Unravelling the Underlying Components of Autophagy-mediated Photomorphogenesis of Arabidopsis thaliana
Yapa Mudiyanselage Akila Maduranga Wijerathna M.Sc. Agric. (Plant Sciences) B.Sc. Agric. (Hons.) Sp. Biotechnology AFHEA (UKPSF)
This thesis is presented for the degree of Doctor of Philosophy (Biochemistry) of the University of Western Australia
Australian Research Council Centre of Excellence in Plant Energy Biology
School of Molecular Sciences
October 2020 Thesis declaration
I, Akila Wijerathna-Yapa (Student ID: 22056957), certify that:
This thesis has been substantially accomplished during enrolment in the degree.
This thesis does not contain material which has been accepted for the award of any other degree or diploma in my name, in any university or other tertiary institution. No part of this work will, in the future, be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of The University of Western Australia and where applicable, any partner institution responsible for the joint-award of this degree. This thesis does not contain any material previously published or written by another person, except where due reference has been made in the text. The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or other rights whatsoever of any person. The following approvals were obtained prior to commencing the relevant work described in this thesis: OGTR (2113), and NLRD (RA/5/1/373). Third party editorial assistance was provided in preparation of the thesis by Professor A. Harvey Millar (Principal and Coordinating supervisor). The work described in this thesis was funded by the Australian Government International Research Training Program Scholarship (RTP). This work was supported by an Australian Research Council Grant (CE140100008- CPEB2 MILLAR). This thesis contains work prepared for publication which has been co-authored.
Signature:
Date: 22/10/2020
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Abstract
Protein homeostasis is critical for the adaptation of cell function to a fluctuating internal and external environment. Proteases, the ubiquitin-proteasome system, and autophagy all contribute to the degradation of cellular organelles. Autophagy-mediated degradation of cytoplasmic proteins and organelles can move whole damaged organelles and cytoplasmic aggregates to the vacuole for degradation. However, it not currently clear how vital this role of autophagy is in changing the organelle proteomes of plants in response to the environment. It is thus important to determine if autophagy deficiency changes organelle protein composition in plant cells.
Mass spectrometry-based proteomics is a powerful technique to study the whole proteome and can support the study of autophagy deficiency that is typically assessed by western blotting, microscopy, and imaging approaches. Protein analysis can help to provide new insights into molecular mechanisms in both space and time during autophagy. Therefore here data-independent targeted proteomics techniques were used to estimate organelle protein abundances in three plant developmental transitions; early seedling development, dark to light greening of seedlings, and light to dark senescence of leaves. These studies were conducted in Arabidopsis thaliana autophagy mutants atg2, atg5, atg7, atg9, and WT Col-0. This showed that the major effect in each mutant was a delay in plastid protein accumulation during photomorphogenesis. Focusing on photomorphogenesis transition, date-dependent shotgun proteomics revealed that a range of plastidial proteins viz. PSII, Calvin-Benson cycle proteins, chlorophyll metabolism protochlorophyllide oxidoreductase, and the plastid ATP synthase complex were lower in abundance in autophagy mutants compared to WT. In contrast, plastidial Type II fatty acid synthase was increased in abundance in autophagy mutants compared to WT. Thus, although multiple cellular protein degradation pathways exist viz. ubiquitin-proteasome, autophagy, and proteases, the results indicated that autophagy played a role in enabling plastid protein biogenesis during photomorphogenesis.
To better understand the basis of this autophagy effect, I complemented proteomics data with transcriptomics using RNA-seq to determine if plastid proteins increased in abundance are targets of autophagy or responses to delayed photomorphogenesis. This analysis showed that de-etiolation transition was associated with a significant increase of gene expression for photosynthesis and enzymes involved in the Calvin cycle, lipid
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biosynthesis, and also induction of gene expression for autophagic and proteasome recycling pathways in autophagy mutants vs. WT.
To assess if the physiological acquisition of photosynthetic maturity during de- etiolation was delayed under autophagy-deficient conditions, the maximum quantum efficiency of PSII photochemistry (Fv/Fm), chlorophyll content, and plastid size were also measured. Chlorophyll, a and b content, were significantly low in autophagy mutants compared to WT plants during the early stages of illumination. Notably, chlorophyll b accumulated less rapidly than chlorophyll a during the de-etiolation process in autophagy mutants, resulting in a high chlorophyll a/b ratio after illumination. WT exhibited a clear increase in Fv/Fm after 12h of light while all the autophagy mutants showed lower photochemical efficiency, a smaller number of plastids per frame, and reduced plastid area.
This study has provided new insights into organelle maintenance under autophagy- deficient conditions and identified an important autophagy-dependent process during plant developmental response; photomorphogenesis or de-etiolation. The outcome of the study suggests that autophagy has a role in maximizing carotenoid and chlorophyll biosynthesis and accumulation of photosynthetic apparatus machinery during chloroplast biogenesis since autophagy defective mutants in Arabidopsis display a slow greening phenotype and lower abundances of key enzymes, despite higher than normal transcripts levels for these components. Further study can provide new insights into chloroplast organelle maintenance under autophagy-deficient conditions, determine if there is a link between enhanced lipid biosynthesis, slow greening, and more accurately pinpoint the timing of autophagy processes during photomorphogenesis. Although the application of proteomics techniques in plant autophagy is still in its nascent stage, it shows great potential for uncovering new insights into plant autophagy.
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Acknowledgements
This is the only place I can write anything without p-value, significance threshold. This section is beyond the statistics and truly based on my heart, feelings, and experiences. There is confidently an uncountable number of people who have helped me along this adventurous journey by providing personal and professional advice, moral and emotional support, brainstorming discussions. Indeed here I am going to try my best to, do far too little to express my gratitude to you all.
I am grateful to the Australian Government, Department of Education, Skills, and Employment for providing the Research Training Program (RTP) scheme scholarship to support my education.
I offer my sincere gratitude to principal and coordinating supervisor Prof. Harvey Millar who has given me this great opportunity to continue my postgraduate study at the ARC Centre of Excellence in Plant Energy Biology, the University of Western Australia, and for his strong, unconditional support throughout my research thesis with his patience, great supervision, encouragement, and knowledge. Also, I am extremely grateful for allowing me to apply for the Plant Energy Biology internode collaboration grant with the Australian National University.
I express my deepest thank to Ms. Ricarda Fenske for her kind supervision, teaching, and training me mass spectrometry and proteomics, and her great friendship.
I am grateful to Dr. Diep Ray Ganguly for his great supervision, teaching, and training me RNA-Seq analysis from wet lab to bioinformatics data analysis. I greatly appreciate his kind guidance and great friendship. Also, I am beholden to Prof. Barry Pogson for his strong support for the collaborative project.
I am thankful to co supervisory team Dr. Elke Stroeher, Dr. Owen Duncan, Prof. Lei Li, and Prof. Ian Small for taking their valuable time for providing valuable feedback for the thesis and establishing the project and giving constructive feedback.
My profound gratitude is expressed to Ms. Geetha Shute, Ms. Deborah Yeoman, and Ms. Rosemarie Farthing for their unconditional support, friendship, and kindness throughout this journey. Thank you very much for creating a beautiful, friendly, and great working environment for me and others.
A huge thank goes to my former supervisors Dr. Nimal Dissanayake at the Rice Research and Development Institute, Department of Agriculture Sri Lanka, Prof.
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Yehoshua Saranga, and Prof. Assaf Mosquna, at the Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, the Hebrew University of Jerusalem, Israel, for making me independent, encouraging always doing great science and differentiate good and bad, and always uplifting and guiding even until today.
I am thankful to Dr. Chirag Parsania, Dr. Jakob Petereit, and Dr. Xiao Zhong for teaching R and Linux programming, and Hui Cao for helping me with troubleshooting my R scripts and mass spectrometry whenever I am in trouble. Also, Dr. Ian Castleden, Dr. Cornelia Hooper for computational data analysis and their great friendship. I am forever grateful for Dr. Yelena Sterlin, Dr. Oded Pri-Tal, and Ben Harpazi at the Hebrew University of Jerusalem for teaching me advanced molecular biology, plant sciences techniques. Further, I thank Maike Bollen and Alysia Hubbard from the Centre for Microscopy, Characterization, and Analysis (CMCA) for their support with fluorescence microscopy.
I am grateful to the European Molecular Biology Organization (EMBO), European Molecular Biology Laboratory (EMBL), for the opportunity given to participate Quantitative Proteomics: Strategies and Tools to Probe Biology workshop, and the scholarship. Also, I am grateful to Dr. Yasin Dagdas at the Gregor Mendel Institute of Molecular Plant Biology, Prof. Koichi Kobayashi at the Osaka Prefecture University, Dr. Wenbin Guo at The James Hutton Institute, Prof. Gary Siuzdak at the Scripps Research Institute, Prof. Clyde Phelix at the University of Texas at San Antonio, Prof. Clarice Garcia Borges Demétrio at the University of São Paulo, and Prof. Leslie Sieburth at the University of Utah, for their selfless act of sharing knowledge.
Especially I am grateful to online biostatistics community members at BioStar, Prof. Istvan Albert, cpad0112, Dr. Kevin Blighe, Dr. Devon Ryan, Gyan Mishra, and Russ Hyde, and StackOverflow community Dr. Maurits Evers, and Dr. Rachael Lappan for their selfless assistance with R programming.
I am forever grateful to my dearest friends, Dr. Marisa Duong, Dr. Karzan Salih and his family, Brian Le, Dr. Lilian Pereira, Prof. Valerie Verhasselt, Dr. Akila Rekima, Dr. Patricia Macchiaverni, Dr. Lieke Vandenelsen, Bhagya Disanayaka, Samalka Wijeweera, and Dr. Adil Khan for their kind support, great friendship, and company. Also, I am thankful to friendly folks at the UWA and ANU, Ro Hook, Dr. Adriana Pruzinska, Dr. Santiago Signorelli, Dr. Emilia Cincu, Dr. Kalia Bernath-Levin, Dr. Christiana Staudinger, Dr. Catherine Small, Dr. Martyna Broda, Dr. Alex Lee, Dr. Joanna Melonek, Karina Price, Ha Xuyen, Dr. Derek Collinge, Dr. Andrew Bowerman, Dr. Estee Tee,
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Aaron Smith, Dr. Xin Hou, Nay Chi Khin, Julie Leroux, Dr. Arun Yadav, and Dr. Marten Moore, for their kind support.
My gratitude also goes to kind and helpful staff at the Graduate Research School of UWA, Ms. Jorja Cenin, Dr. Joanne Edmondston, Dr. Krystyna Haq, Dr. Michael Azariadis, and Ms. Thritty Bhanja for all the support have been given always. Further, My thank also goes to the friendly and supportive staff at molecular sciences, Ranji Thambirajah, Adam Hamilton, Simon Hinton, Marie Hughes, Janice Phoenix, Jacqueline McNally, Prof. Paul Low, Prof. Reto Dorta, Prof. Rob Tuckey, Adarsh Karthikeyan, and Pejman Leylabadi. Further, I am grateful to Ms. Leanne Dusz at the UWA student's wellbeing for her kindness and support and teaching me valuable professional skills.
I am grateful to Ms. Geetha Shute, Mr. William Yeoman, and Ms. Deborah Yeoman for their love, support, great friendship during this journey. I am grateful to Dr. Nimal Dissanayake, Ms. Amita Yatawara, and Ashan Dissanayake family, Prof. Lakshman Alles and family, Dr. Chung Wee, and Dr. Cynthia Vanden Driesen for their great support during my stay at UWA.
I would love to acknowledge all my teachers in the past, and present from Montessori, primary, and secondary schools for their selfless teaching, encouragement, commitment, and love for me. Especially two great teachers from primary school to secondary school, my dearest English literature teacher Mr. Gamini Wijepala, and Science and Biology teacher Mrs. Sandya De Silva, for the motivation, kindness, lifelong friendship, and giving me the wings to explore the world. Their love, blessings are always with me.
The greatest gratitude goes to my beloved mother, Pallegama Mudiyanselage Anulawathie, and Pappa, Yapa Mudiyanselage Wijerathna, who continues to work tirelessly to provide me with all the opportunities that I could have asked for throughout my life. You two are always there for me to support in all the possible ways, and you laid all the foundations and gave me the freedom when I was young to strive to be who I wanted to become. I am grateful to my dear sister Nilakshi Chathurika Wijerathna, brother-in-law Amila Prasad Hathurusinghe, niece Resara Manudi and nephew Rashel Keshawa. Also, the biggest thank extend to my beloved maternal grandmother, Imiya Mudiyanselage Kumarihamy, and paternal grandmother, Wijekoon Mudiyanselage Kalyanawathie who nurtured me, cared me, and always taught me the value of being generous, kind, helpful to anyone regardless of what and how they treat you are.
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Arōgā paramā lābhā
san tutthi paramam dhanan
vis āsa paramā nāthi
Nibbānan paramam sukhan
The Buddha (Dhammapada Verse 204, Tripiṭaka)
Health is the greatest (ultimate) gift (profit), contentment is the greatest (ultimate) wealth, a trusted friend is the best relative, Nibbana is the greatest (ultimate) bliss.
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Table of Contents
Abstract ...... iii
Acknowledgements ...... v
Table of Contents ...... ix
Chapter 1.Introduction ...... 1
1.1 Background ...... 1
1.2 Aim, Objectives, and Significance ...... 4
1.2.1 Aim ...... 4
1.2.2 Objectives ...... 4
1.2.3 Significance ...... 5
1.3 References ...... 6
Chapter 2. Plant autophagy: the potential of application of proteomics for receptor and cargo identification ...... 8
2.1 Summary ...... 8
2.2 Introduction ...... 8
2.3 Types of classical autophagy identified in plants ...... 9
2.4 Core machinery of autophagy is conserved in plants ...... 10
2.4.1 Regulation of autophagy induction ...... 11
2.4.2 Phosphoinositide 3-kinase (PI3K) complex and vesicular nucleation ...... 12
2.4.3 Membrane formation and development ...... 12
2.4.4 Autophagosome expansion and maturation ...... 13
2.4.5 Fusion with the vacuole ...... 14
2.4.6 Degradation ...... 15
2.5 Types of autophagy identified in plants ...... 16
2.5.1 Autophagy of individual proteins...... 17
2.5.2 Organelle-specific degradation by autophagy ...... 17
2.6 Use of proteomics strategies to investigate autophagy ...... 23
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2.6.1 Proteomics of the whole cell ...... 24
2.6.2 Proteomics of the subcellular fractions: autophagosomes and lysosomes ...... 24
2.6.3 Proteomics of the secretome ...... 26
2.7 Proteomics used in the analysis of plant autophagy ...... 27
2.8 How proteomics can be applied to future plant autophagy studies ...... 45
2.9 References ...... 48
Chapter 3...... Determining the contribution of autophagy to protein abundance of energy organelles in Arabidopsis...... 63
3.1 Summary ...... 63
3.2 Introduction ...... 64
3.2.1 Protein synthesis and degradation define protein abundance ...... 64
3.2.2 Targeted proteomics for organelle protein abundance profiling ...... 66
3.2.3 Study of plant protein turnover rate of autophagy ...... 68
3.2.4 Aims and Objectives ...... 71
3.3 Materials and Methods ...... 71
3.3.1 Genotyping of Arabidopsis autophagy mutant lines ...... 71
3.3.2 Plant Growth ...... 71
3.3.3 DNA extraction from Arabidopsis leaves ...... 72
3.3.4 Polymerase Chain Reaction (PCR) ...... 72
3.3.5 Agarose gel electrophoresis ...... 73
3.3.6 Development and optimization of Arabidopsis energy organelle-specific peptide MRM assays for abundance profiling ...... 73
3.3.7 Protein isolation and sample preparation ...... 75
3.3.8 Selection of target peptides for MRM assays ...... 76
3.3.9 Progressive 15N labeling of dark-induced senescence (S3) transition ...... 77
3.3.10 Statistical analysis ...... 79
3.4 Results ...... 80
3.4.1 Genotyping of Arabidopsis autophagy mutant lines ...... 80
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3.4.2 Development and optimization of organelle-specific MRM assays for protein abundance profiling ...... 82
3.4.3 15N labeling of WT to measure the protein turnover rate in dark-induced senescence of leaves (S3 Transition) ...... 101
3.4.4 Effect of autophagy mutants on plastid marker protein accumulation ...... 102
3.5 Discussion ...... 105
3.5.1 Selective degradation of mitochondria, plastids, and peroxisomes under different growth and development transitions...... 105
3.5.2 Contribution of autophagy to degradation of organelles in plant cells during growth and development ...... 106
3.5.3 Selective degradation of specific proteins within organelles under different growth and development transitions ...... 107
3.5.4 Ribosome degradation under different growth and development transitions 108
3.6 References ...... 109
Chapter 4. Role of Autophagy in Photomorphogenesis ...... 116
4.1 Summary ...... 116
4.2 Introduction ...... 118
4.2.1 Origin of chloroplast ...... 118
4.2.2 Etioplasts ...... 118
4.2.3 The role of protochlorophyllide oxidoreductase ...... 119
4.2.4 Etioplasts into chloroplasts transition ...... 120
4.2.5 Photosynthesis ...... 121
4.2.6 Role of light signaling in gene regulation during plant development ...... 124
4.2.7 Chlorophyll biosynthesis ...... 126
4.2.8 Proteome remodeling during photomorphogenesis ...... 127
4.3 Aims and Objectives ...... 128
4.4 Materials and Methods ...... 129
4.4.1 Seedling growth ...... 129
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4.4.2 Chlorophyll Fluorescence Imaging and Photosynthetic Parameters ...... 130
4.4.3 Chlorophyll Extraction and Analysis ...... 130
4.4.4 Microscopic Analysis of Chloroplast ...... 131
4.4.5 Protein isolation and sample preparation ...... 131
4.4.6 LC-MS/MS for shotgun proteomics...... 132
4.4.7 Proteomics Data analysis ...... 132
4.4.8 RNA isolation and quality assessment ...... 133
4.4.9 Library Preparation and Sequencing ...... 133
4.4.10 Quantification of Transcripts ...... 134
4.4.11 Differential Expression Gene Analysis of the RNA-Seq Data ...... 134
4.4.12 Gene Ontology, Enrichment Analysis, and Pathway Analysis ...... 135
4.5 Results ...... 135
4.5.1 Effect of autophagy disruption on plastid fluorescence, chlorophyll abundance, and physiology...... 135
4.5.2 Effect of autophagy disruption on the accumulation of proteins in Arabidopsis seedlings...... 141
4.5.3 Effect of autophagy disruption on the accumulation of plastid localized and photosynthesis-related proteins in Arabidopsis seedlings...... 149
4.5.4 Effect of autophagy mutants on RNA transcript abundance ...... 160
4.5.5 Effect of autophagy disruption on the transcriptome in Arabidopsis seedlings in response to photomorphogenesis...... 168
4.5.6 Effect of autophagy mutants on autophagy underline components: autophagy- related genes expression, ER protein abundance, and bZIP transcription factors...... 179
4.5.7 Correlation between mRNA and protein abundance ...... 184
4.6 Discussion ...... 186
4.6.1 Autophagy induced changes in plastid abundance, chlorophyll content, and function...... 186
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4.6.2 Contribution of autophagy to the dynamic abundance of plastid and photosynthesis-related proteins during photomorphogenesis...... 187
4.6.3 Feedback effect of the loss of autophagy on gene expression during photomorphogenesis...... 188
4.6.4 Transcriptional responses in bZIP transcriptional factors associated with photomorphogenesis in autophagy mutants...... 190
4.6.5 A network of autophagy driven differentially expressed proteins and genes during photomorphogenesis...... 193
4.7 References ...... 196
Chapter 5. General Discussion ...... 209
5.1 Introduction ...... 209
5.2 Autophagy plays a role in both plastid biogenesis and the deposition and breakdown of lipids during photomorphogenesis ...... 210
5.3 A targeted proteomics approach offered high reproducibility and quantitative accuracy over larger sample sets compared to discovery-based proteomics .... 215
5.4 Should we focus on the common effects seen in a range of plant autophagy mutants, or should we focus on gene-specific effects of autophagy in plants to generalize an observed phenomenon? ...... 218
5.5 Potential experimental methods to capture the critical role of autophagy in seedling establishment ...... 220
5.6 Remarks ...... 223
5.7 References ...... 223
Appendix ...... 229
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List of figures Figure 2-1: The molecular mechanisms of autophagosome formation ...... 15
Figure 2-2: Use of proteomics strategies to investigate autophagy ...... 23
Figure 2-3: Receptors and cargo in the autophagy process in plant growth the development...... 46
Figure 3-1: General workflow for targeted proteomics with multiple reaction monitoring ...... 67
Figure 3-2: Establishment of three developmental transitions ...... 75
Figure 3-3: T-DNA insertion in ATG2 and genotyping of atg2-1 (SALK_076727)...... 80
Figure 3-4: T-DNA insertion in ATG5 and genotyping of atg5-1 (SAIL_129_B07). .... 81
Figure 3-5: T-DNA insertion in ATG7 and genotyping of atg7-2 (GK_655B06)...... 81
Figure 3-6: T-DNA insertion in ATG9 and genotyping of atg9-2 (SALK_130796). .... 82
Figure 3-7: Abundance of plastid MRM biomarkers in WT during S1 transition...... 83
Figure 3-8: Abundance of plastid MRM biomarkers in WT during S2 transition...... 83
Figure 3-9: Abundance of plastid MRM biomarkers in WT during S3 transition...... 84
Figure 3-10: Abundance of mitochondria MRM biomarkers in WT during S1 transition...... 85
Figure 3-11: Abundance of mitochondria MRM biomarkers in WT during S2 transition...... 85
Figure 3-12: Abundance of mitochondria MRM biomarkers in WT during S3 transition...... 86
Figure 3-13: Abundance of peroxisomes MRM biomarkers in WT during S1 transition...... 87
Figure 3-14: Abundance of peroxisomes MRM biomarkers in WT during S2 transition...... 87
Figure 3-15: Abundance of peroxisomes MRM biomarkers in WT during S3 transition...... 88
Figure 3-16: Organelles specific protein abundance profiling of WT and autophagy mutants in S1 transition...... 90
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Figure 3-17: Organelles specific protein abundance profiling of WT and autophagy mutants in S2 transition...... 91
Figure 3-18: Organelles specific protein abundance profiling of WT and autophagy mutants in S3 transition...... 92
Figure 3-19: Ribosome specific protein abundance profiling of WT and autophagy mutants in S1 transition ...... 95
Figure 3-20: Ribosome specific protein abundance profiling of WT and autophagy mutants in S2 transition ...... 97
Figure 3-21: Ribosome specific protein abundance profiling of WT and autophagy mutants in S3 transition...... 99
Figure 3-22: PCA of ribosome protein abundance profiling of S1, S2, and S3 transitions...... 100
Figure 3-23: Changes of labeled protein content in covered and uncovered leaves ..... 102
Figure 3-24: MRM based plastid marker protein abundance of S1 early seedling establishing transition...... 103
Figure 3-25: MRM based plastid marker protein abundance of S2 dark to light greening of seedlings; etiolated to de-etiolating; photomorphogenesis transition...... 104
Figure 3-26: MRM based plastid marker protein abundance of S3 dark-induced senescence transition...... 104
Figure 4-1: Etioplast to chloroplast transition in Arabidopsis cotyledon ...... 119
Figure 4-2: Structure of Chloroplast ...... 120
Figure 4-3: Structure of the thylakoid membrane complexes...... 122
Figure 4-4: Photomorphogenesis and skotomorphogenesis in the model plant Arabidopsis thaliana ...... 125
Figure 4-5: Chemical structure of chlorophyll ...... 126
Figure 4-6: Dark to light transition for seedlings greening ...... 130
Figure 4-7: Autophagy mutant growth, greening and maximum quantum efficiency of PSII photochemistry imaging during early photomorphogenesis ...... 136
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Figure 4-8: Measurements of fluorescence parameters associated with PSII integrity in wild type and autophagy mutants during treatment of etiolated Arabidopsis seedlings with light ...... 137
Figure 4-9: Relative chlorophyll contents at a different time in de-etiolating Arabidopsis seedlings of WT and autophagy mutants ...... 138
Figure 4-10: Number of plastids per area at different times in cotyledons of de-etiolating Arabidopsis seedlings of WT and autophagy mutants ...... 139
Figure 4-11: Plastid area measurements at different times in cotyledons of de-etiolating Arabidopsis seedlings of WT and autophagy mutants ...... 139
Figure 4-12: Chlorophyll autofluorescence in Arabidopsis de-etiolating seedlings at different times in WT and autophagy mutants ...... 140
Figure 4-13: Number of identified proteins per sample of identified proteins per sample ...... 141
Figure 4-14: PCA of identified proteins...... 142
Figure 4-15: Heatmap of all proteins with significant changes in abundance in autophagy mutants compared with WT in at least one time-point during photomorphogenesis. .. 143
Figure 4-16: The number of differentially expressed proteins (DEPs) in Arabidopsis autophagy mutants in response to dark to light transition...... 145
Figure 4-17: Venn diagrams of genotypic effect on DEPs compared to WT during photomorphogenesis ...... 146
Figure 4-18: Venn diagrams of the time-course effect on DEPs during photomorphogenesis ...... 147
Figure 4-19: Functional KEGG terms enrichment analysis of all the DEPs in autophagy mutants during the photomorphogenesis ...... 148
Figure 4-20: Partition of the subcellular localization of significantly abundance changed proteins during de-etiolation in Arabidopsis autophagy mutants ...... 151
Figure 4-21:A. Accumulation of proteins biomarkers from Group A in Figure 4.20 in genotypes (WT, atg2, atg5, atg7, and atg9) over the time course during photomorphogenesis...... 153
Figure 4-22: Functional enrichment analysis of differentially expressed proteins of plastid and photosynthesis during photomorphogenesis in autophagy mutants ...... 156
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Figure 4-23: Significant changes in photosystem I and II proteins in autophagy mutants ...... 157
Figure 4-24: Significant changes in Light-harvesting complexes in autophagy mutants ...... 158
Figure 4-25: Significant changes in Carbon fixation pathway...... 158
Figure 4-26: Significant changes in the fatty acid biosynthesis pathway at 0, 12, and 24 hours...... 159
Figure 4-27: Visualization ATG2 (AT3G19190) gene expression of using the Integrative Genomics Viewer ...... 161
Figure 4-28: Visualization ATG5 (AT5G17290) gene expression of using the Integrative Genomics Viewer ...... 162
Figure 4-29: Visualization of ATG9 (AT2G31260) gene expression using the Integrative Genomics Viewer ...... 163
Figure 4-30: Principal component analysis (PCA) of RNA-seq analysis during de- etiolation in Arabidopsis autophagy mutants compared to WT ...... 164
Figure 4-31: MDS analysis of RNA-Seq during de-etiolation in Arabidopsis autophagy mutants compared to WT...... 165
Figure 4-32: The number of differentially expressed genes (DEGs) in Arabidopsis autophagy mutants in response to dark to light transition...... 166
Figure 4-33: Venn diagrams of Genotypic Effect of autophagy mutants’ transcriptome compared to WT during photomorphogenesis ...... 167
Figure 4-34: Venn diagrams of Time course Effect of DEGs during photomorphogenesis ...... 168
Figure 4-35: Gene ontology and KEGG enrichment of up-regulated DEG in autophagy mutants and WT Col-0 at 12 h and 24 h compared to respected 0 h time point...... 171
Figure 4-36: Gene ontology and KEGG enrichment of down-regulated DEG in autophagy mutants and WT Col-0 at 12 h and 24 h compared to respected 0 h time point...... 173
Figure 4-37: Gene ontology and KEGG enrichment of up-regulated DEG in autophagy mutants at 12 h and 24 h compared to WT 0 h time point...... 176
Figure 4-38: Gene ontology and KEGG enrichment of down-regulated DEG in autophagy mutants at 12 h and 24 h compared to WT 0 h time point...... 178
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Figure 4-39: Genotypic and time-course specific autophagy pathway-related genes transcript abundance...... 180
Figure 4-40: Genotypic and time-course specific Transcript abundance of bZIP superfamily genes transcript abundance ...... 182
Figure 4-41: Changes in abundance of endoplasmic reticulum localized proteins ...... 183
Figure 4-42: Correlation analysis of global transcriptomic vs. proteomics ...... 185
Figure 4-43: Quantitative analysis of proteins and mRNA transcript associated with the plastid and photosynthesis DEPs during de-etiolation in Arabidopsis ...... 186
Figure 4-44: Expression of bZIP28 and HY5 transcriptional factors during the photomorphogenesis under autophagy deficit condition...... 191
Figure 4-45: A proposed model depicting autophagy in the regulation of photomorphogenesis ...... 196
Figure 5-1: Schematic diagram of skotomorphogenesis to photomorphogenesis ...... 213
Figure 5-2: Comparison of quantification results obtained by LFQ and MRM biomarkers ...... 217
Figure 5-3: Partition of the predicted plastid localized significantly differentially up- or down-regulated proteins detected using targeted and shotgun proteomics techniques . 218
Figure 5-4: Methods for Monitoring Autophagosome Number and Autophagic Flux . 222
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List of Table Table 2-1: Proteins Involved in Autophagy in Arabidopsis thaliana ...... 11
Table 2-2: Whole cell proteomic assessments of autophagy in yeast and mammalian systems ...... 31
Table 2-3: Proteomic assessments of autophagosomes, lysosomes in yeast and mammalian systems ...... 37
Table 2-4: Proteomic assessments of secretomes associated with autophagy in yeast and mammalian systems ...... 41
Table 2-5: Proteomics assessments of autophagy processes in plant systems ...... 43
Table 3-1: Selected ATG mutant Arabidopsis thaliana lines for use in developmental transitions S1, S2, and S3 and reference to their isolation and characterization...... 72
Table 3-2: Primers used for Arabidopsis thaliana mutant lines genotyping...... 73
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Chapter 1. Introduction
1.1 Background
The protein content of plant cells is constantly being renewed by the actions of protein degradation and protein synthesis. The protein turnover process is critical in plant development and in the daily metabolism of plants that fuel their growth (Doherty and Beynon, 2006; Nelson et al., 2014; Li et al., 2017). Protein function allows adaptation of cell function to a fluctuating internal and external environment (Chen et al., 2016; Dissmeyer et al., 2017). This process involves the coordination of protein degradation, protein synthesis, post-translational modifications, folding, protein-protein interactions, and subcellular localization in the cell (Alvarez-Castelao et al., 2012). Protein turnover generally refers to both protein biosynthesis and degradation and its equilibrium to maintain cellular function (Ohsumi, 2006; Fan et al., 2016). Most proteins are synthesized in the cytosol, while organelle-specific proteins are synthesized inside plastids and mitochondria. Degradation of proteins occurs in both the cytosol and organelles (Nelson et al., 2014). An analysis of protein synthesis and turnover in germinating Arabidopsis seeds revealed that translation and degradation change in a coordinated manner during this developmental transition (Galland et al., 2014). Endogenous signaling mechanisms of plants are also mediated by changes in protein turnover rates in order to respond to environmental stimulants (Yang et al., 2010).
Controlling the degradation of regulatory proteins is a mechanism for the regulation of signaling pathways. In plants, many key components in light sensing, biotic and abiotic stress responses, and hormone responses, as well as developmental programs like flowering, senescence, and cell cycle control are targets of controlled proteolysis. Ubiquitination is a universal stimulus of degradation by tagging substrate proteins for 26S proteasome and autophagy degradation pathways in eukaryotes (Kraft et al., 2010). The receptors for hormone-mediated signaling pathways (auxin, jasmonic acid, gibberellin, and strigolactone) are integral components of the ubiquitin ligase machinery. E3 ubiquitin ligases actively participate in hormone perception, de-repression of hormone signaling pathways, degradation of hormone specific transcription factors, and regulation of hormone biosynthesis (Santner and Estelle, 2010).
1
While the high energy cost of protein synthesis is well known (Piques et al., 2009; Li et al., 2012; Nelson et al., 2014; Li et al., 2017; Basisty et al., 2018), intracellular protein degradation also requires energy for a variety of processes (Ciechanover, 2005). Firstly, protease dependent degradation of peptide bonds is often ATP dependent (Pickart and Cohen, 2004). Secondly, intracellular protein degradation by the ubiquitin- proteasome pathway requires ATP to mark the proteins to be degraded by attaching ubiquitin (Lilienbaum, 2013). In addition, energy is needed for protein transport across the lysosomal membrane (Hayashi et al., 1973) and for the activity of the H+ pump, which is required for the maintenance of the acidic intra-lysosomal pH that is necessary for the optimal activity of proteases (Schneider, 1981). The combination of protein synthesis and degradation is thus one of the most energy-consuming processes in plants depending on the physiological status and environmental stress levels (Izumi et al., 2013; Li et al., 2017). About a third of all the ATP from oxidative phosphorylation is used for protein turnover in Arabidopsis leaves (Li et al., 2017). In rice, protein biosynthesis rate becomes the predominant sink for ATP in anoxic coleoptiles at the cost of almost all other biosynthetic processes (Greco et al., 2012).
Not all proteins are replaced at the same rate, indeed protein half-life may vary from hours to months (Nelson et al., 2013; Nelson et al., 2014; Li et al., 2017) and often relates to a protein’s biological function. Protein complexes such as the proteasome often exhibit a synchronized pattern in turnover rates between subunits, and housekeeping proteins generally have slow turnover rates (Altman and Rathmell, 2012). In a recent study in Arabidopsis leaves, 184 proteins (around 15% of the data) had relatively fast degradation -1 rates which were more than two times the degradation rate of (KD) median (~0.22 d ), and 161 proteins (around 13% of the data) have relatively slow degradation rates which were less than half the median (~0.055 d-1), the majority (72%) showed values in the four- fold range between these KD values (Li et al., 2017). That study revealed that mitochondrial, plastidial, and nuclear proteomes have a median degradation rate while endoplasmic reticulum (ER) and the Golgi apparatus proteomes have a higher median degradation rate (Li et al., 2017). Notably multiple degradation pathways viz. ubiquitin- proteasome, autophagy, and proteases are contributing to this diverse degradation distribution in cellular organelles.
Autophagic degradation of intracellular organelles (plastids, peroxisome, mitochondria, ER), protein aggregates, and lipids both participate in protein degradation but also provides amino acids, lipids, and energy to cells (Kelekar, 2005; Kim et al.,
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2014). Autophagy is a cell survival process that turns over damaged cell components under adverse environmental conditions. In addition to bulk degradation, selective autophagy in plants degrade organelles like mitochondria (mitophagy), chloroplasts (chlorophagy) and peroxisomes (pexophagy), and large cytoplasmic complexes such as 26S proteasomes (proteaphagy) or ribosomes (ribophagy) when dysfunctional or no longer needed (Hafrén et al., 2017). Turnover of intracellular organelles is important to maintain their healthy age profile and prevents the accumulation of damaged or dysfunctional organelles.
Autophagy-mediated turnover is an essential process in cellular homeostasis for removing damaged organelles, cytoplasmic constituents, pathogens, and nutrient recycling, especially during nitrogen, carbon, phosphate deficiency (Michaeli et al., 2016). Nitrogen remobilization efficiency is significantly lower in autophagy mutants irrespective of biomass defects, harvest index reduction, leaf senescence phenotypes, and nitrogen conditions (Guiboileau et al., 2012). Besides that, autophagy mutants accumulate a larger amount of ammonium, amino acid, and proteins in their leaves than wild type and are depleted in sugars (Guiboileau et al., 2013). Over-accumulation of ribosomal proteins subunits viz. RPS6 and RPL13, catalase, and glutamate dehydrogenase in the atg5 mutants occurred despite higher protease activities in mutants. Accumulation of glutamine synthetase 2 and Rubisco large subunit peptides suggests that the autophagy process is required for complete chloroplast protein degradation and that plastidial protein degradation through autophagy might be selective (Guiboileau et al., 2013).
Throughout the autophagic recycling process, cargo proteins are digested by hydrolysis enzymes in the vacuole and later amino acids are released from the vacuole. These amino acids can be utilized as an energy source through the Krebs cycle (Mizushima, 2007). Moreover, recent studies in Arabidopsis have suggested that autophagy can contribute to energy availability at night by supplying amino acids (Izumi et al., 2013). Therefore, autophagy processes are considered to be an energy release process (Anding and Baehrecke, 2017). During seed germination, plant energy organelles, glyoxysomes (specialized peroxisomes), and mitochondria form rapidly and begin converting stored starch and lipids into energy. In parallel, the chloroplasts develop and start providing energy through photosynthesis once the leaves of the seedling have emerged (Michaeli and Galili, 2014). Throughout plant growth and development, maintenance of functional organelles is crucial and damaged proteins aggregates and
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organelles are recycled via macroautophagy or selective autophagy during specific physiological events (Michaeli and Galili, 2014).
However, it not currently clear whether autophagy or intra-organelle proteases are principally responsible for changes in organelle proteomes in plants during growth and development. It is thus important to determine if there is a major role for autophagy in degrading organelles in plant cells and determine the energetic cost of this process to plant cells. It is also important to determine if selective removal of some organelles can occur through autophagy (selective autophagy) or whether organelles of all types are removed without selection. This will determine whether or not autophagy can contribute to the change in role and abundance of specific types of organelles during changes in plant development. 1.2 Aim, Objectives, and Significance
1.2.1 Aim
The overall aim of this thesis was to determine if biogenesis or adaptation of organelles during Arabidopsis growth and development is dependent on autophagy. 1.2.2 Objectives
This aim was planned to be achieved by undertaking the following objectives;
1. To assemble and check homozygosity of a range of Arabidopsis autophagy mutants from different parts of the autophagy process in plants.
2. To set up three developmental transitions during early seedling development, dark to light greening of seedlings and light to dark senescence of leaves for the study of autophagy in Arabidopsis thaliana.
3. To assess the abundance of selected organelle proteins during these transitions in different autophagy mutants by developing targeted mass spectrometry viz. multiple reaction monitoring (MRM) assays across time courses and between genotypes.
4. Based on the outcome of objective three above, to select one transition and a specific biological process to follow with a more comprehensive analysis to independently assess the impact of autophagy on this plant function. For this purpose, the project determined plastidial protein abundances, chloroplast function, plastid microscopy, and gene expression during photomorphogenesis in Arabidopsis thaliana.
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1.2.3 Significance
The expected output from this project was to provide new insights into the following fundamental research questions:
1. What is the contribution of autophagy to the degradation of organelles in plant cells during growth and development?
2. Are organelles including mitochondria, plastids, and peroxisomes selectively degraded under different growth and development transitions?
3. Are specific proteins in these organelles selectively degraded by autophagy under different growth and development transition?
4. Can changes in gene and protein expression be correlated to guide future proteomic research on autophagy?
The answers to these questions will provide new insights into the role of autophagy in organelle homeostasis in plant growth and development.
Figure1-1 Flow diagram of the tasks undertaken in this thesis (relevant chapters are titled)
As summarized in Figure 1-1, Chapter 2 provides a comprehensive review of the literature relating to plant autophagy and the application of mass spectrometry-based proteomics in autophagy studies.
Chapter 3 sets up three developmental transitions during early seedling development, dark to light greening of seedlings and light to dark senescence of leaves for the study 5
Arabidopsis thaliana autophagy. Then MRM markers were used to assess the abundance of selected organelle proteins during these transitions in different autophagy mutants in time-course experiments.
Chapter 4 involves functional and microscopy studies and quantitative proteomics and transcriptomic analysis for plastidial and photosynthesis-related proteins abundances and their gene expression to discover the role of autophagy during photomorphogenesis.
Finally, Chapter 5 summarizes the findings, overall conclusions, and directs future work for using proteomics techniques to develop a consolidated pathway to study the role of autophagy during photomorphogenesis.
1.3 References
Altman, B.J., and Rathmell, J.C. (2012). Metabolic stress in autophagy and cell death pathways. Cold Spring Harb Perspect Biol 4, a008763. Alvarez-Castelao, B., Ruiz-Rivas, C., and Castano, J.G. (2012). A critical appraisal of quantitative studies of protein degradation in the framework of cellular proteostasis. Biochem Res Int 2012, 823597. Anding, A.L., and Baehrecke, E.H. (2017). Cleaning House: Selective Autophagy of Organelles. Dev Cell 41, 10-22. Basisty, N., Meyer, J.G., and Schilling, B. (2018). Protein Turnover in Aging and Longevity. Proteomics 18, e1700108. Chen, W., Smeekens, J.M., and Wu, R. (2016). Systematic study of the dynamics and half-lives of newly synthesized proteins in human cells. Chem Sci 7, 1393-1400. Ciechanover, A. (2005). Intracellular protein degradation: from a vague idea thru the lysosome and the ubiquitin-proteasome system and onto human diseases and drug targeting. Cell Death Differ 12, 1178-1190. Dissmeyer, N., Rivas, S., and Graciet, E. (2017). Life and death of proteins after protease cleavage: protein degradation by the N-end rule pathway. bioRxiv, 115246. Doherty, M.K., and Beynon, R.J. (2006). Protein turnover on the scale of the proteome. Expert Rev Proteomics 3, 97-110. Fan, K.T., Rendahl, A.K., Chen, W.P., Freund, D.M., Gray, W.M., Cohen, J.D., and Hegeman, A.D. (2016). Proteome Scale-Protein Turnover Analysis Using High Resolution Mass Spectrometric Data from Stable-Isotope Labeled Plants. J Proteome Res 15, 851-867. Galland, M., Huguet, R., Arc, E., Cueff, G., Job, D., and Rajjou, L. (2014). Dynamic proteomics emphasizes the importance of selective mRNA translation and protein turnover during Arabidopsis seed germination. Mol Cell Proteom 13, 252-268. Greco, M., Chiappetta, A., Bruno, L., and Bitonti, M.B. (2012). In Posidonia oceanica cadmium induces changes in DNA methylation and chromatin patterning. J Exp Bot 63, 695-709. Guiboileau, A., Yoshimoto, K., Soulay, F., Bataille, M.P., Avice, J.C., and Masclaux- Daubresse, C. (2012). Autophagy machinery controls nitrogen remobilization at the whole-plant level under both limiting and ample nitrate conditions in Arabidopsis. New Phytol 194, 732-740. 6
Guiboileau, A., Avila-Ospina, L., Yoshimoto, K., Soulay, F., Azzopardi, M., Marmagne, A., Lothier, J., and Masclaux-Daubresse, C. (2013). Physiological and metabolic consequences of autophagy deficiency for the management of nitrogen and protein resources in Arabidopsis leaves depending on nitrate availability. New Phytol 199, 683- 694. Hafrén, A., Macia, J.-L., Love, A.J., Milner, J.J., Drucker, M., and Hofius, D. (2017). Selective autophagy limits cauliflower mosaic virus infection by NBR1-mediated targeting of viral capsid protein and particles. PNAS, 2026-2035. Hayashi, M., Hiroi, Y., and Natori, Y. (1973). Effect of ATP on protein degradation in rat liver lysosomes. Nature New Biol 242, 163-166. Izumi, M., Hidema, J., Makino, A., and Ishida, H. (2013). Autophagy contributes to nighttime energy availability for growth in Arabidopsis. Plant Physiol 161, 1682-1693. Kelekar, A. (2005). Autophagy. Ann N Y Acad Sci 1066, 259-271. Kim, J., Lee, H.N., and Chung, T. (2014). Plant cell remodeling by autophagy: switching peroxisomes for green life. Autophagy 10, 702-703. Kraft, C., Peter, M., and Hofmann, K. (2010). Selective autophagy: ubiquitin-mediated recognition and beyond. Nat Cell Biol 12, 836-841. Li, L., Nelson, C.J., Solheim, C., Whelan, J., and Millar, A.H. (2012). Determining degradation and synthesis rates of Arabidopsis proteins using the kinetics of progressive 15N labeling of two-dimensional gel-separated protein spots. Mol Cell Proteomics 11, M111. 010025. Li, L., Nelson, C.J., Trosch, J., Castleden, I., Huang, S., and Millar, A.H. (2017). Protein Degradation Rate in Arabidopsis thaliana Leaf Growth and Development. Plant Cell 29, 207-228. Lilienbaum, A. (2013). Relationship between the proteasomal system and autophagy. Int J Biochem Mol Biol 4, 1-26. Michaeli, S., and Galili, G. (2014). Degradation of organelles or specific organelle components via selective autophagy in plant cells. Int J Mol Sci 15, 7624-7638. Michaeli, S., Galili, G., Genschik, P., Fernie, A.R., and Avin-Wittenberg, T. (2016). Autophagy in Plants--What's New on the Menu? Trends Plant Sci 21, 134-144. Mizushima, N. (2007). Autophagy: process and function. Genes Dev 21, 2861-2873. Nelson, C.J., Li, L., and Millar, A.H. (2014). Quantitative analysis of protein turnover in plants. Proteomics 14, 579-592. Nelson, C.J., Li, L., Jacoby, R.P., and Millar, A.H. (2013). Degradation rate of mitochondrial proteins in Arabidopsis thaliana cells. J Proteome Res 12, 3449-3459. Ohsumi, Y. (2006). Protein turnover. IUBMB Life 58, 363-369. Pickart, C.M., and Cohen, R.E. (2004). Proteasomes and their kin: proteases in the machine age. Nat Rev Mol Cell Biol 5, 177-187. Piques, M., Schulze, W.X., Hohne, M., Usadel, B., Gibon, Y., Rohwer, J., and Stitt, M. (2009). Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis. Mol Syst Biol 5, 314. Santner, A., and Estelle, M. (2010). The ubiquitin-proteasome system regulates plant hormone signaling. Plant J 61, 1029-1040. Schneider, D.L. (1981). ATP-dependent acidification of intact and disrupted lysosomes. Evidence for an ATP-driven proton pump. J Biol Chem 256, 3858-3864. Yang, X.Y., Chen, W.P., Rendahl, A.K., Hegeman, A.D., Gray, W.M., and Cohen, J.D. (2010). Measuring the turnover rates of Arabidopsis proteins using deuterium oxide: an auxin signaling case study. Plant J 63, 680-695. 7
Chapter 2. Plant autophagy: the potential of application of proteomics for receptor and cargo identification
2.1 Summary
Autophagy is a catabolic mechanism facilitating the degradation of cytoplasmic proteins and organelles in a vacuole-dependent manner. A combination of cell biology and reverse genetics has led to the discovery of the protein machinery of autophagy in many eukaryotes. Advancement and application of proteomic technologies have shown how autophagy modifies the proteome and has identified cargo in autophagosomes from yeast and mammalian cells subjected to various extra and intra-cellular stimuli. Proteomics has been used to provide “snapshots” in space and time of protein abundance changes during autophagy processes to highlight the importance of autophagy in cell development and fate. In a small selection of studies, proteomics has been used for the analysis of post-translational modifications and protein-protein interactions, providing a better understanding of the functional and biological processes involved in autophagy processes. While the application of proteomics to plant autophagy is still at a nascent stage, it has great potential to complement plant genetic studies by uncovering the distinct role of autophagy in sculpting organelles and other specific components of the plant proteome and in so doing identify autophagy receptors and cargo in plants.
2.2 Introduction
Plants serve as our main source of food, provide oxygen, and affect global climate and water management. Understanding how plants cope with stress is important to discover potential sites for genetic manipulation in crops to increase yields or improve tolerance to particular climates. Protein synthesis and degradation are critical processes within a cell for adapting function to a fluctuating internal and external environment. It has been said, “life is an equilibrium state between synthesis and degradation of proteins” (Ohsumi, 2016). To ensure the continuing quality of the cellular proteome, proteins are constantly being renewed by a series of protein turnover processes. Plant proteostasis is correlated with cellular protein abundance, which is regulated by DNA transcription, translation, post-translational modifications, and also by stabilization and degradation of proteins (Stitt and Gibon, 2014). Protein turnover rates in plants range from minutes to 8
months for different protein types (Nelson et al., 2014b; Abdelrahman et al., 2017). Proper regulation of protein levels by protein turnover in plants allows cell growth and proliferation (Ishihara et al., 2015) and also enables stress responses (Tyagi and Pedrioli, 2015), nutrient supply, energy balance (Abdelrahman et al., 2017), hormonal signaling and host defense mechanisms against pathogens (Balch et al., 2008).
Autophagy is one of the major catabolic routes in cells and plant cells have a basal level of autophagy under normal conditions to maintain homeostasis (Liu and Bassham, 2012). Under adverse environmental conditions, it dominates as a survival process to turn over damaged cell components (Noda et al., 2000; Doelling et al., 2002; Hanaoka et al., 2002; Mizushima, 2017) to provide anabolic substrates and metabolites for cells in plants (Liu and Bassham, 2012). Recycling intracellular components is essential when outside nutrients are scarce, therefore autophagic turnover of intracellular organelles and the cytoplasmic material is vital to maintain proteostasis and cellular homeostasis during nutrient deficiency (Dikic, 2017; Marshall and Vierstra, 2018). Autophagy induction is also linked to endoplasmic reticulum (ER) stress, where the accumulation of unfolded or misfolded proteins in the ER ultimately cause programmed cell death (Liu et al., 2012; Liu and Bassham, 2013; Pu and Bassham, 2013; Yang et al., 2016).
2.3 Types of classical autophagy identified in plants
Autophagic pathways in eukaryotes, in general, can include microautophagy, chaperone-mediated autophagy, cytoplasm-to-vacuole targeting (Cvt), and macroautophagy (Veljanovski and Batoko, 2014). However, in plants, to date, there is only substantial evidence for microautophagy and macroautophagy. However, there is evidence for mega autophagy, a process associated with programmed cell death, where the whole cell becomes autolytic (van Doorn and Papini, 2013). Micro and macroautophagy processes are similar as some of the key components are shared and both utilize the vacuole as final cargo destination and target lytic compartment (Liu and Bassham, 2012). The difference is in the vesicle formation. During microautophagy, cytoplasmic components are directly engulfed to release into the vacuole by pinching off from the invagination of the tonoplast (Bassham et al., 2006). In macroautophagy, the vesicles transporting the cargo to the vacuole are formed de novo in a cascade of processes. It is important to note that autophagy in general can be a bulk (non-selective) or a selective process. To date, microautophagy is not well explored in plants and has mainly been studied during leaf senescence, seed development, and germination 9
(Levanony et al., 1992; Bassham et al., 2006). This review will focus on macroautophagy, hereafter referred to as autophagy.
2.4 Core machinery of autophagy is conserved in plants
Autophagy is a unique membrane trafficking process where cytosolic parts are initially engulfed by phagophores which are then sealed to form double-membrane bound autophagosomes that are ultimately delivered to the vacuole for degradation. Autophagosome formation proceeds in several stages viz.: Regulation of autophagy induction, Phosphoinositide 3-kinase complex, and vesicular nucleation, membrane formation and development, autophagosome expansion and maturation, fusion with the vacuole and degradation (Liu and Bassham, 2012; Yang and Bassham, 2015; Ohsumi, 2016; Marshall and Vierstra, 2018).
The genes involved in the autophagy machinery were first identified in Saccharomyces cerevisiae, in screens for mutants with defects in protein turnover, peroxisome degradation, and delivery of a resident vacuolar hydrolase (Takeshige, 1992; Mizushima, 2017). So far, about 50 AuTophaGy-related (ATG) genes have been reported, 18 of them are defined as the core ATG genes (Klionsky et al., 2003; Weidberg et al., 2011; Yoshimoto, 2012). Studies have revealed the evolutionary diversity of the autophagic system among a range of organisms (Mizushima, 2017). By comparison to yeast genome sequences, a series of autophagy genes have been identified in multicellular organisms like human (Mizushima et al., 1998), Arabidopsis thaliana (Doelling et al., 2002; Hanaoka et al., 2002; Honig et al., 2012a; Honig et al., 2012b), and agricultural crop plants, such as soybean (Glycine max), pepper (Capsicum annuum), wheat (Triticum aestivum), tomato (Solanum lycopersicum), rice (Oryza sativa), maize (Zea mays), and tobacco (Nicotiana tabacum) (Su et al., 2006; Htwe et al., 2009; Takatsuka et al., 2011; Kuzuoglu-Ozturk et al., 2012; Kurusu et al., 2014; Zhou et al., 2014; Li et al., 2015; Zhai et al., 2017).
In Arabidopsis, about 40 ATG genes have been identified, among them most of the core ATG genes (Zientara-Rytter and Sirko, 2016; Li et al., 2018), but not all have been functionally described yet. Most of the major groups of ATG genes shown in Table 2-1, have functions in Arabidopsis similar to those first identified in Saccharomyces (Avin- Wittenberg et al., 2012a; Avin-Wittenberg et al., 2012b; Floyd et al., 2012; Lv et al., 2014). The following genes viz. ATG1, ATG4, ATG8, ATG12, ATG13, and ATG18 are present in more than one isoform in the Arabidopsis genome and may indicate they 10
display functional redundancy. A further seven ATG genes, viz. ATG1, ATG2, ATG3, ATG13, ATG16, VPS15, and VPS34 were identified by sequence similarity and have not been functionally described yet (Hanaoka et al., 2002; Meijer et al., 2014). However, some of Saccharomyces autophagy genes viz. ATG27, ATG29, and ATG 31 have no homologs in Arabidopsis thaliana (Avin-Wittenberg et al., 2012a; Zhao et al., 2016a).
Table 2-1: Proteins Involved in Autophagy in Arabidopsis thaliana Proteins are grouped according to the steps of autophagosome formation and delivery of cargo to the vacuole, as outlined in Figure 1-1. * indicates that their role has not yet been characterized in Arabidopsis thaliana. Step Key complex Proteins involved References Induction TOR complex TOR kinase, Raptor, LST8 (Montane and Menand, 2013; Pu et al., 2017a; Salem et al., 2017) ATG1 Kinase ATG1, ATG13, ATG11*, (Liu and Bassham, 2010) complex ATG101* Membrane ATG9 complex ATG2, ATG9, ATG18 (Xiong et al., 2005; Wang et al., delivery 2011; Yamamoto et al., 2012; Zhuang et al., 2017), Vesicle nucleation VSP34 complex UVRAG, ATG6, VPS15, (Li and Vierstra, 2012a; He et al., VPS34 (PI3K), ATG14* 2013; Ryabovol and Minibayeva, 2016; Lee et al., 2017). Phagophore ATG8 ATG3, ATG4, ATG5, (Doelling et al., 2002; Suzuki et extension conjugation ATG7, ATG8, ATG10, al., 2005; Phillips et al., 2008; Le systems ATG12, ATG16 Bars et al., 2014; Park et al., 2014) Tonoplast fusion VTI12, CFS1, FREE1, (Surpin et al., 2003; Zouhar et al., VPS2 2009) Digestion Variety of vacuolar hydrolases
2.4.1 Regulation of autophagy induction
Autophagy is initiated by the action of two protein complexes: viz. the TOR kinase complex and the ATG1/ATG13 kinase complex (Li and Vierstra, 2012b; Papinski and Kraft, 2016). TOR is a phosphatidylinositol-3-kinase (PI3K)-related kinase that functions as a serine/threonine-protein kinase and is a negative regulator of autophagy in many organisms (Zheng et al., 1995; Liu and Bassham, 2010; Pu et al., 2017a). It controls ATG1/ATG13 mediated autophagy via phosphorylation of ATG13, thereby preventing interaction with ATG1 and its activation. The TOR signaling pathway is complicated in plants and has a function in growth and development (Son et al., 2018). It is believed that the plant TOR complex regulates autophagy in a similar way as it does in yeast and mammals. However, there is little specific evidence so far of the regulatory mechanism in plants and this is hampered by Arabidopsis tor mutants being embryo lethal (Menand
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et al., 2002). Various approaches to reduce TOR transcript levels or inactivate TOR protein have been used to study its function in Arabidopsis thaliana. These include RNA interference (RNAi) transgenic lines reduced in TOR transcripts (Liu and Bassham, 2010), inducible RNAi lines (Xiong and Sheen, 2012; Caldana et al., 2013), and rapamycin-sensitive transgenic Arabidopsis lines expressing yeast FK506 Binding Protein12 (BP12) (Altman and Rathmell, 2012). ATP-competitive inhibitors (asTORis); AZD8055 (AZD) were recently used to decipher the downstream effectors of TOR in Arabidopsis (Montane and Menand, 2013). A TOR RNAi line with reduced AtTOR transcript level showed activation of autophagy in Arabidopsis (Liu and Bassham, 2010). Two more subunits, ATG11 and ATG101 are part of the fully assembled ATG1/ATG13 complex (Li et al 2014) which is believed to ultimately phosphorylate other autophagy proteins and initiate downstream steps (Li and Vierstra, 2012a; Noda and Fujioka, 2015). In roots, carbon starvation induces the formation of autophagosomes via ATG8 (Slavikova et al., 2008; Michaeli and Galili, 2014) and glucose–TOR signaling networks (Xiong and Sheen, 2013).
2.4.2 Phosphoinositide 3-kinase (PI3K) complex and vesicular nucleation
Plant phosphoinositide 3-kinases (PI3Ks) complex subunit VPS34/PI3K can phosphorylate phosphoinositol (PtdIns) to phosphatidylinositol 3-phosphate (PtdIns(3)P /PI3P) and then initiate the de novo nucleation of a phagophore membrane formation (Ishihara et al., 2001; Jean and Kiger, 2014; Yu et al., 2015). Although conserved subunits of the PI3K complex, PI3K catalytic subunit, activating kinase VPS15, VPS34, UVRAG, and ATG6, have been identified by sequence comparison in plants, their role or interactions have not been reported (Li and Vierstra, 2012a; He et al., 2013; Ryabovol and Minibayeva, 2016; Lee et al., 2017). ATG18 binds with PI3P and directs the ATG18- ATG2 complex to autophagic membranes and plays an important role in vacuolar membrane morphology and localization (Obara et al., 2008).
2.4.3 Membrane formation and development
The transmembrane protein ATG9 functions as a membrane shuttle and delivers lipids to the phagophore assembly site for membrane elongation (Zhuang et al., 2017). ATG9 is usually distributed with the aid of ATG2 and ATG18 (Obara et al., 2008; Orsi et al., 2012). ATG18 binds to PI3P thereby directing the ATG18-ATG2 complex to autophagic membranes. Hence it plays an important role in vacuolar membrane 12
morphology and localization (Obara et al., 2008). Later, ATG9 is dissociated from the fully formed autophagosome and may be recycled (Papinski et al., 2014).
2.4.4 Autophagosome expansion and maturation
In the cytoplasm near the cargo to be degraded, the cup-shaped phagophore membrane is expanded based on two ubiquitin-like conjugation systems (Shpilka et al., 2011). Both of these ubiquitin-like conjugation systems are essential for the regulation of autophagosome size, curvature, expansion rate, and autophagosome closure (Shibutani and Yoshimori, 2014). In the first system; ATG12 conjugates with ATG5 (ATG12– ATG5) in a multistep process. First ATG12 is activated by conjugating its C-terminal glycine with a cysteine residue of the ATG7 (E1 like protein). Active ATG12 binds to a cysteine residue of ATG10 (E2 like protein) and forms a temporary disulfide bridge (Yoshimoto et al., 2010). Then, ATG5 conjugates to ATG12 with the assistance of ATG10. Finally, the ATG12–ATG5 complex non-covalently interacts with the C- terminus of ATG16. Upon dimerization of ATG16, the ATG12-ATG5-ATG16 conjugate forms a multimeric complex (Romanov et al., 2012; Le Bars et al., 2014).
In the second ubiquitin-like conjugation phagophore formation system, ATG8 is modified and covalently links with phosphatidylethanolamine (PE) at the phagophore assembly site. Once the C-terminus of ATG8 is cleaved by the cysteine proteinase ATG4 (Su et al., 2006) and the cleavage exposure; a glycine residue is subsequently conjugated with PE. This reaction is catalyzed by ATG7 (E1 like protein) and ATG3 (E2 like protein) respectively (Ichimura et al., 2000). The ATG12-ATG5-ATG16 conjugate functions as an E3 ligase protein and helps to transfer PE to ATG8. This reaction is reversible by the ATG4protease which can cleave the ATG8–PE adduct and release ATG8 into the cytosol. Both of these ubiquitin-like conjugation systems are essential for the regulation of autophagosome size, curvature, expansion rate, and autophagosome closure (Shibutani and Yoshimori, 2014).
It is well known that ATG4 recycles ATG8. For ATG8-PE modification, the mature ATG8 is processed by ATG4 is the initial step. Once the lipidation of ATG8 occurs, ATG8-PE is delivered to autophagosomes. Since autophagosomes are double-membrane vesicles, ATG8-PEs are decorated inside as well as outside of autophagosomes. Autophagosomes are degraded in the vacuole. During this process, the outside of autophagosomes is fused to the tonoplast, and then, single membrane autophagosomes move into the vacuole lumen for degradation. Although ATG8-PE on the outer membrane 13
will be recycled before the autophagosome fusion with the vacuole, ATG8-PE on the inner membrane will traffic together with the cargo into the vacuol for degradation. Most of the ATG8 molecules outside of autophagosomes are recycled by an ATG4 reaction, whereas all of the ATG8s inside the autophagosomes are subjected to degradation by vacuolar hydrolases. The topology of ATG8s in autophagosomes ultimately decides their fate (Abreu et al., 2017; Kellner et al., 2017). There are several ATG8 homologs in plants (ATG8a-8h), these ATG8s are thought to function in similar ways (Avin-Wittenberg et al., 2012a) and therefore, single atg8 mutants do not have obvious phenotypes.
2.4.5 Fusion with the vacuole
The final stage of the autophagosome life cycle is docking and fusion with the lysosome in animals and the vacuole in yeast and plants. In mammals, some soluble N- ethylmaleimide-sensitive fusion protein attachment receptors (SNAREs), such as VAM7, Vti1b, and VAM9, are involved in the process (Fader et al., 2009; Furuta et al., 2010). Proteins such as tSNARE, VAM3, Vtip, Sec18, and GTP-binding protein Ypt7 are involved in the Saccharomyces fusion process (Darsow et al., 1997; Kirisako et al., 1999).
In plants, endosomal sorting complexes required for transport (ESCRT) homologs play conserved roles in the biogenesis of intraluminal vesicles and multi-vesicular bodies (Gao et al., 2017a). Plant unique ESCRT components such as FYVE DOMAIN PROTEIN REQUIRED FOR ENDOSOMAL SORTING 1 (FREE1) and POSITIVE REGULATOR OF SKD1 (PROS) have also been identified (Gao et al., 2014; Klionsky et al., 2016). Studies carried out recently have shown that FREE1 might play a dual role in regulating autophagosome-vacuole fusion and vacuole biogenesis in Arabidopsis (Gao et al., 2015; Kalinowska et al., 2015; Klionsky et al., 2016). A ubiquitin-binding protein in Arabidopsis SH3P2 functions together with ESCRT-I and the de-ubiquitylating enzyme AMSH3 (Nagel et al., 2017). FREE1 interacts with SH3P2, which displays a higher association with the late endosome and free1 failure in the delivery of autophagosomes to the vacuole. As, SH3P2 binds to the autophagosome membrane and ATG8 (Zhuang et al., 2013), it is hypothesized that FREE1-SH3P2 serves as a bridge for autophagosome fusion with the endosome/vacuole (Soto-Burgos et al., 2018).
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2.4.6 Degradation
Autophagosomes are degraded in the vacuole, by degradative enzymes, including proteases and lipases, for cargo degradation and releases nucleotides, fatty acids, and amino acids to refuel the cells with energy to maintain necessary molecular synthesis (Cui et al., 2016). During this process, the outside of autophagosomes is fused to the tonoplast, and then, autophagic bodies move into the vacuole lumen for degradation. Most of the ATG8 molecules outside of autophagosomes are recycled by ATG4, whereas all of the ATG8s inside the autophagosome is subjected to degradation by vacuole hydrolases. The topology of ATG8s in autophagosomes ultimately decides their fate (Abreu et al., 2017; Kellner et al., 2017).
Figure 2-1: The molecular mechanisms of autophagosome formation The regulatory complex controls autophagy induction—inhibition of the negative autophagy regulator TOR kinase results in ATG13 dephosphorylation. ATG13 binds to ATG1, forming a large complex, including additional proteins such as ATG101. The initiation complex aids autophagy induction and nucleation of the membrane with the ATG6–VPS34 complex playing a central role. ATG6 phosphorylates phosphatidylinositol in a pre-autophagosomal structure which leads to the recruitment of autophagic proteins required for autophagosome formation with the assistance of other initiation complex proteins (UVRAG, VPS34, and VPS15). The nucleation complex of ATG9, ATG18, and ATG2 function as membrane recruiters for vesicle nucleation and expansion. Retrieval of ATG9 from the phagophore assembly sites requires the phosphatidylinositol 3-kinase complex, VPS34, and VPS15, as well as ATG18 and ATG2, ATG13 and ATG1. The conjugation cascade enables autophagosome expansion and maturation. It involves two ubiquitin-like complexes, ATG12-ATG5-ATG16 and ATG8–PE, and regulates autophagosome size, curvature, and expansion rate. The mature autophagosome decorated with ATG8 is transported to the vacuole for fusion. Upon fusion with the vacuole, proteins involved in autophagosome formation dissociate from it, forming a single membraned autophagic body inside the vacuole.
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Autophagosomes and their cargo are degraded in the vacuole by a myriad of lytic enzymes, and amino acids, sugars, and lipids are recycled from the vacuole for use in cellular biosynthesis.
2.5 Types of autophagy identified in plants
Autophagic pathways in other eukaryotes include classification based on the structure; microautophagy, chaperone-mediated autophagy, cytoplasm-to-vacuole targeting (Cvt), macroautophagy, (Veljanovski and Batoko, 2014) and based on the function; bulk autophagy / non selective autophagy and selective autophagy (Münch, and Dikic, 2018). However, in plants, there is substantial evidence to date only for microautophagy and macroautophagy. Both utilize the vacuole as the target lytic compartment and final cargo destination (Liu and Bassham, 2012). To date, microautophagy is not well discovered in plants and has mainly been studied during leaf senescence, seed development, and germination (Levanony et al., 1992; Bassham et al., 2006). During microautophagy, cytoplasmic components associate with the surface of vacuoles and are directly engulfed by the invagination of the tonoplast and pinching off from the membrane to release vesicles named autophagic bodies (Bassham et al., 2006).
Activation of autophagy in response to various types of environmental cues is an evolutionarily conserved strategy crucial for adaptation and survival (Bozhkov, 2018). Accordingly, defective autophagy compromises fitness and viability and in the case of plants leads to the inhibition of both vegetative growth and seed set, accompanied by increased stress sensitivity (Minina et al., 2013; Bozhkov, 2018). Wild-type Arabidopsis Col-0 plants have increased autophagic flux and significantly longer lifespan if grown under reduced light intensity, a phenotype reminiscent of caloric restriction-induced lifespan extension in animals. However, lifespan and vegetative growth of these plants were uncoupled due to decreased photosynthetic activity (Minina et al., 2018). Further, the study has shown by enhancing autophagy flux through a transgenic approach can increase stress resistance, combined with strong growth fitness (Minina et al., 2013). This study showed that transcriptional stimulation of autophagy delays aging promotes vegetative growth, and increases both seed set and seed lipid content (Minina et al., 2013). Previously, 15N labeling experiments showed a nitrogen remobilization defect in autophagy mutants compared to wild type plants. Studies of the cellular and subcellular localization during the embryo development revealed specific expression of ATG8 genes in the phloem of siliques and funiculus, in the outer and the inner integuments, and the seed endosperm (Guiboileau et al., 2012). The autophagic activity has been visualized by microscopy observation of autophagic structures in the developing embryo. Seeds of the
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atg5 autophagy mutant have been morphologically characterized and observed an embryo development arrest at the torpedo stage in many seeds ( Di Berardino, 2018). More extensive studies of atg5 seed composition have been carried out during maturation. This study demonstrated that atg5 mutant seeds are affected by an accelerated maturation and they accumulate more proteins than WT seeds (Guiboileau et al., 2012).
2.5.1 Autophagy of individual proteins
Individual proteins can be degraded via macroautophagy. For example, two of the best-characterized autophagy receptors in mammalian cells that target ubiquitinated proteins are NBR1 (Neighbor of BRCA1 gene 1) and p62/SQSTM1 (Sequestosome-1). NBR1 and p62 have a ubiquitin-associated domain that can bind to ubiquitinated proteins and an LC3-interacting region that interacts with the mammalian ATG8 homolog LC3 (MAP1 light chain 3) (Lamark et al., 2009; Johansen and Lamark, 2014). Recently, in Arabidopsis AtNBR1, which is more similar to mammalian NBR1 than to p62 in structure, was studied and may be involved in the selective degradation of ubiquitin- tagged proteins via autophagy (Svenning et al., 2014). Studies in tobacco identified that JOKA, a member of a family of selective autophagy cargo receptors, is closely related to mammalian NBR1 and p62, colocalizes with both major cytoskeletal components, microtubules, and microfilaments and also resides in close proximity of the ER (Zientara- Rytter et al., 2011; Zientara-Rytter and Sirko, 2014).
2.5.2 Organelle-specific degradation by autophagy
While at one time autophagy was considered as a bulk degradation pathway, lately a range of selective types of autophagy have been described that lead to the elimination of specific organelles or protein aggregates with the help of specific adapters for cargo selection (Fimia et al., 2013; Johansen and Lamark, 2014). Organelles like mitochondria (mitophagy), chloroplasts (chlorophagy) and peroxisomes (pexophagy), and large cytoplasmic complexes such as 26S proteasomes (proteaphagy) or ribosomes (ribophagy) are degraded when dysfunctional or no longer needed (Marshall et al., 2015; Hafrén et al., 2017).
2.5.2.1 Mitophagy
Mitochondria play crucial roles in many biochemical processes. They are involved in the biosynthesis of various protein cofactors like iron-sulfur clusters and the production 17
of ATP by oxidative phosphorylation (Bertram et al., 2006; Stehling and Lill, 2013). ATP assures cellular survival, but the generation of reactive oxygen species (ROS) as a by- product causes damage especially to mitochondria (Yakes and Van Houten, 1997). Turnover of superfluous and damaged mitochondria occurs through mitophagy, a specific autophagy process (Kubli and Gustafsson, 2012; Wang and Klionsky, 2014). The regulation of mitophagy is attained through the retrograde signaling pathway (Journo et al., 2009). The retrograde signaling pathway enables mitochondria to report stresses and metabolic challenges to the nucleus. Hence, mitochondria can play an active role in the regulation of their degradation (Jazwinski, 2013; Simon et al., 2017). In yeast, it is known that there are two distinct mitophagy pathways (Fukuda and Kanki, 2018). The first system is receptor-mediated using ATG32 and ATG11 and ATG8 to deliver the mitochondria to the phagophore. The second pathway is initiated by proteins PINK1 and Parkin. The latter protein ubiquitinates mitochondrial proteins making them a target for classical autophagy receptor proteins (Lazarou et al., 2015). Studies have shown that ATG11 is involved in selective mitophagic quality control also in Arabidopsis (Li et al., 2014). Currently, there is no evidence for an ATG32 orthologue or PINK1 and Parkin orthologues in Arabidopsis (Marshall and Vierstra, 2018) ATG11 binds to the ATG1 and ATG13 and acts as a scaffold connecting the complex with the phagophore assembly site and binding to selective autophagy adapters (Suzuki et al., 2007; Reggiori and Klionsky, 2013). However, the selectivity of mitophagy in plants needs to be analyzed in more detail (Broda et al., 2018), and particularly, the adapter that specifically recognizes mitochondria, possibly via interaction with ATG11, remains to be discovered.
2.5.2.2 Chlorophagy
Chloroplasts host many important biochemical processes such as photosynthesis, and synthesis of essential components. Autophagy is important in maintaining leaf function. In plants in which autophagy is inhibited and abnormal chloroplasts accumulate in the cell, the leaves become easily withered under adverse conditions, such as high light, and autophagy plays a role in removing broken chloroplasts (Izumi et al., 2017; Izumi and Nakamura, 2017b; Otegui, 2017). There are four pathways called chlorophagy in the autophagic decomposition of the chloroplast. These are classified according to differences in chloroplast components transported to the vacuole.
The first, the Rubisco-containing body (RCB) pathway is a partial degradation pathway that transports and degrades only soluble stroma components, whereas the 18
chlorophagic pathway is specific for thylakoid membranes (Wang et al., 2013). The decomposition of chloroplasts and corresponding proteins is particularly important as about 80% of the nitrogen in the leaves of C3 plants is invested in chloroplasts in the amino acids of photosynthetic proteins (Makino et al., 2003). The enzyme ribulose-1,5- bisphosphate carboxylase/oxygenase (Rubisco) which is responsible for the photosynthetic CO2 fixation reaction comprises approximately 12-35% of leaf nitrogen alone (Evans and Seemann, 1989). Therefore, proper regulation of Rubisco's decomposition is important for both photosynthesis and nitrogen recycling. Experimental evidence suggests multiple degradation mechanisms (Feller et al., 2007). Interestingly, Rubisco is known to be degraded inside the chloroplast as it decreases in abundance before the number of chloroplasts in the aging process of leaves and it then degrades further after being released from chloroplasts (Hörtensteiner and Feller, 2002).
Chloroplasts are complex structures and decomposition brings particular challenges for the cell in regards to specific components and timing. This may explain why there are multiple pathways to decompose of them. Besides two ATG-independent degradation pathways, namely Senescing-associated vesicles (SAV) pathway (Ishida et al., 2014; Michaeli and Galili, 2014) and chloroplast vesiculation (CV) pathway (Wang and Blumwald, 2014), four ATG dependent processes have been described. They are classified according to differences in chloroplast components transported to the vacuole.
Soluble components are surrounded by chloroplast membrane, separated, released into the cytoplasm where they are incorporated into autophagosomes mediated by ATG8 and transported to vacuoles for degradation (Ishida et al., 2008). CHMP1A and B are crucial for the correct formation of autophagosomes and recruitment to the vacuole (Spitzer et al., 2015). This pathway has been reported upon nutrient starvation or senescence in plants.
The second pathway, ATI1-PS body pathway requires the membrane-spanning protein ATG8-interacting protein 1 (ATI1). ATI1 was shown to incorporate into chloroplast-associated bodies in response to starvation or following senescence induction and interacts with several chloroplast-localized proteins (Michaeli et al., 2014). ATI1- decorated bodies, containing stromal markers, have been reported to directly bud off from chloroplasts (Michaeli et al., 2014). ATI1 then interacts with ATG8 and other components of the ATG machinery for the delivery to the vacuole. Due to this fact, ATI1 has been described as a chlorophagy receptor (Honig et al., 2012b; Michaeli and Galili, 2014; Michaeli et al., 2014).
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The third pathway uses small starch-like granule (SSLG) bodies to decompose parts for the chloroplast (Wang et al., 2013). The structures are associated with ATG8 and are ultimately recruited to the vacuole.
In the fourth pathway, in contrast to the above described piecemeal processes, whole chloroplasts can be decomposed using ATG depended processes. The entire chloroplast is transported to and degraded in the vacuole (Wada et al., 2008). The transport mechanism to the vacuole is dependent on ATG8 but not yet fully understood (Ishida et al., 2008; Wada et al., 2008; Ishida et al., 2014). A recent study revealed that chlorophagy is also responsible for the elimination of entire damaged chloroplasts after exposure to ultraviolet-B (UVB) radiation. Stress-induced chlorophagy is impaired in atg5 and atg7 mutants, leading to increased UVB-induced cell death and ROS accumulation. When chlorophagy was activated, fluorescently tagged chloroplasts accumulated in the vacuolar lumen (Izumi et al., 2017; Izumi and Nakamura, 2017b, 2018).
Not every step in each of these four ATG dependent processes is known yet. Furthermore, given the multiple parallel degradation processes utilizing ATG-dependent processes, another key question is what cargo is selected and how.
2.5.2.3 Pexophagy
Peroxisomes are usually located between chloroplasts and mitochondria in leaves under normal conditions and their correct location is important for interactions between organelles during photorespiration (Reumann and Weber, 2006). In Arabidopsis thaliana, peroxisomes are involved in photorespiration, decomposition of fatty acids, synthesis of plant hormones, and synthesis of polyamines. Control over peroxisome content is crucial for general quality control. During photorespiration, hydrogen peroxide is produced as a by-product and despite it being largely broken down into water and oxygen by catalase, a proportion escapes to increase the rate of oxidative damage to proteins and membranes (Hu et al., 2012; Kao et al., 2018).
A mutant screen for aggregated peroxisomes led to the identification of Arabidopsis thaliana mutants (peroxisome unusual positioning1 [peup1], peup2, and peup4) which were later found to be alleles for ATG2, ATG18a, and ATG7 (Shibata et al., 2013). Further research on peup1 only revealed the accumulation of low activity catalase in aggregated peroxisomes as well as accumulation of reactive oxygen species and accelerated aging of plants (Shibata et al., 2013; Shibata et al., 2014). Co-localization of
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peroxisome and autophagosome markers suggests that the peroxisomes are degraded by autophagy. Since, inactive and aggregated catalase was also observed in other atg mutants (Yoshimoto et al., 2014). Interestingly, pexophagy is a cell type specific phenomenon, as it could not be observed in root cells (Yoshimoto et al., 2014). A group of peroxisomal membrane proteins called peroxins (PEXs), which mediate various aspects of peroxisome biogenesis and maintenance including the assembly of new membrane structures, peroxisome membrane protein (PMP) targeting, matrix protein import and peroxisome division/proliferation, were identified in Saccharomyces and homologs were identified in mammals and plants (Lopez‐Huertas et al., 2000; Fan et al., 2005; Hoepfner et al., 2005). In yeast, the peroxisomal membrane proteins PEX3 and PEX14, and autophagy proteins ATG8 and ATG11 binding with pexophagy receptor ATG30 have been reported to be involved in yeast macroautophagy (Farre et al., 2008). Phosphorylation of ATG30 coordinates its interaction with ATG8 and ATG11. Also, PEX3 and ATG8, which binds to adaptor ATG11, that links receptors for selective types of autophagy to the core autophagy machinery have been reported in Saccharomyces (Motley et al., 2012).
Glyoxysomes that undergo fatty acid metabolism change their function to green leaf peroxisomes that undergo light respiration under light conditions. This functional change is supported by two routes. One is proteolysis inside the peroxisome by Lon protease 2 (LON 2), which is encoded by ABERRANT PEROXISOME MORPHOLOGY 10 (APEM10), and the other is a system which decomposes whole glyoxysomes by autophagy (Farmer et al., 2013). LON2 is a protease with a chaperone function that was isolated as the causative gene of the apem10 mutant. Analysis of the apem10 mutant and the peup1 (atg2) mutant revealed that these coordinate the functional conversion of peroxisomes (Reumann et al., 2010; Goto-Yamada et al., 2014; Goto-Yamada et al., 2015). For example, when a seedling is exposed to light, some of the glyoxysomes are sequestered within an autophagosome and rapidly degraded in the vacuole. Photorespiratory enzymes are imported to a remaining pool of glyoxysomes, which form transitional peroxisomes. Transitional peroxisomes that still have isocitrate lyase and malate synthase can be targeted by selective autophagy and others may develop into leaf peroxisomes. When they are successfully transitioned, these new peroxisomes catalyze the photorespiration cycle together with chloroplasts and mitochondria (Kim et al., 2013).
The gene mutations responsible for the aggregated peroxisome lines were identified in the core autophagy genes ATG2, ATG7, and ATG18a, by forwarding genetics and map-based cloning (Shibata et al., 2014). This study suggests that the peroxisomes are
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degraded by autophagy as co-localization of peroxisome and autophagosome markers were observed. A higher accumulation of catalase, the most abundant enzyme in peroxisomes, was observed in these autophagy mutants compared to wild-type Arabidopsis, but catalase was less active than in the wild-type, potentially due to sustained oxidative damage and formation of peroxisome aggregates in autophagy-defective mutants (Shibata et al., 2014). It has been shown that peroxisomes show a higher basal rate of autophagy than other organelles (Voitsekhovskaja et al., 2014).
2.5.2.4 Receptors and Adaptors Recent work has focused on identifying receptors and adaptors for selective autophagy. Initial evidence has pointed to the transmembrane protein ATG8 (Marshall et al., 2019), which has been since used as bait in multiple Y2H studies and led to the identification of adapter proteins. ATG8 in higher plants is interesting as it has diversified, and A. thaliana has a small family of 9 ATG8 proteins. Most of the knock out mutants of ATG8 isoforms do not show any phenotype, pointing to the fact that they are redundant (Woo et al., 2014). ATG8 interacts via its LDS motif (AIM (LIR) motif-containing proteins) (Lei and Klionsky, 2019) and its UDS motif (UIM motif containing proteins) (Marshall et al 2019). AIM (LIR) motif-containing proteins are transmembrane proteins that establish the selective autophagy of organelles. During the selective autophagic process, ATG8 (LC3/GABARAP) proteins have a specific role in recognition of cargo and targeting them selectively to autophagosomes by interacting with specific adaptors through the ATG8-interacting motif (AIM)/LC3- interacting region (LIR) (Noda et al., 2010; Rogov et al., 2014). Interestingly AIM has been identified in plants viz. Atg8-interacting proteins (ATI1 and ATI2), which contains two putative AIMs located on either side of a predicted transmembrane domain (Honig et al., 2012b). A recent excellent review has summarized all the known human selective autophagy receptors and their potential orthologs in plants, and all the known Arabidopsis thaliana selective autophagy receptors and their conservation analysis in plants and humans (Stephani and Dagdas, 2019).
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2.6 Use of proteomics strategies to investigate autophagy
The advent of modern peptide mass spectrometry has allowed dissection of various biochemical and signaling pathways, in both in vitro and in vivo systems. Proteomics has been used to investigate cells and disease states, providing insight into the functions of various proteins linked to autophagic machinery. More focused proteomic studies have advanced our knowledge of the content of autophagic structures such as autophagosomes and lysosomes (Shui et al., 2008b) (Figure 2-2). A recent study aiming at providing a repository for highly specific assays for human proteins by SWATH-MS quantified
10,000 proteins (Rosenberger et al., 2014; Schubert et al., 2015). Among them, the majority of the autophagy-related proteins were identified even though some are very low abundant proteins. This gives confidence that targeted proteomics approaches allow the proteins involved in autophagy pathways to be selectively and reproducibly quantified. Therefore, with continual technological advances, it can be expected that targeted proteomics methods will increasingly offer value in the field of autophagy for analysis of autophagy machinery levels and impact on the general proteome in tissue samples grown under different conditions.
Figure 2-2: Use of proteomics strategies to investigate autophagy A. Proteomics of the whole cell. B. Proteomics of the subcellular fractions: autophagosomes and lysosomes. C. Proteomics of the secretome. The secretome comprises of soluble proteins and secreted extracellular vesicles.
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2.6.1 Proteomics of the whole cell
One of the most straightforward applications of MS-based proteomics is the comparison of whole-cell proteomes between autophagy-deficient cells and autophagy- competent cells. Table 2-1 gives an overview of recent publications. An investigation of a human colon cancer cell line (HCT116), using two-dimensional gel electrophoresis (2- DE) and MALDI-TOF MS, uncovered the changes to the proteome during autophagy under starvation conditions (Kang et al., 2011). In total 52 unique proteins, that significantly changed by starvation out of nearly 1500 protein spots, were characterized. ANNEXIN A1 was significantly upregulated following starvation at both mRNA and protein levels and was identified as an important positive regulator of autophagic degradation which was significantly upregulated following starvation at both mRNA and protein levels (Kang et al., 2011). New techniques for labeling and visualizing newly synthesized proteins such as bioorthogonal reactions based methods in combination with MS techniques are particularly useful where direct labeling and antibodies are not available (Dieterich et al., 2006; Best, 2009). The BONCAT (bioorthogonal non- canonical amino-acid tagging) method in combination with iTRAQ for identification has been used to profile newly synthesized proteins during starvation-mediated autophagy. In total 711 de novo synthesized proteins were identified and the positive relationship between these proteins and the pro-survival outcomes of autophagy has been determined (Zhang et al., 2014; Wang et al., 2016a). Further proteomic-based whole cell autophagic studies are summarized in Table 2-1. One of the problems with many of these studies is that origin of the changes of the proteome are not defined, so it is unclear if the proteins represent cargo or changes in proteome due to reorganization of protein degradation pathways.
2.6.2 Proteomics of the subcellular fractions: autophagosomes and lysosomes
Proteomics has further aided autophagy studies by providing direct evidence of the protein content of autophagosomes. Isolation of autophagosomes has been carried out using a combination of different methods, cellular fractionation by differential and/or density gradient centrifugation steps with or without subsequent immuno-precipitation and GFP-tagged immunoprecipitation. During the process, fractions are checked using specific enzyme activity assays, presence of GFP fluorescence of known cargo proteins or by immunoblotting with specific antibodies (Strømhaug et al., 1998; Fujioka et al., 24
2014; Chen et al., 2015). Recent studies are followed up by mass spectrometry for identification. Table 2-3 gives an overview of recent publications. A few studies are highlighted below.
Studies based on the isolation of autophagosomes have led to the most successful use of proteomics to understand cargo and identifying adaptors and receptors for selective autophagy.
In mammals, quantitative MS-based proteomics studies have been carried out to identify proteins associated with mature autophagosomes and compare the protein composition of autophagosomes induced by three different stimuli; following amino acid starvation, treatment with the mTOR1 inhibitor rapamycin or an inhibitor of the lysosomal H+-ATPase Concanamycin A (Dengjel et al., 2012b). Likewise, starvation or chemical induced autophagosomes have been isolated by different density gradient centrifugation and immuno-isolation techniques summarized in Table 2-3. To enrich autophagosome-associated proteins; Concanamycin A (Dengjel et al., 2012b) and Wortmannin (Phosphatidylinositol3-kinase inhibitor) were used to treat autophagy- induced cells (Mancias et al., 2014). SILAC-based quantitative analysis of autophagosomes isolated from MCF7 breast cancer cells treated with mTOR1 inhibitor Rapamycin or Concanamycin A or starved in Hanks' Balanced Salt Solution (HBSS) for seven hours and mixed in a ratio of 1:1:1 showed that several proteins (LC3, p62, and GABARAPL2) involved in autophagy were significantly less abundant in HBSS-starved autophagosomes compared to rapamycin and Concanamycin A-induced treatments (Dengjel et al 2012). This suggests that starvation-induced a higher level of autophagic flux which was confirmed by the rapid depletion of p62 in starved cells (Dengjel et al., 2012b). In another study, light and heavy labeled autophagy-induced cells were either treated with chloroquine (lysosomal inhibitor) to suppress autophagosome formation or Wortmannin (Phosphatidylinositol3-kinase inhibitor) to enhance autophagosome number. This combination in a SILAC based quantitative approach minimized the detection of false-positive among the true autophagosome associated proteins (Mancias et al., 2014).
It is important to recognize that autophagic flux (transport of proteins via the autophagic route to lysosome/vacuole) cannot be determined by simply measuring the number of autophagosomes or steady-state concentration of proteins (Bauvy et al., 2009). Autophagy is responsible for the degradation of long-lived proteins, and so measuring their rate of degradation is one of the classical methods frequently used to monitor the
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autophagic flux in cell cultures and freshly isolated cells. In this method, cellular proteins are pulse-labeled by the incorporation of radioactive amino acids [14C]-leucine or [14C]- valine and then followed by a long nonradioactive chase to allow for the degradation of short-lived proteins (Ogier-Denis et al., 1996; Roberts and Deretic, 2008; Bauvy et al., 2009; Fujioka et al., 2014). Then, the time-dependent release of acid-soluble radioactivity from degrading proteins in intact cells is assayed to estimate autophagic flux. Stable isotope labeling using amino acids in cell culture (SILAC) could replace this method to identify and quantify relative differences in the age of proteins in complex protein samples (Roberts and Deretic, 2008).
Based on a rapid method for the immunopurification of lysosomes (LysoIP) coupled with the advanced LC-MS/MS platform, dynamics of the lysosomal proteome under different conditions of mTORC1 activation have been profiled (Wyant et al., 2018) and more than 800 proteins associated with lysosomes have been detected. The NUFIP1 (Nuclear fragile X mental retardation-interacting protein 1) was identified as a ribosome receptor for starvation-induced ribophagy, due to its increased lysosomal abundance after the inhibition of the mTORC1 pathway. The NUFIP1 is present in the cell nucleus and upon starvation migrates into the cytoplasm and binds to ribosomes and targets them for degradation in the lysosomes. This presents an important survival strategy and ensures the maintenance of the cell (Nofal and Rabinowitz, 2018; Wyant et al., 2018).
Isolations of autophagosomes have thus provided new insights into the exchanges that membranes and cargo proteins undergo and have delivered an understanding of the nature of dynamic autophagic traffic under different growth and stress conditions. Other studies on autophagosome proteomics are summarized in Table 2-3.
2.6.3 Proteomics of the secretome
Proteins secreted by cells, represent potential autophagic biomarkers as they are characteristic for the state of the cellular vesicular trafficking in a specific time and space. Several discoveries have been made in vitro, using cell culture systems, ex vivo, using human tissue specimens and in vivo, using animal models (Table 2-4). The extracellular vesicles released by cancer cells play a role in cell communication and modulate cell growth and immune responses. Secretory and membrane proteins undergo post- translational processing in the Golgi apparatus and ER. Cells under glucose starvation show inhibition of protein glycosylation and oxygen deprivation stress, accumulate unfolded proteins in the ER, and ER stress as a result of the unfolded protein response 26
(UPR) is activated (Schroder and Kaufman, 2005; Pluquet et al., 2015; Wang et al., 2016b). A study has revealed that the ER exit sites (ERES), specialized for sorting proteins into the secretory system, are key factors in the formation of autophagosomes. These sites act as the physical and functional core of autophagosome biogenesis components (Graef et al., 2013). Several studies have identified a relationship between autophagy and ER function. For example, some of the secretory proteins required for autophagosome formation have also been reported as components in ER stress stimulated autophagy (Ishihara et al., 2001; Kouroku et al., 2007; Sakaki and Kaufman, 2008). Proteins related to the autophagy-lysosomal pathway have been identified as novel secretory biomarkers and therapeutic targets for the pathogenesis of age-related macular degeneration (Kang et al., 2014). Using different MS techniques, six proteins of the total of 701 identified proteins were identified as Age-related macular degeneration (AMD) biomarkers (Kang et al., 2014). Another proteomics study, looking at secretomes of human macrophages, identified the dectin-1 pathway induced multi-protein intracellular complex inflammasome activity and an active autophagic process (Ohman et al., 2014).
2.7 Proteomics used in the analysis of plant autophagy
Current insights into plant autophagy are mainly based on molecular genetics. However, this approach can only provide limited information on the impact of autophagy on the plant proteome. Autophagy influences a wide range of processes in plants, such as growth and development, biotic and abiotic stress tolerance. Therefore, it is important to take a broad perspective on the impact of autophagy by using high throughput proteomics techniques, and the work to date is shown in Table 2-5. To understand the importance of autophagy in cellular metabolism and energy homeostasis in Arabidopsis, the whole protein content of carbon starved wild-type and autophagy mutant (atg5 and atg7) seedlings was analyzed by SDS-PAGE, and specific gel bands were analyzed by LC- MS/MS (Avin-Wittenberg et al., 2015). Under carbon starvation, autophagy mutants show delayed growth and exhibit a reduction in amino acid levels, accumulation of proteins, increased respiration, and decreased flux to net protein synthesis. CRUCIFERIN 3 and two other interesting proteins, MALATE SYNTHASE (MLS) and MULTIFUNCTIONAL PROTEIN 2 (MFP2); proteins involved in β-oxidation of fatty acids, were accumulated in autophagy mutants compared with the wild-type. The opposite effect was observed for CYTOSOLIC TRIOSE-PHOSPHATE ISOMERASE,
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an enzyme involved in glycolysis, which accumulated in wild-type seedlings (Avin- Wittenberg et al., 2015).
A recent study has combined transcriptomics, quantitative proteomics, and activity profiling techniques and identified proteases exhibiting differential abundance at the protein level in Arabidopsis autophagy mutant atg5 compared to wild-type plants (Have et al., 2017). The study has revealed that autophagy deficiency, especially under nitrogen- limiting conditions, led to increased abundance and activity of a subset of vacuolar PAPAIN-LIKE CYSTEINE PROTEASES (PLCPs), as well as the 26S proteasome. Further, the study has revealed reduced levels of the chloroplast FILAMENTATION TEMPERATURE SENSITIVE H (FTSH) and DEGRADATION OF PERIPLASMIC PROTEINS (DEG) proteases in atg5 mutants, likely reflecting the impaired plastid maintenance of these plants (Have et al., 2017).
A recent large-scale pioneering analysis of global protein degradation rates in Arabidopsis, determined the half-life of individual proteins vary between hours and months (Li et al., 2017a). Degradation rates represent a sum of the act of the proteasome, organelle proteases, and the basal rate of autophagy in leaf tissues. Of all detected proteins, the degradation rate was reported for TOR; a key component that coordinately regulates the balance between growth and autophagy in response to cellular physiological conditions and environmental stress. The study further revealed that it was not possible to estimate the degradation rate of abundant plant proteins by the presumed N-terminal destabilizing residue of proteins alone (Li et al., 2017a).
iTRAQ was been used to quantify plant proteins in wild-type and atg10-1 mutant Arabidopsis plants upon Verticillium dahliae fungal infection (Wang et al., 2018). The study showed that autophagy played a critical role in host defense against the vascular pathogen V. dahliae in Arabidopsis, and this process has a special association with the activation of defense responses, the degradation of mitochondria and the hyperplasia of xylem cells (Wang et al., 2018).
A recent study has applied in-depth multi-omic approaches to study how plant tissues rely on autophagy under nitrogen starvation conditions in maize (Zea mays) (McLoughlin et al., 2018). Several proteomic studies identified proteasome subunits as cargo in autophagosomes (Gao et al., 2010a; Dengjel et al., 2012a; Le Guerroué et al., 2017), while more recent multi-omics studies in humans and maize confirmed that proteasomes are extensively degraded by both basal and starvation-induced autophagy (Zhang et al., 2016; McLoughlin et al., 2018). The maize nitrogen starvation study showed that broad 28
alterations in the leaf metabolome were evident in plants missing the ubiquitin-like conjugation phagophore formation protein ATG12 even without nitrogen starvation (McLoughlin et al., 2018). Lack of ATG12 particularly affected products of lipid turnover and secondary metabolites, which were underpinned by substantial changes in the transcriptome and proteome. Further cross-comparison of mRNA and protein abundances allowed for the identification of organelles, protein complexes, and individual proteins targeted for selective autophagic clearance, and revealed several processes controlled by this catabolism (McLoughlin et al., 2018). This study performed on maize atg12 mutant confirmed many of the metabolic features found in Arabidopsis. From proteome/transcriptome comparisons, the authors established the role of autophagy in recycling ER, Golgi bodies, ribosomes, peroxisomes, and proteasome, and moreover found a possible role of autophagy in regulating fatty acid catabolism (McLoughlin et al., 2018). Another study from the same group was conduct to understand the how autophagy contributes to carbon starvation in maize, multi-omics molecular characterization was used in autophagy-deficient atg12 leaves under normal and C starvation conditions by leaf shading (McLoughlin et al., 2020). Strong upregulation of catabolic pathways for starch, amino acids, and nucleotides in the absence of autophagy under fixed C starvation was observed. Specially autophagy related target proteins viz. ATG8, NBR1, RPN1, RPN5, and RPT4 subunits of the 26S proteasome were identified from proteomic data sets and confirmed by the immunoblot assays (McLoughlin et al., 2020). These studies together reveal the application of a proteomic strategy for uncovering autophagic substrates and shows that autophagy has a larger role in sculpting plant proteomes and membranes both before and during nitrogen and carbon starvation stress than previously thought (McLoughlin et al., 2018; 2020).
Another latter large scale proteomic and lipidomic analysis have performed to identify the proteins that significantly expressed in the comparisons of both atg5 vs. WT and atg5/sid2 vs. sid2 under different nutrient conditions; nitrogen and sulfur (Have et al., 2019). Salicylic-acid independent effects were determined by comparing the autophagy responsive proteins from WT and sid2 background. The study shows that mutation in atg5 induced the ER stress, and increased the peroxisome and ER proteins involved in the very long-chain fatty acid synthesis and β-oxidation (Have et al., 2019). The contents of sphingolipid, phospholipid, and galactolipid were significantly modified. Changes in the length of fatty acid chains confirmed that the ER lipid-metabolism was modified in atg5. Significant accumulations of phospholipids and ceramides and changes in glycosyl-inositol-phosphoryl-ceramides indicated that atg5 mutants suffer from large 29
modifications in endomembrane and plasma membrane lipid composition (Have et al., 2019). Decreases in chloroplast proteins and galactolipids in atg5 was mostly restricted to low nutrient conditions and suggested that under starvation especially, chloroplasts were used as lipid reservoirs for β-oxidation in atg5 mutants (Have et al., 2019). By application of proteomics and lipidomics techniques, this study uncovers the roles of autophagy on the regulation of ER stress and reveals the role of autophagy in the control of plant lipid metabolism and catabolism, influencing both lipid homeostasis and endomembrane composition (Have et al., 2019).
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Table 2-2: Whole cell proteomic assessments of autophagy in yeast and mammalian systems Cells used / Organism Applied Proteomic Treatment No. of Key autophagy proteins identified or proteins References Technique proteins found to change in abundance during assessed autophagy
Mouse, In vivo Label-free LC-MS/MS EVA1A knockout 5438 28 proteins significantly dysregulated in the (Zhong et al., 2017) absence of EVA1A were identified. They were involved in a variety of pathways such as nicotinate and nicotinamide metabolism (Nnt), amino sugar and nucleotide sugar metabolism (Ugp2), oxidative phosphorylation (Uqcrq), and neurodegenerative disease-relevant pathways (Plcb3, Uqcrq).
Whole-cell (In vitro 2D gel electrophoresis, CpG-ODN (Toll- N/A 18 proteins significantly changing in abundance (Bertin et al., 2008) and in LC-MS/MS like receptor were identified following CpG-ODN induced vivo)DHD/K12/RPOb antagonist), autophagy. These included Glyceraldehyde3- (rat), MCF-7, rapamycin (TOR phosphate dehydrogenase, PC-3, HEK293 inhibitor) Dihydropyrimidinase-related protein 3, Phosphoglycerate mutase-1, Annexin A1, Calponin-3, Tropomyosin α-1 chain, Prohibitin, eIF4A1, Elongation factor 2, Tyrosyl-tRNA synthetase, Citrate synthase, Pyruvate carboxylase, Phosphoglycerate mutase-1, Annexin A1, Mn-superoxide dismutase, Chaperonin subunit 6a (ζ), GRP78, LaminA.
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Whole-cell (partial Dynamic Organellar starvation medium 2848 Three transmembrane cargo proteins, ATG9A, (Davies et al., 2018) separation of organelles Map was generated by with or without SERINC1 and SERINC3, and two AP-4 fractions) of Adaptor differential bafilomycin A1 ( accessory proteins, RUSC1 and RUSC2 protein 4 (AP-4) centrifugation of cell autophagosome proteins were identified via analysis of AP-4 knockout of HeLa cells lysates. Western degradation knockout, providing new insights to ATG9A blotting, GST inhibitor) trafficking as a ubiquitous function of the AP-4 pulldowns from the pathway. cytosol, Immunoprecipitations, Mass spectrometry Whole-cell Ras-driven SILAC, strong cation Starvation, ATG5 7184 3181 proteins were significantly altered in (Mathew et al., 2014) cancer cells / iBMKs exchange (SCX) knockout, abundance comparing autophagy-functional and chromatography, Off- bafilomycin -deficient Ras-driven cancer cells. DNA gel A1 replication proteins, including PCNA, POLA2, fractionation, MCM3, MCM, and LIG1, were among the LC-MS/MS proteins elevated in autophagy-deficient cells.
Whole-cell SH-SY5Y iTRAQ, strong cation Bupivacaine 4139 241 differentially expressed proteins were (Zhao et al., 2016b) Cell Line human exchange (SCX) (anesthetic and identified, of which, 145 were up-regulated and Neuroblast from neural chromatography, neurotoxin) 96 were down-regulated, and the effects linked tissue LC-MS/MS to induction of autophagy. Notable changing proteins included PIK3CB and PIK3R2.
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HeLa cells AHA labeling Amino acid 1176 711 proteins were assessed as de novo (Wang et al., 2016a) (BONCAT), iTRAQ, starvation-induced synthesized proteins during starvation-mediated LC-MS/MS autophagy autophagy. Five proteins were studied in detail: ATP synthase H+ transporting, mitochondrial F1 complex β polypeptide (ATP5B), heat shock protein family E [Hsp10] member 1) (HSPE1), solute carrier family 25 member 3 (SLC25A3), receptor for activated C kinase 1 (RACK1/GNB2L1) and purine nucleoside phosphorylase (PNP) and it was shown their synthesis was required for autophagy.
H3255, H1975 lung SILAC, TiO2 Erlotinib (Receptor 3086 18 Autophagy proteins identified in H3255 and (Zhang et al., 2015) cancer cell lines phosphopeptide tyrosine kinase H1975 cell lines. E.g. ULK1 (S623), ATG16L1, enrichment, SCX, inhibitor) treated Serine/threonine-protein kinase Nek9, LC-MS/MS cells PIK3C2A,PRKAA1, PRKAA2, RABGAP1, RB1CC1 SQSTM1, TRAF2, ULK1.
Human melanoma 2D GE, nano-liquid Ophiobolin A 24 GE spots 24 protein spots whose abundance varied at (Rodolfo et al., 2016) A375 and CHL-1 cell chromatography (blocking the least two-fold in response to Ophiobolin A lines coupled to activation of challenge were detected. E.g. Fructose- electrospray-linear ion calmodulin- bisphosphate aldolase A (ALDOA), trap-tandem mass dependent Triosephosphate isomerase (TPI). spectrometry (nanoLC- phosphodiesterase) ESI-LIT-MS/MS)
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HCT116 cells 2D Gel EBSS (Earle's > 1500 GE 52 unique proteins were characterized. i.e. (Kang et al., 2011) electrophoresis, balanced salts spots Annexin A1 (ANXA1), heat shock cognate 71 MALDI-TOF solution) induced kDa protein (Hsc70), glucose-regulated protein treatment (GRP78), Protein disulfide isomerase A3 starvation (PDIA3), Alpha-enolase (ENO1), Glutathione S-transferase (GST- pi). autophagy-deficient iTRAQ, LC-MS/MS Starvation, 1234 114 proteins that were significantly altered were (Zhuo et al., 2013) mouse embryonic Cytochalasin D identified, including 48 up-regulated proteins fibroblast cells (MEFs) (cell-permeable and and 66 down-regulated proteins. e.g. F-actin, potent inhibitor of p62, LC3-I actin polymerization) for F-actin depolymerization
Mouse brain, iTRAQ, LC-MS/MS Rabies viruses 2285 147 differentially expressed proteins were (Li et al., 2017c) Neuroblastoma (NA) (CVS-11, identified. i.e. Peroxiredoxin-5 (PRDX5), cells SRV9) treated cells mTOR, Superoxide dismutase [Cu-Zn] (SOD1), Superoxide dismutase [Mn] (SOD2), ATP synthase subunit gamma (ATP5C1), Eukaryotic translation initiation factor 3 subunit E (EIF3E), Eukaryotic translation initiation factor 4B (EIF4B).
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Human Umbilical Vein SILAC spike-in,SILAC Treatment with 7565 2221 proteins with significantly regulated levels (Patella et al., 2016) Endothelial Cells spike-in, siATG5 (siRNAs), were identified. i.e. p62/Sequestosome-1, (HUVEC) LC-MS/MS and Complement decay-accelerating factor (CD55), bafilomycin ( Beta-2-microglobulin (B2M). inhibitor of the late phase of autophagy by preventing maturation of autophagic vacuoles by inhibiting fusion between autophagosomes and lysosomes)
Human A427, A549, ATP-binding proteome Mitogen-activated 1925 174 protein kinases or 225 kinases in total (Kim et al., 2016) Calu-1, labeling and protein kinase (including nine lipid kinases and 42 other Calu-6,H157 cell lines enrichment, enzymes MEK1 generic/small-molecule kinases) were identified. LC-MS/MS (MEK) inhibitors i.e. ATG1A/ Serine/threonine-protein kinase (AZD6244 (ULK1), Dual specificity mitogen-activated and MEK162) protein kinase 6 (MAP2K6), Tyrosine-protein kinase JAK1, Aurora kinase A (AURKA).
Human breast cancer Rapid Cells are treated N/A In the presence of calcitriol, the proteins (Tavera-Mendoza et cell line. MCF-7, immunoprecipitation with Calcitriol associated with vitamin D receptor when it is al., 2017) MDA-MB-231, mass spectrometry of (1,25- bound to DNA were identified. e.g. LC3B, MDA-MB-453, endogenous protein dyhydroxyvitamin p62/Sequestosome-1, Heat shock protein HSP ZR-75-1, MCF-12A (RIME) D3 90-beta (HSP90AB1), Tyrosine-protein kinase [1,25(OH)2D3]) Yes (EGFR), ATG1B, ULK2 (ULK2).
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Human ( monocyte / Affinity purification, N/A >10000 Majority of autophagic related proteins were (Rosenberger et al., neutrophil / gut / Size-exclusion able to identified. e.g. BECN1, UVRAG, 2014) kidney / lung / muscle / chromatography, data- ATG5, ATG16L1, Nucleotide-binding blood platelet / blood independent acquisition oligomerization domain-containing protein 2 plasma / 293 cell / (DIA), Sequential (NOD2), LAMP-2, SQSTM1/p62, E3 ubiquitin- THP-1 cell / U2 OS window acquisition of protein ligase parkin (PARK2), cell / HeLa cell / all theoretical Serine/threonine-protein kinase PINK1, NCI60 / LNCAP cell / fragment-ion spectra mitochondrial (PINK1). CAL-51 cell) (SWATH) mass spectrometry (MS)
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Table 2-3: Proteomic assessments of autophagosomes, lysosomes in yeast and mammalian systems No. of Applied Proteomic proteins Key autophagy proteins identified or proteins Cells used / Organism Treatment References Technique assessed found to change in abundance during autophagy
Rat Liver cells Differential Nutrient >1500 39 different known proteins were significantly (Øverbye et centrifugation, starvation, enriched in autophagosomal membranes related to al., 2007) 2D GE, Vinblastine cytoplasmic membranes were identified. e.g., MALDI-TOF (microtubule Phosphatidylethanolamine, binding protein, formation Catechol O, methyltransferase, Betaine: inhibitor) Homocysteine S, methyltransferase, Inorganic pyrophosphatase. Human colon carcinoma cells Immunoisolation, 2D Nutrient 101 Cargo associated proteins viz. LC3, ATG5, (Gao et al., HCT116 and embryonic kidney GE, starvation, ATG16, ATG9, p62/ SQSTM1, UMP-CMP Kinase, 2010b) cells HEK293 MALD-MS/MS calcium GRP-78, Rab4, Rab5 were identified. phosphate precipitate
Saccharomyces cerevisiae GFP-fuse Nutrient 40 Vacuolar aminopeptidase I (Ape1), Cytoplasmic (Suzuki et al., aminopeptidase starvation aldehyde dehydrogenase (Ald6), Pyruvate kinase1, 2014) fluorescence, Iodixanol Yef3 (a subunit of a translational elongation factor), gradient Hsc82 (cytoplasmic chaperone of the Hsp90 centrifugation, LC- family), Eft1 (elongation factor 2) were identified MS/MS in autophagosomes. Human pancreatic carcinoma cell SILAC, density Wortmannin, >2000 33 common proteins were significantly enriched in (Klionsky et lines PANC-1, pancreas gradient Chloroquine autophagosomal membranes related to cytoplasmic al., 2016) adenocarcinoma (PA-TU- centrifugation, membranes were identified. e.g., Nuclear receptor 8988T),MCF7 LC-MS/MS coactivator 4 (NCOA4), ferritin heavy chain (FTH) and ferritin light chain (FTL) subunits, Microtubule Associated Protein 1 Light Chain 3 Beta (MAP1LC3B), SQSTM1, Calcium Binding And Coiled-Coil Domain 1 (CALCOCO1), Sodium-coupled neutral amino acid transporter 2 (SLC38A2), High-affinity cationic
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amino acid transporter 1 (SLC7A1).
Saccharomyces cerevisiae / Lodixanol density Amino acid 7935 94 proteins that significantly change in abundance (Dengjel et al., MCF7-eGFP-LC3 centrifugation, protein starvation, were identified. e.g., Adenylyl cyclase-associated 2012b) correlation profile Rapamycin, protein 1 (CAP1), Vacuolar protein sorting- (PCP)-SILAC, LC- Concanamycin associated protein 35 (VPS35), Elongation factor 1γ MS/MS A (EEF1G), Microtubule-associated protein one light chain 3β (MAP1LC3B), Purine nucleoside phosphorylase, Ribosomal protein SA, Guanine nucleotide-binding protein, Beta polypeptide 2-like 1 (GNB2L1) Mouse macrophages Sucrose gradient 3-methyladenine 546 Specific macrophagic proteins i.e. Early endosomal (Shui et al., centrifugation, 2D (3-MA) associated protein (EEA1), transferrin receptor 2008a) SDS-PAGE, (TfR), soluble phagosomal protease cathepsin D LC-MS/MS (CatD), Vesicle-associated membrane protein 4 (VAMP4), Toll-like receptors 7 and 9 (TLR7/TLR9), microtubule-associated protein 1/light chain 3 (LC3-II), lysosomal-associated membrane protein-1 (LAMP1), Tyrosine-protein kinase JAK3/ Janus kinase 1 (JAK1) were identified. Rat liver tritosomes Differential sucrose Triton-WR1339, 215 Tritosomes specific proteins were identified. e.g. (Bagshaw et density centrifugation, Tyloxapol Lysosome-associated membrane glycoprotein 1 and al., 2005) lysosomal (inhibitor of 2 (LAMP1/2), Lysosome membrane protein 2 subfractionation, lipoprotein (LIMPII), vesicle-associated membrane protein 8 sepharose cation lipase) (VAMP8), C Cytochrome P450 (CYP2A1, CYP2C29, CYP2D2, CYP4A3), Apolipoprotein A-
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exchange, 1D GE, LC- II (APOA-II), MS/MS Apolipoprotein E (APOE), Apolipoprotein B (APOB), and Beta-galactoside alpha-2,6- sialyltransferase 1 (ST6Gal). Lysosomes from HEK-293T, Lysosome Nutrient 5339 828 unique proteins that associated with lysosomes (Wyant et al., MIA-PaCa, 8988T, and P53-/- immunopurification starvation, Torin were identified and 343 proteins designated as 2018) MEFs (LysoIP), UPLC- 1 (mTOR lysosomal under all treated conditions. MS/MS, inhibitor) Data Dependent Acquisition (DDA) mode and Data Independent Acquisition (DIA) mode Lysosomes from Human brain Mannose-6-phosphate Niemann-Pick N/A NPC intracellular cholesterol transporter 2/HE1 (Naureckiene biopsies, rat liver affinity enrichment, disease type C were identified. et al., 2000) 2D SDS-PAGE, (NPC) disease, differential sucrose Controls (No density centrifugation, disease or NP- anion exchange C1 patients), chromatography Triton- WR1339 Lysosomes from Human brain Mannose-6-phosphate LINCL disease N/A Pepstatin (a potent inhibitor of aspartyl proteases) (Sleat et al., biopsies affinity and Controls were identified. 1997) chromatography (No disease) purification, 2D gel electrophoresis, Edman degradation
Rat liver lysosomes, HeLa Differential N/A 734 12 specific proteins were identified viz. (Chapel et al., centrifugation, LOH12CR1, MFSD1, PTTG1IP, SLC37A2, 2013) Nycodenz density SLC38A7, SLC46A3, SLCO2B1, STARD10, gradient TMEM104, TMEM175, TTYH2, and TTYH3. fractionation, liquid- liquid extraction, SDS-PAGE, LC-MS/MS
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Lysosomes from Male Wistar Rat Differential Triton WR1339 1273 Significantly expressed lysosome specific proteins (Della Valle et liver centrifugation, (inhibitor of were identified. e.g. Cathepsin D(CTSD), al., 2011) isopycnic sucrose lipoprotein Lysosomal acid phosphatase 2 (ACP2), Lysosome- density lipase) associated membrane glycoprotein 2 (LAMP2), and centrifugation, iTRAQ, Tartrate-resistant acid phosphatase type 5 (ACP5) 2D SCX and RP peptide separation, MALDI-TOF HeLa lysosomes SPION isolation, LC- NP-C1 2400 53 specific proteins were identified. Interestingly, (Tharkeshwar MS/MS knockout many lysosomal proteins such as LIPA, IFI30, et al., 2017) IGF2R, CLN3, LGMN, autophagy-related proteins (e.g. GABARAPL2, CALCOCO2, SQSTM1) as well as endosomal proteins (FOLR1, ACKR3, RHOB) were found to be upregulated in NPC1-KO LE/LYS. Lysosomes from mouse-derived Differential Listeria 3704 204 specific proteins were identified. Glycosylation (Gao et al., Murine RAW 264.7 centrifugation, monocy- proteins were identified. e.g. 2017b) density gradient togenes, Sodium/potassium/calcium exchanger 6, fractionation, TMT HSV-1, VSV mitochondrial (Slc8b1), Transmembrane protein 26 labeling, LC-MS/MS infections (Tmem26), 60S ribosomal protein L18 (Rpl18), Elongation factor 2 (Eef2), Phospholipase D4 (Pld4), Capping protein (Actin filament) gelsoliN- like (Capg) V-type proton ATPase subunit e1 (Atp6v0e1), Peroxisomal membrane protein PEX14, (Pex14) Lysosomes from MEF23-1SV Mannose-6-phosphate Mannose 6- 38 Proteins that significantly changing in abundance (Kollmann et mouse fibroblast affinity enrichment, phosphate were identified following e.g. Cathepsin B, al., 2005) 2D gel receptors Cathepsin D, Cathepsin F, Cathepsin C, Cathepsin electrophoresis, (MPR46 and L, Cathepsin Z, Ribonuclease 6, and α-D- MALD-TOF, MudPIT MPR300) mannosidase acidic. deficiency, Pepstatin A and Leupeptin
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Table 2-4: Proteomic assessments of secretomes associated with autophagy in yeast and mammalian systems
Cells used / Organism Applied Proteomic Treatment No. of Key autophagy proteins identified or proteins References Technique proteins found to change in abundance during autophagy assessed Secretome of Melanoma cell SDS-PAGE, Label- Tat-BECN1 peptide 599 26 significant expressed proteins in melanoma cells (Kraya et al., lines / Human tumor cells free LC-MS/MS (selectively induce were identified. Eg. FAM3C, Interleukin-1 beta 2015) autophagy, improve (IL1B), Interleukin-8 (CXCL8), Leukemia clearance of protein inhibitory factor (LIF), Dickkopf-related protein 1 aggregates, and (DKK3). improve survival), ATG7 silencing Secretome of Human ultracentrifugation, Lipopolysaccharide, 1597 By pathway analysis using the KEGG database of (Ohman et al., peripheral blood iTRAQ, SCX 1,3-β-Glucans the identified proteins, 14 interested pathways were 2014) mononuclear cell (PMBC)- fraction, (Curdlan), 3- MA, identified. e.g. chemokine signaling pathway, derived primary LC-MS/MS Spleen tyrosine cytokine receptor interaction, and MAPK signaling macrophages, Mouse bone kinase inhibitor pathways, cytosolic DNA-sensing pathway, Jak- marrow-derived dendritic (SykII), Src inhibitor STAT signaling pathway, and NOD-like receptor cells (BMDC) I, Brefeldin A signaling pathways. (inhibits protein transport from the endoplasmic reticulum to the Golgi ), Dectin- deficiency
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The aqueous phase from SDS-PAGE, Paraquat 701 Six biomarker proteins were identified. i.e. actin (Kang et al., ARPE-19, a human retinal LC-MS/MS, (aortic smooth muscle), myosin-9, heat shock 2014) pigment epithelial cell line, LC-MRM protein 70, cathepsin D, cytokeratin 8 and Age-related macular cytokeratin 14 degeneration patients and controls Lipid droplets from Density gradient Aging >400 62 proteins that changed expression significantly (Yu et al., Mortierella alpina fractionation, during aging were identified. Fructose metabolism- 2017) SDS-PAGE, Label- related enzymes were upregulated. free LC-MS/MS Glycerophospholipid metabolism enzymes, Amino Acid Metabolism Related Pathways enzymes, and other metabolic pathway enzymes were upregulated. Glycolysis pathway enzymes were decreased.
Endoplasmic Reticulum His-Tag protein Nitrogen starvation, 27 phospho- Protein transport protein SEC24 (Davis et al., (ER)-derived COPII coated purification, Rapamycin, 0.50% sites (T324/T325/T328), Casein kinase I homolog 2016) vesicles of Saccharomyces LC-MS/MS galactose (HRR25) were identified. (Sec24 induction)
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Table 2-5: Proteomics assessments of autophagy processes in plant systems
Cells used / Applied Treatment No. of Key autophagy proteins identified or proteins found to change References Organism Proteomic proteins in abundance during autophagy Technique assessed
Zea mays SDS- Nitrogen-rich and - >3300 218 proteins significantly increased in abundance in both the (McLoughlin et WT, atg12-1 PAGE,LCMS starvation conditions atg12-1 and atg12-2 plants versus wild type. Protein groups al., 2018) (mu02975), associated with peroxisomes, ER and cytosol, and/or involved in and atg12-2 small molecule, carboxylate, fatty acid, phenylpropanoid and (mu02196) mutants glucose metabolism, some of which were predicted to be autophagy targets and/or were previously shown to be elevated in the same atg12 backgrounds. Proteins associated with thylakoids, plastoglobules and photosynthesis were more abundant in wild- type leaves which probably reflected reduced chloroplast size and abundance in atg12 leaves as they begin to senesce prematurely. Zea mays LCMC Fixed-Carbon >3375 491 proteins significantly increased in abundance in both the (McLoughlin et WT, atg12-1 Starvation conditions atg12-1 and atg12-2 plants versus wild type. Up regulation of the al., 2020) (mu02975), induced by covering levels of autophagy factors ATG8 and NBR1 and the RPN1, and atg12-2 the middle part of the RPN5, and RPT4 subunits of the 26S proteasome were seen in the (mu02196) mutants second leaf with atg12-1 mutant and were confirmed by immunoblot analysis. aluminium foil for 2 d
Arabidopsis thaliana SDS-PAGE, Carbon starvation <10 CRUCIFERIN 3, MALATE SYNTHASE (MLS) and (Avin-Wittenberg seedlings MS (etiolated seedlings MULTIFUNCTIONAL PROTEIN 2 (MFP2) accumulated in atg et al., 2015) grown without sugar) mutants compared with the wild type. CYTOSOLIC of atg mutants TRIOSEPHOSPHATE ISOMERASE, an enzyme involved in glycolysis, was more abundant in wild-type seedlings rather than atg mutants.
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Arabidopsis thaliana iTRAQ Verticillium dahliae 780 Most of the proteins that changed in abundance were involved in (Wang et al., leaf infection in wild-type defense responses, oxidative stress responses, phenylpropanoid 2018) and mutant atg10-1 metabolism and lignin metabolism, and mitochondrial function.
Arabidopsis thaliana LC-MS/MS Low and high nitrogen 181 Chloroplast FTSH and DEG proteases and several extracellular (Have et al., 2017) content 2 h into the 8-h serine proteases were less abundant in atg5 mutants. By contrast, photoperiod wild-type, proteasome related proteins and cytosolic or vacuole cysteine atg5 and atg5/sid2 proteases were more abundant in atg5 mutants. Arabidopsis thaliana 15N labeling, Hydroponically grown 1228 Degradation rate and half-life of growth and autophagy regulatory (Li et al., 2017a) leaf LC-MS/MS plants under 16/8-h protein Target of rapamycin (TOR) was determined (Degradation light/dark conditions Rates KD: 0.122 d-1, Half-life: 5.7 days)
Arabidopsis thaliana LC-MS/MS Nitrogen and sulfer rich 2334 Among the 1,236 proteins modified in atg5 in a SA- (Have et al., 2019) rosettes Col-0 WT, and starvation independent manner, only 54 were identified in atg5 atg5 conditions transcriptome analyses,mainly under low-N. The 54 proteins (SALK_020601), that were modified identified at both transcriptand protein sid2-2, and levels. They were mainly related to senescence and biotic or atg5/sid2 abiotic stresses. Among the 59 ER proteins differentially expressed in atg5and atg5/sid2 vs. Col and sid2, many Protein disulfide-isomerase thioredoxins were more abundantin atg5 lines suggesting strong ER stress.
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2.8 How proteomics can be applied to future plant autophagy studies
While many of the proteins that are needed in the process of autophagosome formation and signaling pathways in plants have been identified (Batoko et al., 2017; Bozhkov, 2018; Soto-Burgos et al., 2018), the primary receptors, adaptors, and cargo enclosed in autophagosomes are still unclear. At first, it was believed that cytosolic proteins targeted to autophagosomes for recycling in plants had no selectivity. However, in yeast and mammals, proteomics-based studies (Table 2-2, Table 2-3, and Table 2-4) have revealed that the autophagic process can be selective in a cargo-specific receptor- dependent manner and have provided an advanced understanding of selective autophagy. Understanding which proteins are responsible for specific cargo degradation is now needed to understand the roles of autophagy in plant growth, development, and stress biology (Figure 2-2).
It is currently unknown whether autophagy or intra-organelle proteases are principally responsible for changes in organelle proteomes in plants. The autophagy network does influence plastid degradation under high light (Izumi et al., 2017; Izumi and Nakamura, 2017b, a, 2018) and the peroxisome beta-oxidation enzyme degradation process during seed germination (Lee et al., 2014; Yoshimoto et al., 2014; Batoko et al., 2017), but no integrated study of autophagy and organelle protease mutants has been undertaken to verify the relative importance or interaction of both sets of processes. Studying seedlings exposed to different stress conditions will allow us to gain insights into how autophagy is involved in early seedling development processes and stress responses. Uncovering the steps of the autophagic pathway will be important to understand the regulation of the dark to light transition which involves moving from heterotrophic to autotrophic growth, with complex changes in chloroplast biogenesis and photorespiration steps in peroxisomes and mitochondria (Figure 2-3).
It is also important to determine if selective removal of some organelles can occur through selective autophagy or whether organelles of all types are removed without selection. This will determine if autophagy can contribute to the change in the role and abundance of specific types of organelles during different stages of plant development. Developing techniques to isolate subpopulations of organelles that are undergoing degradation in plant cells would provide the opportunity to search for receptors and adaptors, and these factors could then be visualized as fluorescent chimeric to connect subcellular proteomics with cell biology investigations.
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Figure 2-3: Receptors and cargo in the autophagy process in plant growth the development. A. Plant development involves transitions from seed to seedling, heterotrophic to autotrophic growth, and the eventual onset of senescence. B. Potential roles for autophagy in organelle alteration and/or removal during plant development. C. Selective autophagy of plant organelles could involve a combination of specific receptors and adaptors recognizing organelles requiring removal. D. Genetic mutants and inhibitors can be used to dissect autophagy proteomes. E. Isolation of organelles in the process of selection for degradation can enable the identification of receptors. F. Isolation of autophagosomes and analysis of enrichment will be needed to fine cargo. G. Direct measurement of protein degradation rates in mutants, rather than just changes in abundance, can confirm if proteins are cargo or receptors.
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The key to distinguishing selective autophagy and bystander autophagy is whether a receptor-ATG8/LC3 interaction is involved (An and Harper, 2018; Munch and Dikic, 2018). Selective autophagy events involve the receptor-ATG8/LC3 interaction while bystander autophagy events do not. To quantify the cross-linked ATG8-receptor versus non-cross-linked receptor abundance inside autophagosome could be a way to uncover this. Targeted proteomic MRM approach could be used here, but it relies on the isolation of autophagosomes.
Plant autophagy can be induced by nutrient starvation (phosphate, nitrogen, carbon) and dark-induced senescence (Guiboileau et al., 2010; Ono et al., 2013; Sankaranarayanan and Samuel, 2015; Masclaux-Daubresse et al., 2017; Zheng et al., 2018). The inhibition of TOR, by either using knock-down mutant lines or inhibitors, is likely to help understand the activation of autophagy and its consequence (Pu et al., 2017b; Soto-Burgos and Bassham, 2017). The application of multiple autophagy mutant lines along with TOR mutants could help to unravel the specialized role of different components in autophagosome formation and their specialized roles during organelle turnover (Figure 2-3).
In Arabidopsis we have T-DNA lines, are able to create Crispr autophagy mutants, and also have proven inhibitors/effectors (inducing autophagy, then stopping it from progressing). These are key resources to apply in high-throughput proteomics studies to uncover the functions of autophagic proteins. At first, maybe we can use the shotgun approach to identify interesting proteins and then use targeted proteomics techniques to quantify their changes in abundance.
There are no reports or widely used methods for the isolation and detailed analysis of autophagosomes in plants. Isolating autophagosomes will be essential to gain a step- change in the list of potential receptors and adaptors and will begin to document with certainty the list of cargo that is transported to vacuoles (Figure 2-3). Such methods will require a number of advances. Firstly we require systems that allow the timed induction of autophagosomes so that substantial numbers can be present at particular points in their development. Cell biology approaches can be used effectively to survey cell cultures and plant tissue types to find and document these processes. A combination of tagged capture of subcellular structures using known autophagosome membrane proteins along with biochemical and biophysical techniques to enrich subcellular structures of a particular size, density, and charge will be needed to isolate autophagosomes. Isolated autophagosomes can be assessed by a wide range of existing proteomic approaches to
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define protein content, protein-protein interaction, and quantitative enrichment of specific proteins in autophagosomes between mutants. A good example of this type of isolation is the identification of cargo proteins of endosomal and secretory pathways in Arabidopsis by the affinity of enrichment of subpopulations with representative proteins (Heard et al., 2015). ATG8 which is kept on the outer membrane of autophagosome would be one option as a bait. ATG8 based enrichment is probably a starting point for the isolation of stepwise autophagosome. To capture highly dynamic autophagosomes, ATG9, ATG11, or other components could be used as baits to isolate early-stage autophagosomes.
To verify plant cargo proteins, it will be important that we move beyond assessments of protein abundance in autophagy mutants. We will need evidence that their degradation rate is slowed in these mutants, leading to an aging population of the cargo of interest, and that under normal conditions the putative cargo is delivered to the vacuole for degradation. This requires a quantitative assessment of degradation rates, such as using heavy isotope labeling proteomics (Li et al., 2012; Nelson et al., 2014a; Li et al., 2017b; Li et al., 2017a). It also requires the development of robust assays and validated autophagy inhibitors to trace protein movement through the cell and delivery to vacuoles. Protein degradation analysis will help identify cargo and receptors. Protein degradation analysis couple with PPI strategies such as cross-linking could be a way to tell apart cargo and receptors.
If proteomics can be deployed to uncover tissue-specific studies in this way, we can achieve a rich understanding of the selectivity of autophagy processes in plants, and the plant-specific cargo and receptors that drive this selectively can be revealed.
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Chapter 3. Determining the contribution of autophagy to protein abundance of energy organelles in Arabidopsis.
3.1 Summary
Autophagy is a catabolic mechanism facilitating the degradation of cytoplasmic proteins and organelles in the cell vacuole. In recent years, we have witnessed the advancement and application of high-throughput proteomic technologies that facilitate large-scale identification and quantification of proteins degraded by autophagy in yeast and mammals. These studies have involved various extra- and intracellular stimuli, which may help to identify new cargo and new components associated with the pathway. Mass spectrometry-based proteomics is a powerful technique to study various aspects of the proteomes of cells, tissues, and organisms and can complement the study of autophagy through microscopy and imaging approaches.
To provide an overview of the influence of autophagy on organelle abundances in the reference plant Arabidopsis thaliana, a targeted proteomics strategy was undertaken based on selecting and analyzing candidate Multiple Reaction Monitoring (MRM, also known as Selective Reaction Monitoring – SRM) peptide transitions for several organelle marker proteins. Only proteins repeatedly localizing to a subcellular compartment and generating non-redundant peptides were selected as markers. To test the utility of these markers to study organelle autophagy processes, it was trialed for the analysis of three developmental transitions in plants during which organelles are reported to change in abundance, but the mechanisms are not defined. These were early seedling development (S1), dark to light greening of seedlings (S2), and light to dark senescence of leaves (S3). The subcellular organelles and structures assessed in this chapter were mitochondria, peroxisome, and chloroplast, and the cytosolic ribosome. To assess the effect of autophagy on changes in organelles, the Arabidopsis thaliana Col-0 ecotype was compared to autophagy mutants atg2, atg5, atg7, and atg9.
MRM results showed that mitochondrial biomarkers were diminished in abundance as the transitions progressed, while plastid biomarkers were increased in both S1 and S2 transitions in all the genotypes, however, the autophagy mutants showed a significant reduction in this response compared to WT. This strongly suggested ATG2, ATG5, ATG7,
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and ATG9-dependent autophagy pathways play a crucial role in chloroplast biogenesis at an early development stage and during plastid greening in plants. This study provides new insights into organelle maintenance under autophagy-deficient conditions and pinpoints the timing of autophagy processes during a key plant developmental response.
3.2 Introduction
3.2.1 Protein synthesis and degradation define protein abundance
The balance between protein synthesis and degradation viz. protein turnover is important for maintaining a functional proteome. Protein turnover requires an investment of cellular energy in both protein biosynthesis and degradation processes. Processes to alter the abundance of specific proteins in response to physiological changes, hormonal status, or environmental stresses need to be adjusted for efficient use of energy and to maximize their performance. To spend the energy efficiently in this balance, adjusting the process to alter the abundance of specific proteins in response to physiological changes, hormonal status, or environmental stresses is important for organisms to maximize their performance (Ohsumi, 2014; Ohsumi, 2016).
An ideal state of balance is achieved by tight regulation of protein synthesis, protein folding, and protein degradation. The process of protein degradation, also called proteolysis, refers to the breakdown of proteins into smaller polypeptides or amino acids and is essential for the maintenance of cellular homeostasis. Numerous proteolysis systems exist in living cells, including many organelle resident proteases, the ubiquitin- proteasome (UPS) pathway, and lysosomal pathways like autophagy and endosome- mediated degradation (Ciechanover, 2005a; Araujo et al., 2011). In animals, the rates of protein turnover were maximal when the growth rate was highest, and the dysregulation of protein turnover has been implicated in the aging process (Zhang et al., 2014). Disruptions in protein degradation can cause cancer and have been implicated in many neurodegenerative diseases, which are associated with abnormal protein aggregates (Bingol and Sheng, 2011). In protein, turnover is important under both normal and environmental stress situations (Zhai et al., 2016). Notably, the endogenous signaling mechanisms are mediated by changes in protein turnover rates in order to respond to rapid environmental stimulants (Yang et al., 2010).
Autophagy maintains the cellular homeostasis by recycling of cellular garbage, cellular proteins, organelles and facilitate to get adapt to metabolic stress during periods
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of abiotic stresses like nutrient deficiency, heat, and light while ensuring vital biological functions in the cells viz. DNA replication, transcription, protein synthesis, and mitosis (Chen et al., 2015b; Guo et al., 2017). Further, the autophagy process aids cells in quality control of damaged proteins and organelles during biotic stresses (Mathew et al., 2014).
Protein synthesis occurs in the cytoplasm inside the cell on 80S ribosomes, while 70S ribosomes facilitate mitochondrion and plastid protein biosynthesis. Secondary structure of mRNA plastid protein biosynthesis along with differences in transcript abundance (Ahmed et al., 2016; Carroll, 2017; Yamaguchi, 2017; Waltz et al., 2019).
Controlling the degradation of regulatory proteins is a key element in the functioning of many signaling pathways. In plants, many important components in light sensing, biotic and abiotic stress signaling, and hormone responses, as well as developmental programs like flowering, senescence, and cell cycle control are targets of controlled proteolysis(Smalle and Vierstra, 2004; Hoecker, 2005; Walsh et al., 2006; Hoecker, 2017). Ubiquitination is a nuanced stimulator of degradation which tags substrate proteins for degradation both by the 26S proteasome and autophagy in eukaryotes (Kraft et al., 2010). The key receptors for hormone-mediated signaling pathways (auxin, jasmonic acid, gibberellin, and strigolactone) are integral components of the ubiquitin ligase machinery. Furthermore, hormone signaling pathway steps involved in the regulation of hormone biosynthesis, degradation of hormone specific transcription factors, and hormone perception are catalyzed by E3 ubiquitin ligases (Santner and Estelle, 2010).
Protein degradation in the cytosol occurs largely through the ubiquitin-proteasome system (UPS) with many links to hormonal signaling and life stage transitions (Ling et al., 2012; Shabek and Zheng, 2014). The UPS consists of a series of enzymes (E1, E2, and E3 ligases) that conjugate ubiquitin tags to proteins destined for degradation, and the proteasome, which is composed of a 20S core and two regulatory 19S lids. Several ubiquitin ligase conjugating enzymes viz. E1, E2, and E3 aid labeled proteins to be catabolized through the UPS. For example, controlling the abundance of specific plastid protein subunits is regulated by components of the UPS at different stages of plastid development (Altman and Rathmell, 2012).
Another pathway for protein catabolism is through the enzymatic action of cellular compartment-specific proteases. These allow finer control of specific proteolytic processes in organelles (Janska et al., 2013; Horner et al., 2017). There are also specific proteases that degrade plastid and mitochondrial proteomes, notably the ATPases Associated with diverse cellular Activities (AAA) class; Long undivided filaments (Lon),
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Filamentation Temperature-Sensitive H (FtsH), and Caseinolytic Protease (Clp) proteases(Janska et al., 2013; van Wijk, 2015; Adamiec et al., 2017).
Intracellular protein degradation requires energy (Ciechanover, 2005b). Across all the kingdoms, proteases have an ATP binding site and utilize ATP for hydrolysis even though peptide bond cleavage is an intrinsically exergonic process (Pickart and Cohen, 2004). In eukaryotes, intracellular protein degradation by the ubiquitin-proteasome pathway, attachment of ubiquitin, and the degradation of marked proteins require energy in the form of ATP (Lilienbaum, 2013). Energy is also needed for protein transport across the lysosomal membrane (Hayashi et al., 1973) and for the activity of the H+ pump, which is required for the maintenance of the acidic intra-lysosomal pH for the optimal activity of the proteases (Schneider, 1981). However, autophagic degradation of mitochondria and other organelles, as well as protein aggregates, cytoplasmic proteins, and long-lived proteins can also yield significant energy benefits for cells (Kelekar, 2005). Autophagy contributes to the maintenance of energy balance through the degradation of energy reserves such as lipids, glycogen, and proteins during increased energy demand (Kim and Lee, 2014). Protein turnover is one of the most energy-consuming processes in plants depending on physiological status and environmental stress levels (Izumi et al., 2013; Abdelrahman et al., 2017). About 16-42% of all the ATP from oxidative phosphorylation is used for protein turnover in Arabidopsis leaves, with about 90% of this ATP needed for protein synthesis and 10% for protein degradation (Abdelrahman et al., 2017).
3.2.2 Targeted proteomics for organelle protein abundance profiling
Increased scientific efforts spent on discovering potential protein biomarkers rely on the reliable determination of their quantitative changes in abundance in control and treatment samples (Parker and Borchers, 2014). Compared to high-abundant proteins in organelles, many novel protein biomarkers have low concentration, usually in the low nanogram range. In data-dependent mass spectrometry (DDA), high abundance precursors are selected for fragmentation preferentially. Therefore, the chance of detecting abundant proteins is higher, while low abundant proteins will only sometimes be observed in repeated experiments (Doerr, 2012b). As a result, during each analysis, the MS will detect and identify a somewhat different list of proteins. For hypothesis- driven analysis, more reproducible answers for changes in abundance of selected protein targets are needed (Hause et al., 2011). To solve this problem, a targeted approach for proteomic analysis has been introduced, also called data-independent analysis (DIA. One
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DIA technique uses a triple quadrupole instrument to perform a dual-stage mass filtering technique with intermediate dissociation of the analyte of interest; so-called SRM (single reaction monitoring or MRM (multiple reaction monitoring). Instead of the abundance- based selection of ions in DDA, each precursor and its product ions to be monitored in DIA in the MRM mode is predetermined (Doerr, 2012a). The product ions and their corresponding precursor ion are considered a “transition.” For the reliable quantification of each proteolytic peptide, several representative transitions are selected and monitored in MRM analysis.
The general workflow for MRM analysis is shown in Figure 3-1. An MRM assay is developed via determination of the m/z values for precursor and product ions in each transition, often using mass spectrum data acquired from precedent DDA (Xiao et al., 2014). Once the method is established, MRM analysis is relatively straightforward and can be conducted in a high throughput manner. After LC-MRM analysis, the identification and quantification results are obtained by specific bioinformatics tools. MRM modes of analysis are performed on triple quadrupole (QqQ), where Q1 serves as the first mass analyzer, q2 as collision cell, and Q3 as the second mass analyzer. Quantitation is achieved in a tandem fashion by monitoring transitions of precursor ion (peptide) to fragment (b- or y-) ions (Elliott et al., 2009). Fragmentations wherein the charge is retained in the N-terminus are called b-ions, while y-ions carry the C-terminus.
Figure 3-1: General workflow for targeted proteomics with multiple reaction monitoring
For the proteins of interest, a transition list is built and optimized. Per peptide, it contains characteristic precursor and product ions and corresponding retention time. Q-TOF-based Arabidopsis thaliana proteome
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libraries were used to select those transitions for multiple peptides per selected protein. Data acquisition is performed on a triple quadrupole mass spectrometer. Q1 and Q3 represent two mass filters for precursor and product ion selection, and Q2 creates product ions via collision-induced dissociation. At the first quadrupole (Q1), targeted precursor ions are isolated based on the transition list, followed by a collision cell where product ions are generated. Finally, Skyline open-source software for targeted proteomics was used for peak picking and area integration. Figure adapted from MtoZ Biolabs (http://www.mtoz- biolabs.com/mrm.html).
For the reliable quantification of a peptide, at least three transitions are typically monitored in the MRM analysis, and three peptides per protein are desirable for accurate quantification (Mani et al., 2012). Thus, to monitor a set of several hundred proteins, the total number of transitions required could add up to several thousand. For most QqQ-MS, only around 170 transitions can be monitored simultaneously with adequate sensitivity. To increase the robustness and throughput of the targeted analysis, scheduled MRM is often the method of choice with the help of bioinformatics tools (Colangelo et al., 2013). For scheduled MRM, each transition is monitored within a short period of the overall time frame of sample elution, known as the retention time window.
An MRM assay for organelle abundance profiling was reported for the reference plant Arabidopsis (Hooper et al., 2017b). This work was based on a large number of Arabidopsis subcellular proteomes that have been collated into SUBA, the Subcellular localization database for Arabidopsis proteins (Hooper et al., 2012; Tanz et al., 2012; Hooper et al., 2014; Hooper et al., 2017a; Hooper et al., 2017b). Ribosome MRMs were those reported previously for Arabidopsis 80S ribosome subunits (Salih et al., 2020). For both organelle and ribosome MRMs, non-redundant peptide transitions for proteins that are reproducibly localizing to a subcellular compartment were selected as specific markers. In this chapter, this organelle abundance profiling MRM assay was used and extended to assess organelle and ribosome abundances during developmental transitions in seedling growth.
3.2.3 Study of plant protein turnover rate of autophagy
Autophagy-mediated turnover is an essential process to maintain cellular homeostasis for removing damaged organelles, cytoplasmic constituents, and pathogens and robust nutrient recycling, especially during nitrogen and fixed-carbon starvation (Michaeli et al., 2016). Nitrogen remobilization efficiency is significantly lower in the autophagy mutants irrespective to biomass defects, harvest index reduction, leaf senescence phenotypes, and nitrogen conditions (Guiboileau et al., 2012). Besides, autophagy mutants accumulate a larger amount of ammonium, amino acid, and proteins
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in their leaves than wild type (WT) and are depleted in sugars (Guiboileau et al., 2013). Over-accumulation of proteins in autophagy mutants occurred despite higher protease activities in mutants. Throughout the autophagic process, cargo proteins are digested by hydrolysis enzymes in the vacuole, and amino acids are then released from the vacuole in a recycling process. These amino acids can be utilized as an energy source through the Krebs cycle (Mizushima, 2007).
Recent studies in Arabidopsis have suggested that autophagy can contribute to energy availability at night by supplying amino acids (Izumi et al., 2013). In general, autophagy processes are considered to be an energy release process (Anding and Baehrecke, 2017). For example, during seed germination, plant energy organelles, glyoxysomes (specialized peroxisomes), and mitochondria accumulate rapidly and begin converting stored starch and lipids into energy. In parallel, the chloroplasts start developing and provide energy through photosynthesis once the leaves of the seedling have emerged (Michaeli and Galili, 2014). Throughout plant growth and development, organelle biogenesis is crucial, and unwanted or damaged proteins aggregates and organelles are recycled via macroautophagy or selective autophagy during these crucial physiological events (Michaeli and Galili, 2014).
In yeast and mammals, autophagy has been shown to be influenced by TOR signaling (Araujo et al., 2011). However, this is not very well understood in plants. One study has revealed that some genes required for autophagy are upregulated in RNAi-TOR Arabidopsis plants, which show constitutive autophagy (Liu and Bassham, 2010). Other research suggests the existence of a link between AtNBR1 (which cooperates with p62 in selective autophagy of ubiquitinated targets) and TOR in sulfur or nitrogen starvation (Tarnowski et al., 2016). Nonetheless, in Arabidopsis, TOR inhibition has an impact on the efficiency of mRNA translation, a reduction in the abundance of polysomes and a decrease in the amount of soluble protein (Deprost et al., 2007).
Ribosomes sequester a large amount of the cell’s resources. In yeast, ribosomal RNA (rRNA) constitutes 80% of cellular RNA and up to 30-40% of the cytoplasmic volume (Warner, 1999). Macroautophagy and selective autophagy of ribosomes (ribophagy), helps to recycle nonfunctional ribosomes and maintain the normal ribosome pool and resource availability for the cells to prevent further use of limited resources (Lafontaine, 2010). Researchers have shown that unnecessary or damaged ribosomes are often degraded through directed autophagy (ribophagy), or they can be repaired through ribosomal protein exchange (Ylä‐Anttila et al., 2009; Mathis et al., 2017). Studies have
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shown that the rate of decay of ribosomes through autophagy is greater than that of general cytosolic components under the nutrient starvation condition in Saccharomyces suggesting selective degradation of ribosomes (Kraft et al., 2008). In the mammalian cell, a recent study identified a ribosome receptor (NUFIP1) that is required for the autophagic targeting of ribosomes upon starvation or mTOR inhibition through its association with the large ribosomal subunit (Wyant et al., 2018). In yeast, it has been found that the ribophagy pathway involves the ubiquitination of the 40S and 60S ribosomal subunits upon nutrient starvation (Kraft et al., 2008). In Arabidopsis thaliana, it has been shown that autophagy mediates rRNA turnover under normal conditions through delivering the rRNA to the vacuole, whereas mutations of autophagy result in rRNA accumulation inside the cytoplasm (Floyd, 2015). Ribophagy shares some de-ubiquitination of ribosomes or associated factors with non‐selective autophagy (Kraft et al., 2008).
Photosynthesis provides energy for plant growth by synthesizing sugars. Plants have developed strategies to control chloroplast homeostasis to cope with environmental and developmental cues (Klionsky et al., 2016; Izumi and Ishida, 2019). The chloroplast can be degraded in a piecemeal degradation or through whole chloroplast autophagy. Piecemeal degradation occurs via rubisco-containing bodies (RCB) and senescence- associated vacuoles (SAV). SAVs are small proteolytic vacuolar compartments that accumulate in senescing leaves (Ishida et al., 2008b; Wada et al., 2008; Ishida et al., 2014). In-plant cells, macroautophagy, and microautophagy pathways contribute to selective chloroplast degradation under carbon nutrient deficiency, light stress, and leaf senescence (Izumi and Nakamura, 2018; Soto-Burgos et al., 2018).
Despite all these observations, it is not currently clear whether autophagy or intra- organelle proteases are principally responsible for changes in organelle proteomes during plant development. It is thus important to determine if there is a major role for autophagy in developing organelles in plant cells. It is also important to determine if selective removal of some organelles can occur through autophagy (selective autophagy) or whether organelles of all types (unwanted or damaged) are removed without selection. This will determine if autophagy can contribute to the change in role and abundance of specific types of organelles during different changes in plant development or not.
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3.2.4 Aims and Objectives
Aim: To determine if organelle-specific protein abundance during Arabidopsis growth and development is dependent on selective autophagy.
This aim required achievement of the following objectives;
1. To collect and check homozygosity of autophagy mutants in Arabidopsis. 2. To set up three developmental transitions during (i). Early seedling development, (ii). Dark to light greening of seedlings and (iii). Light to dark senescence of leaves for the study of Arabidopsis thaliana autophagy mutants. 3. To assess the abundance of selected organelle proteins during these transitions in different mutants by using and extending Multiple Reaction Monitoring (MRM) assays for each organelle and the ribosome across time courses and genotypes.
The results were analyzed to provide answers for the following research questions:
1. What is the contribution of autophagy to the development or degradation of organelles in plant cells under growth and development? 2. Are different organelles, including mitochondria, plastids, and peroxisomes selectively degraded under different growth and development transitions? 3. Are specific proteins in different organelles selectively degraded under different growth and development transitions? 4. Is the ribosome selectively degraded under different growth and development transitions?
3.3 Materials and Methods
3.3.1 Genotyping of Arabidopsis autophagy mutant lines
3.3.2 Plant Growth
The screening of homozygous mutants was performed as described below. Arabidopsis seeds of WT (Col-0), T-DNA insertion lines and other transgenic lines (Table 3-1) were plated on germination medium agar plates containing 1% sucrose (Valvekens et al., 1988) with appropriate antibiotics or herbicides and plates were transferred to a dark, cold (4oC) room for 2-3 days for stratification. Then they were allowed to germinate in a lighted growth room with approximately 100-125 µmolm-2s-1 at 22°C under a 16 h light/8 h dark cycle (long day) for one week, and seedlings were transplanted to soil mix containing compost, perlite and vermiculite (in the ratio of 3:1:1) and then allowed to grow at 100-125 µmolm-2s-1 at 22°C under a 16 h light/8 h dark cycle 71
(a long day for three weeks). For identification of T-DNA insertion sites, 24 randomly selected T-DNA mutant seedlings per line were subjected to genotyping by DNA-PCR to confirm seeds were homozygous.
Table 3-1: Selected ATG mutant Arabidopsis thaliana lines for use in developmental transitions S1, S2, and S3 and reference to their isolation and characterization.
Gene Type of ABRC/NASC References Mutant Mutation Number atg2-1 T-DNA SALK_076727 (Yoshimoto et al., 2009), (Chen et al., 2015a) (atg2*) atg5-1 T-DNA SAIL_129B07 (Yoshimoto et al., 2009), (Chen et al., 2015a) (atg5*) atg7-2 T-DNA GABI_655B06 (Lai et al., 2011) (atg7*) atg9-2 T-DNA SALK_130796 (Zhuang et al., 2017) (atg9*) *hereafter referred to as in the chapters.
3.3.3 DNA extraction from Arabidopsis leaves
About 1 g of Arabidopsis leaves was ground (in a microfuge tube) using liquid nitrogen. 200 μL of extraction buffer (200 mM Tris-HCl (pH 7.5), 250 mM NaCl, 25 mM EDTA, and 0.5% SDS) were added to the tube and mixed well. Tissue residues were removed by centrifuge at 14,000 rpm for five minutes, and the supernatant was transferred to a clean microcentrifuge tube. An equal volume of isopropanol was added to the clarified supernatant and mixed gently by inversion for 5-10 minutes. DNA was pelleted by centrifugation in a microcentrifuge for five minutes at maximum speed and washed with 500 µL of 70% ethanol by inverting the tube several times, and it was centrifuged at maximum speed for 1 min. Ethanol was gently poured out, and tubes were tilted upside down on a Kimwipe to remove remaining ethanol, with the cap open, till the DNA pellet to dry. DNA pellet was re-suspended in 50 µL of sterile water.
3.3.4 Polymerase Chain Reaction (PCR)
PCR was performed in a 96-well microtitre plate or in 0.2 ml tubes in a thermal cycler (BIO-RAD, USA). A premix sufficient for the number of planned reactions was prepared, allowing for a 1x reaction mix once the DNA template was added. The volume and contents of the final reaction are described below. PCR amplification was performed with the specific cycling profile, as described below. Reaction products were visualized by 1% TAE agarose gel electrophoresis and stained with ethidium bromide.
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Primers were designed using Geneious software (http://www.geneious.com, Kearse et al., 2012), and the Salk Institute Genomic Analysis Laboratory (SIGnAL) T-DNA Primer Design (http://signal.salk.edu/cgi-bin/tdnaexpress) (O'Malley et al., 2015), and ordered from IDT, USA. Where possible primers were selected using the following parameters:
. Melting temperature (Tm) between 57°C and 63°C . (G+C) content between 30-80% . Length between 18 - 25 bp For the desired gene mutant, the gene-specific forward primer (Left/LP) and reverse primer (Right/RP) (Table 2) were used to amplify WT allele band while respective T- DNA border and respective gene-specific reverse primers were used to amplify T-DNA band (Table 3-2).
Table 3-2: Primers used for Arabidopsis thaliana mutant lines genotyping.
Gene Forward Primer (LP) Reverse Primer (RP) T-DNA Specific border mutant Primer (LB) atg2-1 ATTACCACGCTTGAT AAGGAATCAGGTGGCATCCT TGGTTCACGTAGTGGGCC GGCAG ATCG atg5-1 ATTTGCTATTTGTTT TACCGTTCATGACAGAGGTC GCCTTTTCAGAAATGGAT GGCACG C AAATAGCCTTGCTT atg7-2 TAATATCCCGAGAG TGCTAATTCCATGGATCCAA CCCATTTGGACGTGAATGT GCGAATC C AGACAC atg9-2 TTATGCCACGAGTTC GGGAAAGAAAGCAAACCTT ATTTTGCCGATTTCGGAAC CGTATC TG
3.3.5 Agarose gel electrophoresis
The PCR product was visualized on a 1% agarose gel in TAE buffer with ethidium bromide. Positive reactions were identified as bright bands that were of expected size as compared to a 100bp and 1Kb molecular weight ladder (Promega). The gel was subjected to electrophoresis at 100 volts for between 30 min to 45 min. The gel was illuminated with ultraviolet light and photographed with a Gel Doc System (BioRad).
3.3.6 Development and optimization of Arabidopsis energy organelle- specific peptide MRM assays for abundance profiling
Three developmental transitions were setup; during early seedling development, dark to light greening of seedlings and light to dark senescence of leaves for the study Arabidopsis thaliana autophagy (Figure 3-2). Prior to work with the selected mutants, preliminary studies were conducted with WT Col-0, and three different strategies were
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applied to investigate the abundance profiling of autophagy-dependent energy organelle- specific proteins for MRM analysis.
1. Early seedling development (Developmental transition; S1):
About 50 mg (500 seeds) of seeds were surface-sterilized by liquid or vapor-phase methods (Desfeux et al., 2000) and washed five times with sterilized water, and were then carefully dispensed on a stainless steel wire mesh platform (mesh size 1 mm; 3 cm x 3 cm x 3 cm) layered previously with 1% sterilized agarose in a round plastic vessel (diameter 120 mm, height 140 mm) containing approximately 300 ml liquid medium (¼- strength Murashige and Skoog medium without vitamins, ¼- strength Gamborg B5 vitamins solution, 2 mM MES, 1% [w/v] sucrose, pH 5.8). Arabidopsis plants were grown under a 16/8 hr light/dark period with a light intensity of 100-125 µmolm-2s-1 at 22oC over seven days. After two days of germination, the developmental transition was studied by collecting and tissue samples (S1_0d) and then after 1day (S1_1d), 3days (S1_3d), and 5days (S1_5d) of growth (Figure 3-2).
2. Dark to light greening of seedlings (Developmental transition; S2):
About 50 mg (500 seeds) of seeds were surface-sterilized by liquid or vapor-phase methods (Desfeux et al., 2000) and washed five times with sterilized water, and were then carefully dispensed on a stainless steel wire mesh platform (mesh size 1 mm; 3 cm x 3 cm x 3 cm) layered previously with 1% sterilized agarose in a round plastic vessel (diameter 120 mm, height 140 mm) containing approximately 300 ml liquid medium (¼- strength Murashige & Skoog medium without vitamins, ¼- strength Gamborg B5 vitamins solution, 2 mM MES, 1% [w/v] sucrose, pH 5.8). Seeds were grown on a mesh in liquid media five days in the dark, and then Arabidopsis plants were grown under 16/8 hr light/dark period with a light intensity of 100-125 µmolm-2s-1 at 22oC. Tissue samples were collected 0 day (S2_0d), 1 day (S2_1d), 3 days (S2_3d), and 5 days (S2_5d) of growth in light (Figure 3-2).
3. Light to dark senescence of leaves (Developmental transition; S3):
Arabidopsis plants were grown in Hoagland’s solution (Waters et al., 2012) under 16/8 hr light/dark period with a light intensity of 100-125 µmolm-2s-1 at 22oC. After 21
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days (S3_0d_UC), leaves were covered (leaf number three, five, and seven) (C) by aluminum foil to study dark-induced senescence. Covered leaves (leaf number: three, five, seven) and uncovered (UC) leaves (leaf number: four, six, eight) were collected over time course (1 day, 3 days and 5 days) (Uncovered leaves: S3_1d_UC, S3_3dU_C, S3_5d_UC, Covered leaves: S3_1d_C, S3_3d_C, S3_5d_C) (Figure 3-2).
Figure 3-2: Establishment of three developmental transitions
Three developmental transitions during (S1) early seedling development, (S2) dark to light greening of seedlings and (S3) light to dark senescence of leaves for the study Arabidopsis thaliana autophagy and Target of Rapamycin (TOR) mutants.
3.3.7 Protein isolation and sample preparation
Here, 200 mg of snap-frozen tissue was ground under liquid nitrogen before extraction in 400 μl of 125 mm Tris–HCl pH 7.5, 7% (w/v) sodium dodecyl sulfate, 10% (v/v) β-mercaptoethanol, 0.5% (w/v) PVP40 with Roche protease inhibitor cocktail (Roche) added at 1 tablet per 50 ml of extraction buffer. Protein extraction was carried out by chloroform/methanol extraction (Wessel and Flügge, 1984) before washing the pellet twice in 80% (v/v) acetone.
Samples were resuspended in freshly prepared buffer (7M Urea, 2M Thiourea,
50mM NH4HCO3, and 10mM DTT) to obtain a protein concentration between 15-25
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μg/μl after resuspension. Protein concentration was quantified by Bradford assay and spectrophotometric measurement at a wavelength of 595 nm using bovine serum albumin as a standard (Kruger, 1994).
200 μg of protein was treated with 25mM Iodoacetamide for 30 min in the dark. The sample solutions were then diluted to below 1 M urea with 50 mM NH4HCO3. For protein digestion, 10 μg of trypsin (dissolved in 0.01% [v/v] trifluoroacetic acid to a concentration of 1 mg/mL) was added to each sample and incubated at 37°C overnight. The samples were acidified to 1% (v/v) with formic acid and solid-phase extraction cleaned using Silica C18 Macrospin columns (The Nest Group). After each of the following steps, solid- phase extraction columns were centrifuged for 3 min at 150g at room temperature. Before loading, the sample columns were washed with 750 μL of 70% (v/v) acetonitrile and 0.1% (v/v) formic acid and charged with 750 μL of 5% (v/v) acetonitrile and 0.1% (v/v) formic acid. After loading the samples onto the columns, two washes with 750 μL of 5% (v/v) acetonitrile and 0.1% (v/v) formic acid were carried out, followed by two elution steps with 750 μL of 70% (v/v) acetonitrile and 0.1% (v/v) formic acid. The eluate was dried under vacuum and resuspended in 5% (v/v) acetonitrile and 0.01% (v/v) formic acid to a final concentration of 1 mg/mL for MS.
3.3.8 Selection of target peptides for MRM assays
Organelle specific biomarkers were selected as previously characterized and available in different bio-informatics (prediction) tools that are publically available viz. SUBAcon (Hooper et al., 2014) and SUBA4 (http://suba.live) (Hooper et al., 2017a), ribosome specific markers (Salih et al., 2020). For each target protein (Table S1), as the first step, proteotypic peptides sequences that are unique were identified by the Arabidopsis PPR Protein Database (Taylor et al., 2014). Then for designing other MRM biomarkers previously published protocol was followed (Kim et al., 2013) Fragment ions were selected using Skyline (http://proteome.gs.washington.edu/ software/skyline) software and analyzing MRM data (Stergachis et al., 2011).
3.3.8.1 LC-MS/MS for dynamic MRM analysis
Using an Agilent 1290 Infinity II LC system, 10 µg of each sample were loaded onto an Agilent AdvanceBio Peptide Map column (2.1 x 250mm 2.7-Micron, P.N. 651750- 902), which was heated to 60 °C. Peptides were eluted over a 30 minute gradient (0-15 min 3% [v/v] acetonitrile 0.1% [v/v] formic acid to 45% [v/v] acetonitrile 0.1% [v/v]
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formic acid; 15-15.5 min 45% [v/v] acetonitrile 0.1% [v/v] formic acid to 100% [v/v] acetonitrile 0.1% [v/v] formic acid; 15.5-16 min 100% [v/v] acetonitrile 0.1% [v/v] formic acid to 3% [v/v] acetonitrile 0.1% [v/v] formic acid; 16-30 min 3% [v/v] acetonitrile 0.1% [v/v] formic acid) directly into the Agilent 6495 Triple Quadrupole MS for detection.
3.3.8.2 Analysis of targeted MRM data
Before conducting MRM analysis for the individual samples, a preliminary MRM analysis was performed using pooled samples of all growth and developmental stages to obtain transition information, such as detectability in tissues and suitability of transition. for organelle-specific assay, 680 peptides (2231 transitions) representing 76 proteins were targeted. After obtaining the resulting MRM data, the final target list was selected using the following criteria: at least one peptide was selected per protein, and at least three transitions (one quantifier and two qualifiers) per peptide were chosen. Individual MRM analysis was performed using 221 transitions, corresponding to 31 proteins.
Raw data files from MRM analysis were processed using Skyline. Peak area integration was confirmed manually to correct potentially wrongly assigned targets. For further analysis, the integrated area of the quantifier ion was used. Normalization was performed to correct for differences between sample runs. Therefore every peptide within a sample was divided by the median of all detected peptides in that sample. Then, to give peptides the same weight, normalized data were scaled by dividing each peptide by the median of that peptide across all samples. For ribosome data, only the scaling step was followed. The normalization step was skipped as it would remove the differences due to changes in total ribosome protein abundance (if any) between the samples and time points.
3.3.9 Progressive 15N labeling of dark-induced senescence (S3) transition
To compare if mitochondria, plastids, and peroxisome proteins turnover rates of autophagy mutants, to WT plants during dark-induced senescence transitions in Arabidopsis, a pilot experiment was conducted to determine the feasibility of 15N labeling during the dark-induced senescence. First, Arabidopsis WT Col-0 plants were grown in unlabelled 14N Hoagland’s (light) media as previously described (Waters et al., 2012) under 16/8 hr light/dark period with a light intensity of 100-125 µmolm-2s-1 at 22oC, until they reached leaf production stage 1.10 (T0) (approximately 21 days) (Boyes et al., 2001).
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On the 21st day (S3_0d_UC), selected leaves were covered for dark-induced senescence as described above. Then for the progressive 15N labeling, plants were transferred to 15N labeled Hoagland’s (heavy) media. The unlabelled growth medium was discarded, and the growth racks rinsed four times with fresh medium without nitrogen (no KNO3 or 15 15 NH4NO3) to ensure the old solution was washed out. Later N medium (6mM K NO3 15 15 and 0.5mM NH4 NO3) was added for every four plants, and the plants were grown for three days before collecting leaves as described above. One rack was pooled as a biological replicate. Three biological replicates were collected at each time point.
3.3.9.1 Protein isolation and sample preparation
Here, 200 mg of snap-frozen tissue was ground under liquid nitrogen before extraction in 400 μl of 125 mm Tris–HCl pH 7.5, 7% (w/v) sodium dodecyl sulfate, 10% (v/v) β-mercaptoethanol, 0.5% (w/v) PVP40 with Roche protease inhibitor cocktail (Roche) added at 1 tablet per 50 ml of extraction buffer. Protein extraction was carried out by chloroform/methanol extraction (Wessel and Flügge, 1984) before washing the pellet twice in 80% (v/v) acetone. Samples were re-suspended in a freshly prepared buffer
(7M Urea, 2M Thiourea, 50mM NH4HCO3, and 10mM DTT) to obtain 15-25 μg/μl range after resuspension. Then protein concentration was quantified by Bradford assay and spectrophotometric measurement at a wavelength of 595 nm using bovine serum albumin as a standard (Kruger, 1994).
Protein (100 µg) in solution from each sample was precipitated by acetone. then pellets were dissolved in 2× sample buffer (4% SDS, 125 mM Tris, 20% glycerol, 0.005% bromophenol blue, and 10% mercaptoethanol, pH 6.8) before being separated on a Bio- Rad protean II electrophoresis system with a 4% (v/v) polyacrylamide stacking gel and 12% (v/v) polyacrylamide separation gel.
Proteins were visualized by colloidal Coomassie Brilliant Blue G 250 staining. The SDS-PAGE gel lane for each sample was cut into nine fractions. The resulting 54 gel pieces were twice decolorized by de-stain solution (10 mM NH4NO3 and 50% acetonitrile) for 45 min. Dried gel pieces from one fraction were digested with 375 ng trypsin at 37°C overnight and then extracted as described previously (Nelson et al., 2014). The digested peptide solutions were dried by vacuum centrifugation at 30°C, re- suspended in 20 μL loading buffer (5% acetonitrile and 0.1% formic acid), and filtered by Millipore 0.22-micron filters before being loaded for HPLC separation. A total of 5 μL of filtered samples were loaded onto a C18 high-capacity nano LC chip (Agilent
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Technologies) using a 1200 series capillary pump (Agilent Technologies) as described previously. Following loading, samples were eluted from the C18 column directly into a 6550 series quadrupole time-of-flight mass spectrometer (Agilent Technologies) with a 1200 series nano pump using the following buffer (0.1% formic acid in acetonitrile) gradient: 5 to 35% in 35 min, 35 to 95% in 2 min, and 95 to 5% in 1 min. Parameter settings in the mass spectrometer were as described previously (Nelson et al., 2014).
3.3.9.2 Determination of peptide 15N incorporation ratio
During protein turnover, newly synthesized proteins increased in the amount of 15N as the amino acid pool was progressively labeled. Calculation of the fraction of the protein labeled with 15N [labeled peptide fraction or LPF, i.e. H/(H+NA)] was determined following a binary differentiation between the natural abundance population before labeling and the newly synthesized labeled population of peptides by a non-linear least- squares analysis as described previously (Li et al., 2017). Agilent .d files were processed as described in (Li et al., 2017) for determining the 15N incorporation to newly synthesize peptides. LPFs of proteins in covered and uncovered leaves in 3 days labeled plants were calculated using three biological replicates.
3.3.10 Statistical analysis
Peak area data were normalized and scaled as described before. Then the averaged peptides of proteins were used to construct the heat maps using the R package ‘pheatmap’ (1.0.12).
The statistical differences of autophagy mutants were compared with WT Col-0 at respective time points by multiple comparisons t-test in R (3.6.1). Results were determined to be statistically different at a probability level of P < 0.05.
Normalized and averaged MRM peptide data were statistically analyzed by Two- way analysis of variance (ANOVA) to detect a significant difference between five genotypes (atg2, atg5, atg7, atg9, WT Col-0) groups at each time-point (0 h, 12 h, and 24 h) means were compared using the Fisher's LSD test at P < 0.05 using R program (3.6.1).
To calculate the Principal Components Analysis (PCA), peak area data were normalized and scaled as described before, and then, data are scaled to unit variance by R package ‘FactoMineR’ (2.3) and visualized by ‘factoextra’ (1.0.7) R package.
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3.4 Results
3.4.1 Genotyping of Arabidopsis autophagy mutant lines
To determine the functional role of ATG2, ATG5, ATG7, and ATG9 during plant growth and development, we analyzed previously studied loss-of-function mutants of the autophagy pathway, viz. ATG2, ATG5, ATG7, and ATG9 T-DNA insertion lines for each gene in the Col-0 background. To confirm that each line was correct, each was genotyped using PCR to determine homozygosity. No amplification products were observed in autophagy mutants for each gene correspondent to the mutation, confirming T-DNA insertion at these sites in these mutant lines.
Figure 3-3 to Figure 3-6 illustrates the molecular verification of T-DNA insertion mutants in autophagy knockout lines used in this study. PCR reactions show a flanking DNA fragment upstream (LP) and downstream (RP) of the insertion site in WT but not in autophagy mutants, and a DNA fragment flanking the T-DNA border (LB) and the downstream (RP) of the insertion site in autophagy mutants but not WT, suggesting that autophagy mutants used are homozygous T-DNA insertion lines. For biological experiments only homozygous T-DNA seeds we used.
Figure 3-3: T-DNA insertion in ATG2 and genotyping of atg2-1 (SALK_076727).
(A) Schematic illustration of the exon-intron structure of ATG2 and the T-DNA insertion position in atg2-1. The white and black boxes indicate UTRs and exons, respectively. Lines between the black boxes indicate introns. Primers used for genotyping are indicated (Chen et al., 2015a). (B) PCR amplification of WT allele band using LP and RP gene-specific primers as well as T-DNA band using SALK LBb1.3 and gene-specific RP primer. Three Col-0 WT seedling and 24 randomly selected SALK_076727 seedlings were used. Primers were used are listed in Table 3-2.
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Figure 3-4: T-DNA insertion in ATG5 and genotyping of atg5-1 (SAIL_129_B07).
(A) Schematic illustration of the exon-intron structure of ATG5 and the T-DNA insertion position in atg5-1. The white and black boxes indicate UTRs and exons, respectively. Lines between the black boxes indicate introns. Primers used for genotyping are indicated (Chen et al., 2015a). (B) PCR amplification of WT allele band using LP and RP gene-specific primers as well as T-DNA band using SAIL LBa1 and gene- specific RP primer. Three Col-0 WT seedling and 24 randomly selected SAIL_129_B07 seedlings were used. Primers were used are listed in Table 3-2.
Figure 3-5: T-DNA insertion in ATG7 and genotyping of atg7-2 (GK_655B06).
(A) Schematic illustration of the exon-intron structure of ATG7 and the T-DNA insertion position in atg7-2. The white and black boxes indicate UTRs and exons, respectively. Lines between the black boxes indicate introns. Primers used for genotyping are indicated (Farmer et al., 2013). (B) PCR amplification of WT allele band using LP and RP gene-specific primers as well as T-DNA band using GABI-KAT LB (8603) and gene-specific RP primer. Three Col-0 WT seedling and 24 randomly selected GK_655B06 seedlings were used. Primers were used are listed in Table 3-2.
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Figure 3-6: T-DNA insertion in ATG9 and genotyping of atg9-2 (SALK_130796).
(A) Schematic illustration of the exon-intron structure of ATG9 and the T-DNA insertion position in atg9-2. The white and black boxes indicate UTRs and exons, respectively. Lines between the black boxes indicate introns. Primers used for genotyping are indicated (Inoue et al., 2006). (B) PCR amplification of WT allele band using LP and RP gene-specific primers as well as T-DNA band using SALK LBb1.3 and gene-specific RP primer. Three Col-0 WT seedling and 24 randomly selected SALK_130796 seedlings were used. Primers were used are listed in Table 3-2.
3.4.2 Development and optimization of organelle-specific MRM assays for protein abundance profiling
A multiple reaction monitoring (MRM) method was used to check the quantity of chloroplast, mitochondria, peroxisome, ER, Golgi, plasma membrane, and ribosomal marker proteins in Arabidopsis thaliana. An organelle abundance profiling pipeline for the reference plant Arabidopsis thaliana was generated by selecting and analyzing candidate MRM peptide transitions for a number of organelle marker proteins based on a previous study (Hooper et al., 2017b) (Table S1). Only proteins repeatedly localizing to a subcellular compartment (Hooper et al., 2017a) and generating non-redundant peptides were selected as markers. To test their utility to study organelle development processes, three transitions were set up; early seedling development (S1), and dark to light greening of seedlings (S2) and light to dark senescence of leaves (S3) as outlined in methods. These transitions were used to study subcellular organelles abundances in Col-0 and autophagy mutants atg2, atg5, atg7, and atg9. The detailed results for plastids, mitochondria, and peroxisomes are shown in Figure 3-7 to Figure 3-15, while results for other subcellular compartments are shown in supplementary tables Table S2-S9.
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Figure 3-7: Abundance of plastid MRM biomarkers in WT during S1 transition.
Heatmap shows the abundance of plastid MRM biomarkers viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), AT1G13930.1, and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), at different time points during the early seedlings development of Col-0 seedlings. The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 3-8: Abundance of plastid MRM biomarkers in WT during S2 transition.
Heatmap shows the abundance of plastid MRM biomarkers viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), AT1G13930.1, and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), at different time points during the dark to light transition of etiolated Col-0 seedlings (photomorphogenesis). The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y- axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
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Figure 3-9: Abundance of plastid MRM biomarkers in WT during S3 transition.
Heatmap shows the abundance of plastid MRM biomarkers viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), AT1G13930.1, and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), at different time points during the dark-induced senescence both covered and uncovered Col-0 leaves. The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
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Figure 3-10: Abundance of mitochondria MRM biomarkers in WT during S1 transition.
Heatmap shows the abundance of mitochondria MRM biomarkers viz. AT2G20360.1 (NADH DEHYDROGENASE [UBIQUINONE] 1 ALPHA SUBCOMPLEX SUBUNIT 9), AT3G01280.1 (VOLTAGE DEPENDENT ANION CHANNEL 1), AT5G47030.1 (ATP SYNTHASE SUBUNIT DELTA), and AT5G66760.1 (SUCCINATE DEHYDROGENASE 1-1) at different time points during the early seedlings development of Col-0 seedlings. The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 3-11: Abundance of mitochondria MRM biomarkers in WT during S2 transition.
Heatmap shows the abundance of mitochondria MRM biomarkers viz. AT2G20360.1 (NADH DEHYDROGENASE [UBIQUINONE] 1 ALPHA SUBCOMPLEX SUBUNIT 9), AT3G01280.1 (VOLTAGE DEPENDENT ANION CHANNEL 1), AT5G47030.1 (ATP SYNTHASE SUBUNIT DELTA), and AT5G66760.1 (SUCCINATE DEHYDROGENASE 1-1) at different time points during the dark to the light transition of etiolated Col-0 seedlings (photomorphogenesis). The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
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Figure 3-12: Abundance of mitochondria MRM biomarkers in WT during S3 transition.
Heatmap shows the abundance of mitochondria MRM biomarkers viz. AT3G01280.1 (VOLTAGE DEPENDENT ANION CHANNEL 1), AT5G47030.1 (ATP SYNTHASE SUBUNIT DELTA), and AT5G66760.1 (SUCCINATE DEHYDROGENASE 1-1) at different time points during the dark-induced senescence both covered and uncovered Col-0 leaves. The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
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Figure 3-13: Abundance of peroxisomes MRM biomarkers in WT during S1 transition.
Heatmap shows the abundance of peroxisome MRM biomarkers viz. AT1G23310.1 (GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1), AT1G35720.1 (ANNEXIN 1), and AT1G78370.1 (GLUTATHIONE S-TRANSFERASE TAU 20) at different time points during the early seedlings development of Col-0 seedlings. The color scale shows an abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 3-14: Abundance of peroxisomes MRM biomarkers in WT during S2 transition.
Heatmap shows the abundance of peroxisome MRM biomarkers viz. AT1G23310.1 (GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1), AT1G35720.1 (ANNEXIN 1), and AT1G78370.1 (GLUTATHIONE S-TRANSFERASE TAU 20) at different time points during the dark to light transition of etiolated Col-0 seedlings (photomorphogenesis). The color scale shows abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y-axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
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Figure 3-15: Abundance of peroxisomes MRM biomarkers in WT during S3 transition.
Heatmap shows the abundance of peroxisome MRM biomarkers viz. AT1G23310.1 (GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1), AT1G35720.1 (ANNEXIN 1), and AT1G78370.1 (GLUTATHIONE S-TRANSFERASE TAU 20) at different time points during the dark- induced senescence both covered and uncovered Col-0 leaves. The color scale shows abundance of peak intensity values with green and red representing down-regulation and up-regulation, respectively. The y- axis of the line graph represents averaged peak intensity of three biological samples (n=3) of these markers over the time course. Statistical analyses were performed by one-way ANOVA, followed by Tukey's HSD test compared to the 0d time (*P < 0.05, **P < 0.01, ***P < 0.001).
The abundance of organelle markers in seedlings (S1 and S2 developmental transition) and mature leaves (S3 transition) tissues differed. Comparing the expression of organelle-specific MRM markers (Hooper et al., 2017a) across all the genotypes, four plastid biomarkers were enriched in WT in both S1 and S2 transitions (Figure 3-7, and Figure 3-8). Biomarkers for mitochondria were diminished in both S1 and S2 transitions (Figure 3-10, and Figure 3-11). Although we hypothesized peroxisome biomarkers should significantly increase in abundance over the time course, only one biomarker protein viz. AT1G23310.1 (GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1), behaved as expected in both S1 and S2 transitions. The MRM results for S1 and S2 transitions show that most plastid biomarker proteins are increased in abundance with time. However, this pattern was not consistent for these biomarker proteins in the S3 transition
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(Figure 3-9, Figure 3-12, and Figure 3-15). Based on the WT Col-0 MRM ( Figure 3-7 to Figure 3-15) significantly changing biomarkers over the time course, were then selected for downstream analysis of autophagy mutants atg2, atg5, atg7, and atg9 along with WT Col-0 for studying each biological transitions (S1, S2, and S3).
Heat maps below (Figure 3-16, Figure 3-17, and Figure 3-18) show abundance patterns of organelle-specific biomarkers in the autophagy mutants’ atg2, atg5, atg7, atg9, and WT Col-0 during the three developmental transitions; S1; early seedlings development, S2; dark to light de-etiolation, and S3; dark-induced senescence transitions. The MRM results of S1 and S2 transitions show that most biomarker proteins are increased in abundance during the time of the transition in all the genotypes. However, this pattern was not consistent for these proteins in the S3 transition. In S1, early seedling development transition, both genotypic and time effect were significant for all the cytosolic ribosomes, mitochondria, plastids biomarkers, while in S2; dark to light de- etiolation transition showed both genotypic and time significant effects in all the mitochondria and plastid biomarkers. However, S3; dark-induced senescence did not show a significant effect for both time and genotypes across all the cytosolic markers.
These heatmaps are each accompanied by the results of a two-way analysis of variance results for the effects of genotypes (G), the effect of the time course (T), and their interacting effect (I). For all the biological transitions, although the genotypic effect was observed for most of the organelle biomarkers, most of these biomarkers were not significantly different in all the autophagy mutants (atg2, atg5, atg7, and atg9) compared to the WT Col-0. Therefore, this genotypic effect did not reflect the changes in organelle abundance of all autophagy mutant genotypes vs. Col-0. This was restricted to one or two autophagy mutants at some time-points and have not observed consistent patterns across all the biomarkers for same autophagy mutants were not observed. For more information, supplementary tables; Table S2 to Table S5 summarizes p values of the posthoc multiple comparison tests for the different biomarker proteins of autophagy mutants (atg2, atg5, atg7, and atg9) compared to WT Col-0 at respected time points (0d, 1d, 3d, and 5d) for all the transitions.
Interestingly, I observed that most of the plastid biomarker proteins were lower in abundance in autophagy mutants compared to WT in all three biological transitions. With respect to all the organelle protein biomarkers, only plastids biomarkers exhibit the genotype effect that extends across all the autophagy mutants (atg2, atg5, at7, and atg9) genotypes compared to WT. In other words, it was only the plastid biomarkers that were
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more abundant in WT compared to all the autophagy mutants in both S1, S2, and S3 transitions. The effect of autophagy mutants on plastid marker protein accumulation is further discussed in detail in this chapter section 3.4.4.
Figure 3-16: Organelles specific protein abundance profiling of WT and autophagy mutants in S1 transition.
Heatmap visualizing organelle biomarker protein abundances of autophagy mutants; atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four-time points (0, 1, 3, and 5 days) in S1 early seedling development transition. Heatmap color scale is based on the normalized and scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. Heatmap is combined with two-way ANOVA results examining independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I) effect. F-values that deviate from the null hypothesis at p < 0.05, are represented in pink color for the effect of individual G, T factors, and their interaction (I).
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Figure 3-17: Organelles specific protein abundance profiling of WT and autophagy mutants in S2 transition.
Heatmap visualizing organelle biomarker protein abundances of autophagy mutants; atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four-time points (0, 1, 3, and 5 days) in S2 de-etiolating dark to light transition. Heatmap color scale is based on the normalized and scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. Heatmap is combined with two-way ANOVA results examining independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I) effect. F-values that deviate from the null hypothesis at p < 0.05, are represented in pink color for the effect of individual G, T factors, and their interaction (I).
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Figure 3-18: Organelles specific protein abundance profiling of WT and autophagy mutants in S3 transition.
Heatmap visualizing organelle biomarker protein abundances of autophagy mutants; atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four-time points (0, 1, 3, and 5 days) in S3 dark-induced senescence transition. Heatmap color scale is based on the normalized and scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. Heatmap is combined with two-way ANOVA results examining independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I) effect. F-values that deviate from the null hypothesis at p < 0.05, are represented in pink color for the effect of individual G, T factors, and their interaction (I).
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There was variability in the abundance of cytosolic marker proteins during the developmental transitions, yet a number of these markers were subunits of the ribosome (Figure 3-16 to Figure 3-21). To further investigate whether autophagy is associated with ribosome-associated proteins during the S1, S2, and S3 developmental transitions, MRM was used to check the abundance level of more ribosomal proteins based on recently developed ribosome specific markers (Salih et al., 2020). During the ribosomal MRM data analysis, the normalization step was skipped, and only a scaling step was followed. This was to ensure that differences due to changes in total ribosome abundance between the samples and time points would be observed. However, this analysis showed that autophagy did not play a crucial role in most ribosomal protein abundances in autophagy mutants compared to WT Col-0, in all the developmental transitions.
Heat maps showed abundance patterns of ribosome specific biomarkers of the autophagy mutants’ atg2, atg5, atg7, atg9, and WT Col-0 during the S1; early seedlings development, S2; dark to light de-etiolation, and S3; dark-induced senescence transitions (Figure 3-19, Figure 3-20, and Figure 3-21). These heatmaps are accompanied by the results of a two-way analysis of variance results for the effects of genotypes (G), the effect of the time course (T), and their interacting effect (I). For S1 and S2 biological transitions, the genotypic effect was observed for most of the ribosomal MRM biomarkers. However except RPS21B (At3g53890.1), and RACK1B (At1g48630.1) biomarkers in S2 transition, none of the biomarkers were significantly different in all the autophagy mutants compared to the WT Col-0 in all three biological transitions. Therefore, the genotypic effect observed did not reflect the changes in ribosomal protein abundance of all autophagy mutant genotypes compared to WT Col-0. For more information, supplementary tables; Table S6 to Table S9 summarizes p values of the posthoc multiple comparison tests for the different ribosomal biomarker proteins of autophagy mutants (atg2, atg5, atg7, and atg9) compared with WT Col-0 at respected time points (0d, 1d, 3d, and 5d) for three biological transitions. Further, PCA was performed on the variance- stabilized counts to check for genotype and time-course effects and overall clustering of the ribosomal biomarker data. As can be seen in Figure 3-22, all the genotype and time course groups cluster together along with the first principal component, while the 0 day and 1 day samples cluster on the opposite side. This suggests that these groups are more similar to each other. This demonstrates that autophagy is not affecting ribosomal abundance under these developmental transitions. However, to have a better understanding of ribosomal stability in autophagy mutants, protein turnover analysis would need to be performed.
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Figure 3-19: Ribosome specific protein abundance profiling of WT and autophagy mutants in S1 transition
Heat-map representing the changes in ribosomal protein biomarker abundances of the autophagy mutants’ atg2, atg5, atg7, atg9, and WT Col-0 during the early seedlings development transitions of Arabidopsis. Rows (Proteins) are centered; no scaling is applied to rows. Targeted proteins are clustered using correlation distance and average linkage. The signal represents the mean result from two independent biological experiments. The color scale is based on the scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. The heatmap is combined with two-way ANOVA results examining the independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I). F-values that deviate from the null hypothesis at p < 0.05, are represented in pink.
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Figure 3-20: Ribosome specific protein abundance profiling of WT and autophagy mutants in S2 transition
Heat-map representing the changes in ribosomal biomarker protein abundances of the autophagy mutants’ atg2, atg5, atg7, atg9, and WT Col-0 during the dark to light transition of de-etiolating seedlings transitions of Arabidopsis. Rows (Proteins) are centered; no scaling is applied to rows. Targeted proteins are clustered using correlation distance and average linkage. The signal represents the mean result from two independent biological experiments. The color scale is based on the scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. The heatmap is combined with two-way ANOVA results examining the independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I). F-values that deviate from the null hypothesis at p < 0.05, are represented in pink.
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Figure 3-21: Ribosome specific protein abundance profiling of WT and autophagy mutants in S3 transition.
Heat-map representing the changes in ribosomal biomarker protein abundances of the autophagy mutant atg5 and WT Col-0 during the dark-induced senescence transitions of Arabidopsis. Rows (Proteins) are centered; no scaling is applied to rows. Targeted proteins are clustered using correlation distance and average linkage. The signal represents the mean result from two independent biological experiments. The color scale is based on the scaled peak area of the averaged peptides of the protein. The red color indicates that the protein was more abundant in the tissue sample, while the green color indicates that the protein was less abundant. The heatmap is combined with two-way ANOVA results examining the independent and interacting effects of autophagy genotype (G) and time course (T), and G x T interaction (I). F-values that deviate from the null hypothesis at p < 0.05, are represented in pink.
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Figure 3-22: PCA of ribosome protein abundance profiling of S1, S2, and S3 transitions.
PCAs were performed on the variance-stabilized counts to check for batch effects and overall clustering of the ribosomal MRM data. A. In S1 early seedling development transition all genotypes across the 0d, 1d, 3d, and 5d groups cluster together along with the first principal component. 0d and 1d, groups’ cluster together along with the second principal component, while the 3d and 5d samples cluster on the opposite side. B. In S2 dark to light de-etiolation transition, all the genotypes across the 0d, 1d, 3d, and 5d 100
groups cluster together along with the first principal component. 0d and 1d, groups’ cluster together along with the second principal component, while the 3d and 5d samples cluster on the opposite side. C. In S3 dark-induced senescence transition, all the genotypes across the 0d, 1d, 3d, and 5d groups cluster together along with the first principal component. 3d and 5d, groups’ cluster together along with the second principal component, while the 0d and 1d samples cluster on the opposite side.
3.4.3 15N labeling of WT to measure the protein turnover rate in dark- induced senescence of leaves (S3 Transition)
15N labelling was performed during the dark treatment to determine if nitrogen assimilation occurs under dark induced senescence conditions, hypothesizing if nitrogen assimilation occurs under dark conditions then this approach could use to determine of the role of autophagic pathway depended protein turnover rates (protein synthesis and degradation), under dark induce senescence transition which involves chloroplast de- greening, and mitochondrial and peroxisome changes. Preliminary 15N labelling study was perform with wild-type Col-0 plants. Labeled protein fractions (LPF) of Arabidopsis thaliana leaves (covered and uncovered) protein were calculated using three biological replicates. 15N enrichment was noticeable in uncovered light exposed leaves compared to the covered dark-induced senescence leaves. Except for one replicate (DR3) the covered samples had no common proteins labeled. The petiole of the DR3 sample was most likely unintentionally slightly exposed to light (Figure 3-23). This result suggests that light intensity profoundly affected the uptake and assimilation of ammonium and nitrate, and the effects were more apparent in the utilization of nitrate by Arabidopsis leaves. The assimilation of the two forms of nitrogen was greatly repressed at a low light intensity. Therefore, 15N labeling for studying dark-induced senescence of autophagy mutants could not proceed as a means of further analysis for S3 transition.
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Figure 3-23: Changes of labeled protein content in covered and uncovered leaves
DR: Dark Replicate 1,2,3, and LR: Light Replicate 1,2,3. The blue bars are the actual peptide data.
3.4.4 Effect of autophagy mutants on plastid marker protein accumulation
The plastid protein marker response evident in Figure 3-18 and 3-19 was the main significant observation linking autophagy with organelle protein abundance. Overall, autophagy mutants showed a delay in plastid protein accumulation compared to WT in early seedling development (S1), dark to light greening of seedlings (S2), and dark- induced senescence (S3) (Figure 3-24, Figure 3-25, and Figure 3-26). Importantly in etiolated to de-etiolating seedlings, all the plastid proteins in autophagy mutants accumulated less compared to WT at day one after exposure to the light. In the seedling development transition (S1), we observed two plastid MRM marker proteins viz. AT1G06680.1 (OXYGEN-EVOLVING ENHANCER PROTEIN 2 / PHOTOSYSTEM II SUBUNIT P) and AT1G09340.1 (CHLOROPLAST RNA BINDING/ HETEROGLYCAN-INTERACTING PROTEIN 1.3), which accumulate slowly in the autophagy mutants compared to WT. In the dark to light transition (S2), we observed AT1G06680.1 (OXYGEN-EVOLVING ENHANCER PROTEIN 2 / PHOTOSYSTEM II SUBUNIT P), AT1G09340.1 (CHLOROPLAST RNA BINDING/ HETEROGLYCAN-INTERACTING PROTEIN 1.3), AT1G15820.1 (LIGHT HARVESTING COMPLEX PHOTOSYSTEM II SUBUNIT 6), and AT1G74470.1
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(GERANYLGERANYL REDUCTASE) accumulation delay in autophagy mutant compared to the WT plants.
In dark-induced senescence (Figure 3-26), plastid biomarker abundances were significantly lower in covered leaves of autophagy mutants than WT Col-0. Interestingly, I also observed that atg2 exhibits the most suppression of plastid protein accumulation of all the autophagy mutants during the time course in S1, S2, and S3 transitions.
Figure 3-24: MRM based plastid marker protein abundance of S1 early seedling establishing transition.
The average peak area of the peptide of plastid biomarker proteins viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), abundances of autophagy mutants atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four time points (0, 1,3, and 5 days) in early seedling development. Means ± SE for n = 4 are shown. Different letters (a–c) denote statistically significant differences at the P < 0.05 level among treatment groups within a time-point.
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Figure 3-25: MRM based plastid marker protein abundance of S2 dark to light greening of seedlings; etiolated to de-etiolating; photomorphogenesis transition.
The Peak area of the peptide of plastid biomarker proteins viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), abundances of autophagy mutants atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four time points (0, 1,3, and 5 days) in de-etiolation transition. Means ± SE for n = 4 are shown. Different letters (a–c) denote statistically significant differences at the P < 0.05 level among treatment groups within a time-point.
Figure 3-26: MRM based plastid marker protein abundance of S3 dark-induced senescence transition.
The peak area of the peptide of plastid biomarker proteins viz. AT1G06680.1 (PHOTOSYSTEM II SUBUNIT P-1), AT1G09340.1 (CHLOROPLAST RNA BINDING), AT1G15820.1 (LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II), and AT1G74470.1 (GERANYLGERANYL DIPHOSPHATE REDUCTASE), abundances of autophagy mutants atg2, atg5, atg7, atg9, and WT Col-0 genotypes at four-time points (0, 1,3, and 5 days) in dark-induced senescence transition. Means ± SE for n = 4 are shown. Different letters (a–d) denote statistically significant differences at the P < 0.05 level among treatment groups within a time-point. UC: Uncovered, C: Covered leaves.
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3.5 Discussion
3.5.1 Selective degradation of mitochondria, plastids, and peroxisomes under different growth and development transitions
Dominant loss of function mutations caused by T-DNA insertions are useful in revealing the functions of genes, however, all such mutants may have minor secondary mutations that cause effects when phenotypes are assessed by new treatments or conditions. Since autophagy is complex and study of multiple independent lines per autophagy gene was not feasible in this study, here I used four independent autophagy T- DNA mutants (atg2, atg5, atg7, atg9) that are commonly studied and shown to each cause disruption of autophagy (Yoshimoto et al., 2009; Lai et al., 2011; Chen et al., 2015a; Zhuang et al., 2017). As only one line of each was used, I focused on common responses found in all the mutants to link the molecular phenotypes observed with autophagy disruption caused by independent gene mutations.
This MRM based preliminary screening of developmental transitions revealed that cellular organelles viz. plastids, mitochondria, and ribosomes have different changes in abundance during early seedling development; dark to light greening of seedlings; and light to dark senescence of leaves. In wild type, the protein biomarkers for mitochondria were diminished with time while plastid biomarkers were increased in the early seedling development transition (S1), and dark to light de-etiolation transition (S2). During the MRM data analysis, raw peak area data were normalized to reduce the variation among each sample run for each MRM target. Therefore the decrease of mitochondrial biomarkers could have occurred due to the growth of chloroplast size and number during seedling growth and development. This would likely change the ratio between chloroplasts and other organelles such as mitochondria.
Further, the S3 dark-induced senescence transition showed the change in plastid abundances during the presence and absence of light and how this related to nitrogen incorporation into these tissues. In contrast, the dark-induced senescence (S3) transition did not show a significant effect during the time course on cytosolic and peroxisome biomarkers. Given the fact that nitrogen assimilation enhances thylakoid density (Moriwaki et al., 2019) and soluble protein and RuBP carboxylase (Terashima and Evans,
1988), increase rate of photosynthetic CO2 uptake in the presence of light, the findings here imply that under dark-induced senescence both nitrogen assimilation and chloroplast biogenesis are limited in plants.
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Previous studies have shown that during the natural senescing process, rubisco- containing bodies (RCB) were found in the cytoplasm and the vacuole (Chiba et al., 2003), RCBs are transported to the vacuole for degradation (Ishida et al., 2008b; Wada et al., 2008). Therefore I suspect that the plastid specific biomarkers I have used in this study are not broad-spectrum enough to capture the dynamics of chloroplasts during senescence. The 15N labeling study in WT provided evidence that nitrogen incorporation during dark-induced senescence is very low compared to in light-exposed leaves, suggesting illumination is crucial for ammonium and nitrate uptake and assimilation in Arabidopsis. Therefore, 15N isotopic labeling can not be used to study dark-induced senescence of autophagy mutants.
During the seed germination process, plants have to rely on limited nutrient sources. A recent study revealed that primary and secondary metabolism is controlled via phytochromes during de-etiolation (Kozuka et al., 2020). That study suggested that during the initial steps of photomorphogenesis, photosynthesis does not play a major role (Kozuka et al., 2020). Since the seed germination is associated with the recycling of stored nutrient reserves (Rajjou et al., 2012), and crucial in providing energy for growth until the growing seedling becomes photoautotrophic (Baker et al., 2006), deficiency in autophagy might negatively influence early seedling establishment.
3.5.2 Contribution of autophagy to degradation of organelles in plant cells during growth and development
In plants, autophagy has been suggested to be involved in seed development and germination, photomorphogenesis, chloroplast maturation, stress protection, pathogen resistance, hormonal responses, and senescence under starvation conditions (Ghiglione et al., 2008; Ishida et al., 2008a; Izumi et al., 2015). To gain insights about the contribution of autophagy to degradation of mitochondria, chloroplasts, and peroxisomes in plant cells during growth and development, the three developmental transitions outlined were used to compare WT and autophagy mutants. To date, a few studies have tackled related questions by using hypersensitivity to carbon, nitrogen, and sulfur starvation in studies of plant autophagy mutants coupled with proteomics and metabolomics techniques (Guiboileau et al., 2013; Masclaux-Daubresse et al., 2014; Avin-Wittenberg et al., 2015; McLoughlin et al., 2018; Have et al., 2019). But the in-depth study has not to be conducted to understand whether autophagy or intra-organelle proteases are principally responsible for changes in organelle proteomes in plant growth and development. Studies
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have shown that proteases (e.g., Cysteine proteases) accumulate during nitrogen deficiency in autophagy mutants (Ali et al., 2018; Davies et al., 2018). T-DNA insertion autophagy mutants display higher sensitivity to various stresses, particularly nutrient starvation. The proteases accumulated in autophagy mutants may compensate for the weaknesses of mutants in nitrogen recycling and provide an alternative degradation pathway regarding autophagy-dependent degradation (Avin-Wittenberg et al., 2015). Therefore the results generated in Chapter 3 were important to determine if there was a significant role of autophagy in degrading organelles in plant cells during the three developmental transitions. Further, this sheds light on the application of targeted proteomics techniques to determine if autophagy can contribute to the change in the role and abundance of organelle-specific proteins during plant growth and development. In summary, the targeted proteomics approach revealed that the role of autophagy was limited to photomorphogenic growth and the developmental transition of the plastid to form chloroplasts.
Nutrient metabolism in photosynthetic tissues during seed germination and early seedling establishment is geared toward the supply of building blocks for organelle biogenesis and maintenance. Previous studies demonstrated that autophagy contributes to the maintenance of the free amino acid pool during nutrient starvation, providing an energy source for respiration (Izumi et al., 2013). During de-etiolation, seedlings face both light and heat stress. Also, their energy resources might be at a low level after five days growing in the dark environment and thus they might be facing nutrient deficiency. Therefore the dark to light transition is an ideal and crucial growth and developmental phase to study the role of autophagy in plants.
3.5.3 Selective degradation of specific proteins within organelles under different growth and development transitions
Although I expected peroxisome biomarkers should significantly increase over the time course in both S1 and S2 transitions (Gonzalez and Vodkin, 2007; Kim et al., 2014; Goto-Yamada et al., 2015), only AT1G23310.1 (GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1), behaved as expected among the selected peroxisomes biomarkers. Although the other two genes viz. ANN1 (AT1G35720) and GSTU20 (AT1G78370) suggested as biomarkers for peroxisomal abundance profiling (Hooper et al., 2017b), previous studies reveal that only GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 1 playing the main role in metabolic and biological processes;
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arginine biosynthesis, alanine, aspartate and glutamate metabolism, photorespiration, glycine, serine and threonine metabolism, 2-Oxocarboxylic acid metabolism, and glyoxylate and dicarboxylate metabolism pathways (Liepman and Olsen, 2003; Proost et al., 2009; Heyndrickx and Vandepoele, 2012). Hence we can suspect this is a potential biomarker for peroxisome in future early seedling development transition (S1), dark to light de-etiolation transition (S2), and light to dark senescence of leaves (S3) plant growth and development studies.
Interestingly, plastidial MRM biomarkers viz. PHOTOSYSTEM II SUBUNIT P-1 (AT1G06680.1), CHLOROPLAST RNA BINDING (AT1G09340.1), LIGHT HARVESTING COMPLEX B6 OF PHOTOSYSTEM II (AT1G15820.1), and GERANYLGERANYL DIPHOSPHATE REDUCTASE (AT1G74470.1) behaved similarly across all the biological transitions. These proteins are playing a key metabolic and biological function in photosynthesis and individual experimental studies suggest that they are localized in chloroplasts (Peltier et al., 2002; Ferro et al., 2003; Froehlich et al., 2003; Proost et al., 2009). Therefore these are ideal for determining plastidial protein abundance both dark to light de-etiolation transition (S2), and light to dark senescence of leaves (S3) plant growth and development studies.
Although extensive studies have conducted previously to uncover the role of autophagy during leaf senescence and seed germination (S1 and S3 transition of this study), the contribution of autophagy in the dark to light de-etiolation transition (S2) has not well studied at the organellular level. Therefore these preliminary plastid MRM biomarker results shed light on elucidating the role of autophagy during photomorphogenesis.
3.5.4 Ribosome degradation under different growth and development transitions
Most protein synthesis occurs in the cytosol on cytosolic ribosomes, while specific components are synthesized inside plastids and mitochondria. Therefore in this study, I have looked into ribosomal protein abundances in the three developmental transitions (S1, S2, and S3) in autophagy mutants and WT to reveal if there is a significant role of autophagy during plant growth and development during early seedling development, photomorphogenesis, and dark-induced senescence transitions. Interestingly, during photomorphogenesis RACK1B (At1g48630.1) was significantly decreased in atg2, atg5, and atg9, while it was significantly increased in atg7 at day 1 of illumination. A recent
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study has revealed that a core ribosomal protein of the eukaryotic small (40S) ribosomal subunit viz. receptor for activated C kinase 1 (RACK1) has a dynamic interaction with ATG5, and further study hypothesized that RACK1 might be a part of the biogenesis of autophagosomes at different stages (Erbil et al., 2016). However, a significant autophagic genotypic effect could not be observed across all the genotypes in these plant growth and developmental transitions. Nonetheless, it shows that in cytosolic ribosome protein abundances, the genotypic effect is associated with the time course effect in both seed germination and photomorphogenesis transitions. Although the overall dynamics of ribosomal abundances during early seedling development and photomorphogenesis implies that autophagic machinery is not playing a significant role in ribosome abundance and quality control, the time course effect suggests that cellular ribosomal abundance is coordinated with protein production required for plant growth and development.
3.6 References
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