulation, and SNIPER7 mainly accumulates in the nucleus, we propose that degradation of CDC48A by SNIPER7 may serve to increase NLR concentrations largely in the nucleus to enhance immune signalling. Interestingly, sniper7-1 mutant does not exhibit any immune defects (Figure 2.12). This is unlikely due to genetic redundancy, as SNIPER7 does not seem to have an evolutionarily close homolog (Figure 2.13). In support, overexpression of FLAG-SNIPER7DN in snc1, like sniper7-1, does not alter snc1 autoimmunity. However, we cannot exclude the possibility that an unrelated E3 ligase exists which evolved to regulate CDC48A through convergent evolution. The most likely explanation for why loss of SNIPER7 does not cause enhanced disease susceptibility or snc1 suppression, is that CDC48A is not the rate-limiting factor in NLR degradation. Instead, the rate-limiting step may be other factors such as substrate-specific E3 ligases. In agreement, overexpression of CDC48A in snc1 does not result in suppression of its autoimmunity (Figure 2.12D). 79 Since loss of CDC48A is lethal (Park et al., 2008), SNIPER7 must be precisely regulated to avoid detrimental effects. This regulation occurs on at least two levels. First, since the expression of SNIPER7 is induced upon pathogen infection and PAMP treatment (Figure 2.1), SNIPER7 levels probably only accumulate in response to specific environmental changes such as infections. Secondly, SNIPER7 is mainly found in the nuclear compartment (Figure 2.11). Such temporal and spatial regulation may allow for stimulus-specific and controlled degradation of CDC48A while avoiding the detrimental effects from removal of an essential protein required for multiple other processes. 80 Chapter 3: TRAF domain protein TC1b is required for the defense responses mediated by plant NLR SNC1 3.1 Summary Immune receptors play important roles in the perception of pathogens and initiation of immune responses in both plants and animals. Intracellular nucleotide-binding domain leucine-rich repeat (NLR) type receptors can provide resistance against pathogens carrying virulence-promoting effectors. In the Arabidopsis thaliana mutant snc1 (suppressor of npr1-1, constitutive 1), a gain-of-function mutation in the NLR gene SNC1 leads to SNC1 overaccumulation and the constitutive activation of defense responses. From a CRISPR-Cas9-based reverse genetics screen in the snc1 autoimmune background, we identified that mutations in TRAF CANDIDATE 1b (TC1b), a gene encoding a TRAF (tumor necrosis factor receptor associated factor) domain protein, can suppress snc1 phenotypes. TC1b does not appear to be a general immune regulator as it is not required for defense mediated by other tested immune receptors. TC1b also does not physically associate with SNC1, affect SNC1 accumulation, nor affect signalling of the downstream regulator ADR1-L2 (ACTIVATED DISEASE RESISTANCE PROTEIN 1-L2), suggesting that TC1b affects snc1 autoimmunity in a unique way. TC1b can form oligomers and localizes to punctate structures of unknown function. The puncta localization of TC1b only strictly requires its coiled-coil domain. Overall, we uncover the TRAF domain protein TC1b as a novel contributor to plant immunity mediated by SNC1. 81 3.2 Introduction Microbial plant pathogens can deliver effector proteins into host cells to suppress plant immunity and promote virulence. Plant genomes encode a large collection of nucleotide-binding domain leucine-rich repeat containing (NLR) receptors which can directly or indirectly recognize the presence or biochemical activity of pathogen effectors to activate downstream defensive responses (Jones et al., 2016; Saur et al., 2021; El Kasmi, 2021). Various classes of NLRs exist including N-terminal Toll/Interleukin-1 receptor (TIR) domain containing TIR-type NLRs (TNLs), N-terminal coiled-coil domain containing NLRs (CNLs), and N-terminal RPW8-like coiled-coil domain containing helper NLRs (CCR-hNLRs). TNLs signal through two parallel downstream pathways. One consists of the lipase-like proteins ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) and PHYTOALEXIN DEFICIENT 4 (PAD4), which associates with the ACTIVATED DISEASE RESISTANCE PROTEIN 1 (ADR1) family of CCR-hNLRs (Wagner et al., 2013; Lapin et al., 2020; Dongus and Parker, 2021; Wu et al., 2021). TNLs such as SUPPRESSOR OF NPR1-1, CONSTITUTIVE 1 (SNC1), CHILLING-SENSITIVE MUTANT 1 (CHS1), and RECOGNITION OF PERONOSPORA PARASITICA 4 (RPP4) mainly rely on the EDS1/PAD4/ADR1s module (Glazebrook et al., 1997; Aarts et al., 1998; Li et al., 2001; van der Biezen et al., 2002; Zhang et al., 2003; Wang et al., 2013; Dong et al., 2016b; Wu et al., 2019). The second pathway consists of the lipase-like proteins EDS1 and SENESCENCE-ASSOCIATED GENE 101 (SAG101), which associates with the N REQUIREMENT GENE 1 (NRG1) family of CCR-hNLRs (Feys et al., 2005; Wagner et al., 2013; Lapin et al., 2019; Sun et al., 2021). CHS3 is an example of a TNL that relies mostly on the EDS1/SAG101/NRG1s module (Xu et al., 2015a; Wu et al., 2019). 82 Current experimental evidence and working models suggest that effector recognition by TNLs results in a conformational change which promotes TNL oligomerization into resistosomes and the induced proximity of the TIR domains (Williams et al., 2014; Zhang et al., 2017c; Duxbury et al., 2020; Ma et al., 2020; Martin et al., 2020). Interestingly, TIR domains have been shown to exhibit enzymatic activities yielding various chemicals that are recognized by downstream components EDS1/PAD4/SAG101, which seem to subsequently activate CCR-hNLRs ADR1s and NRG1s (Horsefield et al., 2019; Wan et al., 2019; Huang et al., 2022; Jia et al., 2022; Manik et al., 2022; Lapin et al., 2022; Yu et al., 2022b). It is thought that activation of CCR-hNLRs leads to the formation of a multimeric wheel-like resistosome complex with membrane-channel function, which triggers downstream signalling events (Jacob et al., 2021). Interestingly, some NLRs also associate with transcriptional co-repressors and transcription factors that are required for NLR function, suggesting that NLRs may also directly activate transcriptional changes (Cui et al., 2015; Tsuda and Somssich, 2015; Sun et al., 2020). One well studied TNL is SNC1 from Arabidopsis thaliana (hereafter Arabidopsis). The snc1 mutant contains a gain-of-function mutation which promotes SNC1 protein accumulation, resulting in the constitutive activation of defense responses and autoimmunity-related dwarfism (Zhang et al., 2003; Cheng et al., 2011). Various forward and reverse genetic screens using snc1 and other backgrounds revealed the importance of pre/post-transcriptional/translational control, nucleocytoplasmic transportation, and protein homeostasis in NLR regulation (Johnson et al., 2012; Li et al., 2015a). For example, two close homologs, MUSE13 (MUTANT, SNC1-ENHANCING 13) and MUSE14, were identified from a snc1 forward genetic screen and shown to facilitate SNC1 protein turnover via the 26S proteasome likely by interacting with proteins required for SNC1 degradation (Huang et al., 2016b). 83 Although remarkable progress has been made in understanding NLRs and TNLs in general, our understanding of SNC1 regulation and downstream signalling is still incomplete. One possible reason for why only a limited number of signalling genes were found through forward genetic screens in snc1 is that some regulators may have closely related homologs with overlapping functions, preventing them from being identified in mutagenesis-based screens. An alternative genetics strategy that circumvents this problem is to use targeted reverse genetic screens to investigate the function of candidate immune-related families. The TRAF (tumor necrosis factor receptor (TNF-R) associated factor) domain, also referred to as the MATH (meprin and TRAF-C homology) domain is a protein-protein interaction domain that can be found in diverse species (Zapata et al., 2007). TRAF domains are present in multiple members of a mammalian E3 ubiquitin ligase family, including TRAF1 to TRAF6, which serve as key signalling components downstream of many receptors including NLRs, Toll-like receptors (TLRs), retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), T-cell receptors, and various cytokine receptors (e.g. interleukin-1 receptor family, TNF-R) (Xie, 2013; Park, 2018). These proteins serve both as a scaffold to link receptors with downstream signalling components (kinases, ubiquitin ligases), and as an E3 ligase that mediates Lys63-linkage polyubiquitination of various substrates (Xie, 2013; Park, 2018). Interestingly, plants possess a largely expanded TRAF domain-containing protein family, though most lack the E3 ligase domain found in many human TRAFs. The Arabidopsis genome encodes more than 70 TRAF proteins, many of which are tandemly encoded in the genome or have close homologs (Oelmüller et al., 2005; Zhao et al., 2013; Kushwaha et al., 2016; Qi et al., 2022). Some members have been characterized to be involved in plant development, abiotic stress, and plant immunity (Kushwaha et al., 2016; Qi et al., 2022). In particular, MUSE13/14 are TRAF 84 proteins that contribute to plant immunity (Huang et al., 2016b). However, the functions of most TRAFs have not been explored. Given the parallels between mammalian and plant immunity, and since mammalian TRAF proteins work as immune adapters and signal transducers downstream of NLRs and TIR domain-containing TLRs (Xie, 2013; Park, 2018), we hypothesized that additional plant TRAF proteins may play a role in the regulation or signalling of TIR-containing TNLs like SNC1. The potential genetic redundancy of Arabidopsis TRAF proteins may have prevented characterization of this family. With the development of CRISPR-Cas9 as an efficient genome-editing tool in plants, we sought to study the potential involvement of the TRAF gene family in plant immunity in more detail. We carried out a reverse genetic screen where we created multi-gene knock-out mutations of members of the TRAF gene family in snc1, in order to find suppressor or enhancer candidates directly involved in innate immunity. Here we describe one gene recovered from this screen, TRAF CANDIDATE 1b (TC1b) which is required for full snc1 autoimmunity. TC1b is not required for the phenotypes of other tested autoimmune mutants, for the protein homeostasis of SNC1, or for ADR1-mediated downstream signalling. Thus, TC1b is a novel component of plant immunity involved in the regulation or activation of SNC1. 3.3 Materials and methods 3.3.1 Plant materials and growth conditions A temperature-controlled growth room was used to grow plants under 22°C and long day light (16h light/8h dark) conditions unless otherwise specified. Light was provided at an intensity of ∼100 μmol/m2/s from fluorescent bulbs. Severely dwarfed plants were grown in a 28°C chamber to suppress autoimmunity and yield seeds. For cold-induced autoimmune mutants, plants 85 were grown in a 16°C growth room under short day conditions or a 10°C chamber under constant light for the indicated times. Seeds were surface sterilized with 15% bleach or chlorine gas, stratified for at least 48h at 4°C in the dark. Seeds were sown on autoclaved Sunshine® Mix #4 soil (Sun Gro® Horticulture), or on half-strength Murashige and Skoog (MS) medium (PhytoTech Labs) supplemented with 0.5% Sucrose and 3g/L Phytogel™ (½ MSA) and adjusted to pH 5.6. All Arabidopsis plants used were in the Col-0 accession. The SALK_103885C (tc1a-1), CS356847 (tc1b-1), and SALK_037106 (tc1a-2) T-DNA lines were obtained from the Arabidopsis Biological Resource Centre (ABRC). Other mutant/transgenic lines (snc1, chs1-2, chs2-1, chs3-2d, mekk1-5, snc2-1D, snc1-r1, eds1-2, ADR1-L2D484V) were previously described (Li et al., 2001; Zhang et al., 2003; Zhang et al., 2010b; Huang et al., 2010; Bi et al., 2011; Roberts et al., 2013; Wang et al., 2013; Bjornson et al., 2014). eds1-25 is a novel CRISPR allele identified using a previously described CRISPR construct (Tian et al., 2021). PCR primers used are listed in (Table B.1). 3.3.2 Plasmid construction CRISPR constructs were made using two strategies. Initially, a ubiquitin promoter driven Cas9 expression system was used (Feng et al., 2013). Annealed oligonucleotides containing sgRNA1 were first cloned into pBS-pAtU6-sgRNA-pAtUBQ-Cas9-tUBQ using BbsI sites, and then the entire cassette was subcloned into binary vector pGreenII0229. An additional construct targeting the entire TC1 locus was generated using an egg-cell Cas9 expression system (Xing et al., 2014; Wang et al., 2015a). Golden Gate cloning was used to introduce two sgRNAs (sgRNA2/3) into the CRISPR binary vector pBEE401E using BsaI sites according to previously published protocols (Xing et al., 2014; Wang et al., 2015a). All sgRNAs are highly specific, with 86 potential off-target sites containing at least 4 base pair (bp) mismatches or a 1bp DNA/RNA bulge (Table B.2). For overexpression and IP-MS analysis, TC1b (with and without a stop codon) was cloned into constitutive expression vectors pBasta-35S:3FLAG, and pBasta-35S:2HA-TurboID using DraIII sites. For full length and truncated TC1b constructs used for confocal microscopy, TC1b and truncations were cloned into pBasta-35S:YFP using DraIII sites. Overlap extension PCR was used for internal domain deletion constructs. For the native promoter driven TC1b-GFP construct used for microscopy, a fragment containing TC1b and an upstream ~800bp sequence was cloned into pBasta-2HA-GFP using KpnI and PstI sites. Cloning primers are listed in Table B.1. 3.3.3 Plant transformation, screening, and Sanger sequencing Binary constructs were transformed into Agrobacterium tumefaciens GV3101 using electroporation. Transgenic lines were generated using Agrobacterium-mediated floral-dip transformation (Clough and Bent, 1998). 100mg/L Basta® (glufosinate-ammonium) was sprayed to select for transgenic plants on soil. For screening CRISPR-Cas9 mutagenized plants, enhancers and suppressors were PCR genotyped at the target locus to identify potential deletions. Deletion fragments for sequencing were PCR amplified from genomic DNA using specific primers, column purified, and sent for Sanger sequencing by a commercial service (Psomagen Inc.). Genotyping primers are listed in Table B.1. 3.3.4 Gene expression analysis A plant RNA extraction kit (Bio Basic; Cat#BS82314) and TRIzol were used to extract total RNA from 2-week-old plate-grown seedlings or from 4-week-old soil-grown plants. 1μg of 87 RNA per sample was reverse-transcribed using the OneScript™ cDNA Synthesis Kit (ABM). cDNA and cDNA-specific primers were mixed with a SYBR premix from a commercial kit (TaKaRa, Cat#RR82LR) according to manufacturer recommendations, and qPCR was completed using a CFX96 Real-Time PCR machine (Bio-Rad). Melting curve analysis was completed to ensure single products were amplified. ACTIN1 was used to normalize the expression values. Primers used are listed in Table B.1. 3.3.5 Infection assays 105 Hyaloperonospora arabidopsidis Noco2 spores/mL H2O were sprayed on 14-day-old soil-grown seedlings and allowed to grow in a humid chamber (12h light/12h dark, 18°C) for 7 days. A light microscope and a hemocytometer were used to quantify spores 7 days after inoculation. 3.3.6 Total protein analysis 50–100 mg of frozen plant tissue per sample was ground to a fine powder using glass beads and a Precellys tissue homogenizer (Bertin Instruments). Total protein was extracted using extraction buffer (100mM Tris-HCl pH 8.0, 0.1% SDS, 2% β-mercaptoethanol), added at a 1:1 ratio. High speed centrifugation (21 130 x g for 10 min) was used to remove cell debris. Samples were mixed with SDS-PAGE sample buffer, boiled for 10 min at 95°C, resolved on 6%, 8% or 10% SDS-PAGE gels, and transferred to nitrocellulose membranes. Immunoblot analysis was completed using standard protocols. Antibody sources and dilutions are listed in Table B.3. 88 3.3.7 TurboID proximity labelling and co-IP assays Nicotiana benthamiana plants were infiltrated with construct-carrying Agrobacterium cultures at the indicated ODs. For TurboID experiments, plants were infiltrated with 100µM of biotin after 48 hours and incubated for 1-2 hours. Around 2g of tissue per sample were then harvested and frozen in liquid nitrogen. Co-immunoprecipitation (IP) was performed following protocols described previously, with minor modifications (Xu et al., 2015). Frozen tissue was ground using a mortar and pestle or using glass beads and a benchtop homogenizer (BeadBug™, Benchmark Scientific Inc.). Total protein was extracted using co-IP buffer (100mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 10% Glycerol, 2% polyvinylpolypyrrolidone (PVPP), 10mM DTT, 0.15% NP-40, protease inhibitor cocktail (no. 11836170001; Sigma-Aldrich), 1 mM PMSF). High speed centrifugation at 21130 x g for 10 min was used to remove cell debris. The supernatant (input) was incubated together with anti-FLAG M2 Affinity gel agarose beads (A2220; Sigma-Aldrich) at 4°C for 2-3 h with gentle agitation. The beads, along with the immunoprecipitated proteins, were collected through gentle centrifugation, washed three times with fresh co-IP buffer without PVPP and mixed with SDS-PAGE sample buffer, and boiled for 10 min at 95°C. Standard western blot protocols were then used to visualize proteins. Antibody sources and dilutions are listed in Table B.3. 3.3.8 Mass Spectrometric Analysis An identical protocol described previously (section 2.3.8) was followed for sample preparation and mass spectrometric analysis. Matches that met several criteria were considered top hits: not identified in control samples and found in 3 of 4 or all 4 IP samples; not identified in control samples and found in 2 IP samples with at least 3 unique peptides per sample; identified in 89 1 or both control samples and all 4 IP samples, with 3 times the average peptides identified per IP samples compared to control samples. 3.3.9 Confocal microscopy To image water-mounted A. thaliana and N. benthamiana leaf discs, a Leica TSC-SP5 confocal laser-scanning microscope with a 20x/0.70 objective (PL APO, CS) or a Leica TSC SP8 Falcon confocal laser-scanning microscope with a 20x/0.75 objective (HCPL APO CS2) was used. YFP and GFP were excited using 514nm and 488nm wavelength light, respectively, emitted by an argon laser line or a pulsed white light laser. mCherry was excited using 561nm wavelength light from a diode-pumped solid-state (DPSS) laser. HyD detectors were used to detect emitted fluorescence at 525–560 nm for YFP, 500–540 nm for GFP, and 580-620 nm for mCherry. A PMT detector was used to detect chlorophyll autofluorescence at 740–770 nm. Images were sequentially scanned. ImageJ was used to generate maximum projections and to merge channels (Rueden et al., 2017). 3.3.10 Bioinformatics and generation of gene and protein diagrams Percent identity and similarity were computed using EMBOSS Needle (Madeira et al., 2019). Protein domain information and annotation data were either gathered from Interpro through the REST webservice client or through Uniprot (Jones et al., 2014; Mitchell et al., 2019; UniProt Consortium, 2019). Intrinsically disordered region predictions were obtained from MobiDB-lite (Necci et al., 2020) and D2P2 (Oates et al., 2013). Gene and protein diagrams and data graphs were generated using the gggenes, ggplot2, and tidyverse packages in R (R Core Team, 2019; Wickham et al., 2019). 90 3.3.11 Phylogenetic analysis Protein sequences of TC1a/b homologs from representative species were obtained from various sources (full list of accessions and data sources listed in Table B.4) (Goodstein et al., 2012; Van Bel et al., 2018; Li et al., 2018b; One Thousand Plant Transcriptomes Initiative, 2019; Grigoriev et al., 2021; Cunningham et al., 2022). Using the blastp utility on the source database websites, sequences significantly aligned with the Chara braunii TC1a/b sequence (uniport accession:A0A388JUC4) were collected (E-value< 1.0E-10, default settings). TC1a/b protein sequences were aligned using a local installation of MAFFT version 7.480 (Katoh and Standley, 2013) accessed through Mesquite version 3.61+ (build 936) (Madison and Madison, 2019). An iterative refinement algorithm (L-INS-i) was used for aligning full protein sequences. A model of sequence evolution was selected using the ModelFinder tool in IQ-TREE ver. 2.1.2 (Kalyaanamoorthy et al., 2017; Minh et al., 2020). JTT+F+G (Jones amino-acid exchange matrix, empirical amino-acid frequencies, discrete Gamma model of rate heterogeneity) was the best-fit model for the protein alignment according to both AIC and BIC criteria and was used as the substitution model for the tree inferences. Using IQ-TREE, 50 independent maximum likelihood (ML) tree searches were done to ensure a thorough search of the treescape, and to increase the chances of finding better trees (Zhou et al., 2018). The tree found with the best log-likelihood was used for further analysis. As the best ML gene tree was found more than once in independent tree searches, the number of replicates was deemed sufficient. Branch supports were obtained through non-parametric bootstrapping in IQ-TREE (n = 500). To determine whether the number of bootstrap replicates were sufficient, the a posteriori bootstrap convergence analysis tool in RAxML was used (Pattengale et al., 2009). A distance-based criterion (-I autoMR) was used to 91 test the convergence of majority-rules consensus trees, and the test suggested sufficient bootstrap replicates were done. A majority-rules consensus gene (protein) tree with a cut-off threshold of 0.75 was generated from the 500 bootstrap gene trees. A parsimony-based program, Notung version 2.9, was used for reconciliation of the non-binary gene (protein) tree and the binary species tree using the Resolve function (Durand et al., 2006). The species phylogeny of the species represented in the gene tree was based on the current consensus of eukaryote evolution (Leliaert et al., 2012; The Angiosperm Phylogeny Group et al., 2016; Burki et al., 2020). 3.3.12 Statistical analysis Graph data is presented as box-plots with the lower and upper hinges representing the first and third percentiles and the centre line representing the median. Whiskers extend from the lower and upper hinge to the smallest and largest values at most 1.5*IQR (inter-quartile range) of the hinge, respectively. Outliers are individually plotted and not linked by whiskers. One-way analysis of variance (ANOVA) was used, followed by Tukey’s HSD test. Compact letter display is used to represent statistically significant differences. Groups not sharing any letters are significantly different (at least p < 0.05). 3.4 Results 3.4.1 A reverse genetic screen to identify regulators of immunity Since TRAF domain proteins work downstream of many mammalian immune receptors, including TIR-domain containing receptors (Park, 2018), and previously studied plant TRAF proteins also contribute to immunity (Huang et al., 2016b), we sought to investigate this protein 92 family in more detail. In order to identify TRAF-domain containing proteins that may play a role in immunity, a CRISPR/Cas9-based reverse genetic screen was completed. Using the Uniprot and Interpro databases, predicted TRAF domain containing proteins were retrieved and analyzed (Jones et al., 2014; Mitchell et al., 2019; UniProt Consortium, 2019). We also classified the proteins into subfamilies according to a previous study of Brassicaceae family TRAF proteins (Zapata et al., 2007; Zhao et al., 2013). As previously noted, many of the TRAF genes are clustered, reminiscent of plant resistance genes (Figure 3.1). We leveraged publicly available gene expression data to identify TRAF genes that are induced or repressed upon pathogen or elicitor treatment, or that are regulated by master immune-related transcription factors like SARD1 (Sun et al., 2015; Waese et al., 2017). We also considered the domain architectures of the TRAF genes. Figure 3.1 Chromosomal locations of genes encoding TRAF domain containing proteins. Chromosome diagram showing genomic locations of genes encoding TRAF domain containing proteins in Arabidopsis. 93 Through these analyses, we restricted our screen to 12 candidate clades/groups with hypothesized immune function for further analysis. CRISPR constructs were designed to delete these genes either individually (unlinked genes) or as a cluster (tandemly encoded genes). Deletion constructs were transformed into the sensitized autoimmune snc1 background to enable the recovery of both enhancers and suppressors (Figure 3.2). Figure 3.2 CRISPR reverse genetic screen for TRAF candidates. A schematic describing the design of the genetic screen. CRISPR constructs were designed to target multiple closely-related TRAF-domain containing proteins. Each construct was individually transformed into snc1 plants using Agrobacterium-mediated floral-dip transformation. T1 plants were selected for the presence of the CRISPR transgene cassette and screened for suppressors or enhancers of the snc1 morphology. If no candidates were identified in the T1 generation, plants were harvested and rescreened in the T2 generation. The targeted CRISPR region in the candidate enhancers or suppressors was sequenced to identify mutations. Confirmed suppressors and enhancers were further characterized. 3.4.2 Mutations in TC1b suppress snc1 In this current study, we report on one candidate isolated from the screen. A CRISPR construct containing one sgRNA was designed to mutate a pair of neighbouring genes encoding 94 TRAF domain proteins: AT2G25330 and AT2G25320, which we hereafter refer to as TC1a (TRAF CANDIDATE 1a) and TC1b, respectively (Figure 3.3A). The sgRNA (sgRNA1) targets an identical sequence region in the first exon of TC1a and TC1b (Figure 3.3A). The TC1a/b CRISPR construct was transformed into snc1 plants, and two independent lines were recovered which partially suppressed the snc1 dwarfism (Figure 3.3B). To determine whether these lines also had suppressed disease resistance compared to snc1, we challenged the plants with the biotrophic oomycete pathogen Hyaloperonospora arabidopsidis (H.a.) Noco2. The suppressor lines were more susceptible to H.a. Noco2 compared with snc1 (Figure 3.3C). The targeted genomic regions with the predicted mutation sites were PCR amplified using specific primers and sequenced (Table B.1). The first line, snc1 tc1ab-1, contains a 3585bp deletion between TC1a and TC1b, resulting in a chimeric gene with a premature stop codon (Figure 3.3D,). The second line, snc1 tc1ab-2, carries a single bp deletion each in TC1a and TC1b (Figure 3.3D). To test whether mutations in both genes are required for the snc1-suppression phenotype, mutants containing T-DNA insertions in the exons of these genes were obtained (Figure 3.3A). When salk_103885c (named tc1a-1), which carries a T-DNA insertion in the first exon of TC1a, was crossed with snc1, the double mutant plants resembled snc1 (Figure 3.3E-F). However, when cs356847 (named tc1b-1), which carries a T-DNA insertion in the fourth exon of TC1b was crossed with snc1, the double mutant plants were larger than snc1 and resembled snc1 tc1ab-1 plants (Figure 3.3E-F). Furthermore, the suppression phenotype perfectly co-segregated with homozygosity of the tc1b-1 T-DNA allele. Thus, loss of TC1b but not TC1a can suppress snc1. As portions of TC1a/TC1b could still be expressed in the tc1ab-1, tc1ab-2, and tc1b-1 alleles, we considered whether the partial snc1 suppression phenotypes in the presence of these alleles were due to incomplete disruption of the genes. A second round of CRISPR mutagenesis 95 Figure 3.3 TC1b, a TRAF-ARM-CC protein, is required for full snc1 autoimmunity. (A) Gene diagram depicting gene models of TC1a and TC1b in their relative genomic context. Exons (blue rectangles), untranslated regions (empty rectangles), and introns (chevrons) are represented. Notable sites, such as T-DNA insertion sites (triangles) and CRISPR sgRNA target sites (circle with X) are highlighted. Axis numbers represent chromosomal coordinates. Arrow represents direction of translation. (B) Morphology of 3.5-week-old soil-grown plants of wild type (WT; Col-0), snc1, and two independent CRISPR gene-edited plants in the snc1 background. Scale bar is 1 cm. (C) Quantification of H.a. Noco2 sporulation on the indicated genotypes 7 days post inoculation with 105 spores/mL. (D) TC1a/b mutations in the snc1 tc1ab-1 and snc1 tc1ab-2 mutants. Horizontal arrows show the direction of the gene. PAM sequences (green text), sgRNA target sites (bold text), sequences hidden for conciseness (//), and deleted bases (-) are marked. Deletion size of alleles are indicated in parentheses on the right. Sanger sequencing results of the mutant allele is shown in chromatograms below the mutant sequence. Vertical arrows indicate mutation sites. (E) Morphology of 3-week-old soil-grown plants of the indicated genotypes are shown. Scale bar is 1 cm. (F) Quantification of fresh weight of plants from (E). (G) A protein diagram TC1a and TC1b proteins (white arrow in N to C orientation), and their predicted domains (coloured) drawn to scale. Axis numbers represent protein/domain length in amino acids. Box plots in (C) and (F) are overlaid with original data points (n = 6). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. 96 was completed (using sgRNA2/3) to fully delete both genes (Figure 3.3A, Table B.2). snc1 plants with the full TC1a/TC1b locus deleted still resembled tc1b-1 snc1, confirming that only TC1b is required for snc1 autoimmunity (Figure 3.4A-B). TC1b encodes a protein with four tandemly repeated TRAF domains, a central ARMADILLO (ARM)-type fold, and a long C-terminal coiled coil (CC) domain (Figure 3.3F). TC1a shares a similar gene architecture to the 5’ end of TC1b but is truncated (Figure 3.3A). As a result, TC1a only possesses the four TRAF domains. The N-terminal TRAF domains of TC1a and TC1b are quite similar, sharing 81% identity and 87% similarity (and 35% identity and 38% similarity overall). TC1a and TC1b are the sole members of a subfamily of TRAF domain containing proteins in Arabidopsis (Zapata et al., 2007; Zhao et al., 2013). Figure 3.4 Morphology and deletion mutations in tc1ab-3, tc1ab-4 lines. (A) Morphology of 3.5-week-old soil-grown plants of indicated genotypes. Scale bar is 1 cm. (B) Quantification of fresh weight of plants from (A). Box plots are overlaid with original data points (n = 6). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. 97 3.4.3 Overexpression of TC1b can enhance snc1 and cause autoimmunity in wild-type plants. Since loss of TC1b can suppress snc1, we examined whether overaccumulation of TC1b lead to the opposite effect. Indeed, snc1 plants overexpressing p35S::TC1b-FLAG exhibited enhanced dwarfism compared with snc1 (Figure 3.5A-B). Consistently, the enhanced dwarfism Figure 3.5 Overexpression of TC1b results in autoimmunity and enhances snc1 dwarfism. (A) Morphology of 3.5-week-old soil grown plants of indicated genotypes. Scale bar is 1cm. (B) Quantification of fresh weight of plants from (A) (n = 7). (C) Western blot analysis of TC1b-FLAG protein levels in plants in (A). Ponceau S staining serves as a loading control. Protein sizes in kDa are indicated to the right. (D) Morphology of 3.5-week-old soil grown plants of indicated genotypes. Scale bar is 1cm. (E) Quantification of H.a. Noco2 sporulation on the indicated genotypes 7 days post inoculation with 2x104 spores/mL (n = 8). Box plots are overlaid with dot plot of raw data points. Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. 98 phenotype co-segregated with the presence of the transgene. TC1b-FLAG protein of approximately the predicted size could be detected in the overexpression plants, suggesting that the phenotype is due to overaccumulation of the full-length protein (Figure 3.5C). In addition, stable transgenic lines overexpressing p35S::TC1b-FLAG in the WT background were stunted in stature (Figure 3.5B,D) and exhibited enhanced resistance to H.a. Noco2 (Figure 3.5E). Thus, TC1b is a positive regulator of SNC1-mediated immunity. 3.4.4 TC1b does not play a general role in receptor-mediated immunity Many previously characterized suppressors/enhancers of autoimmune mutants such as snc1 are general immune regulators (Johnson et al., 2012; Li et al., 2015a). Others play specific roles in NLR-mediated defense pathways (Cheng et al., 2011; Gou et al., 2012). To differentiate which category of regulator TC1b belongs to, autoimmune mutants activating different pathways were used for epistasis analysis (van Wersch et al., 2016). snc2-1D is a gain-of-function (g-o-f) mutant of the RLP SNC2 that causes constitutive activation of SNC2 defense pathways and autoimmunity (Zhang et al., 2010b). tc1b-1 could not suppress snc2-1D, suggesting that TC1b is not involved in the pathways of RLPs like SNC2 (Figure 3.6A). MEKK1 is a component of a MAP kinase cascade that is guarded by the CNL SUMM2. In mekk1-5, disruption of MEKK1 results in constitutive activation of SUMM2 and autoimmunity (Zhang et al., 2012; Bjornson et al., 2014). tc1b-1 could not suppress the stunted stature of mekk1-5, suggesting that TC1b may not work downstream of CNLs like SUMM2 (Figure 3.6B). chs3-2D is a g-o-f mutant of the atypical TNL CHS3, which mainly relies on the EDS1/SAG101/NRG1s downstream module (Bi et al., 2011; Wu et al., 2019). tc1b-1 could not suppress the severe dwarfism of chs3-2D, suggesting that TC1b may not be required for NRG1s-dependent TNLs (Figure 3.6C). Similar to snc1, the typical TNL 99 RPP4 and the truncated TN protein CHS1 both mainly rely on the EDS1/PAD4/ADR1s module (Wu et al., 2019). chs2-1, a g-o-f mutant of RPP4, and chs1-2, a g-o-f mutant of CHS1, both exhibit autoimmunity-related dwarfism and cell death when grown at low temperatures (Huang et al., 2010; Wang et al., 2013). tc1b-1 could not suppress the cold-induced cell death of chs2-1 or chs1-2 (Figure 3.6D-E), suggesting that TC1b may not be required for other ADR1s-dependent TNLs besides SNC1. Overall, TC1b does not seem to be a general immune regulator. Alternatively, TC1b may be a general regulator that only contributes to selective pathways. Figure 3.6 TC1b does not contribute to autoimmunity mediated by snc2-1D, mekk1-5, chs3-2D, chs1-2, or chs2-1. (A-C) Morphology of 3.5-week-old soil-grown plants of the indicated genotypes. Scale bar is 1cm. (D) Morphology of 2.5-week-old soil-grown plants of the indicated genotypes. Plants were grown at 22°C long day conditions for 10 days, then at 16°C short day conditions for 7 days. Scale bar is 1cm. (E) Morphology of 2.5-week-old soil-grown plants of the indicated genotypes. Plants were grown at 22°C long day conditions for 10 days, then at 10°C 24h light for 7 days. Scale bar is 1cm. 100 3.4.5 TC1b acts genetically upstream of ADR1s, but does not physically interact with SNC1 Since TC1b is not required for other immune receptor-mediated pathways, we focused on how TC1b affects SNC1-related autoimmunity. First, we investigated whether loss of TC1b affects SNC1 downstream signalling. SNC1 mainly signals through the EDS1/PAD4/ADR1s module (Zhang et al., 2003; Wu et al., 2019). An Asp to Val mutation in the MHD motif of ADR1-L2 results in autoactivation of the ADR1-L2 pathway and autoimmunity (Roberts et al. 2013). We tested whether TC1b is required for the autoimmunity of ADR1-L2D484V expressing plants. ADR1-L2D484V tc1b-1 plants are similar in stature to ADR1-L2D484V plants (Figure 3.7A), suggesting that TC1b does not act downstream of the ADR1s. Many regulators of SNC1 can directly interact with SNC1 (Zhu et al., 2010b; Cheng et al., 2011; Gou et al., 2012; Xu et al., 2014a). To test whether TC1b can associate with SNC1, a TurboID proximity labelling assay was used (Zhang et al., 2019). TurboID is a biotin ligase that can biotinylate proximal proteins in the presence of biotin (Branon et al., 2018). tc1ab-1 snc1 plants overexpressing p35S::TC1b-2HA-TurboID had enhanced dwarfism compared to tc1ab-1 snc1, suggesting that the fusion protein is functional (Figure 3.7B-C). TC1b-2HA-TurboID could not biotinylate or be co-immunoprecipitated (co-IPed) by SNC1-FLAG (Figure 3.7D). In a reciprocal assay, SNC1-2HA-TurboID could not biotinylate or be co-IPed by TC1b-FLAG but could self-biotinylate (Figure 3.7D). In contrast, TC1-2HA-TurboID could biotinylate and be co-IPed by TC1-FLAG and could self-biotinylate (Figure 3.7D). These results suggest that TC1b does not associate with SNC1 but can form homo-dimers or homo-oligomers. 101 Figure 3.7 TC1b acts upstream of ADR1s/PAD4 and does not interact with SNC1. (A) Morphology of 3.5-week-old soil-grown plants of the indicated genotypes are shown. Scale bar is 1cm. (B) Morphology of 3.5-week-old soil-grown plants of the indicated genotypes. Scale bar is 1 cm. (C) Quantification of fresh weight of plants from (B). Box plots are overlaid with dot plot of raw data points (n = 6). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (D) TurboID proximity labelling assay of Nicotiana benthamiana-expressed SNC1-FLAG or TC1b-FLAG with TC1b-2HA-TurboID or SNC1-2HA-TurboID. FLAG-tagged proteins were immunoprecipitated using anti-FLAG beads. Proteins were detected using indicated antibodies or streptavidin-HRP. Reference molecular mass markers are shown on right in kilodaltons. Input – total protein extract, IP FLAG – immunoprecipitated samples. Orange arrows – IPed FLAG-tagged protein, orange asterisk – co-IPed TurboID-tagged protein, blue asterisk – self-biotinylated TurboID-tagged protein, green asterisk – trans-biotinylated FLAG-tagged protein. 3.4.6 TC1b does not obviously affect SNC1 protein levels Multiple previous studies have revealed processes that modulate SNC1 function including protein turnover, nucleocytoplasmic trafficking, or expression regulation (Johnson et al., 2012; Li 102 et al., 2015a). Many genes required for snc1 autoimmunity affect protein homeostasis either directly (SNC1 degradation) or indirectly (reduced transcription). Since defects in SNC1 protein turnover or SNC1 gene expression both affect SNC1 protein accumulation, we measured SNC1 levels in the tc1b-1 and tc1ab-1 lines. SNC1 protein levels were similar in tc1b-1 plants compared with WT (Figure 3.8A). Since the level of SNC1 protein is low in WT plants and small differences may not be obvious, we also measured SNC1 protein level in the snc1 eds1-25 background. eds1-25 is a newly generated loss-of-function allele of EDS1a/b carrying a deletion of both EDS1a and EDS1b generated by CRISPR-Cas9 using a previously described construct (Tian et al., 2021). In snc1 eds1-25 plants, SNC1 protein accumulation is higher but the autoimmune positive feedback expression of SNC1 is blocked. Thus, snc1 eds1-25 plants provide more meaningful interpretation of alterations in SNC1 protein levels compared to snc1 plants. SNC1 protein levels were similar in tc1b-1 snc1 eds1-25 and p35S::TC1b-FLAG snc1 eds1-25 plants compared to snc1 eds1-25 (Figure 3.8B), suggesting that TC1b may not be involved in modulating SNC1 protein homeostasis or in SNC1 expression. Figure 3.8 TC1b does not obviously affect SNC1 protein levels. (A-B) Western blot analysis of SNC1 protein levels in indicated lines using a previously characterized SNC1-specific antibody. Reference molecular mass markers are shown on the left in kDa. Ponceau S staining serves as a loading control. 103 3.4.7 TC1b localizes to punctate structures in the cytoplasm Multiple components necessary for snc1 autoimmunity include nuclear transport receptors and nuclear pore complex components, which can affect the nucleocytoplasmic distribution of SNC1 (Palma et al., 2005; Cheng et al., 2009). Some of these regulators have been shown to localize to the nuclear envelope or have nucleocytoplasmic distributions. To test whether TC1b may be a nuclear or cytoplasmic regulator, or whether TC1b may be associated with the nuclear envelope, we investigated the subcellular localization of TC1b. snc1 plants expressing either p35S or pTC1b driven fluorescent protein-tagged TC1b exhibited enhanced dwarfism, suggesting that the fusion proteins are functional and localize to the native compartments (Figure 3.9A-D). When transiently expressed in Nicotiana benthamiana, TC1b-YFP appears to localize in punctate structures in the cytoplasm of epidermal cells (Figure 3.9E). To ensure the localization was not a result of protein aggregation due to overexpression, a native-promoter driven pTC1b::TC1b-2HA-GFP construct was also used. TC1b-2HA-GFP was localized to similar puncta in N. benthamiana (Figure 3.9F). The observed puncta do not resemble ER or mitochondrial localization patterns. To identify whether these puncta correspond to other known subcellular compartments, we used fluorescent-tagged markers for co-localization analysis. TC1b-YFP does not co-localize with a peroxisomal marker, px-rb, suggesting that these puncta are not peroxisomes (Figure 3.10) (Nelson et al., 2007). We also considered that TC1b may localize to a unique compartment. TC1b possesses multiple domains, TRAF, ARM, and CC, which have all been shown to be involved in homotypic and/or heterotypic protein-protein interactions in other proteins. It is possible that the puncta are large hetero or homo-dimeric/oligomeric complexes. A second hypothesis is based on the observation that the properties of these puncta are reminiscent of membraneless organelles (liquid- 104 Figure 3.9 TC1b-YFP and TC1b-2HA-GFP are functional and localize to cytoplasmic puncta. (A) and (C) Morphology of approximately 3.5-week-old soil-grown plants of the indicated genotypes. Scale bar is 1 cm. (B) and (D) Quantification of fresh weight of plants from (A) and (C), respectively. Box plots are overlaid with dot plot of raw data points (n = 6). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (E-F) Confocal scanning laser microscopy images of Nicotiana benthamiana epidermal cells expressing p35S::TC1b-YFP (E) or pTC1b::TC1b-2HA-GFP (F). Bars = 30µm. Z-stack images are shown. 105 Figure 3.10 TC1b-YFP does not co-localize with peroxisomal marker px-rb. Confocal scanning laser microscopy images of Nicotiana benthamiana epidermal cells expressing p35S::TC1b-YFP with px-rb, an mcherry-tagged peroxisome marker. Bars = 30µm. Both a single plane image, and a Z-stack image is shown. The overlap merge image is a composite of the three channels. liquid phase separated compartments). Proteins that undergo liquid-liquid phase transitions usually possess intrinsically disordered regions that drive the formation of these structures (Uversky, 2017). TC1b was predicted to contain some disordered regions (Figure 3.3G). To test these hypotheses, we generated TC1b-YFP domain truncations to determine which domains or regions are necessary for localization to cytoplasmic puncta. Interestingly, deletion of the N-terminal or C-terminal disordered regions did not abolish the localization of TC1b-YFP, suggesting that these puncta may not be membraneless organelles. In addition, the TRAF and ARM deletion variants could still localize to cytoplasmic puncta, suggesting they are not solely required (Figure 3.11A-B). However, when the CC domain and the C-terminal disordered region were deleted together, the cytoplasmic puncta could no longer be observed, suggesting that the CC domain is necessary for localization (Figure 3.11A-B). 106 Figure 3.11 The puncta localization of TC1b relies on the CC domains. (A) Confocal scanning laser microscopy images of Nicotiana benthamiana epidermal cells expressing 35S::TC1b-YFP or truncations. Bars = 30µm. Z-stack images are shown. (B) A scale diagram of truncations of TC1b-YFP made for domain deletion analysis. Domains are color-coded. Predicted sizes of truncated proteins are noted on the right in kDa. Internal domain deletions are shown with a line. Axis numbers show relative protein length in amino acids. 3.4.8 Identification of TC1b interactors Some TRAF domain proteins work together with or inhibit TRAF-E3s through TRAF-TRAF associations (Qi et al., 2017). In another example, mammalian TRAF1, which is a TRAF domain protein that is missing the RING (really interesting new gene) E3 ligase domain, can interact with other TRAF-E3 proteins (TRAF2) (Park, 2018). Thus, we sought to identify potential 107 TRAF-domain proteins, and other interactors that may mediate dimerization/oligomerization, localization, or function of TC1b. Immunoprecipitation coupled with mass-spectrometry (IP-MS) was carried out using TC1b-FLAG-expressing plants in the tc1ab-1 snc1 background. Since TC1b-FLAG overexpression lines have severely dwarfed phenotypes, they likely possess a significantly different proteome than WT or GFP-overexpression lines (Figure 3.12A-B). An alternative control line was generated where a stop codon was inserted between the TC1b gene and the FLAG epitope. This TC1bSTOP-FLAG control line closely resembles the TC1b-FLAG lines (Figure 3.12A-B). TC1b-FLAG could be enriched upon co-IP of tissue from the tagged lines, but not control lines Figure 3.12 TC1b-FLAG can be immunoprecipitated. (A) Morphology of 3.5-week-old soil-grown plants of the indicated genotypes. Bar, 1cm. (B) Quantification of fresh weight of plants from (A). Box plots are overlaid with dot plot of raw data points (n = 6). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (C) Western blot analysis of immunoprecipitated TC1b-FLAG from Arabidopsis transgenic lines, compared with non-tagged controls. Approximate protein sizes are marked on right in kDa. (D) Silver-stained SDS-PAGE gel containing immunoprecipitated TC1b-FLAG from Arabidopsis transgenic lines. Arrows show the bait protein. Asterisks denote high abundance proteins that were excised and sequenced separately. The data from high and low abundance bands were combined upon data analysis. 108 (Figure 3.12C). Furthermore, the enriched TC1b-FLAG can be detected on a silver-stained SDS-PAGE gel (Figure 3.12D). Thus, TC1b-FLAG can be successfully immunoprecipitated. By analyzing peptides from the IP-MS experiment, we identified potential candidate interactors that may function with TC1b (Table 3.1). Peptides from TC1b were the most abundantly detected peptides (Table 3.1), confirming that the IP was successful. Interestingly Table 3.1 Potential TC1b interactors from IP-MS analysis. Top candidate proteins that interact with TC1b are listed. Columns state the molecular weight (MW), total number of queries for control and IP samples, and the number of control and IP samples that detected each protein. Blue – TC1b, red – ribosomal proteins, black – proteasomal proteins, grey – other proteins. Locus Protein name/description MW (Da) Ctrl # queries IP # queries # Ctrl samples matched # IP samples matched AT2G25320 TC1b 187558 2 2895 2 4 AT3G07110 Ribosomal protein L13 family protein 23451 11 41 2 4 AT5G02870 RIBOSOMAL LARGE SUBUNIT 4 (RPL4) 44694 4 26 1 4 AT3G24830 Ribosomal protein L13 family protein 23444 0 20 0 2 AT5G10360 RIBOSOMAL PROTEIN SMALL SUBUNIT 6B (RPS6B) 28144 0 19 0 3 AT5G09590 HEAT SHOCK COGNATE (HSC70-5) 72946 0 17 0 3 AT1G07320 RIBOSOMAL PROTEIN L4 (RPL4) 30540 3 17 1 4 AT2G47610 Ribosomal protein L7Ae/L30e/S12e/Gadd45 family protein 29111 0 14 0 2 AT2G07698 ATPase, F1 complex, alpha subunit protein 85879 0 13 0 2 AT1G54270 Eukaryotic initiation factor 4A-2 (EIF4A-2) 46733 0 12 0 2 AT3G52580 Ribosomal protein S11 family protein 16228 0 12 0 2 AT1G02500 S-ADENOSYLMETHIONINE SYNTHETASE 1 (SAM1) 43131 0 11 0 2 AT4G18100 Ribosomal protein L32e 15493 1 10 1 4 AT1G02780 EMBRYO DEFECTIVE 2386 (emb2386) 24591 0 9 0 3 AT1G18540 Ribosomal protein L6 family protein 26136 0 8 0 2 AT4G34200 EMBRYO SAC DEVELOPMENT ARREST 9 (EDA9) 63286 0 7 0 3 AT3G56910 PLASTID-SPECIFIC 50S RIBOSOMAL PROTEIN 5 (PSRP5) 16351 1 7 1 4 AT1G35680 CHLOROPLAST RIBOSOMAL PROTEIN L21 (RPL21C) 24024 0 5 0 4 AT5G65220 PLASTID RIBOSOMAL PROTEINS OF THE 50S SUBUNIT 29 (PRPL29) 19365 0 5 0 3 AT3G13300 VARICOSE (VCS) 145630 0 5 0 3 AT5G02450 60S ribosomal protein L36-3 12182 0 4 0 4 AT1G09090 RESPIRATORY BURST OXIDASE HOMOLOG B (RBOHB) 96329 0 4 0 4 AT1G36240 Ribosomal protein L7Ae/L30e/S12e/Gadd45 family protein (RPL30A) 12310 0 4 0 4 AT5G23540 Mov34/MPN/PAD-1 family protein (RPN11) 34332 0 4 0 3 AT3G08940 LIGHT HARVESTING COMPLEX PHOTOSYSTEM II (LHCB4.2) 24986 0 4 0 3 AT5G58330 Lactate/malate dehydrogenase family protein 48286 0 4 0 3 AT5G42570 B-cell receptor-associated 31-like protein 28551 0 4 0 3 AT3G01370 CRM FAMILY MEMBER 2 (CFM2) 114527 0 4 0 3 AT4G24820 26S proteasome regulatory subunit Rpn7 44254 0 3 0 3 AT3G46440 UDP-XYL SYNTHASE 5 (UXS5) 38365 0 3 0 3 AT2G26080 GLYCINE DECARBOXYLASE P-PROTEIN 2 (GLDP2) 113703 0 3 0 3 109 many ribosomal components and proteasomal components were identified to be specifically enriched in TC1b-FLAG samples, suggesting that TC1b may function in these processes. 3.4.9 TC1a is not conserved and TC1A overexpression leads to autoimmunity TC1b is an ancient gene and can be found in early diverging species such as green algae (Figure 3.13A). On the other hand, TC1a is a recent duplication of TC1b, and only exists in a relatively small clade of Brassicaceae species (Figure 3.13A-B). TC1a is absent in the Brassica genus but is present in some close Arabidopsis relatives (Figure 3.13B). Furthermore, at least 70 ecotypes of Arabidopsis contain alleles of TC1a that have high-impact SNPs and indels, compared to 1 ecotype carrying a deleterious TC1b allele, suggesting that TC1a function may be dispensable (Figure 3.13C). Interestingly, an allele of TC1a (SALK_037106, named tc1a-2) that carries a T-DNA insertion in the promoter of TC1a could enhance snc1 dwarfism (Figure 3.3A, Figure 3.14A-B). Since this phenotype is the opposite of the tc1a-1 allele, we considered whether the tc1a-2 allele may be a g-o-f function allele that overexpresses TC1a. This overexpression could be from an expression-promoting element on the T-DNA or from disrupted negative regulatory elements in the 5’ UTR of TC1a due to the inserted T-DNA. Alternatively, the T-DNA could induce the expression of the 3’ downstream TC1b. When the expression of TC1a and TC1b was measured in tc1a-2 plants, TC1a, but not TC1b was expressed significantly higher, suggesting that the overexpression of TC1a in tc1a-2 can enhance snc1 autoimmunity (Figure 3.14C-D). 110 Figure 3.13 Phylogenetic analysis of TC1a and TC1b in select plant species. (A) Cladogram showing the reconciliation between the consensus maximum likelihood TC1a/b protein tree and the species tree, generated using Notung. Inferred duplication events are denoted with a red label at nodes. Loses are denoted in tip labels as "LOST", along with the species name(s). Homologs within the same species are denoted alphabetically. The tree was rooted between Chlorophyta and Streptophyta clades. (B) Table showing occurrence of TC1a and TC1b in various Brassicaceae plants, along with a phylogenetic tree of species relationships. (C) Number of Arabidopsis ecotypes that carry high impact SNPs or indels in TC1a or TC1b. Populus trichocarpa_TC1_aPopulus trichocarpa_TC1_bPopulus trichocarpa_TC1_cArabidopsi haliana_ TC1_aArabidopsis thaliana_TC1_bSolanum lycopersicum_TC1_aDaucus carota_TC1_aLOST in Arabidopsis thalianaPopulus trichocarpa_TC1_dLOST in Solanum lycopersicum, Daucus carotaPapaver somniferum_TC1_aLOST in Oryza sativa, Asparagus officinalisAmborella trichopoda_TC1_aThuja plicata_TC1_aPinus taeda_TC1_aSalvinia cucullata_TC1_aSalvinia cucullata_TC1_bCeratopteris richardii_TC1_aCeratopteris richardii_ TC1_bSelaginella moellendorffii_ TC1_aSelaginella moellendorffii_ TC1_bLeiosporoceros dussii_TC1_aParaphymatoceros hallii_TC1_aMarchantia polymorpha_TC1_aPhyscomitrium patens_TC1_aPhyscomitriumpatens_TC1_bPhyscomitriumpatens_TC1_dPhyscomitrium patens_TC1_cChara braunii_TC1_aKlebsormidium nitens_TC1_aChlorokybus atmophyticus_TC1_aMesostigma viride_TC1_aLOST in Chlamydomonas reinhardtiiUlva mutabilis_TC1_aCoccomyxa subellipsoidea_TC1_aTetraselmis striata_TC1_aLOST in Ostreococcus lucimarinusDDDDDDDDDD 111 Figure 3.14 Overexpression of TC1a can enhance snc1 autoimmunity. (A) Morphology of 4-week-old soil grown plants of indicated genotypes. Bar, 1cm. (B) Quantification of fresh weight of plants from (A). Box plots are overlaid with dot plot of raw data points (n = 4). Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. (C-D) Quantification of TC1a (C) and TC1b (D) expression in indicated genotypes using qPCR. Expression values were normalized with ACTIN1 expression. Bars represent means ± SD. Means not sharing any letter are significantly different (p < 0.05), determined using one-way ANOVA with post-hoc Tukey HSD test. 3.5 Discussion 3.5.1 TC1b as a novel TRAF protein involved in plant immunity In our study, we revealed that TC1b, but not its close homolog TC1a, is required for snc1 autoimmunity (Figure 3.3, Figure 3.4). According to epistasis data, TC1b is not a general downstream regulator of NLRs or PRRs, or a general regulator of NLR expression or homeostasis 112 (Figure 3.6, Figure 3.8). TC1b likely also does not directly work in a complex with SNC1 (Figure 3.7), suggesting that it may play a more indirect role in SNC1 activation or regulation. Multiple plant TRAF domain proteins have been previously reported to be involved in immunity (Qi et al., 2022). Deubiquitinating enzymes UBP12 (UBIQUITIN-SPECIFIC PROTEASE 12) and UBP13, which possess an N-terminal TRAF domain, are negative regulators of bacterial resistance against Pseudomonas syringae pv. tomato DC3000 in Arabidopsis and of the HR (hypersensitive response) elicited by the PRR Cf-9 with Avr9 in tobacco (Ewan et al., 2011). In tomato, SlSINA3 (SEVEN IN ABSENTIA 3), a TRAF-RING E3 ligase, ubiquitinates and mediates degradation of a defense related transcription factor SlNAC1 (NAM, ATAF1,2, CUC2 1) (Miao et al., 2016). Interestingly, co-expression of SlSINA1/2/3/5/6 can suppress the HR elicited from transient expression of various autoactive NLRs in tobacco, whereas independent expression of SlSINA4 can cause HR, suggesting they may differentially regulate of NLR- mediated immunity (Miao et al., 2016; Wang et al., 2018). Finally, RESTRICTED TEV MOVEMENT 3 (RTM3) is a coiled-coil domain containing TRAF protein that prevents the long-distance movement of potyviruses through an unclear mechanism (Cosson et al., 2010). The finding that TC1b contributes to SNC1-mediated responses provides another example of a TRAF domain protein that is involved in immunity. Interestingly, TRAF proteins MUSE13/14 are negative regulators of SNC1 defense (Huang et al., 2016b), whereas TC1b plays an opposite role. It is possible that one regulator may negatively regulate the other to prevent its activity. This could occur through TRAF-TRAF interactions as both MUSE13/14 and TC1b have TRAF domains that may hetero-dimerize/oligomerize. However, MUSE13/14 were not identified in the IP-MS data, suggesting that: it does not interact with TC1b-FLAG, it is quickly degraded during immunoprecipitation, and/or it is lowly expressed. 113 Interestingly, MUSE13/14 is targeted by E3 ligase complex SCFSNIPER4 for degradation, suggesting that its native protein level may be low (Huang et al., 2018). MUSE13/14 is a regulator of SNC1 homeostasis whereas TC1b does not affect SNC1 protein levels (Figure 3.8), further suggesting these regulators work differently. Of note, one study reported that MUSE13/14 interacts with TRAF domain containing RING-E3 ligases SINAT1/2/6 (SEVEN IN ABSENTIA OF ARABIDOPSIS THALIANA 1/2/6) and regulates the degradation of autophagy related proteins AUTOPHAGY PROTEIN 6/13 (ATG6/ATG13) (Qi et al., 2017; Qi et al., 2020). It is unclear whether TC1b functions through interacting with SINATs, or other TRAF proteins. 3.5.2 Potential mechanism for the suppression of snc1 by loss of TC1b The finding that TC1b does not contribute to the autoimmunity of autoimmune mutants representing different receptor pathways except snc1 suggest that TC1b is not a general regulator (Figure 3.6). This is in contrast to many of the previously identified regulators important for snc1 autoimmunity which contribute to general immunity such as the nuclear pore component MOS7/NUP88, and MOS4-associated complex components (Zhang et al., 2005; Palma et al., 2007; Monaghan et al., 2009; Cheng et al., 2009; Xu et al., 2011; Xu et al., 2012). TC1b also likely does not contribute to pathways of other known SNC1 protein homeostasis regulators like the SNC1-targeting E3 ligase CPR1 (Cheng et al., 2011; Gou et al., 2012) or NLR-targeting E3 ligases SNIPER1/2 (Wu et al., 2020). There are several known regulators that affect SNC1 function but are not involved in SNC1 protein homeostasis. One potential group of regulators that TC1b may function with is the bHLH84 transcription factor family. Similar to TC1b loss of function mutants, snc1 autoimmunity is partially blocked in triple mutants of the bHLH84 family members (Figure 3.3) (Xu et al., 2014a). 114 In addition, overexpression of both TC1b and bHLH84 family members results in enhanced dwarfism in snc1 (Figure 3.5) (Xu et al., 2014a). Importantly, neither TC1b nor bHLH84 affect SNC1 protein levels (Figure 3.8) (Xu et al., 2014a). It is possible that TC1b and bHLH84 members function together, or that TC1b affects the expression or homeostasis of bHLH84 members or vice versa. TC1b could also function with TOPLESS/TOPLESS-RELATED (TPL/TPR) proteins, transcriptional co-repressors which have been shown to be required for snc1 autoimmunity (Zhu et al., 2010b). The knockout and overexpression phenotypes of TC1b and TPR are similar, and SNC1 expression was not significantly different in TPR mutant plants (Figure 3.3, Figure 3.5) (Zhu et al., 2010b). Although TPL/TPR was suggested to be required for downstream transcriptional signalling, it is also possible that TPL/TPR may be SNC1 guardees since TPR1 can associate with SNC1. TC1b could function through TPL/TPR1 using various mechanisms. TPR1 is known to be SUMOylated, which inhibits its repressor activity (Niu et al., 2019). In addition, TPR2 and TPR3 are negative regulators of SNC1, potentially by competing with TPR1 for SNC1 binding (Garner et al., 2021). TC1b could potentially interfere with or activate these processes. Another potential mechanism is that TC1b may work with MUSE1/2, RING-type E3 ubiquitin ligases, or with SIDEKICK SNC1 1/2/3 (SIKIC1/2/3), SNC1-related TNLs that were previously reported to be targeted by MUSE1/2 for degradation (Dong et al., 2018). Loss of both MUSE1 and MUSE2 results in severe dwarfism that is dependent on SNC1, whereas overexpression of MUSE1 can fully suppress snc1 phenotypes. TC1b could potentially contribute to turnover or inactivation of MUSE1 and/or MUSE2, thus suppressing snc1. Alternatively, TC1b may directly promote the stability or accumulation of the SIKICs. 115 3.5.3 Implications of TC1b protein domains The TC1b TRAF domains likely serve scaffold functions, similar to mammalian TRAF proteins. Evidence from mammalian TRAF structures show that TRAF-TRAF interactions are mediated through extensive interfaces, and TRAF-interactor/receptor interactions are mediated through a shallow minimal interface (Park et al., 1999; Park, 2018). Presumably, the TC1b TRAF domains mediate both intramolecular TRAF-TRAF interaction between its domains, and intermolecular TRAF-TRAF interactions to form larger TRAF complexes similar to mammalian trimeric complexes (Figure 3.7D) (Park, 2018). These larger complexes could consist of homo-dimers/oligomers, hetero-dimers/oligomers (for example with TC1a, TRAF-BTBs, SINA/SINATs), or both. It is unclear why TC1b possesses four TRAF domains. Some multi-TRAF domain proteins are also found in the genomes of carrot and C. elegans (e.g. MATH-4, MATH-26, MATH-28, MATH-42). It is possible that the larger number of TRAF domains allows for recruitment of more units of a putative interactor. Two scenarios for TC1b TRAF function can be imagined. TC1b could bind to previously oligomerized receptors/binding partners through the TRAF domains to mediate downstream signalling through its C-terminal domains (similar to the mammalian TRAF model). Alternatively, the signal-transduction mechanism may be similar to the NLR resistosome model, where oligomerized TRAF-mediated recruitment of multiple interactor units by TC1b results in their induced proximity, oligomerization, and activation. Since TC1b does not have E3 ligase domains, the ARM and CC domains provide additional functionality. Our results indicate that the CC domain is required for the cytosolic puncta localization (Figure 3.11). CC domains can form trimeric or dimeric coils through protein-protein interactions. Long coils such as the one in TC1b may function as a molecular spacer, a molecular tether, or a scaffold for protein complex assembly (Gillingham and Munro, 2003; Truebestein and 116 Leonard, 2016). Because the C-terminal end of TC1b is a short, disordered region rather than a structural domain, it is unclear how TC1b may bind a distal protein as part of a spacer or tether function. Alternatively, the disordered region may be a ligand bound by a receptor type protein or may be an anchor to attach to an unknown membrane component or protein complex. The ARM domain is a common binding domain found in various proteins, including E3 ligases and alpha-importins (Mudgil et al., 2004; Wirthmueller et al., 2013), and could interact with many different types of substrates. The ARM domain likely connects its substrate to the interactor bound by the TRAF domain. 3.5.4 Implications of TC1b localization to cytoplasmic puncta It is still unclear whether TC1b cytoplasmic foci are known structures such as membrane-bound vesicles/multi-vesicular bodies, RNA processing bodies, stress granules, or autophagosomes. Co-localization with addition fluorescent markers would help distinguish between these possibilities and provide clues for its function. Interestingly, wheat TRAF-BTB protein TaMAB2 localizes to cytoplasmic foci in Arabidopsis protoplasts, and overexpression of TaMAB2 leads to growth defects resembling autoimmune mutants (Bauer et al., 2019). In addition, maize TRAF-BTB ZmMAB1 localizes to speckles in the nucleus and cytoplasm of tobacco suspension cells, which is dependent on the cell cycle stage (Juranič et al., 2012). Additional SNC1 regulators SRFR1 (SUPPRESSOR OF RPS4-RLD 1) and TAF15b (TATA-BINDING PROTEIN-ASSOCIATED FACTOR 15B) also localize to cytoplasmic puncta. SRFR1 is a negative regulator of ETI and is involved in the regulation of NLRs like SNC1 (Li et al., 2010b; Kim et al., 2010). SRFR1 can localize to cytoplasmic puncta when transiently expressed in tobacco (Kwon et al., 2009). SRFR1 also can interact with SNC1 and RPS4 117 specifically in the cytoplasmic microsomal fraction (Kim et al., 2010) and can co-localize with EDS1 in cytoplasmic puncta (Bhattacharjee et al., 2011). TAF15b is an RNA-binding protein required for snc1 autoimmunity (Dong et al., 2016a) and can localize to puncta-like RNA-processing bodies (p-bodies). Potentially, TC1b may work in a complex with these components to regulate SNC1. 3.5.5 TRAF proteins in plants and animals Immune receptors are used by both plants and animals as a common tool to recognize the presence of pathogens and activate downstream responses to handle pathogen threats. Compared with mammals, NLR families are expanded in many plant lineages and have large interspecific and intraspecific diversity. Many NLRs are encoded tandemly and/or in clusters, which is thought to mediate rapid diversification and adaptation under evolutionary pressure (Michelmore and Meyers, 1998; Hulbert et al., 2001; Meyers et al., 2003; Jacob et al., 2013). Similar to NLR genes, plant TRAF genes are frequently tandemly encoded, possibly to mediate adaptation to external challenges. Indeed, RTM3, a viral resistance protein is encoded in a large tandem cluster of TRAF genes on Arabidopsis chromosome 3 (Cosson et al., 2010), suggesting that genes in this cluster may have evolved to handle pressure from viruses. Beyond plants, TRAF gene clustering is also found in other species. For example, 50 MATH genes are clustered together on chromosome 2 in Caenorhabditis elegans (Thomas, 2006). The biological significance of TRAF gene clustering awaits future analysis. 118 Chapter 4: Summary, future directions, and research implications 4.1 Summary The goal of this research was to identify new players involved in plant immunity regulation. This was addressed using two reverse genetic approaches: E3 ligase overexpression and CRISPR-mediated directed mutagenesis. Characterization of the recovered genes resulted in two significant contributions to the field of NLR receptor biology. Firstly, the positive role of the F-box adapter protein SNIPER7 in immunity was uncovered (Figure 2.3). SNIPER7 interacts with E3 ligase complex components, suggesting that it is likely part of an SCFSNIPER7 complex (Figure 2.5). The interaction between SNIPER7 and CDC48A in planta was further established (Figure 2.5). SNIPER7 overexpression plants resemble cdc48a-4 mutant plants (Figure 2.7) and SNIPER7 promotes CDC48A degradation (Figure 2.8, Figure 2.9), supporting the hypothesis that SNIPER7 targets CDC48A for degradation. As SNIPER7 is largely found in the nucleus (Figure 2.11), SNIPER7 may be limited to a nuclear subpool of CDC48A to restrict CDC48A degradation. Overall, the evidence suggests that SNIPER7 contributes to SNC1 accumulation by promoting CDC48A turnover (Figure 2.14). Secondly, the role of TRAF domain protein TC1b in snc1 autoimmunity was studied. TC1b was shown to be a positive regulator of defense (Figure 3.3, Figure 3.5). TC1b is not required for immunity conferred by representative CNL, PRR, and TNL receptors, except SNC1, suggesting that TC1b is not involved in a general immunity process (Figure 3.6). TC1b does not interact with or affect the protein levels of SNC1 (Figure 3.7, Figure 3.8), suggesting that it may indirectly affect SNC1 immunity by affecting the activation or homeostasis of SNC1 regulators. Interestingly, TC1b localizes to cytoplasmic puncta through its C-terminal CC domain (Figure 3.9, Figure 3.11), 119 and this localization is likely important for its function. Taken together, TC1b is involved in snc1 autoimmunity through a yet-to-be-identified mechanism. Overall, with the identification of SNIPER7 and TC1b as players in SNC1 NLR immunity, two additional aspects of NLR regulation are unveiled. 4.2 Future directions 4.2.1 Further molecular characterization of SNIPER7 Various studies could be considered for further characterization of SNIPER7 and its regulation of CDC48A. Although SNIPER7 was shown to also interact with other CDC48 family members CDC48B and CDC48C (Figure 2.5D), these interactions were not investigated in detail and their relevance in plant immunity is unclear. Interestingly, CDC48A but not its homologs, is involved in plant immunity (Copeland et al., 2016), and loss of CDC48A is lethal (Park et al., 2008), suggesting that the homologs do not function redundantly. Future experiments could address whether SCFSNIPER7 also affects the protein levels of CDC48B and CDC48C, and whether this regulation affects their role in other biological processes. It is possible that CDC48A is the major functional unit which can form oligomers with other CDC48 proteins, thus resulting in their co-immunoprecipitation with SNIPER7. Alternatively, SNIPER7 may have a broader substrate range that includes the CDC48 homologs. Although SCFSNIPER7 was suggested to interact with and degrade CDC48A, the mechanistic details of this were not tested in detail. Since CDC48A was found to be ubiquitinated and multiple ubiquitinated lysine residues of CDC48A were identified in proteomics experiments (Maor et al., 2007; Saracco et al., 2009; Kim et al., 2013; Svozil et al., 2014; Walton et al., 2016), the specific requirement of individual lysine residues for ubiquitination and degradation could be tested 120 through site directed mutagenesis. Furthermore, the types of ubiquitination linkages catalyzed by SCFSNIPER7 were not established. Although it was assumed that lysine-48 ubiquitin linkages were attached to CDC48A through SCFSNIPER7, multiple other linkages are possible (Komander and Rape, 2012). The ones mediated by SCFSNIPER7 could be revealed in co-immunoprecipitation experiments by probing with ubiquitin-linkage specific antibodies. Another interesting finding is the nuclear localization of SNIPER7 despite the lack of a confidently predicted NLS. One proposed mechanism is that SNIPER7 is imported as a complex with other components (such as SCF complex components) possessing NLSs. Alternatively, SNIPER7 may carry a noncanonical NLS. It is also possible that SNIPER7 is transported through the pore passively. However, the protein is ~50kDa, which is near the predicted upper size limit for passive diffusion (Wang and Brattain, 2007). Furthermore, passive diffusion does not explain why SNIPER7 protein accumulation in the cytoplasm appears to be lower compared to the nucleus. Several experiments can be performed to test these possibilities. To determine if SNIPER7 is imported as an SCF complex, the nuclear localization of SNIPER7 F-box deletion variants, which can no longer interact with the SCF complex, can be tested. Furthermore, the localization of SNIPER7 variants carrying mutations in candidate NLS sites can be determined. One candidate is the N-terminal serine-rich portion of SNIPER7 upstream of the F-box domain (Figure 2.6). Interestingly, SNIPER7 variants carrying a deletion of the polyserine tract could still enhance snc1 when overexpressed (data not shown), suggesting that these serines are not important for its function. Finally, the biological functions of SNIPER7 should be investigated in more detail. Loss of SNIPER7 does not result in any loss in resistance to virulent or avirulent pathogens (Figure 2.12G-I). It is possible that there are additional E3 ligases mediating CDC48A degradation. This 121 can be tested for example in sniper7 mutant lines by examining CDC48A ubiquitination. Furthermore, it would be informative to test whether CDC48A mediates the turnover of NLRs other than SNC1. If only SNC1 is affected, it may explain why loss of SNIPER7 has minimal effects in overall immunity. Additional pathogen strains and infection conditions can be tested to further find minor immune defects. 4.2.2 Additional research avenues for TC1b and TRAF proteins The molecular function of TC1b, and how it contributes to snc1 autoimmunity is not fully understood. It is possible that TC1b may function with known regulators like bHLH84 family members, TPL/TPR, MUSE1/2, SIKIC1/2/3, SRFR1, or TAF15b. Since these regulators have been studied previously in our lab, proposed hypotheses could be easily tested. TC1b could function in the same pathway as the bHLH84 TF family or the TPL/TPR co-repressor family to positively regulate snc1. For example, TC1b may be directly involved in forming complexes with these regulators. This could be tested biochemically through protein interaction assays between TC1b and bHLH84, or TC1b and TPL/TPR. Alternatively, a genetic approach could be used by determining if loss of bHLH84 members can further suppress tc1b-1 snc1. TC1b may play a more indirect role by controlling the expression of these proteins. This possibility can be examined by measuring bHLH84 protein and transcript levels in TC1b overexpression and loss-of-function plants. TNLs SIKIC1/2/3 was also reported to be important for SNC1 function (Dong et al., 2018). TC1b may regulate SIKIC1/2/3 levels directly by affecting SIKIC1/2/3 expression/function or indirectly through affecting the SIKIC-targeting E3s MUSE1/2. This could be tested by measuring the expression/protein levels of the SIKICs in the presence or absence of TC1b in stable transgenic 122 lines or in transient experiments. Additionally, co-IP experiments could be used to test the direct interaction of TC1b with these components. It would be interesting to test whether SRFR1 or TAF15b, which can localize to cytosolic puncta, can co-localize with TC1b and whether they may function in the same pathway. SRFR1 nuclear accumulation is required for its function (Kim et al., 2014), thus TC1b potentially promotes the release of SRFR1 from cytoplasmic compartments which to activate its nuclear function. Alternatively, TC1b may form a complex with TAF15b to mediate its function in the cytosol. These hypotheses can be tested by protein-protein interaction, confocal and subcellular fractionation experiments. Additional experiments focusing on the TRAF protein family may be fruitful. Since mammalian TRAFs can work downstream of PRRs, it would be informative to test whether any plant TRAFs work downstream of plant PRRs. Interestingly, some TRAF proteins possess predicted transmembrane domains, and could potentially work with membrane associated receptors like PRRs and CNLs. Since the CRISPR-Cas9 genome editing tools are already generated from the work reported here, a similar genetic screen can be done to address these questions and hypotheses. For example, the snc2-1D mutant can be used to test for the requirement of various TRAF proteins in RLP/RLK-mediated pathways. Furthermore, other autoimmune mutants could be used to test other pathways (van Wersch et al., 2016). Alternatively, a collection of higher-order TRAF mutants in the WT background could be generated to test for susceptibility to various pathogens. The largest group of TRAF genes in Arabidopsis is tandemly repeated on the bottom arm of chromosome 3 (Figure 3.1). The organization of these genes is reminiscent of NLR gene clusters, which are thought to often exist in clusters in order to mediate diversification and 123 adaptation in response to pathogen challenge (Michelmore and Meyers, 1998; Hulbert et al., 2001; Meyers et al., 2003; Jacob et al., 2013). In particular, the large cluster on chromosome 3 may be an adaptation to quickly evolving environmental challenges (such as pathogens/viruses). Deletion mutants of this clade can be generated and tested for defects in immunity or resistance to other stresses. Preliminary results suggest that deletion of this cluster does not render plants significantly more susceptible to virulent bacterial and oomycete pathogens (data not shown). Additional clade members not in the tandem cluster may need to be deleted in order to uncover any phenotypic consequences. One of the functions of the TRAF domain is the formation of TRAF-TRAF domain interactions (Xie, 2013; Park, 2018). It has been shown that some distantly related plant TRAF proteins (MUSE13/14 and SINATs) can interact, likely through their TRAF domains (Qi et al., 2017). Similarly, TRAF-RING proteins such as SlSINAs and MtSINAs can interact with one another in yeast-2-hybrid experiments, suggesting that they form hetero-oligomers (Den Herder et al., 2008; Wang et al., 2018). From IP-MS results, TC1b does not seem to specifically interact with any TRAF proteins (Figure 3.12). It would be interesting to determine which clades/subfamilies of TRAF proteins can interact with E3 ligases like the SINATs or the TRAF-BTB proteins, and whether these interactions mediate their function. In addition, it would be informative to carry out untargeted domain-domain screens to identify additional interactors of both the TRAF and ARM domains. Yeast-2-hybrid screens, for example, can address both these questions. 124 4.3 Research implications Plant pathogens are a threat that should be monitored and combated. With increasing global travel and climate change, important crops will continue to be exposed to virulent pathogens that they are ineffective in defending against (Bebber, 2015). For example, with the emergence of a virulent wheat stem rust lineage called Ug99 in Africa and the Middle East, global wheat crops are at risk (Singh et al., 2011). Genetic engineering and biotechnology applications can be used to manage such problems. However, genetic resources are needed for the basis of engineering. Luckily, plants are adaptable to change and have evolved strategies to manage threats. Many pathogen-derived problems may already have plant-based solutions that are waiting to be uncovered. Data from large-scale sequencing efforts and high-throughput assays are providing more opportunities to identify interesting biotechnology candidates from both plants and pathogens (Van de Weyer et al., 2019; Depotter and Doehlemann, 2020; Laflamme et al., 2020). Careful molecular characterization and engineering guided by additional cryo-EM structures of NLRs and NLR resistosomes will result in fruitful, agriculturally relevant discoveries and science-based engineering. The research presented here is a small contribution towards our overall understanding of NLR regulation that may lead to effective innovations in the field. 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The Arabidopsis Chromatin-Remodeling Factor CHR5 Regulates Plant Immune Responses and Nucleosome Occupancy. Plant Cell Physiol 58:2202–2216. 166 Appendices Appendix A - Supplemental materials for Chapter 2 Table A.1 Primers used in Chapter 2. Primer Name Primer Sequence 5'-3' Primer purpose SNIPER7_SfiI_F1 CCCGGCCGTCAAGGCCAATGCCTCTGCCTATATTCCC Overlap PCR for F-box deletion construct SNIPER7DN_R1 GAGATTGTTGGTGTTGCTCAAGCTTGGATCGATTCG Overlap PCR for F-box deletion construct SNIPER7DN_F2 CGAATCGATCCAAGCTTGAGCAACACCAACAATCTC Overlap PCR for F-box deletion construct SNIPER7_SfiI_R2 CGCGAATTCGGCCCATGAGGCCCTACAATCGGATGGTGCAC Overlap PCR for F-box deletion construct SALK_121520_LP ACCGGTTTGTCGTAATGAGC sniper7-1 T-DNA genotyping, qPCR SALK_121520_RP GACGATTCCGTTTCATTGATC sniper7-1 T-DNA genotyping SALK_LBb1.3 ATTTTGCCGATTTCGGAAC sniper7-1 T-DNA genotyping KpnI-TagRFP-F TGGGTACCCTCGAGATGGTGTCTAAGGGCGAAG tagRFP cloning EcoRI-TagRFP-R GGCCGAATTCCGTCGACCTTGTACAGCTCGTCCATG tagRFP cloning M13F GTAAAACGACGGCCAG tagRFP-SNIPER7 amplification XbaI-SNIPER7-R CCTCTAGAGGCCCATGAGGCCCTACAATCGGAT tagRFP-SNIPER7 amplification CDC48A-GFP_rtF CATCAGAGAAAACATTGAGAAGGAC qPCR for CDC48A-GFP expression CDC48A-GFP_rtR GCATCACCTTCACCCTCTCC qPCR for CDC48A-GFP expression CDC48A-KpnI-F GCGGTACCATGTCTACCCCAGCTGAATCTTC CDC48A-FLAG, CDC48A-nLUC cloning CDC48A-SalI-R GCGTCGACATTGTAGAGATCATCATC CDC48A-FLAG, CDC48A-nLUC cloning ACTIN8_rtF GGTTTTCCCCAGTGTTGTTG qPCR ACTIN8_rtR CTCCATGTCATCCCAGTTGC qPCR PR1_F GTAGGTGCTCTTGTTCTTCCC qPCR PR1_R CACATAATTCCCACGAGGATC qPCR ACTIN1_F CGATGAAGCTCAATCCAAACGA qPCR ACTIN1_R CAGAGTCGAGCACAATACCG qPCR SNIPER7_rt GGGATTCTTCCTGTTGATGAG qPCR 167 Table A.2 Antibodies used in Chapter 2. Antibody Manufacturer Item # Dilution Anti-HA Roche #11867423001 1:2000 Anti-FLAG Sigma #F1804 1:2000 Anti-GFP Roche #11814460001 1:2000-5000 Anti-TagRFP Evrogen #AB233 1:5000 Anti-SNC1 In-house NA 1:800 Anti-Ubiquitin Sigma #U0508 1:3000 Anti-PEPC Rockland #200-4163 1:10000 Anti-Histone H3 Abcam #ab1791 1:5000 Anti-rat Santa Cruz Biotechnology SC-2065 1:5000 Anti-mouse Thermo Fisher Scientific #32230, #32430 1:5000 Anti-rabbit Thermo Fischer Scientific #32460 1:5000 168 Table A.3 List of CDC48A, CDC48B, and CDC48C peptides identified from IP-MS. Family member Peptides identified in family member Unique peptide identified in family member? CDC48A M.STPAESSDSK.S + Acetyl (Protein N-term) YES CDC48A K.KDFSTAILER.K - CDC48A K.DFSTAILER.K - CDC48A R.LVVDEAINDDNSVVSLHPATMEK.L YES CDC48A K.LQLFR.G - CDC48A R.GDTILIK.G - CDC48A R.VHILPVDDTVEGVTGNLFDAYLKPYFLEAYRPVR.K - CDC48A R.KGDLFLVR.G - CDC48A K.GDLFLVR.G - CDC48A R.EDEERLDDVGYDDVGGVR.K YES CDC48A R.EDEERLDDVGYDDVGGVRK.Q YES CDC48A R.LDDVGYDDVGGVR.K YES CDC48A R.LDDVGYDDVGGVRK.Q YES CDC48A R.KQMAQIR.E - CDC48A K.QMAQIR.E - CDC48A R.ELVELPLR.H - CDC48A R.ELVELPLRHPQLFK.S - CDC48A R.HPQLFK.S - CDC48A K.SIGVKPPK.G - CDC48A K.GILLYGPPGSGK.T - CDC48A K.LAGESESNLR.K - CDC48A K.LAGESESNLRK.A - CDC48A K.AFEEAEK.N - CDC48A K.NAPSIIFIDEIDSIAPK.R - CDC48A K.NAPSIIFIDEIDSIAPKR.E - CDC48A R.RIVSQLLTLMDGLK.S - CDC48A R.IVSQLLTLMDGLK.S - CDC48A R.IVSQLLTLMDGLK.S + Oxidation (M) - CDC48A R.AHVIVMGATNRPNSIDPALR.R - CDC48A R.AHVIVMGATNRPNSIDPALR.R + Oxidation (M) - CDC48A R.AHVIVMGATNRPNSIDPALRR.F - CDC48A R.FDREIDIGVPDEIGR.L - CDC48A R.FDREIDIGVPDEIGRLEVLR.I - CDC48A R.EIDIGVPDEIGR.L - CDC48A R.EIDIGVPDEIGRLEVLR.I - CDC48A R.LEVLR.I - CDC48A K.LAEDVDLER.I - CDC48A R.EKMDVIDLEDDSIDAEILNSMAVTNEHFHTALGNSNPSALR.E YES CDC48A K.MDVIDLEDDSIDAEILNSMAVTNEHFHTALGNSNPSALR.E YES CDC48A R.ETVVEVPNVSWNDIGGLENVK.R YES CDC48A R.ETVVEVPNVSWNDIGGLENVKR.E YES CDC48A R.ELQETVQYPVEHPEK.F - CDC48A R.ELQETVQYPVEHPEKFEK.F - CDC48A K.FGMSPSK.G - CDC48A K.GPELLTMWFGESEANVR.E - CDC48A K.GPELLTMWFGESEANVR.E + Oxidation (M) - CDC48A R.EIFDK.A - CDC48A R.VLNQLLTEMDGMNAK.K - CDC48A R.VLNQLLTEMDGMNAK.K + Oxidation (M) - CDC48A R.VLNQLLTEMDGMNAKK.T - CDC48A K.TVFIIGATNRPDIIDSALLRPGR.L - CDC48A R.LDQLIYIPLPDEDSR.L - CDC48A R.LDQLIYIPLPDEDSRLNIFK.A - CDC48A R.LNIFK.A - CDC48A K.DVDIGALAK.Y YES CDC48A R.ENIEKDIEK.E - CDC48A R.ENIEKDIEKEK.R YES CDC48A R.RSENPEAMEEDGVDEVSEIK.A YES CDC48A R.SENPEAMEEDGVDEVSEIK.A YES CDC48A R.SENPEAMEEDGVDEVSEIK.A + Oxidation (M) YES 169 Family member Peptides identified in family member Unique peptide identified in family member? CDC48A K.AAHFEESMK.Y - CDC48A K.AAHFEESMK.Y + Oxidation (M) - CDC48A R.RSVSDADIR.K - CDC48A R.RSVSDADIRK.Y - CDC48A R.SVSDADIR.K - CDC48A R.SVSDADIRK.Y - CDC48A R.KYQAFAQTLQQSR.G - CDC48A K.YQAFAQTLQQSR.G - CDC48A R.GFGSEFR.F - CDC48B K.DFSTAILEK.K YES CDC48B R.LVVDEAINDDNSVVSLHPDTMEK.L YES CDC48B K.LQLFR.G - CDC48B R.GDTILIK.G - CDC48B R.KGDLFLVR.G - CDC48B K.GDLFLVR.G - CDC48B R.EDEERLDEVGYDDVGGVR.K - CDC48B R.KQMAQIR.E - CDC48B K.QMAQIR.E - CDC48B R.ELVELPLR.H - CDC48B R.ELVELPLRHPQLFK.S - CDC48B R.HPQLFK.S - CDC48B K.SIGVKPPK.G - CDC48B K.GILLYGPPGSGK.T - CDC48B K.LAGESESNLR.K - CDC48B K.LAGESESNLRK.A - CDC48B K.AFEEAEK.N - CDC48B K.NAPSIIFIDEIDSIAPK.R - CDC48B K.NAPSIIFIDEIDSIAPKR.E - CDC48B R.RIVSQLLTLMDGLK.S - CDC48B R.IVSQLLTLMDGLK.S - CDC48B R.IVSQLLTLMDGLK.S + Oxidation (M) - CDC48B R.AHVIVMGATNRPNSIDPALR.R - CDC48B R.AHVIVMGATNRPNSIDPALR.R + Oxidation (M) - CDC48B R.AHVIVMGATNRPNSIDPALRR.F - CDC48B R.FDREIDIGVPDEIGR.L - CDC48B R.FDREIDIGVPDEIGRLEVLR.I - CDC48B R.EIDIGVPDEIGR.L - CDC48B R.EIDIGVPDEIGRLEVLR.I - CDC48B R.LEVLR.I - CDC48B K.LAEDVDLER.V - CDC48B R.ETVVEVPNVSWEDIGGLENVKR.E - CDC48B R.ELQETVQYPVEHPEK.F - CDC48B R.ELQETVQYPVEHPEKFEK.F - CDC48B K.FGMSPSK.G - CDC48B K.GPELLTMWFGESEANVR.E - CDC48B K.GPELLTMWFGESEANVR.E + Oxidation (M) - CDC48B R.EIFDK.A - CDC48B R.VLNQLLTEMDGMNAK.K - CDC48B R.VLNQLLTEMDGMNAK.K + Oxidation (M) - CDC48B R.VLNQLLTEMDGMNAKK.T - CDC48B R.LDQLIYIPLPDEESR.Y YES CDC48B R.ENIEKDIEK.E - CDC48B K.AGHFEESMK.Y YES CDC48B R.RSVSDADIR.K - CDC48B R.RSVSDADIRK.Y - CDC48B R.SVSDADIR.K - CDC48B R.SVSDADIRK.Y - CDC48B R.KYQAFAQTLQQSR.G - CDC48B K.YQAFAQTLQQSR.G - CDC48B R.GFGSEFR.F - CDC48C K.KDFSTAILER.K - 170 Family member Peptides identified in family member Unique peptide identified in family member? CDC48C K.GDLFLVR.G - CDC48C R.EDEERLDEVGYDDVGGVR.K - CDC48C R.KQMAQIR.E - CDC48C K.QMAQIR.E - CDC48C R.ELVELPLR.H - CDC48C R.ELVELPLRHPQLFK.S - CDC48C R.HPQLFK.S - CDC48C K.SIGVKPPK.G - CDC48C K.GILLYGPPGSGK.T - CDC48C K.LAGESESNLR.K - CDC48C K.LAGESESNLRK.A - CDC48C K.AFEEAEK.N - CDC48C K.NAPSIIFIDEIDSIAPK.R - CDC48C K.NAPSIIFIDEIDSIAPKR.E - CDC48C R.RIVSQLLTLMDGLK.S - CDC48C R.IVSQLLTLMDGLK.S - CDC48C R.IVSQLLTLMDGLK.S + Oxidation (M) - CDC48C R.AHVIVMGATNRPNSIDPALR.R - CDC48C R.AHVIVMGATNRPNSIDPALR.R + Oxidation (M) - CDC48C R.AHVIVMGATNRPNSIDPALRR.F - CDC48C R.FDREIDIGVPDEIGR.L - CDC48C R.FDREIDIGVPDEIGRLEVLR.I - CDC48C R.EIDIGVPDEIGR.L - CDC48C R.EIDIGVPDEIGRLEVLR.I - CDC48C R.LEVLR.I - CDC48C K.LAEDVDLER.I - CDC48C R.ETVVEVPNVSWEDIGGLENVKR.E - CDC48C R.ELQETVQYPVEHPEK.F - CDC48C R.ELQETVQYPVEHPEKFEK.F - CDC48C K.FGMSPSK.G - CDC48C K.GPELLTMWFGESEANVR.E - CDC48C K.GPELLTMWFGESEANVR.E + Oxidation (M) - CDC48C R.EIFDK.A - CDC48C R.VLNQLLTEMDGMNAK.K - CDC48C R.VLNQLLTEMDGMNAK.K + Oxidation (M) - CDC48C R.VLNQLLTEMDGMNAKK.T - CDC48C K.TVFIIGATNRPDIIDSALLRPGR.L - CDC48C R.LDQLIYIPLPDEDSR.L - CDC48C R.LDQLIYIPLPDEDSRLNIFK.A - CDC48C R.LNIFK.A - CDC48C K.DVDVTALAK.Y YES CDC48C R.ENIEKDIENER.R YES CDC48C R.AAHFEESMK.Y - CDC48C R.AAHFEESMK.Y + Oxidation (M) - CDC48C R.RSVSDADIR.K - CDC48C R.RSVSDADIRK.Y - CDC48C R.SVSDADIR.K - CDC48C R.SVSDADIRK.Y - CDC48C R.KYQAFAQTLQQSR.G - CDC48C K.YQAFAQTLQQSR.G - CDC48C R.GFGSEFR.F - 171 172 173 174 175 Figure A.1 CDC48A is ubiquitinated. Multiple sequence alignment of select CDC48 homologs from representative species. Shading represents level of residue conservation. Specific lysines previously identified to be ubiquitinated are colored in green, and the position is outlined in orange. Residues from Arabidopsis are marked with * (Walton et al., 2016) or # (Kim et al., 2013) and from rice are marked with † (Xie et al., 2015) or ‡ (Li et al., 2015b). Domains are denoted using colored bars: blue – yeast NLS site, dark green – N-terminal domain, lime green – domain 2, yellow – ATPase domains, pink – lid domains. WA – Walker A motif, WB – Walker B motif. Species included in the alignment: Schizosaccharomyces pombe, Neurospora crassa, Saccharomyces cerevisiae, Phytophthora infestans, Dictyostelium discoideum, Chlamydomonas reinhardtii, Oryza sativa, Zea mays, Glycine max, Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Xenopus laevis, Mus musculus, Homo sapiens, Anolis carolinensis, Gallus. 176 Appendix B - Supplemental materials for Chapter 3 Table B.1 Primers used in Chapter 3. Primer Name Primer Sequence 5'-3' Primer purpose TC1ab-CRISPR-F1 GATTGAACAATAAGTCGACAATCC CRISPR construct cloning (sgRNA1) TC1ab-CRISPR-R1 AAACGGATTGTCGACTTATTGTTC CRISPR construct cloning (sgRNA1) TC1ab-DelPCR-F CACAGAAGATAACGAGTCCTG Genotyping presence of TC1ab deletion TC1ab-DelPCR-F GTCGGAGAAACTGAATTAGTTGAG Genotyping presence of TC1ab deletion TC1a-CRISPR-PCR-F TGTGAAACTGAACTAGCAGATTCTG Genotyping TC1a region presence TC1b-CRISPR-PCR-F GAGGATCAGTGTGTACCAGAGTG Genotyping TC1b region presence SALK_03106_LP ATAGTAGTACTGTTGGATGTTAT Genotyping salk_103885c, salk_03106 T-DNAs SALK_03106_RP ATTGAGAATCATAATATCAGCCG Genotyping salk_103885c, salk_03106 T-DNA SALK_LBb1.3 ATTTTGCCGATTTCGGAAC Genotyping salk_103885c T-DNA GabiKat o8474 ATAATAACGCTGCGGACATCTACATTTT Genotyping CS356848 T-DNA CS356848-RP2 GGATAATAAAGACGGCTCAGAGTG Genotyping CS356848 T-DNA, colony PCR TC1abΔ-DT1-FF0 ATATATGGTCTCGATTGACGTCGGACAGGCAGTGTGGTTTTAGAGCTAGAAATAGC TC1abΔ CRISPR construct cloning TC1abΔ-DT2-RR0 ATTATTGGTCTCGAAACCATGGTCGTAAGAGTACCCCAATCTCTTAGTCGACTCTAC TC1abΔ CRISPR construct cloning TC1abΔ_del_F CTAAGCGAGCATAGAAGGATC Genotyping presence of TC1abΔ deletion TC1abΔ_del_R ATGATTAAGCCCCATTTAAACCC Genotyping presence of TC1abΔ deletion DraIII-TC1b-F TACACTCAGTGATGAAGCAAAGCTCATCGGAG TC1b cloning DraIII-TC1bns-R ATCACATGGTGGGTGTGGTGACTAAACCAAGG TC1b cloning DraIII-TC1bstop-R ATCACATGGTGTTAGGTGTGGTGACTAAACCA TC1b cloning KpnI-pTC1b::TC1b-F TTGGGTACCGTCTAGGTGCTGATATTGCCGA pTC1b construct cloning PstI-TC1b-ns-R GGGCTGCAGGGTGTGGTGACTAAACCAAGG pTC1b construct cloning M13F GTAAAACGACGGCCAG Colony PCR SALK_03106_RP2 TTCCATCTCTCGACGATCCAA Colony PCR 35S_F CCCACTATCCTTCGCAAGAC Colony PCR DraIII_TC1bΔNdis_F accCACTCAGTGATGCAAGAGATCGTAGCCGTC TC1b-YFP truncation cloning DraIII_TC1bΔCdis_R agtCACATGGTGCTTACGTTGTTGAAGGGCATGG TC1b-YFP truncation cloning DraIII_TC1bΔCC_R agtCACATGGTGAATATTTGCATGTTCCACAACC TC1b-YFP truncation cloning SDM_TC1bΔTRAF_F GATGGAAGCGGAGGTGCATGGTTTGAGTTTTCAGAC TC1b-YFP truncation cloning, site-directed mutagenesis SDM_TC1bΔTRAF_R GTCTGAAAACTCAAACCATGCACCTCCGCTTCCATC TC1b-YFP truncation cloning, site-directed mutagenesis SDM_TC1bΔARM_F GCATAAGCGGAGATGAGGAAGACAGGGCGGCCCTGTGG TC1b-YFP truncation cloning, site-directed mutagenesis 177 Primer Name Primer Sequence 5'-3' Primer purpose SDM_TC1bΔARM_R CCACAGGGCCGCCCTGTCTTCCTCATCTCCGCTTATGC TC1b-YFP truncation cloning, site-directed mutagenesis TC1a-qPCR-F GGCCTTCAAGGGAATAATGGAAAA qPCR TC1a-qPCR-R TCACGGACTGACCTGACGGAA qPCR TC1b-qPCR-F AGCGTTGCGTGAACCAGTATAC qPCR TC1b-qPCR-R CACGGACTGCATTTACGGCTTC qPCR 178 Table B.2 Potential target sites for CRISPR sgRNAs. Construct/sgRNA Bulge type crRNA DNA Chromosome Position Direction Mismatches Bulge size On-target site TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAATAAGTCGACAATCCCGG chr2 10790131 + 0 0 exon AT2G25330 (TC1a) TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAATAAGTCGACAATCCCGG chr2 10786730 + 0 0 exon AT2G25320 (TC1b) TC1ab sgRNA1 DNA A-ACAATAAGTCGACAATCCNGG AAACAATAAGTCGACAATCCCGG chr2 10790130 + 0 1 TC1ab sgRNA1 DNA A-ACAATAAGTCGACAATCCNGG AAACAATAAGTCGACAATCCCGG chr2 10786729 + 0 1 TC1ab sgRNA1 DNA AA-CAATAAGTCGACAATCCNGG AAACAATAAGTCGACAATCCCGG chr2 10790130 + 0 1 TC1ab sgRNA1 DNA AA-CAATAAGTCGACAATCCNGG AAACAATAAGTCGACAATCCCGG chr2 10786729 + 0 1 TC1ab sgRNA1 RNA AACAATAAGTCGACAATCCNGG A-CAATAAGTCGACAATCCCGG chr2 10790132 + 0 1 TC1ab sgRNA1 RNA AACAATAAGTCGACAATCCNGG A-CAATAAGTCGACAATCCCGG chr2 10786731 + 0 1 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAAcAAcTCaACAATaCTGG chr5 26972571 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAATAAaTCGACAAgaaTGG chr1 17463185 - 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AAgAATAAcgCGACAAaCCTGG chr1 29512105 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAcaAAGTCGcCAATtCAGG chr1 29961578 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACcATgAGTCcAaAATCCCGG chr2 2459476 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AAtAATAAcTCGACAtTtCTGG chr2 5014339 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAAcAAGTgGtCAATaCTGG chr2 9837902 - 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAtTAAGggaACAATCCAGG chr2 10957480 - 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAATAAGTCaAtAAcCtTGG chr4 9271828 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AAaAATAAGTCcAtAATaCGGG chr4 15632100 - 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAAaAAGTCcAaAATtCAGG chr3 3757871 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACAAgAAGaCGAaAAaCCCGG chr3 7475236 + 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AAtAATAAcTCGACAtTtCCGG chr3 12702235 - 4 0 TC1ab sgRNA1 X AACAATAAGTCGACAATCCNGG AACgATAtGTCacCAATCCTGG chr3 18483463 + 4 0 TC1abΔ sgRNA2 X ACGTCGGACAGGCAGTGTGNGG ACGTCGGACAGGCAGTGTGTGG chr2 10791731 + 0 0 5' of AT2G25330 (TC1a) TC1abΔ sgRNA2 X ACGTCGGACAGGCAGTGTGNGG ACcTCGGctAGGCAGcGTGTGG chrM 330549 - 4 0 TC1abΔ sgRNA2 X ACGTCGGACAGGCAGTGTGNGG ACcTCGGctAGGCAGcGTGTGG chr2 3335841 + 4 0 TC1abΔ sgRNA2 X ACGTCGGACAGGCAGTGTGNGG ACGTaGGAgAGGCAaaGTGCGG chr2 9958666 + 4 0 TC1abΔ sgRNA2 X ACGTCGGACAGGCAGTGTGNGG AaGTtGGtCAtGCAGTGTGTGG chr2 13176241 + 4 0 TC1abΔ sgRNA2 DNA A-CGTCGGACAGGCAGTGTGNGG AACGTCGGACAGGCAGTGTGTGG chr2 10791730 + 0 1 TC1abΔ sgRNA2 DNA A--CGTCGGACAGGCAGTGTGNGG AAACGTCGGACAGGCAGTGTGTGG chr2 10791729 + 0 2 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGTACTCTTACGACCATGAGG chr2 10781182 + 0 0 3' of AT2G25320 (TC1b) TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGaACTacTACGAtCATGTGG chr5 18857050 + 4 0 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGaACTacTACGAtCATGTGG chr5 18759616 - 4 0 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GcGTAtTCTTACtACaATGTGG chr5 23076515 + 4 0 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGTtCTCgTACGAgtATGGGG chr1 25621838 - 4 0 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGTACTaTaACGAtCtTGTGG chr2 6356375 + 4 0 TC1abΔ sgRNA3 X GGGTACTCTTACGACCATGNGG GGGTACTaTaACGAtCtTGTGG chr3 16261686 + 4 0 TC1abΔ sgRNA3 DNA G-GGTACTCTTACGACCATGNGG GGGGTACTCTTACGACCATGAGG chr2 10781181 + 0 1 TC1abΔ sgRNA3 DNA GG-GTACTCTTACGACCATGNGG GGGGTACTCTTACGACCATGAGG chr2 10781181 + 0 1 TC1abΔ sgRNA3 DNA GGG-TACTCTTACGACCATGNGG GGGGTACTCTTACGACCATGAGG chr2 10781181 + 0 1 TC1abΔ sgRNA3 RNA GGGTACTCTTACGACCATGNGG G-GTACTCTTACGACCATGAGG chr2 10781183 + 0 1 TC1abΔ sgRNA3 RNA GGGTACTCTTACGACCATGNGG GG-TACTCTTACGACCATGAGG chr2 10781183 + 0 1 TC1abΔ sgRNA3 RNA GGGTACTCTTACGACCATGNGG G--TACTCTTACGACCATGAGG chr2 10781184 + 0 2 179 Table B.3 Antibodies and protein detection reagents used in Chapter 3. Antibody Manufacturer Item # Dilution Anti-HA Roche #11867423001 1:2000 Anti-FLAG Sigma #F1804 1:2000 Anti-GFP Roche #11814460001 1:2000-5000 Anti-SNC1 In-house NA 1:800 Anti-rat Santa Cruz Biotechnology SC-2065 1:5000 Anti-mouse Thermo Fisher Scientific #32230, #32430 1:5000 Anti-rabbit Thermo Fischer Scientific #32460 1:5000 Streptavidin-HRP Abcam #ab7403 1:10000 Table B.4 Protein accessions and data sources for phylogenetic tree Species Uniprot accession Gene code Data source Arabidopsis thaliana Q0KKQ1 AT2G25320 Ensembl Arabidopsis thaliana Q9SIR0 AT2G25330 Ensembl Chara braunii A0A388JUC4 CBR_g20432 Ensembl Daucus carota A0A161YBC2 DCAR_001543 Ensembl Physcomitrium patens A0A2K1L7Y1 Pp3c1_12560 Ensembl Physcomitrium patens A0A7I3Z8D6 Pp3c2_25330 Ensembl Physcomitrium patens A0A7I4D1F8 Pp3c2_25350 Ensembl Physcomitrium patens A0A7I4D3L1 Pp3c2_25380 Ensembl Papaver somniferum A0A4Y7JQA7 C5167_023739 Ensembl Marchantia polymorpha A0A176WFE5 AXG93_4510s1050 Ensembl Populus trichocarpa A0A2K2CE77 POPTR_001G466600 Ensembl Populus trichocarpa A0A2K1ZXW4 POPTR_006G065300 Ensembl Populus trichocarpa A0A2K2A8B1 POPTR_006G256400 Ensembl Populus trichocarpa A0A3N7G5T9 POPTR_018G025550 Ensembl Solanum lycopersicum A0A3Q7GPK7 Solyc06g017970.3 Ensembl Selaginella moellendorffii D8RS70 SELMODRAFT_173767 Ensembl Selaginella moellendorffii D8SJC1 SELMODRAFT_180190 Ensembl Amborella trichopoda U5DDU9 AMTR_s00065p00122030 Ensembl Salvinia cucullata NA Sacu_v1.1_s0159.g023847 Fernbase Salvinia cucullata NA Sacu_v1.1_s0066.g016184 Fernbase Leiosporoceros dussii NA gnl|onekp|ANON_scaffold_2036459 onekp project Paraphymatoceros hallii NA gnl|onekp|FAJB_scaffold_2057873 onekp project Pinus taeda NA PTA00020891 Plaza Thuja plicata NA Thupl.29378649s0002.1.p Phytozome Ceratopteris richardii NA Ceric.07G070900.1.p Phytozome Ceratopteris richardii NA Ceric.02G028800.1.p Phytozome Klebsormidium nitens NA rna-KFL_003080060 Phycocosm Mesostigma viride NA Mesvi380S05630 Phycocosm Chlorokybus atmophyticus NA Chrsp3S05628 Phycocosm Ulva mutabilis NA UM048_0073.1 Phycocosm Coccomyxa subellipsoidea NA estExt_Genemark1.C_250037 Phycocosm Tetraselmis striata NA TSEL_025136.t1 Phycocosm 180 Figure B.1 Protein consequences of mutations in tc1ab-1 and tc1ab-2 lines. Protein sequences of WT and mutant alleles of TC1a and TC1b are shown. Red font indicates the frame-shifted sequences.
Thesis/Dissertation
eng
Botany
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Li, Xin
Characterization of two regulators in plant immune receptor-mediated defense
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