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1 Pheophorbide a, a chlorophyll catabolite may regulate jasmonate 2 signalling during dark-induced senescence in Arabidopsis 3 4 Sylvain Aubrya1, Niklaus Fankhauserb, Serguei Ovinnikova, Krzysztof Zienkiewiczc,d, 5 Ivo Feussnerc,d,e, Stefan Hörtensteinera1 6 7 aInstitute of Plant and Microbial Biology, University of Zürich, Zollikerstrasse 107, 8 8008 Zürich, Switzerland 9 bDepartment for Clinical Research, Clinical Trials Unit, University of Bern, 10 Finkenhubelweg 11, 3012 Bern, Switzerland 11 cDepartment of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, 12 University of Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany 13 dGöttingen Metabolomics and Lipidomics Laboratory, Göttingen Center for Molecular 14 Biosciences (GZMB), University of Göttingen, Justus-von-Liebig-Weg 11, 37077 15 Göttingen, Germany 16 eDepartment of Plant Biochemistry, Göttingen Center for Molecular Biosciences 17 (GZMB), University of Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, 18 Germany 19 20 1corresponding authors 21 22 Summary: Transcriptome and metabolite profiles of key chlorophyll breakdown 23 mutants reveal complex interplay between speed of chlorophyll degradation and 24 jasmonic acid signalling 25 26 List of author contributions 27 S.A. and S.H. conceived the original research plans; S.A. and S.O. performed most 28 of the experiments; K.Z. and I.F. analyzed jasmonic acid metabolites; S.A. and N.F. 29 analyzed the data; S.A. and S.H. wrote the article with contribution of all the authors. 30 31 Financial sources 32 This work was supported by the European Union (Plant Fellow program), the Swiss 33 National Foundation/ERA-NET (grant No 163504) and the German Research 34 Foundation (DFG, grant No INST 186/822-1).

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35 36 Corresponding authors 37 Sylvain Aubry ([email protected]) and Stefan Hörtensteiner 38 ([email protected]) 39 40 41 ABSTRACT 42 Chlorophyll degradation is one of the most visible landmarks of leaf senescence. 43 During senescence, chlorophyll is degraded in the multi-step pheophorbide a 44 oxygenase (PAO)/phyllobilin pathway, which is tightly regulated at the transcriptional 45 level. This regulation allows a coordinated and efficient remobilisation of nitrogen 46 towards sink organs. Taking advantage of combined transcriptome and metabolite 47 analyses during dark-induced senescence of Arabidopsis thaliana mutants deficient 48 in key steps of the PAO/phyllobilin pathway, we show an unanticipated role for one of 49 the pathway intermediates, i.e. pheophorbide a. Both jasmonic acid-related gene 50 expression and jasmonic acid precursors specifically accumulated in pao1, deficient 51 in PAO. We propose that pheophorbide a, the last intact porphyrin intermediate of 52 chlorophyll degradation and unique pathway ‘bottleneck’, has been recruited as a 53 signalling molecule of the chloroplast metabolic status. Our work challenges the 54 assumption that chlorophyll breakdown is merely a senescence output, but propose 55 that the flux of pheophorbide a through the pathway acts in a feed-forward loop that 56 remodels the nuclear transcriptome and controls the pace of chlorophyll degradation 57 in senescing leaves. 58 59 60 INTRODUCTION 61 In higher plants, leaf senescence is a tightly regulated process that is responsible for 62 remobilisation of nutrients like nitrogen and phosphorus from source to sink organs 63 (Hörtensteiner and Feller, 2002). Degradation of photosynthetic proteins, 64 representing up to 70% of total leaf proteins, is co-regulated with chlorophyll (chl) 65 degradation, while carotenoids are largely retained (Kusaba et al., 2009). Chl is 66 degraded via a cascade of coordinated enzymes leading to cleavage and export of 67 chl catabolites to the vacuole in the form of non-toxic linear tetrapyrroles, termed 68 phyllobilins (Süssenbacher et al., 2014). Since all final phyllobilins are ultimately

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69 derived from the porphyrin ring-opening activity of PHEOPHORBIDE A OXYGENASE 70 (PAO), this pathway of chl breakdown is referred to as the “PAO/phyllobilin pathway” 71 (Hörtensteiner, 2006). 72 Two key chlorophyll catabolic genes (CCGs) that encode the chlorophyll catabolic 73 enzymes (CCEs) and precede the opening of the porphyrin ring of chl, i.e. the 74 magnesium dechelating enzyme NON YELLOWING (NYE) and PHEOPHYTIN 75 PHEOPHORBIDE HYDROLASE (PPH), hydrolyzing the phytol tail, are tightly co- 76 regulated with PAO at the transcriptional level during leaf senescence. In addition, all 77 three CCEs were shown to physically interact (Pružinská et al., 2007; Ren et al., 78 2007; Aubry et al., 2008; Sakuraba et al., 2012). This regulation may allow quick 79 metabolic channelling of potentially phototoxic chl catabolites. A model based on the 80 apparent coordinated expression of these genes and under the control of one or a 81 few main transcriptional regulator(s) could therefore be hypothesised. However, the 82 mechanism underlying this transcriptional coordination remains unclear. 83 Senescence is a complex process integrating hormonal and environmental signals 84 from very distinct pathways (Kim et al., 2018). Only considering CCGs, at least three 85 distinct hormonal signals and their respective signalling pathways have been shown 86 to interact via some of their components with CCG promoters (for a recent review, 87 see (Kuai et al., 2018))(Kuai et al., 2017)(Kuai et al., 2017). Jasmonic acid (JA), 88 ethylene (ET) and abscisic acid (ABA) signalling pathways together with some 89 components of the light signalling cascade have been shown to modulate CCG 90 expression directly (Kuai et al., 2018). 91 In particular, JA and its derivatives are key regulators of senescence (He et al., 2002) 92 and typically synthesised in response to insects and necrotrophic pathogens (Kim et 93 al., 2018; Wasternack and Feussner, 2018). Levels of JA increase during natural or 94 dark-induced senescence (Breeze et al., 2011) and ectopic methyl jasmonic acid 95 induces early senescence (Ueda and Kato, 1980). JA and associated oxylipin 96 signalling have pleiotropic effects on the cellular fate, for example changing 97 expression of defense genes (Hickman et al., 2017). Default JA signalling is 98 perceived via CORONATINE-INSENSITIVE 1 (COI1) that in turn degrades the 99 transcriptional repressors JA ZIM-domain proteins (JAZ) (Howe et al., 2018). For 100 example, JAZ7 blocks MYC2 transcription factor activity that act upstream of many 101 genes involved in dark-induced leaf senescence (Yu et al., 2016). MYC2/3/4 and 102 their downstream targets NAC019/055/072 directly interact with NYE1, NYE2 and

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103 NYC1 promoters (Zhu et al., 2015). In a very similar manner, NAC019 and MYC2 104 interact with each other to synergistically up-regulate NYE1 (Zhu et al., 2015). Other 105 transcription factors involved in ET signalling (EIN3, EEL, ORE1 and ERF17), ABA 106 signalling (NAC016, NAC046, NAP, ABF2/3/4, ABI5) were also reported as direct 107 interacting transcription factors of some cis-elements in CCG promoters (Kim et al., 108 2014; Sakuraba et al., 2014; Qiu et al., 2015; Sakuraba et al., 2016; Yin et al., 2016). 109 These multiple intertwined hormonal cues eventually lead to chlorosis, by way of 110 degradation of chl, as a visible landmark of dark-induced, aged-induced and also 111 (a)biotic stress-induced senescence. Interestingly, constitutive overexpression of 112 single CCGs in Arabidopsis thaliana (Arabidopsis) led in most cases to an 113 acceleration of chl breakdown after senescence induction (Sakuraba et al., 2012). 114 This suggests a feedback mechanism by which the chloroplast coordinates the rate 115 of chl degradation during leaf senescence. The extent to which the speed of chl 116 degradation itself could regulate the various hormonal cues and thereby inform cells 117 about the current status of their senescing chloroplasts remains to be shown. 118 Here, in an attempt to identify such a link and simultaneously shed more light onto 119 these complex regulatory networks, we used genome-wide transcriptome analysis of 120 CCG mutants during early dark-induced senescence. By combining these data with 121 metabolite profiling, we aim at understanding processes that regulate the dynamics of 122 the production of chl catabolites in the PAO/phyllobilin pathway and the extent to 123 which accumulation of pathway intermediates remodel nuclear gene expression, and 124 more precisely the JA response. 125 Based on our data, we propose a model where transient accumulation of the 126 intermediate pheophorbide (pheide) a acts as a sensor for the rate of chl 127 degradation, and thereby regulates the speed of leaf senescence tuned by JA 128 signalling. This model highlights a new function for the PAO/phyllobilin pathway of chl 129 breakdown, not only as an irreversible prerequisite to senescence-driven nitrogen 130 remobilization, but also as a sensing mechanism of the stress status of the 131 chloroplast. 132 133 134 RESULTS 135 Coordinated Variations of the Leaf Transcriptome During Dark-Induced 136 Senescence

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137 The extent of variations in gene expression during dark incubation of detached 138 leaves (DET) was assessed using RNAseq. Mature leaves number six (see Materials 139 and Methods) were sampled in triplicate at 0 and 2 days in the dark (dd). Using these 140 time points allowed us to profile early events of the senescence program before any 141 distinct visible phenotype (Fig. 1A). 142 In wild type (WT) leaf, a total of 21,403 genes were detected (genes with normalised 143 counts ≥ 1 in at least one of the samples), amongst these 6,124 (29% of detected) 144 genes were considered as being differentially expressed during DET (after applying 145 EBSeq test using a posterior probability of differential expression ≥ 0.95 and a 146 minimum fold change of two times). Of these, 3,389 genes were upregulated after 147 dark treatment (Table 1). In an analogous experiment on Arabidopsis leaf 148 senescence using microarrays, 2,153 genes were differentially expressed between 0 149 and 2 dd, of which 65% (1,353) were common to our dataset (Supplemental Fig S1) 150 (Van der Graaff et al., 2006). Another microarray-based analysis of the transcriptome 151 during natural leaf senescence in Arabidopsis showed perturbation in 6,370 genes, of 152 which 2,825 (44%) were common to our differentially expressed genes Supplemental 153 Fig S1) (Breeze et al., 2011). These results show the biological relevance of our data. 154 The differences observed being likely due to the biases associated with different 155 techniques used to induce senescence. A relatively high number of genes have been 156 shown to be similarly expressed when comparing different methods of senescence 157 induction, such as DET, dark incubation of attached leaves and natural senescence 158 (Van der Graaff et al., 2006). Analysis of the WT transcriptome signature using Gene 159 Ontology (GO) terms revealed that photosynthesis, starch metabolism, 160 glucosinolates, tetrapyrrole synthesis and redox terms were under-represented 161 during DET (Fig. 2A), while terms gathering genes involved in lipid, amino acid and 162 protein degradation but, more interestingly, also micro-RNA, retrotransposons and 163 the bZIP family of transcription factors were over-represented during senescence 164 (Fig. 2A). This is consistent with described major gene expression changes during 165 leaf senescence (Van der Graaff et al., 2006; Breeze et al., 2011). 166 167 Disruption of Specific CCGs Modifies Phyllobilin Accumulation that Lead to 168 Distinct Stay-Green Phenotypes 169 In order to determine the extent to which the disruption of the PAO/phyllobilin 170 pathway influences the global leaf senescence process, we analysed three mutants

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171 that are defective in this pathway: nye1-1, pph-1 and pao1, and analysed 172 senescence of detached leaves after dark incubation (Pružinská et al., 2003; Ren et 173 al., 2007; Schelbert et al., 2009). After 2 dd, chl was retained in these lines (Fig. 1A 174 and B). Noteworthy, pao1 contained slightly less chl before dark incubation (0 dd) in 175 comparison to WT (Fig. 1B). In addition to chl retention, pao1 accumulated pheide a 176 during dark incubation and exhibited a light-independent cell death (LICD) phenotype 177 (Pružinská et al., 2003; Hirashima et al., 2009), as deduced from an increase in 178 electrolyte leakage of pao1 leaf tissue in the dark (Fig. 1C). The molecular basis of 179 LICD in this line is unclear, but this phenotype is specific to pao1, and may to some 180 extent be linked to pheide a accumulation (Fig. 1D) (Hirashima et al., 2009). Absence 181 of PAO in pao1 led to a complete halt of the PAO/phyllobilin pathway with virtually no 182 phyllobillin accumulation (Fig. 1D). By contrast, nye1-1 and pph-1 accumulated the 183 major phyllobilins of Arabidopsis to about one third of the WT level after two days of 184 dark treatment but virtually no pheide a (Fig. 1D) (Christ et al., 2013). 185 Stopping the PAO/phyllobilin pathway artificially at various levels seems to imply 186 largely distinct phenotypic modifications. On the top of its relatively well described 187 implication on nitrogen remobilisation (mostly due to photosystem degradation), the 188 control of chl catabolite homeostasis within the degradation pathway is a potentially 189 overlooked signal that may inform the cell about the status of chloroplast integrity or 190 metabolism. 191 192 Transcriptome Analysis of CCG Mutants Gives Insight Into Molecular Bases of 193 Phenotypic Variations Observed in the Dark 194 We then assessed alterations in the leaf transcriptome during DET in all three 195 mutants. In nye1-1 and pph-1, 6,227 and 6,764 genes were differentially expressed 196 between 0 and 2 dd, respectively, numbers that were comparable to the changes 197 observed in WT (Table 1). By contrast, about two times more genes (11,408) were 198 differentially expressed in pao1 during DET (Table 1). In order to detect variations in 199 gene expression that were specific to mutations of one or several of the CCGs, 200 genes differentially expressed during DET in every line were compared (Fig. 2B, C). 201 2,692 and 3,524 genes, respectively, were specifically down- and up-regulated in 202 pao1, while for pph-1 (76 and 139 genes, respectively) and nye1-1 (265 and 326 203 genes, respectively) these numbers were much smaller (Fig. 2C). A core set of 3,203 204 genes (1,912 up- and 1,291 down-regulated) showed similar patterns of expression

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205 in all four lines. The most enriched genes among these were genes involved in 206 catabolic processes, senescence, aging and autophagy, while genes involved in 207 chloroplast and various photosynthesis-related processes were the most repressed 208 ones (Supplemental Dataset 2). Analysis of GO term enrichment showed that altered 209 genes showing a very similar pattern in all four lines included genes involved in 210 photosynthesis, starch metabolism, glucosinolate synthesis (down-regulated) as well 211 as protein and amino acid degradation (up-regulated) (Fig. 2A). Collectively, this 212 indicated that mutations in any of the three CCGs, despite clear phenotypic 213 differences in these lines, had little effect on general background senescence 214 processes. 215 Most of the genes whose expression specifically changed in pao1 while remaining 216 unchanged in all other lines belonged to GO terms related to ethylene and WRKY 217 and PHOR1 transcriptional regulators (Fig. 2A). Among GO terms that were 218 significantly enriched in pao1, categories of genes involved in various stresses were 219 the most enriched ones: these include response to stress, stimulus, chitin, 220 carbohydrate, chemical stimuli as well as genes involved in post-transcriptional 221 processes (Supplemental Dataset 2). 222 Thirty-six of the 50 most highly expressed genes after 2 dd were different between 223 pao1 and WT (Supplemental Dataset 3), among them, PLEIOTROPIC DRUG 224 RESISTANCE 12 (PDR12/ABCG40), involved in ABA transport (Kang et al., 225 2010)(Kang et al., 2010)(Kang et al., 2010), LIPOXYGENASE 1 (LOX2) involved in 226 JA synthesis (Wasternack and Feussner, 2018), as well as NYE1. Taken together 227 our data suggest major remodelling of gene expression in pao1 leaves upon dark 228 incubation, while absence of NYE1 or PPH only mildly affect the senescence leaf 229 transcriptome, at least at an early stage of senescence. 230 231 The PAO/Phyllobilin Pathway Is Mainly Regulated at the Transcriptional Level 232 Most of the core CCGs like PAO, PPH and NYE1, as well as genes encoding some 233 catabolite-modifying enzymes, i.e. METHYLESTERASE 16 (MES16) and 234 CYTOCHROME P450 MONOOXYGENASE 89A9 (CYP89A9) were transcriptionally 235 up-regulated during DET in WT (Supplemental Dataset 1 and Fig. 3) (Sakuraba et al., 236 2012). In all four lines studied, genes encoding enzymes involved in the oxidative half 237 of the chl cycle, namely CHLOROPHYLL A OXYGENASE (CAO) and 238 CHLOROPHYLL SYNTHASE (CHLG), were down-regulated, whereas genes

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239 involved in chl b to chl a conversion (NYC1 and NOL) were up-regulated. This is 240 consistent with the assumption that conversion of chl b to chl a is a prerequisite for 241 chl degradation (Sakuraba et al., 2010). Noteworthy, expression of RCCR was 242 repressed during DET and, thus, not correlated with the expression of PAO or of any 243 of its proposed interacting partners (Fig. 3) (Sakuraba et al., 2012). Except for a slight 244 decrease in HCAR, nye1-1 and pph-1 did not exhibit significant differences in CCG 245 expression compared to WT. By contrast, major changes were observed in pao1 with 246 strong overexpression of NYE1 and NYC1 (but not NOL) and down-regulation of 247 CAO and HCAR, suggesting a “feed-forward” regulation of the catabolic pathway. 248 249 The PAO/Phyllobilin Pathway Is Controlled by Multiple Intertwined Signalling 250 Pathways 251 In order to evaluate the impact of CCG mutations on upstream regulators of the 252 pathway, we extracted expression data for signalling pathways involving JA, ET, ABA 253 and light signalling in all four lines as described (Kuai et al., 2018) (Fig. 4). Out of the 254 41 genes represented here that are key genes involved in these hormonal pathways, 255 only JAZ10 was significantly downregulated in pao1 as compared to WT (none in 256 pph-1 or nye1-1, Supplemental Dataset 5), suggesting minor function of the hormonal 257 cues before dark incubation. Expression of genes involved in ET and ABA signalling 258 was mostly up-regulated in all lines during dark-induced senescence. The most 259 striking difference between pao1 and all other three lines was the pattern of 260 expression of genes involved in JA signalling: COI1 expression was increased 261 significantly during dark treatment in WT, pph-1 and nye1-1, but not in pao1, while 262 nine of the twelve jasmonate-ZIM domain (JAZ) proteins showed an inverse pattern 263 of expression. Intriguingly, among the very few genes differentially expressed after 264 dark treatment in both nye1-1 and pph-1, a subset of JAZ genes, namely JAZ1, 265 JAZ5, JAZ7, JAZ8 and JAZ10, were significantly down-regulated compared to WT 266 (Supplemental Dataset 1). 267 It also appears that expression of transcription factors that are repressed by JAZ 268 proteins like MYC2/3 and downstream factors like NAC019/055 and 072 were also 269 up-regulated exclusively in pao1 at 2 dd (Fig. 4). 270 271 Accumulation of Pheide a in pao1 Modifies JA-Related Signalling

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272 Having noticed strong variations of JA-related gene expression in pao1 after dark 273 incubation, we analysed whether JA synthesis and levels of JA metabolites were also 274 modified in this line. To this end, JA precursors (12-OPDA, dn-OPDA, OPC6, and 275 OPC4) as well as JA and some of its derivatives (JA-Val, JA-Ile, JA-Leu, 12OH-JA-

276 Ile, 12COOH-JA-Ile, 12O-Glc-JA and 12HSO4-JA) were quantified in both WT and 277 pao1 (Fig. 5 and Supplemental Dataset 4). JA levels were significantly increased in 278 WT during dark incubation, but in pao1, JA accumulated with an order of magnitude 279 higher, i.e. up to 2 nmol g-1 fresh weight (Fig. 5 and Supplemental Dataset 4). Levels 280 of endogenous JA after dark treatment are known to increase in WT (Seltmann et al., 281 2010a) and are regulated under strong circadian control (Goodspeed et al., 2012). 282 However, the dark-induced increase of JA in WT not necessarily triggers JA- 283 signalling pathways (Seltmann et al., 2010b). In pao1, not only JA levels were 284 dramatically increased, but also downstream metabolites, i.e. JA-Val, JA-Leu, 12OH- 285 JA-Ile and the active phytohormone JA-Ile (Fig. 5 and Supplemental Dataset 4). 286 Genes involved JA biosynthesis (LOX2, AOC1, AOC2, OPR3) and degradation 287 (CYP94B1, CYP94B3) were also strongly upregulated in pao1. Interestingly, 288 expression of JMT and JAR1 were unchanged in both lines. 289 Taken together, these data show a complex rewiring of the JA signalling pathway and 290 indicate a link between the pao1 phenotype and JA responses. Next, we tried to 291 decipher the exact extend of this feedback using patterns of gene co-expression. 292 293 Co-Expression Analysis Reveals Structure of Regulatory Networks of the 294 PAO/Phyllobilin Pathway 295 To further characterise a possible link between the PAO/phyllobilin pathway and JA 296 signalling, we computed the genome-wide expression data for all four lines studied 297 and tried to decipher co-expression patterns underpinning relevant gene networks. 298 The basic assumption being that genes that show a similar pattern of expression 299 during DET and/or in various genetic backgrounds could be involved in a similar 300 process and most probably share similar regulating pathways. We used Weighted 301 Genome Co-expression Network Analysis (WGCNA) (Zhao et al., 2010) to perform 302 comparative analysis of gene co-expressed modules among darkness treatment in all 303 four lines. Genes were clustered in 16 co-expression modules, each harbouring 304 genes that generally showed a similar pattern of expression across genetic 305 background and treatment (Fig. 6A, Supplemental Fig. S2). Three modules (blue,

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306 pink and yellow) were highly correlated with the darkness treatment. The pink and 307 yellow modules contained genes that showed consistent changes expression during 308 dark incubation in all four lines, but not the blue motif that did not correlate to pao1 309 after 2 dd (Fig. 6B). Modules were subsequently characterised using GO term 310 enrichment (Fig. 6B & Supplemental Dataset 6). All three motives were enriched in 311 terms related to mRNA catabolic process, fatty acid catabolism, senescence and 312 autophagy (Supplemental Dataset 6). Red, black and green modules that mostly 313 correlated with pao1 after dark incubation were enriched in terms representing 314 various responses to stress as well as hormonal response (namely ET, JA and ABA 315 responses, Supplemental Dataset 6). Interestingly, PPH is the hub gene. i.e. the 316 most highly connected gene in this module (Langfelder and Horvath, 2008), of the 317 blue module that contains most CCGs (PAO, CYP89A9, PPH, NYE2) (Fig. 6C). This 318 module may gather conserved elements of the response to darkness. Finally, 319 networks of genes neighbouring expression for CCGs and known transcriptional 320 regulators of the PAO/phyllobilin pathway (as in Fig. 4) were extracted and their 321 respective position in the networks visualised (Fig. 6C, for the sake of clarity, only the 322 three most correlated genes are shown here). Surprisingly, not all CCGs were co- 323 expressed in a unique cluster, and not necessarily with the predicted pathway, they 324 were shown to interact with (Fig. 6C). For example, MYC2/3/4 and JAZ genes were 325 scattered across various modules, whereas genes involved in ethylene signalling 326 (EIN2, EIN3 and ERF17) were mostly grouped within the blue module. As shown 327 before (Hickman et al., 2017), differences in the networks of JA-related genes may 328 be explained by the interplay between several factors that are linked to the treatment 329 and genotypes used here and that are thus represented in these data, i.e. dark 330 treatment, pheide a and JA. 331 Validation of the clustering approach can be seen, for example, by the fact that 332 NAC019, NAC055 and NAC072, clustering closely together, have already been 333 shown to be homologs (Zheng et al., 2012). Similarly, ORE1 and ANAC046 are 334 closely related, but act in distinct clusters, suggesting a distinct regulation mechanism 335 as shown recently (Park et al., 2018). The WGCNA approach can also be a fruitful 336 approach to identify new candidates in the PAO/phyllobilin pathway, like for example 337 phytanoyl CoA 2-hydrolase (phyH) that was suggested to be involved in phytol chain 338 degradation (Araújo et al., 2011) and clustered within the yellow module with MES16 339 and NYC1. Taken together, the co-expression data suggest that the PAO/phyllobilin

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340 pathway is regulated by multiple layers of transcriptional factors. This approach may 341 help deciphering multiple gene networks involved in the regulation of chl degradation 342 that are tightly associated with developmental cues, nitrogen levels, and biotic and 343 abiotic stresses. Further work is necessary to confirm the relative influence of each of 344 these clusters. 345 346 347 DISCUSSION 348 We have shown that the PAO/phyllobilin pathway is mostly regulated at the 349 transcriptional level during dark-induced senescence. A tight control of the 350 expression of genes involved in this pathway is necessary to prevent possible 351 oxidative damage due to a release of toxic tetrapyrrole breakdown intermediates. By 352 genetically modulating the homeostasis of chl catabolites, we unravelled a retrograde 353 signalling function for the PAO/phyllobilin pathway that uses JA-signalling to 354 coordinate chloroplasts and the nucleus during dark-induced senescence. 355 356 Pheide a Is a Key Signalling Molecule of Chloroplast Function 357 The pao1 mutant has originally been identified in a screen for lines that show 358 abnormal response to pathogens by accelerated severe cell death (Greenberg and 359 Ausubel, 1993). The basis of this light-dependent cell death phenotype is relatively 360 well understood (Yang et al., 2004; Pružinská et al., 2005). Pheide a phototoxicity is 361 even observable in mammalian systems (Jonker et al., 2002). However, another 362 peculiar feature of pao1 is a light-independent cell death phenotype, whose 363 underlying molecular basis is still unclear (Hirashima et al., 2009) (Fig. 1C). Two 364 hypotheses were proposed to explain cell death caused by pheide a accumulation in 365 the dark: it may act directly on chloroplast membrane integrity (via lipid peroxidation 366 or increased oxidative stress levels) or it may itself be a signalling molecule 367 regulating cell death. The acd2-2 mutant that is deficient in the next committed step 368 of the PAO/phyllobilin pathway, i.e. red chlorophyll catabolite reductase (RCCR) 369 accumulates red chlorophyll catabolite (RCC), a linear tetrapyrrole. Surprisingly, 370 although RCC is phototoxic like pheide a, acd2-2 is exclusively affected in a light- 371 dependent manner (Supplemental Fig. S3) (Greenberg et al., 1994; Pružinská et al., 372 2007). The main difference is that pheide a, unlike RCC, is likely trapped within the 373 chloroplast. Indeed, miss-targeting of the cytosolic phyllobilin-modifying enzyme

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374 MES16 into the chloroplast in a pao1 background revealed that in vivo pheide a is 375 not a substrate for MES16. It can, therefore, be concluded that pheide a is unlikely to 376 be released from the chloroplast (Christ et al., 2012), in contrast to RCC, which has 377 been shown to (partially) localize to the vacuole (Pružinská et al., 2007). 378 Independent of the exact molecular basis underlying light-independent cell death in 379 pao1, pheide a appears to have two specific properties: it is a metabolic “bottleneck” 380 of degradation, i.e. once formed, chl molecules must be irreversibly degraded further, 381 and it exhibits certain light-independent bioactive properties that act on chloroplast 382 homeostasis. These two features render pheide a a very good candidate compound 383 for sensing the rate at which chl is degraded, not only in the context of 384 (natural/induced) senescence, but also during the pathogen-induced hypersensitive 385 response (Mur et al., 2010). Sensing the rate at which chl is degraded is essential to 386 coordinate various senescence processes such as nitrogen remobilisation 387 (Hörtensteiner and Feller, 2002). Taking advantage of our large dataset, we propose 388 a model of how pheide a-dependent signalling possibly works. 389 390 Pheide a Metabolism Underpins a Specific Jasmonic Acid Response 391 Absence of PAO during dark incubation and the concomitant accumulation of pheide 392 a seem to be characterised by enhanced gene expression of most of the genes from 393 JA synthesis and signalling pathways, as well as by an increase in JA and many of its 394 derivatives. One common feature among the CCG mutant lines studied here is the 395 variation of levels/flux of pheide a: lower amounts in nye1-1/pph-1 (formation blocked 396 by up-stream mutations) vs. higher amounts (further degradation blocked) in pao1. 397 We propose a model, in which the quantity of pheide a that accumulates in/flows 398 through the PAO/phyllobilin pathway at a defined time may act as a signal that 399 triggers a specific JA response (Fig. 7). 400 Noteworthy, JA levels increase during dark-induced senescence in WT plants, but 401 this is not necessarily followed by a coordinated JA response (Seltmann et al., 402 2010b). Prolonged darkness treatment is thought to induce degradation of 403 chloroplast membranes and leads to an increase in lipid β-oxidation (Seltmann et al., 404 2010a). Breakdown of membrane lipids leads to a considerable increase in free α- 405 linolenic acid, a precursor of JA. However, in pao1, even if the extent remains 406 unknown to which dark-dependent pheide a accumulation may damage chloroplast 407 membranes, we could observe a massive increase of JA, way above the levels of

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408 WT, and a change in most genes associated with the JA response. All these 409 elements are strongly indicative of a fully coordinated and transduced JA response. 410 This response is partially similar to JA responses observed during defense processes 411 against insects, necrotrophic pathogens or ozone stress (Howe et al., 2018). It is 412 important to note, however, that increase in LOX expression resulting in increased 413 LOX activity might in itself be sufficient to increase lipid peroxidation and changes in 414 α-linolenic acid availability and modulate oxidative stress that in turn would 415 deteriorate chloroplast homeostasis (Wasternack, 2014; Mata-Perez et al., 2015; 416 Wasternack and Feussner, 2018). 417 The pheide a-dependent JA response could also be differentiated across the CCG 418 mutants studied here depending on their level of impairment in the pathway. Indeed, 419 expression of several JAZ genes was reversed in pao1 as compared to nye1-1/pph-1 420 (Fig. 4). Simultaneous increase in JAZ expression and JA accumulation might be 421 indicative of a heavy transduction load of the pathway (i.e. JAZ degradation by 422 SCFCOI1) making new synthesis of JAZ transcriptional inhibitors necessary. 423 The co-expression patterns of the JA signalling networks confirm that some of the JA 424 signalling elements were co-expressed with various CCGs, but also illustrate the 425 underlying complexity of the JA response (Fig. 6) (Hickman et al., 2017). For 426 example, not all MYC2 target genes are triggered in the same manner after pheide a 427 accumulation: the defensin gene PDF1.2 (At5g44420), a known marker of ET and JA 428 (Lorrain et al., 2003), or VSP1 (At5g24780), induced by wounding and JA, were not 429 differentially expressed in any of the CCG mutants, whereas PR4 (At3g04720), a 430 pathogenesis-related gene, was significantly overexpressed during dark incubation in 431 pao1 exclusively. Further studies deciphering the various interacting sub-networks 432 involved in the JA response upon developmental and various pathogenic cues will be 433 needed to possibly explain these discrepancies. 434 Several independent pieces of evidence point towards an effect of chl degradation on 435 JA signalling. For example, NYE mutants are less sensitive to pathogens and have a 436 reduced JA response compared to WT (Mecey et al., 2011). Thus, preventing chl 437 catabolites to be metabolised by the pathway by mutating NYE can apparently have 438 some protective effect. Interestingly, pxa1, a mutant impaired in a peroxisomal ABC 439 transporter essential for fatty acid degradation, accumulates α-linolenic acid and 440 pheide a during extended darkness (Kunz et al., 2009; Nyathi et al., 2010). This

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441 further supports the idea of an intricate link between chloroplast membrane integrity, 442 levels of pheide a and JA signalling. 443 444 Pheide a Signalling: Another Porphyrin-Based Retrograde Signal? 445 Tetrapyrrole intermediates, like Mg-protoprophyrin IX or heme have been long 446 suggested to be involved in plastid-to-nucleus retrograde signalling (Chi et al., 2013). 447 Coordination of chloroplast function with nuclear genome expression is equally 448 important during early developmental stages as during senescence. Interestingly, 449 hy1-101 (also referred to as gun2), a mutant deficient in HEME OXYGENASE 1 450 (HO1) that catalyses heme degradation into biliverdin IX as a key step for 451 phytochrome chromophore biosynthesis, constitutively accumulates high amounts of 452 JA and high levels of JA-responsive genes (Zhai et al., 2007). Deficiency in HO1 453 leads to the accumulation in the chloroplast of protoporphyrin IX, a circular porphyrin 454 with a structure similar to pheide a. It remains to be shown, to which extent this 455 phenotype is similar to the one observed in pao1 and whether porphyrin-induced JA 456 responses could be effectively coordinated signalling mechanisms, by which the 457 status of the chloroplast can be further transduced to the nucleus during both 458 synthesis and degradation of chl (Lin et al., 2016). 459 460 461 CONCLUSION 462 Taken together, our data show that the homeostasis of chl derivatives in the 463 PAO/phyllobilin pathway impacts leaf metabolism; specifically, the rate of 464 accumulation of pheide a triggers JA-related responses that, to a certain extent, 465 mimic pathogen responses. The JA-induced transcription factor MYC2 is involved in 466 PAO/phyllobilin pathway activation by directly binding to the promoter of various 467 CCG, like PAO, NYC1 and NYE1 (Zhu et al., 2015; Kuai et al., 2018). Here, we show 468 a positive feedback loop mediated by pheide a, that in turn activates JA-responsive 469 genes. While JA signalling is central to senescence regulation, this suggests an 470 additional signalling function of the PAO/phyllobilin pathway besides default 471 porphyrin detoxification. 472 Pheide a has likely been recruited during evolution as a signalling molecule of the 473 chloroplast metabolic status, due to its particular position within the chl degradation 474 pathway and because of its intricate chloroplast toxicity. Pheide a signalling may act

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475 via accumulation of JA and its bioactive derivatives that in turn induce JA-dependent 476 responses. However, the exact molecular mechanism, in particular the nature of the 477 retrograde signal(s) that links chloroplast pheide a-sensing to nuclear variation in 478 gene expression remains to be identified. To the best of our knowledge, this report is 479 the first to postulate retrograde signalling during leaf senescence. We show how 480 critical the control of such signals is during late leaf development stages. This 481 proposed mechanism allows a chloroplast-controlled remodelling of the nuclear 482 transcriptome and aims at an efficient coordination of the cellular fate during 483 senescence. 484 485 486 MATERIALS AND METHODS 487 Plant Material 488 WT and CCG mutant lines, i.e. the T-DNA lines pao1 (Pružinská et al., 2005) and 489 pph-1 (Schelbert et al., 2009) and the EMS line nye1-1 (Ren et al., 2007), were 490 grown in short day condition (8 h light/16 h dark, 23°C, 65% humidity) for eight 491 weeks. At least four leaves n°8 for each triplicates were harvested and frozen in

492 liquid nitrogen at 0 days in the dark (dd) and after 2 days incubation on H20-soaked 493 filter paper in complete darkness at 23°C. 494 495 RNA Isolation and Sequencing 496 RNA was isolated using RNAeasy minikit (Qiagen) together with on-column DNAse 497 treatment. Quality was assessed using Bioanalyzer RNA nanochip (Agilent). Three 498 replicates samples for each condition were multiplexed randomly on two lanes (12 499 samples per lane) of HiSeq 2500 (Illumina). 500 501 Read Processing and Gene Expression Analysis 502 Single-end 100 bp reads were subjected to adapter trimming and removal of low 503 quality bases in leading, trailing and sliding window (4 bp) mode with Trimmomatic 504 v0.35 (Bolger et al., 2014). Reads shorter than 40 bp after trimming were discarded. 505 Remaining reads were aligned to the protein-coding transcripts from the ENSEMBL 506 release of the TAIR10 Arabidopsis thaliana transcriptome (Swarbreck et al., 2008) 507 using Bowtie v1.0.1 (Langmead, 2010). Expression of genes and transcripts was 508 quantified using RSEM v1.2.11 taking into account strand-specific information (Li and

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509 Dewey, 2011). Differential expression was estimated using EBSeq by estimating the 510 posterior probability of genes to be differentially expressed across all conditions 511 (Leng et al., 2013). Coverage data were visualized using IGV viewer 2.3.34 512 (Thorvaldsdottir et al., 2013) using RSEM-generated .bam files (see Supplemental 513 Data). Gene ontology enrichment was performed using a corrected Benjamini- 514 Hochberg enrichment score implemented in Pageman (Usadel et al., 2006). 515 516 Co-Expression Network Analysis 517 WGCNA was used to identify modules gathering genes showing similar pattern of 518 expression across all conditions (Langfelder and Horvath, 2008). Genes below 50 519 mean read count were excluded, leaving 14,691 genes in the analysis. An unsigned 520 network was constructed from a signed topological overlap matrix and module 521 detection was performed using the default deepSplit setting of 2. In order to visualize 522 the direct subset of genes co-regulated with CCG and selected regulatory gene 523 candidates, subnetworks were generated and visualized using VisANT 5.51 (Hu et 524 al., 2007). In order to evaluate the extent to which expression of genes involved in 525 the regulation of the PAO/phyllobilin pathway (all present in Fig. 4) were linked to 526 CCE genes, subnetworks containing either of these genes (CCGs and regulators) 527 were extracted from the WGCNA networks and the three most connected genes for 528 each gene were displayed (Fig. 6C). Larger nodes show the input genes and smaller 529 nodes the top three connected genes for each input gene. Edges represent 530 connection between the genes and node colors represent the modules in which the 531 genes clustered. 532 533 Chlorophyll Extraction 534 Chl was extracted from liquid-nitrogen homogenised tissue using extraction buffer 535 (90% cold acetone and 10% 0. 2 M Tris-HCl, pH 8) (Guyer et al., 2014). Chl content

536 was determined by photospectrometry at A649 and A665. Chl concentrations were 537 calculated as published (Strain et al., 1971). 538 539 Chlorophyll Catabolites Profiling 540 Metabolite profiling was performed by liquid chromatography (LC)-tandem mass 541 spectrometry (MS) (LC-MS/MS) according to a published protocol (Christ et al., 542 2016). Briefly, leaf samples from 5 replicates were harvested, frozen and

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543 homogenized in liquid nitrogen. Metabolites were extracted in five volumes of ice-cold 544 extraction buffer [80% methanol, 20% water, 0.1% formic acid (v/v/v)] and centrifuged 545 (5 min at 14,000 rpm, 4°C). Supernatants were then analyzed by LC-MS/MS. 546 Samples were run on an Ultimate 3000 Rapid Separation LC system (Thermo Fisher 547 Scientific) coupled to a Bruker Compact ESI-Q-TOF (Bruker Daltonics). The system 548 consisted of a 150 mm C18 column (ACQUITY UPLC BEH, 1.7 μm; Waters Corp., 549 Milford, MA, USA). In order to efficiently separate phyllobilins, the following gradient 550 of solvent B [acetonitrile with 0.1% (v/v) formic acid] in solvent A [water with 0.1% 551 (v/v) formic acid] was run at a flow rate of 0.3 mL min−1: 5% B for 0.5 min, 5% B to 552 100% B in 11.5 min, 100% B for 4 min, 100% B to 5% B in 1 min and 5% B for 1 min. 553 Pheide a and phyllobilins were quantified from extracted ion chromatograms as 554 relative peak areas using QuantAnalysis (Bruker Daltonics). 555 556 Determination of Phytohormones 557 Extraction was performed as previously described for lipids (Matyash et al., 2008) 558 with some modifications. Five replicates were used for each condition and each time 559 point. Plant material (100 mg) was extracted with 0.75 mL of methanol containing 10 560 ng D5-JA (C/D/N Isotopes Inc., Pointe-Claire, Canada), 30 ng D5-oPDA, 10 ng D4- 561 JA-Leu (both kindly provided by Dr. Otto Miersch, Halle, Germany) as internal 562 standards. After vortexing, 2.5 mL of methyl-tert-butyl ether (MTBE) were added and 563 the extract was shaken for 1 h at room temperature. For phase separation, 0.6 mL

564 H2O were added. The mixture was incubated for 10 min at room temperature and 565 centrifuged at 450 g for 15 min. The upper phase was collected and the lower phase 566 re-extracted with 0.7 mL methanol/water (3:2.5, v/v) and 1.3 mL MTBE as described 567 above. The combined upper phases were dried under streaming nitrogen and re-

568 suspended in 100 μL of acetonitrile/water (1:4, v/v) containing 0.3 mM NH4COOH 569 (adjusted to pH 3.5 with formic acid). 570 Reversed phase separation of constituents was achieved by LC using an ACQUITY 571 UPLC system (Waters) equipped with an ACQUITY UPLC HSS T3 column (100 mm 572 x 1 mm, 1.8 µm; Waters). Aliquots of 10 µL were injected. Elution was adapted from 573 a published procedure (Balcke et al., 2012). Solvent A and B were water and

574 acetonitrile/water (9:1, v/v), respectively, both containing 0.3 mM NH4COOH 575 (adjusted to pH 3.5 with formic acid). The flow rate was 0.16 mL min-1 and the 576 separation temperature held at 40°C. Elution was performed with two different binary

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577 gradients. Elution profile 1 was as follows: 10% B for 0.5 min, to 40% B in 1.5 min, 578 40% B for 2 min, to 95% B in 1 min, 95% B for 2.5 min; elution profile 2: 10% B for 579 0.5 min, to 95% B in 5 min, 95% B for 2.5 min. In both elution profiles, the column 580 was re-equilibrated in 10% B in 3 min. 581 Nano-electrospray ionization (nanoESI) analysis was achieved using a chip ion 582 source (TriVersa Nanomate; Advion BioSciences, Ithaca, NY, USA). For stable 583 nanoESI, 70 µL min-1 of 2-propanol/acetonitrile/water (7:2:1, v/v/v) containing 0.3 mM

584 NH4COOH (adjusted to pH 3.5 with formic acid) delivered by a Pharmacia 2248 585 HPLC pump (GE Healthcare, Munich, Germany) were added just after the column via 586 a mixing tee valve. By using another post column splitter, 502 nL min-1 of the eluent 587 were directed to the nanoESI chip with 5 µm internal diameter nozzles. Jasmonates 588 were ionized in negative mode at -1.7 kV (after UPLC separation with elution profile 589 1) and in positive mode at 1.3 kV (after UPLC separation with elution profile 2), 590 respectively, and determined in scheduled multiple reaction monitoring mode with an 591 AB Sciex 4000 QTRAP tandem mass spectrometer (AB Sciex, Framingham, MA, 592 USA). Mass transitions were as previously described (Iven et al., 2012), with some 593 modifications as follows: 214/62 [declustering potential (DP) 35 V, entrance potential 594 (EP) 8.5 V, collision energy (CE) 24 V] for D5-JA, 209/59 (DP 30 V, EP 4.5 V, CE 24 595 V) for JA, 237/165 (DP 45 V, EP 6 V, CE 24 V) for OPC4, 265/221 (DP 50 V, EP 6 V,

596 CE 24 V) for OPC6, 305/97 (DP 30 V, EP 4 V, CE 32 V) for 12HSO4-JA, 338/130 (DP 597 45 V, EP 10 V, CE 30 V) for 12OH-JA-IIe, 352/130 (DP 45 V, EP 10 V, CE 30 V) for 598 12COOH-JA-IIe, 387/59 (DP 85 V, EP 9 V, CE 52 V) for 12O-Glc-JA, 325/133 (DP 65 599 V, EP 4 V, CE 30 V) for D4-JA-Leu, 308/116 (DP 45 V, EP 5 V, CE 28 V) for JA-Val, 600 322/130 (DP 45 V, EP 5 V, CE 28 V) for JA-Ile, 296/170.2 (DP 65 V, EP 4 V, CE 28 601 V) for D5-OPDA, 263/165 (DP 40 V, EP 5 V, CE 20 V) for dnOPDA and 291/165 (DP 602 50 V, EP 5 V, CE 26 V) for 12-OPDA. The mass analyzers were adjusted to a 603 resolution of 0.7 amu full width at half-height. The ion source temperature was 40°C, 604 and the curtain gas was set at 10 (given in arbitrary units). Quantification was carried 605 out using a calibration curve of intensity (m/z) ratios of [unlabeled]/[deuterium- 606 labeled] vs. molar amounts of unlabeled (0.3-1000 pmol) compound. Due to the lack

607 of standards, only relative amounts of 12HSO4-JA, 12OH-JA-Ile, 12COOH-JA-Ile and 608 12O-Glc-JA were determined. 609 610 Ion Leakage Measurements

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611 For determining cell death in the lines during senescence, leaf discs (0.4 cm 612 diameter) were punched with a cork-borer under green safe light, avoiding the mid 613 vein. They were placed in a multi-well plate ion conductivity meter (Reid &

614 Associates, South Africa) (1.5 mL H2O and two discs per well) and relative ion 615 leakage (displayed as µS) was determined in the dark. 616 617 Accession Number 618 The raw sequencing data from RNAseq are available in the ArrayExpress database 619 (www.ebi.ac.uk/arrayexpress) under accession number (E-MTAB-6965). 620 621 622 Supplemental Data 623 The following supplemental materials are available. 624 625 Supplemental Figure S1. Overlap between the data presented here and two 626 independent leaf senescence transcriptome datasets. 627 628 Supplemental Figure S2. Dendrogram of the modules generated by WGCNA 629 630 Supplemental Figure S3. Electrolyte leakage data of pao1 and acd2-2 mutants 631 during dark-induced senescence. 632 633 Supplemental Figure S4. Mapping of the RNAseq reads to genes of interest in 634 respective mutant lines. 635 636 Supplemental Dataset S1. RNAseq gene expression data during DET in the four 637 lines. 638 639 Supplemental Dataset S2. GO terms enrichment for each pairwise comparison of 640 gene expression. 641 642 Supplemental Dataset S3. List of 50 most highly expressed genes after dark 643 incubation. 644

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645 Supplemental Dataset S4. Data from quantification of jasmonic acid and its 646 derivatives used to draw Fig. 5. 647 648 Supplemental Dataset S5. Expression of hormone-related genes in the four lines 649 before senescence induction. 650 651 Supplemental Dataset S6. GO terms enrichment of all 16 clusters originating from 652 the WGCNA analysis and WGCNA scoring matrix for dark-treated leaves across all 653 lines and for pao1 after 2 dd incubation. 654 655 656 ACKNOWLEDGEMENTS 657 We are thankful to Sirisha Aluri and Lennart Opitz from the Functional Genomics 658 Centre Zürich for sequencing, to Otto Miersch from the University of Halle, Germany, 659 for providing JA metabolite standards, and to Kathrin Salinger for technical 660 assistance. 661 662 663 LITERATURE CITED 664 Aubry S, Mani J, Hörtensteiner S (2008) Stay-green protein, defective in Mendel's 665 green cotyledon mutant, acts independent and upstream of pheophorbide a 666 oxygenase in the chlorophyll catabolic pathway. Plant Mol Biol 67: 243-256 667 Balcke GU, Handrick V, Bergau N, Fichtner M, Henning A, Stellmach H, Tissier 668 A, Hause B, Frolov A (2012) An UPLC-MS/MS method for highly sensitive 669 high-throughput analysis of phytohormones in plant tissues. Plant Methods 8: 670 47 671 Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina 672 sequence data. Bioinformatics 30: 2114-2120 673 Breeze E, Harrison E, McHattie S, Hughes L, Hickman R, Hill C, Kiddle S, Kim 674 YS, Penfold CA, Jenkins D, Zhang C, Morris K, Jenner C, Jackson S, 675 Thomas B, Tabrett A, Legaie R, Moore JD, Wild DL, Ott S, Rand D, 676 Beynon J, Denby K, Mead A, Buchanan-Wollaston V (2011) High- 677 resolution temporal profiling of transcripts during Arabidopsis leaf senescence

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746 Kusaba M, Maoka T, Morita R, Takaichi S (2009) A novel carotenoid derivative 747 lutein 3-acetate, accumulates in senescent leaves of rice. Plant Cell Physiol 748 50: 1573-1577 749 Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation 750 network analysis. BMC Bioinformatics 9: 559 751 Langmead B (2010) Aligning short sequencing reads with Bowtie. Curr Protoc 752 Bioinformatics Chapter 11: Unit 11.17 753 Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, Haag JD, 754 Gould MN, Stewart RM, Kendziorski C (2013) EBSeq: an empirical Bayes 755 hierarchical model for inference in RNA-seq experiments. Bioinformatics 29: 756 1035-1043 757 Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data 758 with or without a reference genome. BMC Bioinformatics 12: 323 759 Lin Y-T, Chen L-J, Herrfurth C, Feussner I, Li H-M (2016) Reduced biosynthesis of 760 digalactosyldiacylglycerol, a major chloroplast membrane lipid, leads to 761 oxylipin overproduction and phloem cap lignification in Arabidopsis. Plant Cell 762 28: 219-232 763 Lorrain S, Vailleau F, Balaqué C, Roby D (2003) Lesion mimic mutants: keys for 764 deciphering cell death and defense pathways in plants? Trends Plant Sci 8: 765 263-271 766 Mata-Perez C, Sanchez-Calvo B, Begara-Morales JC, Luque F, Jimenez-Ruiz J, 767 Padilla MN, Fierro-Risco J, Valderrama R, Fernandez-Ocana A, Corpas 768 FJ, Barroso JB (2015) Transcriptomic profiling of linolenic acid-responsive 769 genes in ROS signaling from RNA-seq data in Arabidopsis. Front Plant Sci 6: 770 122 771 Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D (2008) Lipid 772 extraction by methyl-tert-butyl ether for high-throughput lipidomics. J Lipid Res 773 49: 1137-1146 774 Mecey C, Hauck P, Trapp M, Pumplin N, Plovanich A, Yao J, He SY (2011) A 775 critical role of STAYGREEN/Mendel's I locus in controlling disease symptom 776 development during Pseudomonas syringae pv tomato infection of 777 Arabidopsis. Plant Physiol 157: 1965-1974 778 Mur LAJ, Aubry S, Mondhe M, Kingston-Smith A, Gallagher J, Timms-Taravella 779 E, James C, Papp I, Hörtensteiner S, Thomas H, Ougham H (2010)

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780 Accumulation of chlorophyll catabolites photosensitizes the hypersensitive 781 response elicited by Pseudomonas syringae in Arabidopsis. New Phytol 188: 782 161-174 783 Nyathi Y, De Marcos Lousa C, van Roermund CW, Wanders RJ, Johnson B, 784 Baldwin SA, Theodoulou FL, Baker A (2010) The Arabidopsis peroxisomal 785 ABC transporter, comatose, complements the Saccharomyces cerevisiae 786 pxa1 pxa2Δ mutant for metabolism of long-chain fatty acids and exhibits fatty 787 acyl-CoA-stimulated ATPase activity. J Biol Chem 285: 29892-29902 788 Pružinská A, Anders I, Aubry S, Schenk N, Tapernoux-Lüthi E, Müller T, 789 Kräutler B, Hörtensteiner S (2007) In vivo participation of red chlorophyll 790 catabolite reductase in chlorophyll breakdown. Plant Cell 19: 369-387 791 Pružinská A, Anders I, Tanner G, Roca M, Hörtensteiner S (2003) Chlorophyll 792 breakdown: pheophorbide a oxygenase is a Rieske-type iron-sulfur protein, 793 encoded by the accelerated cell death 1 gene. Proc Natl Acad Sci USA 100: 794 15259-15264 795 Pružinská A, Tanner G, Aubry S, Anders I, Moser S, Müller T, Ongania K-H, 796 Kräutler B, Youn J-Y, Liljegren SJ, Hörtensteiner S (2005) Chlorophyll 797 breakdown in senescent Arabidopsis leaves: characterization of chlorophyll 798 catabolites and of chlorophyll catabolic enzymes involved in the degreening 799 reaction. Plant Physiol 139: 52-63 800 Qiu K, Li Z, Yang Z, Chen J, Wu S, Zhu X, Gao S, Gao J, Ren G, Kuai B, Zhou X 801 (2015) EIN3 and ORE1 accelerate degreening during ethylene-mediated leaf 802 senescence by directly activating chlorophyll catabolic genes in Arabidopsis. 803 PLOS Genet 11: e1005399 804 Ren G, An K, Liao Y, Zhou X, Cao Y, Zhao H, Ge X, Kuai B (2007) Identification of 805 a novel chloroplast protein AtNYE1 regulating chlorophyll degradation during 806 leaf senescence in Arabidopsis. Plant Physiol 144: 1429-1441 807 Sakuraba Y, Han SH, Lee SH, Hörtensteiner S, Paek NC (2016) Arabidopsis 808 NAC016 promotes chlorophyll breakdown by directly upregulating 809 STAYGREEN1 transcription. Plant Cell Rep 35: 155-166 810 Sakuraba Y, Park SY, Kim YS, Wang SH, Yoo SC, Hörtensteiner S, Paek NC 811 (2014) Arabidopsis STAY-GREEN2 is a negative regulator of chlorophyll 812 degradation during leaf senescence. Mol Plant 7: 1288-1302

24 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

813 Sakuraba Y, Schelbert S, Park S-Y, Han S-H, Lee B-D, Besagni Andrès C, 814 Kessler F, Hörtensteiner S, Paek N-C (2012) STAY-GREEN and chlorophyll 815 catabolic enzymes interact at light-harvesting complex II for chlorophyll 816 detoxification during leaf senescence in Arabidopsis. Plant Cell 24: 507-518 817 Sakuraba Y, Yokono M, Akimoto S, Tanaka R, Tanaka A (2010) Deregulated 818 chlorophyll b synthesis reduces the energy transfer rate between 819 photosynthetic pigments and induces photodamage in Arabidopsis thaliana. 820 Plant Cell Physiol 51: 1055-1065 821 Schelbert S, Aubry S, Burla B, Agne B, Kessler F, Krupinska K, Hörtensteiner S 822 (2009) Pheophytin pheophorbide hydrolase (pheophytinase) is involved in 823 chlorophyll breakdown during leaf senescence in Arabidopsis. Plant Cell 21: 824 767-785 825 Seltmann MA, Hussels W, Berger S (2010a) Jasmonates during senescence: 826 signals or products of metabolism? Plant Signal Behav 5: 1493-1496 827 Seltmann MA, Stingl NE, Lautenschlaeger JK, Krischke M, Mueller MJ, Berger S 828 (2010b) Differential impact of lipoxygenase 2 and jasmonates on natural and 829 stress-induced senescence in Arabidopsis. Plant Physiol 152: 1940-1950 830 Süssenbacher I, Christ B, Hörtensteiner S, Kräutler B (2014) Hydroxymethylated 831 phyllobilins: A puzzling new feature of the dioxobilin branch of chlorophyll 832 breakdown. Chem-Eur J 20: 87-92 833 Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster 834 H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, 835 Tissier C, Zhang P, Huala E (2008) The Arabidopsis Information Resource 836 (TAIR): gene structure and function annotation. Nucleic Acids Res 36: D1009- 837 1014 838 Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer 839 (IGV): high-performance genomics data visualization and exploration. Brief 840 Bioinform 14: 178-192 841 Ueda J, Kato J (1980) Isolation and identification of a senescence-promoting 842 substance from wormwood (Artemisia absinthium L.). Plant Physiol 66: 246- 843 249 844 Usadel B, Nagel A, Steinhauser D, Gibon Y, Blasing OE, Redestig H, 845 Sreenivasulu N, Krall L, Hannah MA, Poree F, Fernie AR, Stitt M (2006)

25 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

846 PageMan: an interactive ontology tool to generate, display, and annotate 847 overview graphs for profiling experiments. BMC Bioinformatics 7: 535 848 Van der Graaff E, Schwacke R, Schneider A, Desimone M, Flügge UI, Kunze R 849 (2006) Transcription analysis of Arabidopsis membrane transporters and 850 hormone pathways during developmental and induced leaf senescence. Plant 851 Physiol 141: 776-792 852 Wasternack C (2014) Action of jasmonates in plant stress responses and 853 development--applied aspects. Biotechnol Adv 32: 31-39 854 Wasternack C, Feussner I (2018) The oxylipin pathways: biochemistry and function. 855 Annu Rev Plant Biol 69: 363-386 856 Yang M, Wardzala E, Johal GS, Gray J (2004) The wound-inducible Lls1 gene from 857 maize is an orthologue of the Arabidopsis Acd1 gene, and the LLS1 protein is 858 present in non-photosynthetic tissues. Plant Mol Biol 54: 175-191 859 Yin XR, Xie XL, Xia XJ, Yu JQ, Ferguson IB, Giovannoni JJ, Chen KS (2016) 860 Involvement of an ethylene response factor in chlorophyll degradation during 861 citrus fruit degreening. Plant J 86: 403-412 862 Yu J, Zhang Y, Di C, Zhang Q, Zhang K, Wang C, You Q, Yan H, Dai SY, Yuan 863 JS, Xu W, Su Z (2016) JAZ7 negatively regulates dark-induced leaf 864 senescence in Arabidopsis. J Exp Bot 67: 751-762 865 Zhai Q, Li CB, Zheng W, Wu X, Zhao J, Zhou G, Jiang H, Sun J, Lou Y, Li C 866 (2007) Phytochrome chromophore deficiency leads to overproduction of 867 jasmonic acid and elevated expression of jasmonate-responsive genes in 868 Arabidopsis. Plant Cell Physiol 48: 1061-1071 869 Zhao W, Langfelder P, Fuller T, Dong J, Li A, Hovarth S (2010) Weighted gene 870 coexpression network analysis: state of the art. J Biopharm Stat 20: 281-300 871 Zheng XY, Spivey NW, Zeng W, Liu PP, Fu ZQ, Klessig DF, He SY, Dong X 872 (2012) Coronatine promotes Pseudomonas syringae virulence in plants by 873 activating a signaling cascade that inhibits salicylic acid accumulation. Cell 874 Host Microbe 11: 587-596 875 Zhu X, Chen J, Xie Z, Gao J, Ren G, Gao S, Zhou X, Kuai B (2015) Jasmonic acid 876 promotes degreening via MYC2/3/4- and ANAC019/055/072-mediated 877 regulation of major chlorophyll catabolic genes. Plant J 84: 597-610 878 879

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880 FIGURE LEGENDS 881 Figure 1. Phenotypic characterisation of CCGs mutants during dark-induced 882 senescence of detached leaves. 883 A WT, pao1, nye1-1 and pph-1 detached leaves before and after 2 and 5 days of 884 dark induced senescence (dd). B Chlorophyll degradation of CCG mutants during 885 dark-induced senescence. C Electrolyte leakage of CCG mutants during dark- 886 induced senescence. D Profile of the accumulation of pheophorbide a and the major 887 phyllobilin (DNCC_618) in CCG mutants during dark-induced senescence. Data in B- 888 D are mean values of a representative experiment with three (B), at least ten (C) and 889 five (D) replicates, respectively. Error bars indicate SD. 890 891 Figure 2. RNAseq profiling of CCG mutants provide new insight into the 892 relationship of the PAO/phyllobilin pathway to global leaf senescence. 893 A Major enriched gene ontology terms identified in the three CCG mutants during 894 dark-induced senescence (0 dd vs 2 dd) using Wilcoxon test implemented in 895 Pageman tool (Usadel et al., 2006). B Principal Component Analysis of the RNAseq 896 data. C Venn diagrams showing common patterns of differential expression (0 dd vs 897 2 dd) of up- and down-regulated genes during dark-induced senescence. 898 899 Figure 3. Influence of dark-induced senscence on the expression of the genes 900 involved in the PAO/phyllobilin pathway.

901 Heat maps represent log2 (fold change) of gene expression in each of the four 902 studied lines during dark-induced senescence. Genes/enzymes: CAO, chlorophyll a 903 oxygenase; CHLG, chlorophyll synthase; CYP89A9, cytochrome P450 904 monooxygenase 89A9; HCAR, 7-hydroxymethyl chlorophyll a reductase; MES16, 905 methylesterase 16; NYC1, non-yellow coloring 1 (chlorophyll b reductase); NYE1, 906 non yellowing 1 (magnesium dechelatase); NYE2, non yellowing 2; PAO, 907 pheophorbide a oxygenase; PPH, pheophytinase; RCCR, RCC reductase; TIC55, 908 translocon at the inner chloroplast envelope 55. Phyllobilins: DNCC, dioxobilin-type 909 NCC; NCC, non-fluorescent chlorophyll catabolite; pFCC, primary fluorescent 910 chlorophyll catabolite; RCC, red chlorophyll catabolite. 911 912 Figure 4. Transcriptional regulation of the PAO/phyllobilin pathway during 913 dark-induced senescence is mainly affected in pao1.

27 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

914 Heat maps represent log2 (fold change) of gene expression in each of the four 915 studied lines during dark-induced senescence. JA, jasmonic acid; ET, ethylene; ABA, 916 abscisic acid; COI1, coronatine insensitive 1; JAZ, jasmonate-ZIM domain; NAC, 917 NAM, ATAF1/2 and CUC2 domain protein; NAP, NAC-like, activated by PA3/PI; EIN, 918 ethylene insensitive; ELF3, early flowering 3; PIF, phytochrome interacting factor; 919 SOC1, suppressor of overexpression of coi1; ERF17, ethylene response factor; 920 ORE1, oresara 1; EEL, enhance em level ; ABI5, ABA insensitive 5; ABF, ABA- 921 responsive element binding factor; SnRK2, serine/threonine kinase 2; PYL9, 922 pyrabactin resistance 1-like 9. 923 924 Figure 5. Jasmonic acid metabolism during dark-induced senescence in WT 925 and pao1. 926 Levels of JA and JA-related metabolites in grey (0 dd) and black (2 dd) for WT and 927 pao1 are shown as histograms. Expression levels are shown using heat maps of

928 log2(fold change). Genes/enzymes: DAD1, delayed anther dehiscence 1; LOX2, 13- 929 lipoxygenase 2; AOS, allene oxide synthase; AOC, allene oxide cyclase; OPR3, 930 OPDA reductase 3; IAR3, IAA-alanine resistant 3; JMT, jasmonate 931 methyltransferase; JAR1, JA-amino acid synthetase 1; JOX, JA-induced oxygenase; 932 CYP, cytochrome P450 monooxygenase; ST2A, sulfotransferase 2. Metabolites: 933 OPDA, 12-oxo-phytodienoic acid; OPC 3-oxo-2-cis-2-pentenyl cyclopentyl-octanoic 934 acid; JA-CoA, jasmonate-coenzyme A. Asterisks indicate significant differences (p < 935 0.05). 936 937 Figure 6. Weighted gene co-expression analysis (WGCNA) sheds new light on 938 the regulation of the PAO/phyllobilin pathway. 939 A Heat map showing a module-sample association matrix. Each row corresponds to 940 a module. The heat map colour code from blue to red indicates the correlation 941 coefficient between the module and either the treatment (first column; darkness) or 942 the genetic background. B Patterns of expression (left panel) and size (right panel) of 943 gene co-expression modules. On the left panel, heat maps indicate mean expression

944 [log2 (fold change)] of the 10% most representative genes (highest connectivity) for 945 each WGCNA module during dark-induced senescence. C The regulatory network of 946 the PAO/phyllobilin pathway as exported from WGCNA and visualized in VisANT 947 5.51 (Hu et al., 2007). Larger nodes show the input genes (CCGs, transcriptional

28 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

948 regulators according to Fig. 4), smaller nodes were limited to the top 3 most 949 connected genes for each input gene, the edges represent connections between the 950 genes. Node colours represent the module in which the genes clustered during 951 WGCNA analysis. 952 953 Figure 7. Model illustrating the influence of pheophorbide a homeostasis on JA 954 signalling. 955 The middle panel shows the PAO/phyllobilin pathway under normal senescence 956 conditions, leading to the complete degradation of chlorophyll to vacuole-localized 957 phyllobilins. Left and right panels show modulation of catabolite homeostasis caused 958 by mutations of either nye1-1 or pph1 (left panel) or pao1 (right panel), and the 959 respective observed downstream modulation of the JA response (hatched arrows). 960 Arrow sizes schematically represent relative flux (metabolite) and response (JA 961 signalling) intensities. Among the few genes differentially expressed in nye1-1 and 962 pph-1, JAZ genes were downregulated compared to WT. On the other hand, in pao1, 963 JA biosynthesis and signalling genes as well as some JA bioactive derivatives were 964 induced.

29 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Table 1. Number of genes differentially expressed during dark incubation of detached leaves (DET) in WT and three CCG mutant lines. 25,920 genes were detected in at least one of the 24 samples.

Total transcripts detected (non zero) 21,403

Differentially expressed during DET Total Up-regulated Down-regulated (PPDE ≥0.95 and FC≥2)

WT 6,124 3,389 2,735 nye1-1 6,227 3,325 2,902 pph-1 6,764 3,777 2,987 pao1 11,408 5,723 5,685

30 A B

bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights30 reserved. No reuse allowed without permission. ) 2

20

10 Chlorophyll (mg/mm

0 WT pao1 nye1-1 pph-1

0 dd D C 2 dd 30000 5 dd Pheophorbide a

20000 40

10000 30

Relative catabolite abundance 0 250000 20 DNCC_618 200000 10 150000 Electrolyte leakage (μS)

100000 0 WT pao1 nye1-1 pph-1 50000 Relative catabolite abundance 0 0dd 2dd 0dd 2dd 0dd 2dd 0dd 2dd WT pao1 nye1-1 pph-1

Figure 1. Phenotypic characterisaon of CCGs mutants during dark-induced senescence of detached leaves. A WT, pao1, nye1-1 and pph-1 detached leaves before and aer 2 and 5 days of dark induced senescence (dd). B Chlorophyll degradaon of CCG mutants during dark-induced senescence. C Electrolyte leakage of CCG mutants during dark-induced senescence. D Profile of the accumulaon of pheophorbide a and the major phyllobilin (DNCC_618) in CCG mutants during dark-induced senescence. Data in B-D are mean values of a representave experiment with three (B), at least ten (C) and five (D) replicates, respecvely. Error bars indicate SD. A 0 dd vs 2 dd

bioRxiv preprint doi: https://doi.org/10.1101/486886nye1-1 pph-1 pao1 ; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified byWT peer review) is the author/funder. All rights reserved. No reuse allowed without permission. PS 4 PS.PSII PS.PSI sen+ 2 PS.NADH DH PS.photorespiration 0 PS. calvin cycle PS.calvin cycle.RBCS -2 major CHO. synthesis.starch major CHO.degradation.starch sen- -4 cell wall modification lipid metabolism.transfer protein lipid metabolism.lipases lipid metabolism.FA desaturation amino acid metabolism.synthesis.aspartate amino acid metabolism.degradation.branched-chain group secondary metabolism.sulfur-containgin.glucosinolate.synthesis hormone.ethylene tetrapyrrole synthesis misc.glutathione S transferase redox stress.biotic misc.GDSL-motif lipase misc.beta 1,3-beta-glucosidase RNA.regulation.bZIP TF RNA.regulation.WRKY TF RNA.regulation.Aux/IAA TF RNA.regulation.PHOR1 TF DNA.synthesis/chromatin structure.retrotransposon protein.synthesis.ribosomal protein.prokaryotyic.chloroplast protein.postranslational modification protein degradation.subtilases protein.degradation.autophagy protein.degradation.ubiquitin.E3.RING protein.degradation.ubiquitin.E3.RSCF.FBOX protein folding micro RNA, natural antisense development not assigned.pentatricopeptide (PPR) not assigned.DC1 domain containing protein signalling.receptor kinases.LRR cell.organisation B nye1-1 0 dd 60 nye1-1 2 dd pao1 0 dd pao1 2 dd pph-1 0 dd 40 pph-1 2 dd WT 0 dd WT 2 dd 20 PC2 0

−20

−40 05− 0 05 001 PC1 C pao1 pph-1 pao1 pph-1 WT WT 2692 76 nye1-1 3524 139 nye1-1

87 280 119 157 219 165

192 310 172 113 265 246 134 326

1912 1291 159 109 88 107 468 77 746 48 101 86

up-regulated down-regulated Figure 2. RNAseq profiling of CCG mutants provide new insight into the relaonship of the PAO/phyllobilin pathway to global leaf senescence. A Major enriched gene ontology terms idenfied in the three CCG mutants during dark-induced senescence (0 dd vs 2 dd) using Wilcoxon test implemented in Pageman tool (Usadel et al., 2006). B Principal Component Analysis of the RNAseq data. C Venn diagrams showing common paerns of differenal expression (0 dd vs 2 dd) of up- and down-regulated genes during dark-induced senescence. 0 dd vs 2 dd bioRxiv preprint doi: https://doi.org/10.1101/486886chloroplast ; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is theChlorophyll author/funder. Allb rights reserved.Chlorophyllide No reuse allowed b without permission. HCAR CHLG NYC1 CAO NOL CHLG Chlorophyll a Chlorophyllide a NYE1 NYE2

Pheophytin a

PPH

Pheophorbide a PAO RCCR TIC55 pFCC OH-pFCC

CYP89A9

MES16 log2(0 dd/2 dd) Down Up vacuole −50 5 (D)NCCs WT pao1 pph-1 nye1-1

Figure 3. Influence of dark-induced senscence on the expression of the genes involved in the PAO/phyllobilin pathway. Heat maps represent log2 (fold change) of gene expression in each of the four studied lines during dark-induced senescence. Genes/enzymes: CAO, chlorophyll a oxygenase; CHLG, chlorophyll synthase; CYP89A9, cytochrome P450 monooxygenase 89A9; HCAR, 7-hydroxymethyl chlorophyll a reductase; MES16, methylesterase 16; NYC1, non-yellow coloring 1 (chlorophyll b reductase); NYE1, non yellowing 1 (magnesium dechelatase); NYE2, non yellowing 2; PAO, pheophorbide a oxygenase; PPH, pheophynase; RCCR, RCC reductase; TIC55, translocon at the inner chloroplast envelope 55. Phyllobilins: DNCC, dioxobilin-type NCC; NCC, non-fluorescent chlorophyll catabolite; pFCC, primary fluorescent chlorophyll catabolite; RCC, red chlorophyll catabolite. pao1. Figure 4. Transcriponal regulaon of the PAO/phyllobilin pathw serine/threonine kinase2;PYL9, pyrabacn resistance 1-like 9. factor; ORE1, oresara 1; EEL, enhance em level ; ABI5, ABA inse suppresso SOC1, factor; interacng phytochromePIF, 3; flowering early ELF3, insensive; ethylene EIN, PA3/PI; by NAP,acvatedNAC-like, protein; domain ATAF1/2CUC2 NAM, and NAC, senescence. JA, jasmonicacid;ET, ethylene; ABA, abscisicacid;COI1, coronane insensive 1;JAZ, jasmonate-ZIM domain; Down NAC072 NAC055 NAC019 log 55 −5 2 Heat maps represent log represent maps Heat (0 dd/2dd) WT bioRxiv preprint

pph-1 0 nye1-1

pao1 Up doi: not certifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. JAZ10 JAZ12 JAZ11 MYC4 MYC3 MYC2 JAZ9 JAZ8 JAZ7 JAZ6 JAZ5 JAZ4 JAZ3 JAZ2 JAZ1 https://doi.org/10.1101/486886 COI1 AET JA 2 (fold change) of gene expression in each of the four studied lines during dark-induced during lines studied four the of each in expression gene of change) (fold ERF17 EIN3 EIN2 ; this versionpostedDecember4,2018. ORE1 Chlorophyll CatabolicGenes Age/Senescence ELF3 phyB PIF5 PIF4 E ABI5 EEL nsive 5; ABF, ABA-responsive element binding factor; SnRK2,

ay during dark-induced senescence is mainly affected in Light r of overexpression of coi1; ERF17, ethylene response ethylene ERF17, coi1; of overexpression of r SOC1 SnRK2.6 SnRK2.3 SnRK2.2 ABF4 ABF3 ABF2 PYL9 The copyrightholderforthispreprint(whichwas ABA A A06NAC016 NAC046 NAP Phospholipids Metabolites bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peerDAD1 review) is the author/funder. All rights reserved. No reuse allowed without permission. (?) dd α-Linolenic acid Roughanic acid 0 2 0 2

LOX2 WT pao1 Gene expression * AOS * WT pao1 12-OPDA dn-OPDA

AOC1 log2(0 dd/2 dd) AOC2 OPC-8AOC3 AOC4 -7 0 7

OPR3

OPC6

* OPC4 * 4,5-ddh-JA * * * JA-Val

JA-CoA * * JA-Leu 7iso-JA JA * * * * * JAR1 JA-Ile

JMT CYP94b1 JOX2 CYP94b3 * 7isoMe-JA Me-JA JOX3 * * JOX4 12OH-JA-Ile CYP94c1 nd ? * 12OH-JA * * ST2a 12COOH-JA-Ile 12O-Glc-JA 12HSO -JA IAR3 4 * * * * * * * * 12COOH-JA

Figure 5. Jasmonic acid metabolism during dark-induced senescence in WT and pao1. Levels of JA and JA-related metabo- lites in grey (0 dd) and black (2 dd) for WT and pao1 are shown as histograms. Expression levels are shown using heat maps of log2(fold change). Genes/enzymes: DAD1, delayed anther dehiscence 1; LOX2, 13-lipoxygenase 2; AOS, allene oxide synthase; AOC, allene oxide cyclase; OPR3, OPDA reductase 3; IAR3, IAA-alanine resistant 3; JMT, jasmonate methyltransfer- ase; JAR1, JA-amino acid synthetase 1; JOX, JA-induced oxygenase; CYP, cytochrome P450 monooxygenase; ST2A, sulfotrans- ferase 2. Metabolites: OPDA, 12-oxo-phytodienoic acid; OPC 3-oxo-2-cis-2-pentenyl cyclopentyl-octanoic acid; JA-CoA, jasmonate-coenzyme A. Asterisks indicate significant differences (p < 0.05). A Modules blue

brown

bioRxiv preprint doi: https://doi.org/10.1101/486886pink ; this version posted December 4, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. yellow

red

black

green salmon

cyan midnightblue Module-trait purple relationship tan 1 turquoise 0.5 greenyellow 0 magenta −0.5 grey −1

0 dd 2 dd 0 dd 2 dd 0 dd 2 dd 0 dd 2 dd darkness WT nye1-1 pph-1 pao1

B Gene expression Number of genes Enriched GO terms mRNA catabolic process Modules associated senescence to darkness fatty acid beta-oxidation

JA mediated signaling pathway Modules associated ABA-activated signaling pathway to pheide a response to chitin

log2(0 dd/2 dd) JA biosynthetic process Up during DET 4

0

Down during DET -4 N/A WT pao1 pph-1 nye1-1 0 1000 2000 3000 4000 5000 6000 7000 8000 C JAZ6 SnRK2.6 CYP89a9 PAO MYC2 NAC016 JAZ1 At4g00830 At1g80980 JAZ5 ABF2 ABF3 At1g05890 At2g35680 At4g5420 At2g21240 At2g18790 At1g36320 At3g19520 At2g43680 PIF4 PIF5 EIN2 PHYB At1g13150 SGR2 SnRK2.2 At2g02080 At3g51370 At1g27430 At2g23980 At1g70620

At3g25150 At1g14650 At2g02760 At1g43700 At4g31410 PPC2 At5g04440 At2g30410 At3g56850 At5g19350 At5g08100 At4g34070 At1g13980 At5g02200 At1g29355 At5g25560 At4g28880

At3g18030 At5g22320 At3g59990 At5g42820 At3g14010 At1g14400 PPH At1g55450 At2g03890 At5g42080 JAZ10 At1g50660 At1g79750 At4g04740

JAZ9 SnRK2.3 JAZ2 EEL At3g09830 ERF17 EIN3

JAZ3 At5g46910 At2g28305 At1g56700 At5g66250 JAZ11

At5g42650 At5g50670 JAZ12 At5g32440 At2g18690NAC072 At1g08200 At1g14370

At5g58800 At1g27100 At2g37620 At5g61780 PhyH ABF4 At4g39580 At2g17705 JAZ8

At2g01490 At2g26800 NAC055 At5g89785 At1g70790 ORE1 At1g26300 At3g55840 NAC019 MYC4 At1g19800 NAC046 At3g19280 JAZ7

At4g10810 At1g29470 MES16 At3g08010 TIC55 NYC1 At4g38980 At2g45970 At401030At5g15500

At2g20990 At2g16400 At2g04842 At5g27670 At5g12340 CAO At1g78820 MYC3 At1g63970

At4g32330 At5g37310 RCCR At1g74740 At2g30766 At4g04950 SOC1 At5g25040 NAC029 At1g76150

ELF3 NYE1

At5g54130 At5g62540 Figure 6. Weighted gene co-expression analysis (WGCNA) sheds new light on the regulaon of the PAO/phyllobilin path- way. A Heat map showing a module-sample associaon matrix. Each row corresponds to a module. The heat map colour code from blue to red indicates the correlaon coefficient between the module and either the treatment (first column; dark- ness) or the genec background. B Paerns of expression (le panel) and size (right panel) of gene co-expression modules.

On the le panel, heat maps indicate mean expression [log2 (fold change)] of the 10% most representave genes (highest connecvity) for each WGCNA module during dark-induced senescence. C The regulatory network of the PAO/phyllobilin pathway as exported from WGCNA and visualized in VisANT 5.51 (Hu et al., 2007). Larger nodes show the input genes (CCGs, transcriponal regulators according to Fig. 4), smaller nodes were limited to the top 3 most connected genes for each input gene, the edges represent connecons between the genes. Node colours represent the module in which the genes clustered during WGCNA analysis. nye1-1 or pph-1 WT pao1 bioRxiv preprint doi: https://doi.org/10.1101/486886; this version posted December 4, 2018. The copyright holder for this preprint (which was not certifiedchloroplast by peer review) is the author/funder. All rightschloroplast reserved. No reuse allowed without permission.chloroplast Chlorophyll Chlorophyll Chlorophyll NYE1 NYE1 NYE1 PPH PPH PPH

Pheophorbide a Pheophorbide a Pheophorbide a

PAO PAO PAO

pFCC pFCC

JA signalling «Default» downregulated JA signalling JA signalling upregulated

vacuole vacuole vacuole (D)NCCs (D)NCCs

Figure 7. Model illustrang the influence of pheophorbide a homeostasis on JA signalling. The middle panel shows the PAO/phyllobilin pathway under normal senescence condions, leading to the complete degradaon of chlorophyll to vacu- ole-localized phyllobilins. Le and right panels show modulaon of catabolite homeostasis caused by mutaons of either nye1-1 or pph1 (le panel) or pao1 (right panel), and the respecve observed downstream modulaon of the JA response (hatched arrows). Arrow sizes schemacally represent relave flux (metabolite) and response (JA signalling) intensies. Among the few genes differenally expressed in nye1-1 and pph-1, JAZ genes were downregulated compared to WT. On the other hand, in pao1, JA biosynthesis and signalling genes as well as some JA bioacve derivaves were induced.