Defects in Autophagy Lead to Selective in Vivo Changes in Turnover of Cytosolic and Organelle Proteins in Arabidopsis

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bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 Defects in autophagy lead to selective in vivo changes in turnover of cytosolic and 2 organelle proteins in Arabidopsis 3 4 Lei Li1,2, Chun Pong Lee2 , Akila Wijerathna-Yapa2, Martyna Broda2, Marisa S. Otegui3,4, A. 5 Harvey Millar2 6 7 1. Frontiers Science Center for Cell Responses, Department of Plant Biology and Ecology, 8 College of Life Sciences, Nankai University, 300071 Tianjin, China. 9 2. ARC Centre of Excellence in Plant Energy Biology, School of Molecular Science, The 10 University of Western Australia, 6009 Crawley, WA, Australia. 11 3. Department of Botany and Center for Quantitative Cell Imaging, University of 12 Wisconsin-Madison, Madison, WI, 53706, USA. 13 4. Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, 14 53706, USA. 15 16 17 Single Sentence Summary: Protein cargo of autophagy in plants can be discovered by 18 identifying proteins that increase in abundance and decrease in degradation rate in mutants 19 deficient in autophagy machinery 20 21 Keywords: autophagy, protein degradation, mitochondrion, chloroplast, phosphate 22 starvation 23 24 The author responsible for distribution of materials integral to the findings presented in this 25 article is: 26 27 A. Harvey Millar 28 ARC Centre of Excellence in Plant Energy Biology, Bayliss Building M316 University of 29 Western Australia, 35 Stirling Highway, Crawley 6009, Western Australia, Australia 30 Tel +61 8 6488 7245 31 [email protected] 32

33 Abstract

34 Identification of autophagic protein cargo in plants by their abundance in autophagy related

35 genes (ATG) mutants is complicated by changes in both protein synthesis and protein

36 degradation. To detect autophagic cargo, we measured protein degradation rate in shoots

37 and roots of Arabidopsis atg5 and atg11 mutant plants. These data show that less than a

38 quarter of proteins changing in abundance are probable cargo and revealed roles of ATG11

39 and ATG5 in degradation of specific cytosol, chloroplast and ER-resident proteins, and a

40 specialized role for ATG11 in degradation of proteins from mitochondria and chloroplasts.

41 Our data support a role for autophagy in degrading glycolytic enzymes and the chaperonin

42 containing T-complex polypeptide-1 complex. Autophagy induction by Pi limitation changed

43 metabolic profiles and the protein synthesis and degradation rates of atg5 and atg11 plants.

44 A general decrease in the abundance of amino acids and increase in several secondary

45 metabolites in autophagy mutants was consistent with altered catabolism and changes in

46 energy conversion caused by reduced degradation rate of specific proteins. Combining

47 measures of changes in protein abundance and degradation rates, we also identify ATG11

48 and ATG5 associated protein cargo of low Pi induced autophagy in chloroplasts and

49 ER-resident proteins involved in secondary metabolism.

51 Introduction

52 Autophagy enables cellular sugar, lipid and protein recycling and maintenance through the

53 trafficking of cellular material into the hydrolytic environment of the vacuolar lumen.

54 Autophagic degradation involves general and selective processes and is controlled through

55 both autophagy related genes (ATGs) and a range of receptor recognition mechanisms (An

56 and Harper, 2018; Marshall and Vierstra, 2018). Large protein complexes like ribosomes,

57 proteasomes, and protein aggregates are recognized through receptors-adaptor interaction

58 and engulfed by autophagosomes for delivery to vacuoles (Marshall et al., 2015; Floyd et al.,

59 2016; Jung et al., 2020). Autophagic degradation is also involved in clearance of chloroplasts,

60 mitochondria, peroxisomes, and ER during developmental transitions or stress responses

61 (Liu et al., 2012b; Farmer et al., 2013; Li et al., 2014; Khaminets et al., 2015; Izumi et al.,

62 2017; Zhang et al., 2020).

64 ATG proteins participate in autophagosome induction, membrane delivery, vesicle

65 nucleation, cargo recognition, and phagophore expansion and closure (Marshall and Vierstra,

66 2018). While some ATGs are encoded by single or duplicated genes in plants, there are

67 notable exceptions like ATG8 and ATG18 which are encoded in multi-gene families

68 (Thompson et al., 2005; Xiong et al., 2005; Yoshimoto et al., 2009a). In the multi-stage

69 conjugation system that mediates phagophore formation, ATG8 and ATG12 are each typically

70 conjugated to ATG7 and transferred separately to ATG3 and ATG10, respectively.

71 Subsequently, ATG8 is covalently attached to phosphatidylethanolamine (PE) and ATG12 is

72 attached to ATG5, forming an E3 ligase complex. The ATG5-ATG12 conjugate mainly

73 contributes to phagophore expansion and maturation. ATG5 mutants in Arabidopsis fail to

74 form autophagosomes, show a general disruption in subsequent autophagy-related

75 processes, and senesce under nitrogen- and carbon-limiting conditions (Thompson et al.,

76 2005; Yoshimoto et al., 2009b). ATG11 is an accessory protein that aids the scaffolding of the

77 ATG1 kinase regulatory complex to the expanding phagophore. ATG11 is reported to

78 promote vesicle delivery to vacuoles by stabilizing the ATG1/13 complex, but does not

79 appear to influence ATG12-ATG5 or ATG8-PE conjugates. Arabidopsis mutants deficient in

80 ATG11 also senesce rapidly under nitrogen- and carbon-limiting conditions and fail to

81 degrade mitochondrial proteins during dark-induced senescence (Li et al., 2014; Li and

82 Vierstra, 2014).

84 The apparent accumulation of specific sets of proteins in atg mutant plants (Avin-Wittenberg

85 et al., 2015; McLoughlin et al., 2018; Have et al., 2019; McLoughlin et al., 2020) may be

86 caused directly by a failure in autophagy-dependent protein degradation, or indirectly

87 through an increase in protein synthesis rate due to their enhanced gene expression or

88 translation. A failure in protein degradation could also be accompanied by lower levels of

89 protein synthesis through either feedback attenuation of gene expression or translational

90 control. Thus, using steady-state protein abundance as sole criterion to identify autophagic

91 protein targets is prone to errors (Wijerathna-Yapa et al., 2021). A couple of multi-omics

92 studies surveying the effect of autophagic recycling on proteome remodeling attempted to

93 use comparisons of mRNA and protein abundance in maize atg12 genotypes to resolve this

94 issue (McLoughlin et al., 2018; McLoughlin et al., 2020). These studies found that more than

95 half of the proteins that accumulated in atg12 plants did not have consistent changes in the

96 abundance of their mRNA. Using a similar approach, a lack of correlation in

97 protein-transcript changes was also observed in atg5 plants (Have et al., 2019). In addition,

98 differential regulation of translational rates imply that the same amount of mRNA will not

99 always result in the same level of translation, especially in autophagy mutants in which

100 ribosome accumulation has been reported (Gretzmeier et al., 2017; McLoughlin et al., 2018).

101 It therefore remains an open question as to which proteins that accumulate in atg mutants

102 are actual autophagy cargo and which are autophagy-related changes in protein synthesis

103 rate.

104

105 Autophagy deficiency also leads to changes in the abundance of metabolic intermediates in

106 plants. Metabolic profiling shows that ATG-deficient mutants respond differently to

107 prolonged darkness or nutrient limitation by undergoing extensive rearrangement in primary

108 and secondary metabolism (Masclaux-Daubresse et al., 2014; Avin-Wittenberg et al., 2015;

109 Barros et al., 2017; McLoughlin et al., 2018; Have et al., 2019; McLoughlin et al., 2020;

110 Barros et al., 2021). These reports show that the changes in metabolic profiles and protein

111 abundance in atg lines following nutrient limitation is complex, depends deeply on

112 environmental, developmental, and tissue/organ context.

113

114 Our previous use of stable isotope progressive labelling to measure protein turnover rates in

115 barley and Arabidopsis revealed that organelles and intra-organellar components are

116 degraded at different rates (Nelson et al., 2014; Li et al., 2017). These rates resulted from the

117 combined action of specific proteases in different subcellular compartments, the

118 proteasome, and autophagy-dependent and autophagy-independent vacuolar degradation.

119 In this study, we combined a quantitative analysis of changes in protein abundance with a

120 stable-isotope progressive labelling strategy to measure protein degradation rates in atg5

121 and atg11 lines of Arabidopsis. Using these data, we quantified the contribution of

122 autophagic degradation to the clearance of different organelles under control and Pi-limiting

123 conditions. Changes in protein abundance and turnover rate provide clues to understand

124 broad changes in metabolite levels in autophagy mutants, links between cellular trafficking

125 and autophagic flux, and identity a range of autophagy target proteins in shoot and root

126 tissues.

127

128 Results

129

130 Arabidopsis atg5 and atg11 mutants do not show accelerated senescence in hydroponics

131 at early stages of leaf production

132 The early onset of leaf senescence after 35 days of growth, or following bolting, is a widely

133 reported Arabidopsis phenotype in well-studied autophagy deficient mutants, including atg5

134 and atg11 (Yoshimoto et al., 2009b; Li et al., 2014). We choose an earlier developmental

135 stage prior to leaf senescence for our experiments to avoid senescence-associated protein

136 abundance and degradation rate changes from dominating our analysis. After visual

137 inspection of hydroponically grown wild type (WT) and atg mutant plants we decided to use

138 21-day-old plants for analysis as no visible signs of senescence were observed in 10 rosette

139 leaves which resembled growth stage 1.10 plants as reported previously (Boyes et al., 2001)

140 (Fig S1A). Consistent with the visible appearance of plants, the quantum efficiency of

141 photosystem II (Fv/Fm) was the same in WT, atg5, and atg11 leaves and no evidence of early

142 senescence hot spots were observed in pulse-amplitude-modulation (PAM) fluorometry

143 images (Fig S1B-C).

144

145 Deficient autophagic flux leads to broad changes in the abundance of proteins in

146 Arabidopsis roots and shoots.

147 Changes in relative protein abundance between different genotypes and their biological

148 replicates were measured using a 15N reference sample as a control. Total root or shoot

149 proteins extracted from WT, atg5 and atg11 grown in 14N media were mixed with equal

150 amounts of reference samples of 15N fully labeled WT shoot or root protein (Li et al., 2017).

151 The combined samples were digested by trypsin and the resulting peptides fractionated and

152 analysed by mass spectrometry. In total, 25,771 non-redundant peptides from root tissues

153 and 18,939 peptides from shoot samples could be quantified using ratios of 14N sample

154 peptides to 15N reference peptides. These peptides mapped to 1,265 non-redundant

155 proteins in roots and 777 in shoots that could be quantitatively compared between WT and

156 atg lines (DataS1). We performed pairwise comparisons between WT, atg5 and atg11 using

157 protein sets that were quantified in all three biological replicates. Volcano plots showed that

158 both autophagy mutants exhibited symmetric distributions for sets of proteins increasing or

159 decreasing in abundance. Fold changes (FC) in protein abundance in atg11/WT show a

160 relatively narrow range of FC (2-fold FC in root and 4-fold FC in shoot) compared with a wide

161 range of FC in atg5/ WT protein abundance (4-fold FC in root and 8-fold FC in shoot) (FigS2).

162

163 To dissect the role of autophagy in protein homeostasis in different cellular compartments,

164 we displayed the distributions of relative change in protein abundance according to the

165 known subcellular localization of each protein (Hooper et al., 2014; Hooper et al., 2017)

166 (Fig1, DataS2). Proteins located in the cytosol and peroxisomes of both shoots and roots

167 showed higher median abundance in atg11 and atg5 mutants than in WT. Conversely,

168 proteins in the nucleus, plasma membrane, vacuoles, and those secreted to the extracellular

169 space, showed lower median abundances in the autophagy mutants compared to WT. The

170 majority of the 53 plastid proteins found in roots showed lower abundance in the mutants,

171 but an overall increase of chloroplast protein abundance was observed in shoot tissue from

172 both mutant lines (Fig 1). ER-resident proteins were more abundant in the root but not in

173 shoot of the mutants. A higher abundance of mitochondrial proteins was found in both

174 shoots and roots of atg11, but only in the shoot of atg5 compared to WT.

175

176 To further understand the role of autophagy in regulating the protein synthesis and

177 degradation machineries, we investigated abundance changes in ribosomal and proteasomal

178 subunits in both mutants compared to WT (FigS3, DataS2). For ribosomes, 70-80% of

179 r-subunits showed a trend of higher abundance in the mutants, with 17 out of 61 r-protein

180 in root and 2 out of 7 ribosomal r-proteins in shoot showing statistically significant increases

181 (Student’s T-test, P<0.05). More than half of the proteasomal subunit proteins identified also

182 tended to be more abundant in both mutants, with 2 out of 23 proteasomal subunit proteins

183 in root and 1 out of 6 in shoot showing statistically significant increases (Student’s T-test,

184 P<0.05).

185

186 Specific proteins changed in abundance in a manner unexpected for their function or

187 subcellular location in autophagy-deficient plants.

188 To investigate specific protein abundance changes in root (Fig S4) and shoot (Fig S5),

189 proteins with statistically significant changes in atg5 and atg11 were then categorized by

190 their subcellular localizations and functions.

191

192 In roots, the cytosolic Chaperonin Containing T-complex polypeptide-1 (CCT) protein

193 complex subunits, ribosomal subunits, enzymes of amino acid metabolism (GAD1, ASP2,

194 MMT and OLD3) and glycolytic enzymes accumulated in atg11 and atg5 (Fig S4). In contrast,

195 cytoskeleton-related proteins including villins (VLN4), actin (ACT7) and tubulin

196 (TUB2,4,6,8,9), enzymes of amino acid metabolism (MAT3 and BCAT4), and

197 phosphatidylinositol transfer proteins (At1g30690 and At1g72160) from the secretory

198 pathway showed decreased abundance in both atg11 and atg5. Eleven mitochondrial

199 proteins, including components of the electron transport chain and TCA cycle, showed

200 increases in abundance, several mitochondrial stress response proteins, such as mtHsc70-1,

201 mtHsc70-2 and GPX6, displayed a decreased abundance in both mutants. Ten mitochondrial

202 proteins (including ATP synthase beta subunit, CPN10, ATPHB3, TOM5 and carbonic

203 anhydrase) showed different patterns in atg11 and atg5, with their abundance typically

204 increased in atg11 but decreased in atg5 (Fig S4). We also found that in roots, proteins

205 involved in vesicle transport specifically accumulated in atg5 but not in atg11; these proteins

206 included clathrin heavy chain1 (At3g11130) associated with plasma membrane and Golgi,

207 and the coatomer alpha, delta and gamma-subunits (At2g21390, At5g05010 and At4g34450)

208 of the COP1 coat, which is required for intra Golgi-transport, retrograde transport from Golgi

209 to ER, and Golgi maintenance. ER-resident proteins, such as AtBAG7(At5g62390), CNX1

210 (At5g61790) and PDIL1-3(At3g54960), also show a higher abundance in atg5 than atg11

211 when compared to WT (31% in atg5 vs 6% in atg11).

212

213 Different sets of proteins were quantified in shoots compared with roots due to the variation

214 in their absolute abundance in photosynthetic and non-photosynthetic tissues. In shoots,

215 almost half of quantified cytosolic proteins with significant changes in abundances were less

216 abundant in mutant lines (Fig S5). Similar to the protein set from the roots, cytosolic

217 ribosomal subunits, enzymes of amino acid metabolism (MAT3 and ATCIMS) and glycolytic

218 enzymes from shoots showed increased abundance, while profilin1 and profilin2, which

219 regulate the organization of actin cytoskeleton, showed reduced abundance in both atg11

220 and atg5. Peptidylprolyl isomerase enzymes (FKBP12, ROC1, ROC3 and ROC5) and proteins

221 with redox activity (TRX3, TPX1 and CSD1) showed decreased abundance in both atg11 and

222 atg5 shoots. In shoots, the mitochondrial redox proteins (GPX6, PRXIIF), CPN10, and

223 membrane-localized electron transport chain subunits showed decreased abundance while

224 TCA cycle enzymes and matrix-localized ETC subunits accumulated in both atg11 and atg5.

225 In chloroplasts, most stromal proteins showed increased abundance, while photosystem II

226 (PSII) subunits, photosystem I (PSI) reaction center (PSAN), cytochrome b6/f (PetC),

227 plastocyanin (DRT112 and PETE1), thioredoxins, and protein folding associated proteins

228 were less abundant in both atg11 and atg5 (Fig S5). Most quantified shoot plastid proteins

229 showed consistent changes in abundance in both mutant lines with few exceptions.

230

231 To determine whether these many changes in the abundance of specific root proteins were

232 reflected in changes in the cellular architecture, we analysed the ultrastructure of root tips

233 of 24-day-old WT, atg5, and atg11 plants processed by high-pressure freezing/

234 freeze-substitution and resin-embedding. In longitudinal sections of root tips, we identified

235 two areas of interest, the meristematic area (up to 100 microns from the quiescent center

236 towards the elongation zone) and the area where cells started to develop large vacuoles

237 (between 100 and 200 microns from the quiescent center) (Fig 2 A-C). We imaged multiple

238 middle sections of two roots of each genotype and measured the cell area and the area

239 occupied by the nucleus, mitochondria, and vacuoles as well as the tonoplast length per

240 section (Fig 2 D-H). We did not find statistically significant differences in any of these

241 parameters between WT and atg mutants; however, there were consistent trends showing

242 slight increase in vacuole surface and a reduction in tonoplast length/perimeter in the two

243 atg mutants, in actively vacuolating cells. These results indicating that the changes in the

244 proteome of atg5 and atg11, did not induce drastic changes in the cellular organization of

245 mutant root cells.

246

247 However, we noticed that approximately 25% of the cells in the atg5 root tips contained

248 abnormal Trans-Golgi networks (TGN) with largely dilated bulges or vesicles (Fig 3 A-B) and

249 large concentric membranous systems (Fig 3 C-F). In some cases, the edges of these

250 abnormal large membranes had bulges and budding profiles reminiscent of Golgi/TGN

251 cisternae (Fig 3C and D). In some other examples, we were able to image coats assembled

252 on budding sites on the membrane edges (Fig 3E, arrowheads), which is consistent with the

253 abnormal accumulation of COP1 coatomer subunits and clathrin in atg5. Whereas most of

254 these structures seem to enclose ribosomes and cytoplasm, approximately 10% of them

255 displayed rounded electron dense aggregates 2-3 times larger than a ribosome (Fig 3E).

256

257

258

259 Autophagy deficiency changes the degradation rate of specific organelle proteins in

260 Arabidopsis root and shoot

261 To compare specific protein abundance changes with changes of specific protein

262 degradation rates, we utilized a 15N progressive labelling strategy (Li et al., 2017) to quantify

263 protein degradation rates in the three genotypes. For this, the media of hydroponically

264 grown plants was switched from 14N to 15N nutrient salts to label newly synthesized proteins

265 over three days, and the fraction of each peptide that was 15N-labelled (Labelled Peptide

266 Fraction; LPF) was calculated using peptide mass spectrometry. In total, LPF for 11,179

267 peptides in roots and 7,145 peptides in shoots was quantified in three biological replicates

-1 268 across the three genotypes. From these LPFs, the degradation rates (KD, d ) of 558 root

269 proteins and 505 shoot proteins were obtained (Data S3) and relative changes in KD values

270 were visualize by volcano plots (Fig S6). In roots, most proteins with slower degradation

271 rates in atg11 (68%) and atg5 (82%) were located in the cytosol, followed by smaller

272 proportions that were located in mitochondria and ER (Table 1). In shoots, proteins that

273 degraded slowly were predominantly from the cytosol, chloroplasts, and mitochondria. A

274 higher proportion of mitochondrial proteins with slower degradation rates were detected in

275 atg11 roots and shoots (17%, 21%) compared to atg5 (2%, 0%). There was also a higher

276 proportion of chloroplastic proteins with slower degradation rates in shoots of atg11 (33%)

277 compared to shoots of atg5 (7%).

278

279 In roots, proteins with significant slower degradation rate in both atg5 and atg11 (Fig 4)

280 included forty cytosolic proteins, two ER proteins (the chaperones CRT1 and CNX1), and one

281 mitochondrial protein (ATP synthase D chain). Cytosolic proteins in this list can be broadly

282 placed into three major functional categories: metabolism, ribosome subunits, and glycolytic

283 enzymes. In shoots, twelve cytosolic proteins showed slower degradation rates in both atg5

284 and atg11. We also found proteins with slower rates of protein degradation but with

285 statistical significance only in one of the two mutants (Table S1). One example from this

286 group was RPN10, which has been reported to be an autophagy receptor for the

287 proteasome (Marshall et al., 2015). In contrast, four mitochondrial and eight chloroplastic

288 proteins showed very different changes in degradation rate between atg5 and atg11. These

289 proteins show slower degradation rate in atg11, but no change or faster rates of degradation

290 in atg5. These patterns suggested a specialized role of ATG11 in mitochondrial and

291 chloroplast protein homeostasis.

292

293 Identification of ATG5 and ATG11 targets from the combined protein degradation rate and

294 abundance data

295 Protein abundance and degradation rate changes were then plotted orthogonally to

296 pinpoint probable autophagy protein targets (Fig 5). When atg11 and atg5 were compared

297 to WT, we found that 140 and 200 root proteins and 116 and 187 shoot proteins showed

298 significant changes in abundance and/or degradation rate (Student’s T-test, P<0.05). The

299 response of these proteins could be grouped into the four quadrants with different

300 responses as explained in Fig 5.

301

302 Of the significantly changed proteins in roots, 80% were more abundant and slow degrading

303 (Group 1-green) or less abundant and faster degrading (Group 2-red) (Fig 5 A,B). The former

304 are potential autophagy targets, and constituted more than half of the significantly changed

305 proteins in atg11 but less than 20% in atg5 (Data S5). These proteins are typically localized

306 to the ER, cytosol, nucleus, peroxisomes, and mitochondria (Fig 5C). Furthermore, they are

307 components of mitochondrial oxidative phosphorylation, amino acid metabolism, glycolysis,

308 the ribosome and proteasome, TCP-1 chaperones, and protein folding and processing in the

309 ER (Table S1). In comparison, most of the proteins that were degraded faster but

310 accumulated less in the mutants (Group 2-red) were potential components of alternative

311 and/or compensatory pathways and were localized to vacuoles, plasma membrane, plastids,

312 and apoplast. From these, it was apparent that the mitochondrial TCA cycle and oxidative

313 phosphorylation proteins showed the most distinct differences between the mutants, with

314 17 mitochondrial proteins belonging to Group 1 in atg11 but not in atg5.

315

316 In shoots, only 50% of proteins that significantly changed were in Group 1 and 2, and Group

317 1 accounted for less than 20% of proteins in both mutants (Fig 5D, E). In atg11, most of the

318 remaining proteins were in Group 4 while in atg5, a higher proportion of proteins were in

319 Group 3. Group 1 from shoots included mainly resident peroxisome, cytosolic, mitochondrial

320 and chloroplastic proteins, with a smaller proportion of nuclear and vacuolar proteins (Fig

321 5F). Proteins in the chloroplast showed different responses between mutant lines; ten

322 chloroplastic proteins, including RUBISCO large subunit, fell into Group 1 in atg11 but in

323 Group 3 in atg5 (Table S2). This again was consistent with ATG11 playing a specialized role in

324 mitochondrial and chloroplastic protein degradation. A high proportion of proteins from

325 shoots in Group 2 localize to plastids, vacuoles, or the apoplast (Fig 5D,E). The higher

326 proportion of proteins that fell into Group 4 in shoots compared to roots (Fig 5C,F) suggests

327 that protein synthesis attenuation might mask autophagy degradation in shoot tissues.

328

329 Pi limitation induces autophagy and changes metabolite abundances in hydroponically

330 grown Arabidopsis

331 Nitrogen, phosphate or carbon limitation are reported to activate autophagy, promote

332 cellular content degradation in plants and lead to early senescence in autophagy mutants

333 (Yoshimoto et al., 2009b; Avin-Wittenberg et al., 2015; Barros et al., 2017; McLoughlin et al.,

334 2018; Naumann et al., 2019; McLoughlin et al., 2020). However, it is unclear if such

335 conditions lead to a generic induction of autophagy or of selective autophagy of

336 stress-related targets. Nitrogen limitation conditions would limit our ability to use a 15N

337 labelling and darkness would limit both carbon and 15N incorporation into amino acids

338 (Nelson et al., 2014). Therefore, we subjected plants to Pi starvation to investigate its effect

339 on protein abundance and degradation rate in all three genotypes (Fig 6).

340

341 No visible phenotypic changes were observed in plants grown under Pi-limited conditions

342 over 3 days of treatment (Fig 6A), although both root and shoot Pi content was significantly

343 reduced in all genotypes (Fig 6B). To monitor autophagy induction and autophagic flux, we

344 performed an imaging analysis of a line expressing GFP-ATG8a, which is localized to

345 autophagic membranes and autophagosomes in root cells. Abundant GFP-ATG8a-decorated

346 organelles were evident in the elongation zone of Pi-limited roots but not in the equivalent

347 root zone from control plants (Fig 6C). In shoots, the Fv/Fm ratio remained at 0.8 in all three

348 genotypes under control and Pi-limiting conditions (Fig 6D). Consistent with the reduced Pi

349 content, the transcript of the Pi sensor SPX1 was induced in all three genotypes when plants

350 were grown under Pi-limiting conditions (Fig 6E). Autophagy associated genes, ATG8H and

351 ATG7, were also induced under limited Pi in WT and atg5 but not in atg11 plants (Fig 6E).

352 Extension of Pi-limited conditions to >10 days led to purple coloration of rosette leaves,

353 indicating stress-induced anthocyanin accumulation.

354

355 To further assess the impact of Pi limitation on metabolism, we performed a mass

356 spectrometry-based profiling of selected primary and secondary metabolites in shoots and

357 roots both between WT and autophagy mutants and within each genotype (Fig 7, Data S11).

358

359 In roots, there was a drastic decrease of amino acids commonly reported to be highly

360 responsive to nutrient limitation conditions (Araújo et al., 2011; Masclaux-Daubresse et al.,

361 2014; Avin-Wittenberg et al., 2015; Barros et al., 2017). However, we observed very little

362 effect on the abundance of amino acids and sugars in both mutants upon Pi limitation.

363 Organic acids in roots generally did not change in abundance except for isocitrate and

364 D-2-hydroxyglutarate, which were slightly more abundant in atg11 under Pi starvation.

365 Several glucosinolates changed in abundance in roots of autophagy mutants only under Pi

366 limitation. Salicylic acid (SA) levels did not change in mutants under control conditions but

367 accumulated in atg5 under limited Pi (Fig 7), as also previously reported in dark-induced

368 senescence (Yoshimoto et al., 2009b). Interestingly, SA-sugar conjugates, including

369 2,3-dihydroxybenzoate glucoside 2,3-dihydroxybenzoate xyloside, and

370 2,5-dihydroxybenzoate xyloside accumulated in autophagy mutants under both control and

371 Pi-limiting conditions. SA conjugation inactivates SA; the accumulation of these compounds

372 in autophagy mutants might be part of a mechanism to partially prevent the SA-dependent

373 early senescence typical of autophagy mutants (Yoshimoto et al., 2009b).

374

375 In shoots, the levels of amino acids alanine, threonine, serine, and phenylalanine were

376 reduced in the mutant lines. Asparagine and aspartic acid showed some accumulation in

377 atg11 but not in atg5 under both control and Pi-limiting conditions. Organic acids did not

378 show significant changes in abundance, with the exception of fumarate, which was reduced

379 in atg11, and succinate, which decreased in both autophagy mutants but only under Pi

380 limitation. Few changes in sugar and sugar derivative abundances were present in shoots,

381 with glucose and glucose-6-phosphate decreasing slightly in atg11 or atg5, respectively. All

382 glucosinolates, except 8-methylsulfinyloctyl glucosinolate, decreased in abundance in shoots

383 of autophagy mutants. Consistent with the patterns seen in roots, SA-sugar conjugates in

384 shoots were more abundant in autophagy mutants, but this accumulation was only

385 statistically significant for 2,3-DHBX in atg11. Overall these profiles indicate that many of the

386 metabolic effects of autophagy disruption are already evident under control conditions,

387 while some of them were enhanced by Pi limitation.

388

389

390 Pi limitation had only a mild impact on root cytosolic protein degradation in mutant lines

391 and on mitochondria abundance in atg11

392 To determine if protein degradation rates were similarly affected by Pi limitation, we

393 compared protein abundance for 1045 proteins and degradation rates for 476 proteins

394 among WT, atg5, and atg11 roots under both control and Pi-limiting conditions. A principal

395 component analysis (PCA) of these datasets showed that each genotype/treatment group

396 could be separated by protein abundance and degradation rate (Fig 8A-B). Low Pi increased

397 the abundance of vacuole proteins and decreased the abundance of Golgi proteins in WT;

398 however, the same treatment caused an increase in vacuolar proteins in atg11 but not in

399 atg5 whereas Golgi proteins were not significantly altered in either mutant (Fig S7). Low Pi

400 did not induce significant changes in protein degradation rates in WT; however, it did

401 decrease mitochondrial protein degradation rates in both atg11 and atg5, and peroxisomal

402 protein degradation rates in atg5 (Fig S8).

403

404 We then expressed the root datasets as relative changes in mutants and compared them

405 between control and Pi-limiting conditions (Fig 8C-F). We found that mild Pi limitation

406 further increased mitochondrial protein abundance in atg11 compared to WT, but not in

407 atg5. Unexpectedly, Pi limitation decreased the degree of differences in degradation rates of

408 cytosolic proteins between the mutant lines and WT (Fig 8D,F). This is seen in the narrower

409 distribution of relative ∆KD values under Pi limited conditions. However, we found nine

410 proteins, including five cytosolic glycolytic enzymes, Annexin 1, the glutathione transferase

411 ATGSTF8 and the ER-localized beta-glucosylase BGLU22, with significantly faster degradation

412 rates under Pi limitation in WT (Fig 8G, DataS10). Intriguingly, the faster degradation of

413 these proteins under low Pi did not lead to a decrease in their abundance; rather, four out of

414 nine proteins were more abundant under low Pi conditions. This pattern is consistent with

415 induced protein synthesis as a means to compensate for faster protein degradation under Pi

416 limitation. Faster degradation of AtGSTF8 and BGLU22 under low Pi were only detected in

417 WT but not in the autophagy mutants (Fig 8G, DataS10).

418

419

420 Pi limitation affects to degree of relative changes in abundance of chloroplast proteins in

421 shoots and their degradation rates in both atg5 and agt11

422 We also compared protein abundance of 782 proteins and degradation rates of 505 proteins

423 among WT, atg5, and atg11 shoots under control and Pi-limiting conditions. By applying a

424 PCA, we found that protein abundance in WT samples could be separated from atg5 and

425 atg11 under both control and Pi-limiting conditions, while atg5 and atg11 samples can be

426 clearly separated under Pi limitation but not under control conditions (Fig 9A). In terms of

427 protein degradation rates, WT samples could be fully separated by PCA from atg5 and atg11

428 under low Pi, but not under control conditions (Fig 9B). Pi limitation led to a decrease in

429 cytosolic protein abundance in WT and atg11 but not in atg5. Chloroplastic proteins

430 accumulated in WT under low Pi whereas under similar conditions, chloroplastic protein

431 abundances decreased in both mutant lines (FigS7). Pi limitation conditions altered protein

432 degradation rates of chloroplastic proteins only in atg5 (FigS8).

433

434 As done for the root datasets, we then expressed the shoot data as relative changes in

435 mutants to facilitate comparisons between samples grown under control and Pi-limiting

436 conditions (Fig9C-F). Low Pi again led to a narrower distribution of the abundance changes

437 of chloroplastic proteins in both mutant lines compared to WT and smaller changes of

438 protein degradation rate for chloroplast proteins in atg5. Conversely in atg11, Pi limitation

439 was associated with a narrower distribution of changes in cytosolic and mitochondrial

440 protein abundance without affecting the changes in protein degradation rate. Although

441 there was no overall change in organellar degradation rate, six shoot proteins (PSBO-1,

442 At3g47070, thylakoid phosphoprotein-At3g47070, thioredoxin-At1g03680, PSAC, LHCA3 and

443 plastocyanin 1) showed significantly faster degradation rates under Pi limiting conditions in

444 WT (Fig 9G, Data S10). Four of them, namely PSBO-1 (PSII), thioredoxin (chloroplast stroma),

445 PSAC (PSI), and LHCA3 (PSI) showed unchanged or slower degradation rate in atg11 and

446 atg5 under low Pi. These are therefore potential ATG11 and ATG5 associated targets of

447 induced autophagy under low Pi. Plastocyanin 1 (thylakoid) showed a slower degradation

448 rate and significant increase in abundance in atg11 but faster degradation in atg5 and

449 non-significant change in abundance, which suggests its degradation is dependent on ATG11

450 but not ATG5. A notable exception to these trends was the thylakoid TSP9 phosphoprotein

451 which is involved in photosystem state transition (Fristedt et al., 2009) that had an increased

452 degradation rate but also accumulated in abundance in WT and mutant lines under low Pi,

453 indicating its high protein synthesis rate and its turnover in all lines under Pi limitation.

454

455

456 Discussion

457

458 Investigation of protein turnover in autophagy mutant lines

459 Defects in autophagy can cause accumulation of autophagic protein cargo due to impaired

460 degradation, but also lead to many changes in metabolite and transcript abundances,

461 complicating the interpretation of cause and effect. Increase in transcript abundance may

462 reveal up-regulation of gene expression (McLoughlin et al., 2018; McLoughlin et al., 2020),

463 but that does not necessarily correlate with protein synthesis. In addition, estimating

464 protein synthesis by ribosome profiling (Juntawong et al., 2014; Chotewutmontri and Barkan,

465 2016) or newly made protein labelling strategies (Wang et al., 2016) in autophagy mutants

466 can be misleading since ribosomes themselves are targets of autophagy (Gretzmeier et al.,

467 2017; McLoughlin et al., 2018), Table S2, FigS4). Focusing on steady-state protein abundance

468 alone in impaired autophagy mutants also fails to identify proteins that may maintain

469 homeostasis, either by using alternative degradative pathways or reducing protein synthesis.

470 By focusing on protein degradation rates and correlating these with protein abundance in

471 autophagy mutants, we circumvent some of these problems to reveal subsets of proteins

472 that are directly influenced by autophagic processes, with or without compensatory changes

473 in protein synthesis. Similar approaches in human fibroblasts (Zhang et al., 2016) and

474 Drosophila melanogaster (Vincow et al., 2019) have also pinpointed specific protein

475 complexes and organelles that are differentially affected by autophagy in other organisms.

476

477 Common changes in cytosolic protein abundance and degradation rates in atg11 and atg5

478 The ATG11 and ATG5 mutants used in this report were previously shown to be bona fide

479 single gene mutants in Arabidopsis, to have impaired autophagic fluxes detected by vacuolar

480 delivery of ATG8-GFP, and to share typical early senescence phenotypes at late

481 developmental stage and under nutrient limitation conditions (Thompson et al., 2005;

482 Yoshimoto et al., 2009b; Li et al., 2014; Li and Vierstra, 2014). We show atg11 and atg5 share

483 many common differences in cytosolic and organellar protein abundances and associated

484 changes in protein degradation rates, although atg5 typically showed larger relative changes

485 in protein abundance (Fig 1&4, Fig S4&S5). The larger differences in atg5 are broadly

486 consistent with the severity of this mutant’s senescence phenotype compared with atg11

487 (Yoshimoto et al., 2009b; Li et al., 2014), and the established roles of ATG5 and ATG11 in the

488 autophagy process; namely ATG5 acting in the core conjugation cascade and ATG11 acting in

489 a regulatory complex.

490

491 Glycolytic enzymes including phosphoglycerate kinase, enolase, triosephosphate isomerase

492 and fructose bisphosphate aldolase showed slower protein degradation rates in both atg11

493 and atg5, but these led to only a mild increase in abundance of these enzymes (0-24%) in

494 autophagy mutants. Interactions between autophagy and glycolytic enzymes have been

495 previously reported in plants and other organisms and can be complex (Han et al., 2015;

496 Henry et al., 2015; Watson et al., 2015; Qian et al., 2017a; Qian et al., 2017b). Firstly,

497 glycolytic enzymes play roles in autophagic flux regulation. For example,

498 glyceraldehyde-3-phosphate dehydrogenases (GAPDHs) negatively regulate autophagy in

499 Arabidopsis (Henry et al., 2015) and in tobacco GAPDHs can reduce autophagy activities by

500 binding to ATG3 (Han et al., 2015). In contrast, phosphoglycerate kinase 1 (PGK1) can induce

501 autophagy under cellular stress conditions in mammals through phosphorylating of Beclin1

502 (Qian et al., 2017b). Secondly, autophagy can downregulate glycolysis metabolism through

503 selective degradation of enzymes. For example, selective degradation of hexokinase (HK) in

504 human liver cancer cells during autophagy (Jiao et al., 2017). Here we show the basal rate of

505 degradation of glycolytic enzymes in Arabidopsis is partially due to autophagy, but impaired

506 autophagy may be compensated for by changes in glycolytic enzyme synthesis that prevent

507 their accumulation. Recently, proximity-dependent biotinylation screening of in vivo

508 interactions confirmed GAPDH and fructose bisphosphate aldolase are bound by ATG8 in

509 plants (Macharia et al., 2019).

510

511 The Chaperonin Containing T-complex polypeptide-1 (CCT) protein complex in human cell

512 lines is present in immunopurified autophagosomes, degrades slowly in autophagy mutants

513 (Dengjel et al., 2012; Zhang et al., 2016), and can restrict neuropathogenic protein

514 aggregation via autophagy in human cell lines and fruit fly (Pavel et al., 2016). Although well

515 documented in animals, a 20S protein complex consisting of eight CCT subunits was only

516 recently reported in plants (Ahn et al., 2019; McWhite et al., 2020). In this study, we found

517 five CCT protein complex subunits (CCT1-4, 8) increased in abundance and had slower rates

518 of degradation in roots of both autophagy mutants (Table S1, Fig S4), supporting the

519 hypothesis that CCT is an autophagy target in Arabidopsis roots. CCT is known to affect the

520 folding and stability of tubulin in Arabidopsis and mutants with deficient CCT function show

521 depletion of cortical microtubules and reduced alpha and beta tubulin abundance due to

522 increased degradation (Ahn et al., 2019). Interestingly, we found that five beta tubulins

523 (TUB2,4,6,8,9) but not alpha tubulins, showed decreased abundance in atg11 and atg5. It is

524 unclear whether the decreased abundance of beta tubulin is a direct or indirect effect of

525 impaired autophagy, but microtubules are important for autophagy. Microtubules can

526 interact with autophagic proteins and play roles in pre-autophagosome structure, autophagy

527 induction, formation and movement (Mackeh et al., 2013). In plants, microtubules are

528 proposed to aid autophagosome delivery to the vacuole with the help of FYVE and

529 coiled-coil domain–containing (FYCO) proteins that bind ATG8 and PI3K on the

530 autophagosome outer membrane (Marshall and Vierstra, 2018). We also found actin (ACT7)

531 and the actin-interacting proteins VILIN4 and PROFILIN1/2 showed decreased abundance in

532 both mutants. In yeast, actin filaments are only involved in selective but not bulk autophagy

533 (Hamasaki et al., 2005; Reggiori et al., 2005; Monastyrska et al., 2009). In mammals, actin

534 was found to be required for both selective and bulk autophagic degradation (Kast and

535 Dominguez, 2017; Xu et al., 2018). In plants, actin filaments seem to be dispensable for bulk

536 autophagy in tobacco (Zheng et al., 2019), but it is unclear whether actin is needed for any

537 form of selective autophagy in plants. Our results thus suggest that the homeostasis of the

538 plant CCT complex is controlled by autophagic degradation and that accumulation of CCT

539 complex subunits correlated with decreases in the abundance of components of the

540 cytoskeleton, which warrants further investigation.

541

542 Changes in organelle abundance and degradation rates in atg11 and atg5

543 Many proteins with decreased degradation rates in both root and shoot tissues of

544 autophagy mutants localize to the ER, peroxisomes, or mitochondria (Fig 5). This supports

545 the notion that ER, peroxisome and mitochondrial proteins are autophagy cargo in both

546 photosynthetic and non-photosynthetic tissues. Interestingly, we found that in roots, a high

547 proportion of these proteins increased in abundance, while in shoots a high proportion

548 decreased in abundance (Fig 1). This means that proteins with slower degradation showed

549 reduced abundance in shoot but accumulated in roots, suggesting that photosynthetic

550 tissues have more plasticity for transcription and translational control during autophagy than

551 root tissues. The more prominent deployment of alternative proteostasis/protein recycling

552 mechanisms in shoots than in roots is also consistent with the more drastic decrease in

553 amino acids levels in roots than in shoots of autophagy mutants (Fig 7).

554

555 In roots, organelle proteins with faster degradation rates in autophagy mutants were

556 predominantly localized to plastids, apoplast, plasma membrane, and vacuoles. Whereas we

557 did not find evidence of increases in plastid protease abundance in roots (Data S1), other

558 degradative pathways can deliver portions of plastids to the vacuole (Izumi et al., 2017;

559 Otegui, 2018; Zhuang and Jiang, 2019). Therefore, it is possible that

560 ATG5/ATG11-independent pathways that mediate plastid turnover are stimulated in roots of

561 autophagy-deficient mutants. The content of extracellular, plasma membrane, and vacuolar

562 proteins is closely associated with the rate of intracellular vesicle trafficking. Proteins reach

563 the vacuole through the secretory and endocytic/endosomal pathways as well as through

564 autophagy (Marty, 1999; Pereira et al., 2014; Zhang et al., 2014; Shimada et al., 2018).

565 Deficient autophagy in atg5 and atg11 correlated with a decreased abundance of both

566 tonoplast and vacuolar lumen proteins (Fig S4, Fig S5). The outer membrane of the

567 autophagosome is integrated into the tonoplast upon fusion and it is therefore assumed to

568 supply large quantities of membrane to vacuoles. Consistently, although not statistically

569 significant, we noticed by electron microscopy a consistent decrease in tonoplast membrane

570 in actively vacuolating cells of both atg5 and atg11 root cells. We also found that CLATHRIN

571 HEAVY CHAIN 1 (CHC1, At3g11130) was more abundant in roots of atg5 (Fig S4). Clathrin is

572 associated with endocytosis at the plasma membrane and sorting at the TGN and

573 endosomes (Gao et al., 2019). The altered abundance of clathrin and other trafficking

574 components could alter both endocytosis/endosomal and exocytosis rates, contributing to

575 the fast turnover and low abundance for both plasma membrane and extracellular proteins

576 seen here in autophagy mutants. However, future experimental evidence is still needed to

577 investigate the endocytosis/exocytosis processes in atg5 and other autophagy mutants.

578

579 Protein abundance and degradation rate specific changes in atg11 and atg5

580 The most severe molecular alterations in atg5 were the five-fold increases in specific

581 ER-resident proteins. This differential effect on ER homeostasis in atg5 also correlated with

582 accumulation of vesicle transport-associated proteins, such as three COP1 coatomers (alpha,

583 delta and gamma subunits) in atg5. COP1 is essential for retrieval of proteins with di-lysine

584 motifs from Golgi stacks back to the ER (Wang et al., 2018), intra Golgi transport, and Golgi

585 maintenance. Interestingly, we observed in atg5 but not in atg11 root cells abnormal

586 membranous structures with assembled coats of unknown nature reminiscent of Golgi

587 and/or ER membranes (Fig 3). Whereas the origin of these abnormal, coated, membranous

588 structures in atg5 cells is unknown, the mis-regulation of COP1 components in mammalian

589 cells induces the re-localization of Golgi, TGN, and ER markers into large membranous

590 structures (Styers et al., 2008). The fungal toxin brefeldin A inhibits the assembly of the

591 COP1 coat and also results in large abnormal membranous bodies (BFA bodies) in plants,

592 that contain Golgi, TGN, and endosomal proteins (Nebenfuhr et al., 2002; Lam et al., 2009;

593 Berson et al., 2014). In addition, the loss of COP1 subunits also leads to the accumulation

594 of abnormal autophagosomes not fully capable to fusing with lysosomes (Razi et al., 2009).

595

596 A higher proportion of mitochondrial proteins with slower degradation rates was found in

597 atg11 compared with atg5, in both root and shoot tissues (Table S1, S2). Higher abundance

598 of mitochondrial proteins was also common in both shoot and root of atg11, but only in

599 shoots of atg5 (Fig 1). ATG11 has been reported to be essential for senescence-induced

600 mitophagy in Arabidopsis photosynthetic tissues (Li et al., 2014; Li and Vierstra, 2014) and

601 ATG5-dependent mitophagy has been recently reported in Arabidopsis cotyledons and roots

602 (Ma et al 2021); however, in vivo changes in degradation rate of specific mitochondrial

603 proteins in either atg11 or atg5 under control conditions has not been reported previously

604 to our knowledge. Interestingly, although chloroplast proteins show general increases in

605 abundance in shoots of both atg11 and atg5, we only found a higher proportion of

606 chloroplast proteins with slower degradation rates in shoots of atg11 (Fig 2, Table S2). These

607 same chloroplast proteins showed faster protein turnover rates in atg5. We interpret this to

608 mean that chloroplast proteins accumulated in atg11 through deficient degradation but

609 through enhanced synthesis in atg5. Taken together, the different patterns of degradation

610 and abundance changes in mitochondrial and chloroplastic proteins in the mutant lines

611 support a specialized role of ATG11 in basal level mitochondrial and chloroplast protein

612 homeostasis and highlight specific organelle proteins that are good indicators of this role.

613

614 Pi limitation caused autophagy-dependent cytosolic protein degradation and vacuole

615 biogenesis in roots, and chloroplast degradation in shoots.

616 Pi limitation has been reported to induce autophagy in yeast and plants (Tasaki et al., 2014;

617 Yokota et al., 2017; Naumann et al., 2019) and our data indicate we could reproduce this

618 effect in hydroponically-grown Arabidopsis plants (Fig 6). Pi limitation-induced autophagy is

619 reported to contribute to vacuole biogenesis (Gao et al., 2017), which is consistent with the

620 general increase in abundance of vacuolar proteins in roots of WT plants grown in Pi-limiting

621 conditions (Fig S7). A similar increase in vacuolar proteins was observed in roots of atg11

622 but not of atg5, suggesting ATG11 is not essential for autophagy-dependent vacuole

623 biogenesis under Pi-limiting conditions. A new ATG1-independent autophagy mechanism in

624 prolonged carbon starvation conditions was recently reported (Huang et al., 2019), so this

625 might explain the activation of an autophagic pathway independent of the ATG1 kinase

626 complex in atg11. However, the role of ATG11 in mitochondrial degradation was not

627 diminished under Pi limitation (Fig 8C), indicating that ATG1-independent autophagy in

628 atg11 cannot compensate for deficient mitochondria degradation. We also found that Pi

629 limitation attenuated the cytosolic protein abundance differences observed between WT

630 and autophagy mutants, without affecting protein degradation rates. This could indicate

631 complementary transcription/translation changes induced by Pi limitation in these

632 autophagy mutants.

633

634 In roots, BGLU22 and ATGSTF8 had faster turnover rates under Pi limitation in WT but not in

635 either autophagy mutant (Fig 8G). While BGLU22 showed a slight abundance reduction,

636 ATGSTF8 accumulated in WT. This indicates that in WT plants growing in Pi-limiting

637 conditions, BGLU22 turnover is associated with the stimulated autophagic degradation while

638 ATGSTF8 increased turnover is compensated with even greater protein synthesis. BGLU22

639 localizes to root ER bodies is an EE-type myrosinase that can break down aliphatic

640 glucosinolates during stress conditions (Sugiyama and Hirai, 2019). The faster degradation of

641 BGLU22 under Pi-starvation suggest that ER bodies containing BGLU22 may be delivered to

642 the vacuoles containing the aliphatic glucosinolates by autophagy.

643

644 All six proteins that degraded faster under Pi limiting conditions in WT shoots were

645 chloroplast-resident proteins. Four of them showed unchanged or slower degradation rate in

646 atg11 and atg5 (Fig 9G), suggesting a role for Pi limitation in ATG11/5 associated autophagic

647 chloroplast degradation. These unchanged or slower degrading proteins were components

648 of photosystems and their light harvesting complexes (PSBO-1, PSAC, LHCA3), and a plastid

649 thioredoxin (thioredoxin M1) that regulates photosynthetic acclimation in fluctuating light

650 intensities by regulating the export of excess reductive power from the chloroplasts

651 (Thormahlen et al., 2017). An electron carrier between photosystems (plastocyanin 1)

652 showed slower degradation and a significant increase in abundance in atg11 but faster

653 degradation rate in atg5 and no change in abundance, which suggest its degradation is

654 dependent on ATG11 but not ATG5 (Data S10). These represent useful protein markers to

655 study general or selective chloroplast degradation by autophagy.

656

657

658 Methods

659

660 Arabidopsis hydroponic plants preparation and 15N labelling

661 Arabidopsis thaliana accession Columbia-0 (WT), atg5 and atg11 plants were grown under

662 16/8-h light/dark conditions with cool white T8 tubular fluorescent lamps 4000K 3350 lm

663 (Osram, Germany) with intensity of 100–125 μmol m-2 s-1 at 22 °C. The hydroponic protocol

664 was as described previously (Waters et al., 2012) and used a modified Hoagland solution (2

665 mM CaCl2, 6 mM KNO3, 0.5 mM NH4NO3, 0.5 mM MgSO4, 0.25 mM KH2PO4, 0.05 mM KCl

666 and 0.04 mM Fe-EDTA) supplemented with micro elements (25 μM H3BO3, 2 μM MnCl2, 2

667 μM ZnSO4, 0.5 μM CuSO4, 0.15 μM CoCl2 and 0.25 μM (NH4)6Mo7O24) and 2.6 mM MES, and

668 the pH was adjusted to 5.8-6.0. Seeds of different lines (WT, atg5 and atg11) were planted

669 on the growth hole of agar stuffed in lids of 1.5-ml black tubes sitting in 24-well floater tubes

670 racks containing 160 ml growth medium. The seeds were vernalized under 4 °C for 2-3 days

671 before being transferred to the growth chambers. Half-strength growth medium was used

672 for the first week. A single plant was placed in every tube lid and four tubes lids in each

673 floater tube rack (FigS1). The growth medium was changed every 5 days. Unlabelled

674 Arabidopsis plants were grown for 21 days until they reached leaf production stage 1.10 (T0)

675 (Boyes et al., 2001) in natural abundance medium as noted above. To obtain a fully labeled

15 15 15 15 15 676 N protein reference standard, N medium (with 6 mM K NO3, 0.5 mM NH4 NO3) was

677 used to replace the natural abundance nitrogen in the medium and plants were grown from

678 seed in this medium for 26 days. For progressive 15N labelling, the growth medium was

679 discarded and the growth racks rinsed four times with fresh medium without nitrogen (no

15 680 KNO3 or NH4NO3) to ensure the old solution was washed out. A total of 160 ml of N

15 15 15 681 medium (6 mM K NO3, 0.5 mM NH4 NO3) was added for every four plants and the plants

682 were grown for three days before collecting leaf and root tissues for separate total protein

683 extraction (FigS9). Root/shoot from two plants in one rack were pooled as a biological

684 replicate, three biological replicates were collected.

685

686 Protein extraction, in-solution digestion, high pH HPLC separation and LC-MS analysis of

687 tryptic peptides

688 The root/shoot samples (0.1-0.2 g) from fully 15N labelled reference, 15N progressively

689 labeled and unlabeled of three lines (WT, atg5 and atg11) were snap frozen in liquid

690 nitrogen and homogenized using Qiagen tissue lysis beads (5 mm) by vortex. A total plant

691 protein extraction kit (PE0230-1KT, Sigma Chemicals) was used to extract root/shoot total

692 proteins. The final pellet of total protein was dissolved in Solution 4 and then reduced and

693 alkylated by tributylphosphine (TBP) and iodoacetamide (IAA) as described in the Sigma

694 manual. The suspension was centrifuged at 16,000 g for 30 min and the supernatant was

695 assay for protein concentration by amido black quantification as described previously (Liu et

696 al., 2012a).

697 A total of 100 µg root/shoot proteins from progressively 15N labelled samples were digested

698 in solution as described previously (Nelson et al., 2014). A total of 50 µg of unlabeled

699 root/leaf protein samples noted above was mixed individually with 50 µg of the fully

700 15N-labelled reference and digested in solution by trypsin. Each sample was separated into

701 96 fractions by high pH HPLC separation and further pooled into 12 fractions and each

702 fraction was analyzed by mass spectrometry. Filtered samples (5 µl each) were loaded onto a

703 C18 high-capacity nano LC chip (Agilent Technologies) using a 1200 series capillary pump

704 (Agilent Technologies) as described previously (Li et al., 2017).

705

706 MS data analysis, calculations of KD and relative abundance values

707 Agilent .d files were converted to mzML using the Msconvert package (version 2.2.2973)

708 from the Proteowizard project, and mzML files were subsequently converted to Mascot

709 generic files using the mzxml2 search tool from the TPPL version 4.6.2. Mascot generic file

710 peak lists were searched against an in-house Arabidopsis database comprising ATH1.pep

711 (release 10) from The Arabidopsis Information Resource (TAIR) and the Arabidopsis

712 mitochondrial and plastid protein sets (33621 sequences; 13487170 residues) (Lamesch et

713 al., 2012), using the Mascot search engine version 2.3 and utilizing error tolerances of 100

714 ppm for MS and 0.5 Da for MS/MS; “Max Missed Cleavages” set to 1; variable modifications

715 of oxidation (Met) and carbamidomethyl (Cys). We used iProphet and ProteinProphet from

716 the Trans Proteomic Pipeline (TPP) to analyze peptide and protein probability and global

717 false discovery rate (FDR) (Nesvizhskii et al., 2003; Deutsch et al., 2010; Shteynberg et al.,

718 2011). The reported peptide lists with p=0.8 have FDRs of <3% and protein lists with p=0.95

719 have FDRs of <0.5%. Quantification of LPFs (labeled peptide fraction) were accomplished by

720 an in-house R script which was written originally in Mathematica (Nelson et al., 2014). A

721 median polish method described previously was used for data analysis (Li et al., 2017).

722 Measured protein degradation rate 0.1 d-1 was used to calculate the FCP (fold change

723 protein) for shoot samples. For root tissues, measured FCP based on fresh weight was

724 measured before and after progressive 15N labelling. A measured degradation rate 0.5 d-1

725 was determined and then applied to calculate FCP in samples of Wt and mutant lines, which

726 were applied for degradation rate calculations. We determined changes in specific protein

727 abundance using a fully labeled 15N protein reference standard. Protein abundance was

728 represented as ratio to reference and normalized to all samples (three lines under both

729 control and Pi starvation conditions) as previously reported (Li et al., 2017). Relative

730 ∆Abundance (i.e. (mutant–Wt)/Average (mutant and Wt)) was used to describe the level of

731 changes between mutant vs Wt or treatment vs control.

732

733 RNA extraction and Q-PCR analysis

734 We collected leaf 5 from all three lines at 21 days, and collected leaf 6 after three days Pi

735 starvation treatment. The shoot samples (~0.1g) from three lines (Wt, atg5 and atg11)

736 under control/Pi starvation conditions were snap frozen in liquid nitrogen and homogenized

737 to powder using Qiagen tissue lysis beads (2 mm) by a homogenizer. RNA was extracted

738 using SpectrumTM Plant Total RNA kit (Sigma-Aldrich, STRN250-1KT)with On-Column DNase

739 treatment (Sigma-Aldrich, DNASE70) following manufacturer’s instructions. 500ng of RNA

740 was used for cDNA synthesis with iScript cDNA synthesis kit (Bio-rad, 1708890). Transcripts

741 of spx1 (F- TGCCGCCTCTACAGTTAAATGGC, R-TGGCTTCTTGCTCCAACAATGG), atg8h

742 (F-TGCAGTTAGATCCATCCAAAGCTC, R-TCCATGCGACTAGCGGTTTGAG) and atg7

743 (F-ACGTGGTTGCACCTCAGGATTC, R- ACTAAGAGTTCAACGGCGAGAGC) were quantified

744 using QuantiNova SYBR green PCR kit (Qiagen, 208056) with LightCycler380 in Wt, atg5/11

745 lines under both control and Pi starvation conditions. We did four biological replicates for

746 most samples except atg11 post starvation treatment. QPCR data were normalized to

747 housekeeping genes AKT2 (F- GGTAACATTGTGCTCAGTGGTGG,

748 R-AACGACCTTAATCTTCATGCTGC) and UBQ10(F-CTGCGACTCAGGGAATCTTCTA,

749 R-TTGTGCCATTGAATTGAACCC) before analysed using geometric averaging of multiple

750 control genes (Vandesompele et al., 2002; Czechowski et al., 2005) before being compared.

751

752 Pi concentration measurement by a colorimetric assay

753 Wt, atg5 and atg11 lines were grown hydroponically till leaf production stage 1.10 (T0).

754 Growth containers were rinsed with water for complete phosphate depletion. For Pi

755 starvation treatment, plant growth media was replaced with Hoagland solution without

756 phosphate and grown for three days (T3-Pi starvation). Hoagland solution with phosphate

757 was used for control plants (T3). Inorganic concentration in root/shoot tissues of three lines

758 were measured by a colorimetric assay. Inorganic phosphate was extracted in 500 µl water

759 from 10-mg frozen powdered samples. The concentration of Pi was determined

760 spectrophotometrically at 820 nm after a 90-min reaction at 37°C in the presence of 1.4%

761 w/v ascorbate and 0.36% w/v ammonium molybdate in 1 N H2SO4 (Ames, 1966).

762

763 Maximum quantum yield of PSII measurement by IMAGING-PAM

764 Leaf production stage 1.10 Arabidopsis plants (T0-grown in hydroponics for ~21 days

765 post-germination) were washed by Hoagland media without phosphate and then grown for

766 another three days in fresh growth media (T3-control) or growth media without phosphate

767 (T3-phosphate starvation). Whole plants were dark adapted least 20 mins before being

768 measured by a MAXI version of the IMAGING-PAM. A color gradient was used to

769 demonstrate the Fv/Fm (maximum quantum yield of PSII) values which were measured by

770 IMAGING-PAM in leaves of the whole rosette. One biological replicate was a combination of

771 measured Fv/Fm values in six leaves in two Arabidopsis plants.

772

773 Confocal laser scanning microscopy

774 GFP-ATG8a plants were grown hydroponically till leaf production stage 1.10. Whole plants

775 were transferred into normal or -Pi growth media and grown for another three days. E64d

776 was supplemented into growth media 24 hours before the confocal laser scanning

777 microscopy experiment to a final concentration of 100 µM. A Nikon A1Si confocal

778 microscope equipped with laser line 488-nm excitation and emission band-pass filter of

779 500-520 nm, and controlled by a NIS element AR software package (version 4.13.01, Build

780 916) was used. Images were acquired using a 20x lens (Nikon CFI Plan Apo VC 20x 0.75 N.A.)

781 with pinhole diameter of 2.5 airy units (corresponds to the optical slice of 4.37 um).

782 Autophagic puncta (AP) of representative images were counted by the ‘Analyze Particles’

783 function of ImageJ. AP numbers in each Z-stack were plotted. The distribution of AP number

784 under control and Pi limitation conditions in the representative image were compared by

785 Kolmogorov-Smirnov test for significance.

786

787 Transmission electron microscopy

788 Arabidopsis seedlings were grown for 24 days in hydroponic conditions as described above.

789 Root tips were excised and placed in freezing planchettes containing 0.1M sucrose and

790 high-pressure frozen in a Baltec HPM 010. Samples were high-pressure frozen in 2% (w/v)

791 OsO4 in anhydrous acetone in dry ice overnight and warmed to room temperature on a

792 rocker with slow agitation for several hours, until they reached at room temperature. After

793 several acetone rinses and the planchets removed, samples were infiltrated in a series of

794 Epon resin changes polymerizing at 60°C for 24 h. Sections were stained with 2% uranyl

795 acetate and lead citrate (2.6% lead nitrate and 3.5% sodium citrate, pH 12) and imaged in a

796 Philips CM120 transmission electron microscope. Morphological measurements were done

797 using FIJI (Schindelin et al., 2012).

798

799 Metabolite Extraction

800 Plant tissues (15–50 mg) were collected at specified time points and immediately

801 snap-frozen in liquid nitrogen. Samples were ground to fine powder and 500 μl of cold

802 metabolite extraction solution (90% [v/v] methanol, spiked with 2 mg/ml ribitol, 6 mg/ml

803 adipic acid, and 2 mg/ml and 13C-leucine as internal standards). Samples were immediately

804 vortexed and shaken at 1,400 rpm for 20 min at 75°C. Cell debris was removed by

805 centrifugation at 20,000 x g for 5 minutes. For each sample, 100 or 400 μl of supernatant

806 was transferred to a new tube and either proceeded to derivatization for LC-MS analysis or

807 dried using a SpeedVac.

808

809 Analyses of salicylic acid, organic acids and amino acids by selective reaction monitoring

810 using triple quadrupole (QQQ) mass spectrometry

811 For LC-MS analysis of organic acids, sample derivatization was carried out based on

812 previously published methods with modifications (Han et al., 2013). Briefly, for each of 100

813 µL of sample, 50 µL of 250 mM 3-nitrophenylhydrazine in 50% methanol, 50 µL of 150 mM

814 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide in methanol, and 50 µL of 7.5% pyridine in

815 75% methanol were mixed and allowed to react on ice for 60 minutes. To terminate the

816 reaction, 50 µL of 2 mg/mL butylated-hydroxytoluene in methanol was added, followed by

817 the addition of 700 µL of water. Derivatized organic acids were separated on a Phenomenex

818 Kinetex XB-C18 column (50 x 2.1mm, 5µm particle size) using 0.1% formic acid in water

819 (solvent A) and methanol with 0.1% formic acid (solvent B) as the mobile phase. The elution

820 gradient was 18% B at 1 min, 90% B at 10 min, 100% B at 11 min, 100% B at 12 min, 18% B at

821 13 min and 18% B at 20 min. The column flow rate was 0.3 mL/min and the column

822 temperature was maintained at 40 °C. The QQQ-MS was operated in the negative ion mode

823 with multiple reaction monitoring (MRM) mode.

824

825 For measuring salicylic acid and amino acids, dried samples were resuspended in 100 µL

826 HPLC-grade water before they were filtered to remove insoluble debris. Metabolites were

827 separated on an Agilent Poroshell 120 Bonus-BP column (100 x 2.1 mm, 2.7µm internal

828 diameter) using 0.1% formic acid in water (solvent A) and acetonitrile with 0.1% formic acid

829 (solvent B) as the mobile phase. For the analysis of amino acids and sugars, the elution

830 gradient was 0% B at 1 min, 1% B at 4 min, 10% B at 6 min, 100% B at 6.5 min, 100% B at 8

831 min, 0% B at 8.5 min and 0% B at 15 min. The column flow rate was 0.25 mL/min, the

832 column temperature was kept at 40 °C. The QQQ-MS was operated in the positive ion mode

833 with MRM mode. For salicylic acid, the elution gradient was 0% B at 1 min, 1% B at 3 min, 95%

834 B at 23 min, 100% B at 23.2 min, 100% B at 25 min, 0% B at 25.5 min and 0% B at 34 min.

835 The column flow rate was 0.20 mL/min and the column temperature was set to 40 °C. The

836 LC-MS was operated in the negative ion mode with MRM mode.

837

838 A 0.5 µL or a 15 µL aliquot of each sample were injected and analysed by an Agilent 1100

839 HPLC system coupled to an Agilent 6430 Triple Quadrupole (QQQ) mass spectrometer

840 equipped with an electrospray ion source. Data acquisition and LC-MS control were done

841 using the Agilent MassHunter Data Acquisition software (version B06.00 Build 6.0.6025.4).

842 The autosampler was kept at 10°C. The QQQ-MS was operated in MRM mode using the

843 following operation settings: capillary voltage, 4000V; drying N2 gas and temperature, 11

844 L/min and 125 °C respectively; Nebulizer, 15 psi. All optimised MRM transitions for each

845 target were listed in Data S12. All data was analysed using MassHunter Quantitative Analysis

846 Software (version B.07.01, Build 7.1.524.0). Metabolites were quantified by comparing the

847 integrated peak area with a calibration curve obtained using authentic standards, and

848 normalised against fresh weight and internal standards.

849

850 Measurement and identification of sugars and secondary metabolites by

851 quadrupole/time-of-flight mass (Q-TOF) spectrometry

852 Analyses of sugars and secondary metabolites were performed using an Agilent 1100 HPLC

853 system coupled to an Agilent 6510 Quadrupole/Time-of-Flight (Q-TOF) mass spectrometer

854 equipped with an electrospray ion source. Data acquisition and LC-MS control were carried

855 out using the Agilent MassHunter Data Acquisition software (version B02.00). Separation of

856 metabolites was performed using a Luna C18 column (Phenomenex; 150 × 2 mm, 3 µm

857 particle size). The mobile phase consisted of 97:3 water:methanol with 10 mM tributylamine

858 and 15 mM acetic acid (solvent A) and 100% methanol (solvent B). The gradient program

859 was 0% B 0 min, 1% B 5 min, 5% B 15 min, 10% B 22 min, 15% B 23 min, 24% B 25 min, 29%

860 B 80 min, 95% B 81 min, 95% B 82 min, 0% B 83 min and 0% B 97min. The flow rate was 0.2

861 mL/min, with column temperature kept at 35°C and samples at 10°C. The Q-TOF was

862 operated in MS mode with negative ion polarity using the following operation settings:

863 capillary voltage, 4000V; drying N2 gas and temperature, 10 L/min and 250 °C respectively;

864 Nebulizer, 30 psi. Fragmentor, skimmer and octopole radio frequency (Oct1 RF Vpp) voltages

865 were set to 110V, 65V and 750V respectively. The scan range was 70-1200 m/z and spectra

866 were collected at 4.4 spectra/s which corresponded to 2148 transients/spectrum. All MS

867 scan data was analysed using MassHunter Quantitative Analysis Software (version B.07.01,

868 Build 7.1.524.0). Peaks were normalised against sample weight and the internal standard.

869 For identification of metabolites without authentic standards, Q-TOF was operated in

870 Targeted MS/MS mode with negative ion polarity using the same MS settings as outlined

871 above. The MS/MS scan range was 40-1000 m/z and spectra were collected at 3.7 spectra/s

872 which corresponded to 2603 transients/spectra. For each metabolite target, the retention

873 time window was set to ±1 min, isolation width was set to narrow (~1.3 m/z), 10- to 20- and

874 40-eV collision energies were used and the acquisition time was set to 180 ms/spectra. The

875 identity of each unknown was verified by comparing MS/MS fragment ions with published

876 data (Stobiecki et al., 2006; Lee et al., 2008; Rochfort et al., 2008; Matsuda et al., 2009;

877 Bartsch et al., 2010; Bialecki et al., 2010; Zhang et al., 2013; Lin et al., 2014; Hohner et al.,

878 2018). The expected m/z, retention time and the method for identification were listed in

879 DataS12.

880

881 Open accessible data:

882 PRIDE Project Name: To investigate the role of autophagy in Arabidopsis root cellular protein

883 turnover and proteostasis (15N Spike-in root)

884 Project accession: PXD010992

885 PRIDE Project Name: To investigate the role of autophagy in Arabidopsis shoot cellular

886 protein turnover and proteostasis (15N Spike-in shoot)

887 Project accession: PXD010948

888 PRIDE Project Name: To investigate the role of autophagy in Arabidopsis root cellular protein

889 turnover and proteostasis

890 Project accession: PXD010900

891 PRIDE Project Name: To investigate the role of autophagy in Arabidopsis shoot cellular

892 protein turnover and proteostasis

893 Project accession: PXD010932

894

895 Acknowledgements

896 atg5-1 (SAIL_129_B07), atg11-1 (SAIL_1166_G10) and the GFP-ATG8a line were kindly

897 provided by Professor Richard Vierstra, Washington University, St Louis, MO. We thank Julio

898 Paez-Valencia for his assistance growing plants for transmission electron microscopy analysis

899 and Zhirui Chen for her assistance for confocal image quantifications.

900

901 Author Contribution

902 LL and AHM designed the research; CPL, AWY and MB performed plant culture and

903 biochemical experiments; MSO performed and analyzed TEM data. Mass spectrometry and

904 analysis was performed by LL. LL, AHM and MSO contributing to the writing and revision

905 of the article.

906

907

908 Funding

909 This work was supported through funding by the Australian Research Council (CE140100008,

910 DP180104136) to AHM, National Natural Science Foundation of China (31970294) to LL and

911 NSF IOS-1840687 grant to MSO.

912

913 Competing Interests

914 The Authors declare that there are no competing interests associated with the manuscript. 915 916

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1215 Figure Legends 1216 Fig 1. Changes in abundance of proteins in atg5 and atg11 that are resident in different 1217 subcellular locations. Box plots of relative changes in abundance of 241 root (A) and 265 1218 shoot (B) proteins from atg5 and atg11 which significantly changed in abundance in 1219 comparison with WT (p<0.05). These were from the larger set of 1114 root and 698 shoot 1220 proteins that were quantified in all genotypes. ΔAbundance changes of specific root and 1221 shoot proteins are shown in Fig S2 and Fig S3. Numbers of significantly changing (x) and total 1222 quantified (y) proteins for each subcellular location (x/y) are shown. A comparison of 1223 k-samples distributions (Kruskal-Wallis) was performed in XLSTAT to evaluate the level of 1224 changes in subcellular locations. Changes in abundance of proteins in root can be divided 1225 into A-G groups with increasing values. Changes in abundance of proteins in shoot can be 1226 only divided into A group. An explanation of values defining each group would be important. 1227 1228 Fig 2. Transmission electron microscopy analysis of WT, atg5, and atg11 root cells. (A) 1229 Longitudinal section of a WT root showing the areas selected for analysis: meristem region 1230 (up to 100 m from quiescent center; QC) and the adjacent area up to 200 m from the QC 1231 where cells are actively developing vacuoles. Asterisks indicate examples of cells that were 1232 analyzed. (B) Cell with developing vacuoles (C) Meristematic cells; D-E Quantification of 1233 vacuolated cell area per section (D), nuclear area in meristematic cells (E), mitochondrial and 1234 vacuolar area per section of vacuolated cells (F,G) and length of tonoplast per section of 1235 vacuolated cells (H). Between 10 and 13 cells from two roots of each genotype were used for 1236 this analysis. M, mitochondria,; N, nucleus; V, vacuole. Scales bars= 10 m in (A); 2 m in 1237 (B,C). 1238 1239 Fig 3. Abnormal organelles in atg5 root cells. (A) Golgi stack in WT cells. (B) Golgi stack and 1240 associated TGN with dilated vesicle profiles (asterisks) in atg5. (C)-(F) Large membranous 1241 structures with concentric membranes in atg5. Some of these structures displayed budding 1242 profiles at their edges (asterisks in C). Budding sites with assembled coats (E, asterisks) are 1243 commonly seen on these structures. Enclosed by these membranes, there are electron 1244 dense aggregates 2-3 times larger than a cytosolic ribosomes (E and inset). G, Golgi stack; M, 1245 mitochondrion. Scale bars= 200 nm (A,B, E); 400 nm (C,D); 500 nm (F). 1246 1247 Fig 4. Specific proteins that degrade more slowly in atg5 and atg11 roots and shoots 1248 compared with wild-type Arabidopsis. (A) A heatmap of 43 root proteins with significant

1249 slower degradation rate (relative ΔKD) in both atg5 and atg11. (B) A heatmap of 31 shoot 1250 proteins with significant slower ΔKD in atg5 or atg11. Proteins with significance differences 1251 in both atg5 and atg11 and shown in bold font. Proteins are group according to the top 1252 three functional categories in root and top 3 organelles in shoot tissues. Specific protein 1253 degradation rates in WT, atg5 and atg11 can be found in Data S3. 1254 1255 Fig 5. Combination of changes in abundance and degradation rate for proteins in atg5 and 1256 atg11 from different cellular compartments. Matching sets of protein degradation rate 1257 changes (Relative ΔKD) and protein abundance changes (Relative ΔAbundance) were 1258 graphed orthogonally to identify putative autophagy cargo. 140 root proteins and 116 shoot 40

1259 proteins with significant changes in abundance or degradation (Student’s T-test, P<0.05) in 1260 atg11 are plotted in A and D. 200 root proteins and 187 shoot proteins with significant 1261 changes in abundance/degradation (Student’s T-test, P<0.05) in atg5 are plotted in B and E. 1262 The proportion of proteins shown in each quadrant that reside in a particular subcellular 1263 location in either mutant is shown for root data in C and shoot data in F. The four colors 1264 represent the four quadrants. Group 1 (green) represents proteins with slower degradation 1265 rates and greater abundance, consistent with direct changes driven by deficient autophagy 1266 substrate degradation; Group 2 (red) represents proteins with faster degradation rates and a 1267 lower steady-state abundance, potentially driven by alternative degradation pathways 1268 compensating for defects in autophagy; Group 3 (blue) contains proteins with faster 1269 degradation rates and greater abundance (likely driven by enhanced protein synthesis); and 1270 Group 4 (gray) contains proteins with slower degradation rates and lower abundance, 1271 putative examples of feedback regulated response to impaired autophagy degradation 1272 triggering decreasing protein synthesis. 1273 1274 Fig 6. Pi limitation induces changes in atg5, agt11 and WT Arabidopsis plants. (A) Arabidopsis 1275 plants grown in hydroponics for 21 days were transferred to fresh growth media with/without 1276 Pi for three days. (B) Free inorganic Pi concentration per fresh weight of root and shoot tissues 1277 was measured by a colorimetric assay method. (C) Root tips of a GFP-ATG8a line under control 1278 and Pi starvation conditions were treated with E64d (protease inhibitor) overnight before 1279 confocal imaging. Confocal image of elongation region are presented as well as differential 1280 interference contrast (DIC) images. Number of autophagic organelles (AO) per z-stack in 1281 roots grown under control or Pi limitation conditions (Kolmogorov-Smirnov two distribution 1282 test, **P<0.01). (D) Shoot tissue quantum efficiency of photosystem II (Fv/Fm) in Wt, atg5 and 1283 atg11 lines. (E) Transcript abundance of SPX1, ATG8H and ATG7 in Wt, atg5 and atg11 under 1284 both control and Pi starvation conditions. Student’s T-test, *P<0.05, **P<0.01. Error bars show 1285 standard deviations of four biological replicates. 1286 1287 Fig 7. Primary and secondary metabolite profile changes under control conditions and Pi 1288 limitation in atg5 and atg11. Heatmaps of changes in metabolite abundance in shoots and 1289 roots are shown. Metabolites were determined by LC/MS. Two-way ANOVA analyses were 1290 carried out to determine the genotype and Pi starvation effects on metabolite abundance 1291 changes. Metabolite content significantly altered due to Pi starvation, regardless of genotype, 1292 was labelled by a hashtag (#) (p< 0.05 for both T0 vs T3-P and T3-P vs T3+P). Metabolite 1293 content significantly altered due to genotype was determined by Tukey Ad hoc analysis for 1294 Col-0 vs either atg5 or atg11 and labelled by an asterisk (*); *p< 0.05 or ** p < 0.01. 1295 Metabolites with unquantifiable abundance in a given sample are shown in grey. 1296 1297 Fig 8. Pi limitation effects on changes of root protein abundance and degradation in atg5 1298 and atg11. (A,B) PCA analysis was applied to evaluate Pi limitation effects on protein 1299 abundance and degradation changes in Wt, atg11 and atg5 using 1045 and 476 proteins, 1300 respectively. Protein abundance data were LN transformed before being used for PCA 1301 analysis. Principle components 1 and 2 (x and y axis) for all genotypes under both control and 1302 Pi starvation conditions are shown for protein abundance (A) and protein degradation (B). 41

1303 Relative changes of protein abundance and degradation between Wt and autophagy mutant 1304 lines were plotted to visualize Pi limitation effects on specific proteins of known location in 1305 root cells. Relative changes in protein abundance from 194 proteins in atg11/Wt 1306 comparisons (C) and from 233 proteins in atg5/Wt comparisons (E) are shown as 1307 scattergrams. Relative changes in protein degradation rates from 111 proteins in atg11/Wt 1308 comparisons (D) and from 115 proteins in atg5/Wt comparisons (F) are also shown as 1309 scattergrams. A nonparametric Kolmogorov-Smirno test was utilized for comparison of 1310 control and Pi starvation on distribution of relative changes in protein abundance and 1311 degradation rate of cellular localisations to evaluate the Pi limitation effect (** P<0.01, 1312 *P<0.05). Nine proteins show significantly faster degradation rates under Pi starvation

1313 conditions compared with control in Wt root. Relative degradation rate changes (relative ΔKD) 1314 and relative abundance changes (relative Δabundance) of these proteins in Wt, atg5 and 1315 atg11 between control and Pi limiting conditions are shown as heatmaps (G). 1316 1317 Fig 9. Pi limitation effects on changes of shoot protein abundance and degradation in atg5 1318 and atg11. (A,B) PCA analysis was applied to evaluate Pi limitation effects on protein 1319 abundance and degradation changes in Wt, atg11 and atg5 using 782 and 505 proteins, 1320 respectively. Protein abundance data were LN transformed before being used for PCA 1321 analysis. Principle components 1 and 2 (x and y axis) for all genotypes under both control and 1322 Pi starvation conditions are shown for protein abundance (A) and protein degradation (B). 1323 Relative changes of protein abundance and degradation between Wt and autophagy mutant 1324 lines were plotted to visualize Pi limitation effects on specific proteins of known location in 1325 shoot cells. Relative changes in protein abundance from 238 proteins in atg11/Wt 1326 comparisons (C) and from 234 proteins in atg5/Wt comparisons (E) are shown as 1327 scattergrams. Relative changes in protein degradation rates from 50 proteins in atg11/Wt 1328 comparisons (D) and from 52 proteins in atg5/Wt comparisons (F) are also shown as 1329 scattergrams. A nonparametric Kolmogorov-Smirno test was utilized for comparison of 1330 control and Pi starvation on distribution of relative changes in protein abundance and 1331 degradation rate of cellular localisations to evaluate the Pi limitation effect (** P<0.01, 1332 *P<0.05). Six proteins show significantly faster degradation rates under Pi limiting conditions

1333 compared with control in Wt shoot. Their relative degradation rate changes (relative ΔKD) 1334 and relative abundance changes (relative Δabundance) in Wt, atg5 and atg11 between 1335 control and Pi limiting conditions are shown as heatmaps (G). 1336 1337

1338 Tables 1339 Table 1. Cellular compartments with significant slower protein degradation rate in atg5/11 1340 mutants compared with wild-type Arabidopsis. 1341 Percentage of proteins resident in major cellular compartments that showed significant 1342 slower degradation rate in atg11 and atg5 than in Wt. In root, the majority of proteins with 1343 slower degradation rate are located in the cytosol, mitochondrion or ER. In shoot, the 1344 majority are in the cytosol, chloroplast and mitochondrion. Cellular localization of proteins 1345 beyond the top three are marked as others. Numbers in brackets are the number of proteins 1346 with slower protein degradation rate divided by the total number of proteins from that 1347 location that were analysed. 1348

atg11/Wt atg5/Wt Subcellular location

68% (45/66) 82% (42/51) Cytosol 17% (11/66) 2% (1/51) mitochondrion Root 5% (3/66) 4% (2/51) ER 11% (7/66) 12% (6/51) Others

33% (8/24) 64% (9/14) Cytosol 33% (8/24) 7% (1/14) chloroplast Shoot 21% (5/24) 0% (0/14) mitochondrion 13% (3/24) 29% (3/14) Others 1349 1350

1351 Supplemental Tables 1352 Table S1 Identification of putative ATG5 and ATG11 targets in Arabidopsis roots as proteins 1353 with higher abundance and slower degradation rates in atg5 or atg11 compared with 1354 wild-type. 1355 Table S2 Identification of putative ATG5 and ATG11 targets in Arabidopsis shoots as proteins 1356 with higher abundance and slower degradation rates in atg5 or atg11 compared with 1357 wild-type. 1358 1359 Supplemental Figures 1360 Fig S1 Arabidopsis atg5 and atg11 phenotypes compared to Wt plants. 1361 Fig S2 Changes in protein abundance in roots and shoots of Arabidopsis autophagy mutants. 1362 Fig S3 Significant changes in abundance of ribosome and proteasome subunits in 1363 Arabidopsis autophagy mutants. 1364 Fig S4 Significant changes in relative Δabundance of 241 root proteins in Arabidopsis 1365 autophagy mutants. 1366 Fig S5 Significant changes in relative Δabundance of 265 shoot proteins in Arabidopsis 1367 autophagy mutants.

1368 Fig S6 Changes in protein degradation rate (KD) in roots and shoots of Arabidopsis autophagy 1369 mutants compared to Wt. 1370 Fig S7 Effects of Pi limitation on protein abundance in roots and shoots of Wt and autophagy 1371 mutants. 1372 Fig S8 Effects of Pi limitation on protein degradation rates in roots and shoots of Wt and 1373 autophagy mutants. 1374 Fig S9 Workflow of analysis to determine protein abundance, protein degradation rates and 1375 metabolite abundances in samples from Arabidopsis plant tissues. 1376 1377 Supplemental Data 1378 DataS1 Changes in protein abundance in roots and shoots of hydroponically grown 1379 Arabidopsis autophagy mutants compared to wild type. 1380 DataS2 Changes in protein abundance of proteins belonging to different subcellular 1381 locations in autophagy mutants compared to wild type. 1382 DataS3 Changes in protein degradation rate in autophagy mutants. 1383 DataS4 Changes in protein abundance and degradation rate in autophagy mutants. 1384 DataS5 Proteins with significant differences in protein abundance & degradation rate. 1385 DataS6 Root proteins with significant changes in their abundance & degradation rate. 1386 DataS7 Changes in protein abundance in wild type and autophagy mutants under control 1387 and Pi limiting conditions. 1388 DataS8 Changes in protein degradation rate in wild type and autophagy mutants under 1389 control and Pi limiting conditions. 1390 DataS9 Protein degradation rates and protein abundance data for PCA analysis. 1391 DataS10 Proteins with faster turnover rates under Pi limitation conditions in wild type 1392 DataS11 Metabolites measurement using mass spectrometry. 1393 DataS12 Precursor masses of metabolites used in LC-MS analysis.

44 bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A 1 Root

F FG 0.8 atg11 atg5 0.6 G F FG 0.4 DE CD DEF EF EF DEF CD 0.2 AB BCD BC Abundance BC ∆ 0 ABC A ABC

-0.2 AB

Relative Relative -0.4

-0.6

-0.8 (89/404) (16/56) (20/88) (4/28) (26/158) (10/78) (7/27) (12/55) (53/125) (4/19) B 2 Shoot

A 1.5 A A A A A A A 1

0.5 Abundance

∆ A 0 A A A A A A A -0.5 A A A

Relative Relative A -1

-1.5 (48/130） (1/8) (18/46) (1/5) (24/72) (9/23) (9/17) (1/9) (148/344) (6/11) Cytosol ER Extracellular Golgi Mito Nucleus Pero PM Pt Vacuole

1 Fig 1. Changes in abundance of proteins in atg5 and atg11 that are resident in 2 different subcellular loca�ons. Box plots of rela�ve changes in abundance of 241 3 root (A) and 265 shoot (B) proteins from atg5 and atg11 which significantly changed 4 in abundance in comparison with WT (p<0.05). These were from the larger set of 5 1114 root and 698 shoot proteins that were quan�fied in all genotypes. ΔAbundance 6 changes of specific root and shoot proteins are shown in Fig S2 and Fig S3. Numbers 7 of significantly changing (x) and total quan�fied (y) proteins for each subcellular 8 loca�on (x/y) are shown. A comparison of k-samples distribu�ons (Kruskal-Wallis) 9 was performed in XLSTAT to evaluate the level of changes in subcellular loca�ons. 10 Changes in abundance of proteins in root can be divided into A-G groups with 11 increasing values. Changes in abundance of proteins in shoot can be only divided into 12 A group. An explana�on of values defining each group would be important. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

WT atg5 atg11 A B M M M V 200µm 1 2 *3 N N 4 * * V N 5 V * * 4* * * * V 100µm *9 * 8 7 6 C * ** * * * * * * 0 N N N

D E 300 F G 120 H ) 2

) 250 100 100 ) 2 2 16 14 200 10 80 80 12 150 8 60 60 10 8 6 100 40 40 Cell area (µm

6 area (µm Vacuole 4 4 50 20 (µm) Tonoplast 20

Nucleus area (µm 2 2 Mitochondria area (µm ²) WT atg5 atg11 WT atg5 atg11 WT atg5 atg11 WT atg5 atg11 WT atg5 atg11

1 Fig 2. Transmission electron microscopy analysis of WT,atg5 , and atg11 root cells. 2 (A) Longitudinal sec�on of a WT root showing the areas selected for analysis: 3 meristem region (up to 100 µm from quiescent center; QC) and the adjacent area up 4 to 200 µm from the QC where cells are ac�vely developing vacuoles. Asterisks 5 indicate examples of cells that were analyzed. (B) Cell with developing vacuoles (C) 6 Meristema�c cells; D-E Quan�ﬁca�on of vacuolated cell area per sec�on (D), nuclear 7 area in meristema�c cells (E), mitochondrial and vacuolar area per section of 8 vacuolated cells (F,G) and length of tonoplast per sec�on of vacuolated cells (H). 9 Between 10 and 13 cells from two roots of each genotype were used for this analysis. 10 M, mitochondria,; N, nucleus; V, vacuole. Scales bars= 10 µm in (A); 2 µm in (B,C). bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A C D M G M

B M G

* * * *

E F

1 Fig 3. Abnormal organelles in atg5 root cells. (A) Golgi stack in WT cells. (B) Golgi 2 stack and associated TGN with dilated vesicle proﬁles (asterisks) in atg5. (C)-(F) Large 3 membranous structures with concentric membranes in atg5. Some of these 4 structures displayed budding proﬁles at their edges (asterisks in C). Budding sites 5 with assembled coats (E, asterisks) are commonly seen on these structures. Enclosed 6 by these membranes, there are electron dense aggregates 2-3 �mes larger than a 7 cytosolic ribosomes (E and inset). G, Golgi stack; M, mitochondrion. Scale bars= 200 8 nm (A,B, E); 400 nm (C,D); 500 nm (F). bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Shoot

atg11 atg5 Protein Root atg11 atg5 Protein AT3G15730.1 PLDALPHA1 AT5G19550.1 ASP2 AT4G14880.1 OASA1 AT5G17920.1 ATCIMS AT4G14040.1 EDA38 AT3G28940.1 AIG2-like AT2G31390.1 kinase AT4G23670.1 Polyketide cyclase AT5G03630.1 ATMDAR2 AT1G08830.1 CSD1 AT4G09320.1 NDPK1 AT5G02240.1 NAD(P)-binding protein AT4G13930.1 SHM4 metabolism AT5G51970.1 alcohol dehydrogenase AT2G36160.1 S11 AT4G38740.1 ROC1 AT1G22780.1 PFL AT2G16600.1 ROC3 AT2G41840.1 S5 AT4G34870.1 ROC5 AT3G04920.1 S24e AT3G55440.1 Isomerase AT1G72370.1 P40 cytosol AT3G52930.1 Aldolase AT3G04840.1 S3Ae AT1G24020.1 MLP423 AT3G02560.1 S7e AT1G11860.1 Glycine cleavage T AT5G16130.1 S7e AT3G06050.1 PRXIIF AT5G20290.1 S8e AT3G46560.1 TIM9 AT1G02780.1 L19e AT1G14980.1 CPN10 AT1G04480.1 L14p/L23e mitochondrion AT1G53240.1 mMDH1 AT2G19730.1 L28e AT4G37300.1 MEE59 AT2G39460.1 ATRPL23A AT1G08110.1 lactoylglutathione lyase AT2G01250.1 L30/L7 AT2G33150.1 PKT3 AT5G47700.1 60S protein AT5G38430.1 RuBisCO (small chain) AT1G30230.1 GST AT3G55800.1 SBPASE AT1G56070.1 EF G/III/V AT3G52960.1 Thioredoxin AT1G57720.1 EF1B AT2G47730.1 ATGSTF8 AT5G19510.1 EF1B ribosome AT1G32990.1 PRPL11 AT2G24200.1 peptidase AT3G44890.1 RPL9 AT3G03960.1 TCP-1/cpn60 AT4G34620.1 SSR16 AT1G56340.1 CRT1 AT5G20720.1 CPN20 AT3G63190.1 RRF AT1G62480.1 Ca2+ related chloroplast AT1G04410.1 MDH1 AT5G61790.1 CNX1 AT4G34870.1 ROC5 AT2G44060.1 embryogenesis AT3G16640.1 TCTP AT4G02450.1 HSP20-like AT4G22670.1 AtHip1 AT1G30580.1 GTP binding -0.6 0.6 AT1G79550.1 PGK Relative∆K AT2G36530.1 Enolase D AT3G55440.1 Isomerase AT3G52930.1 Aldolase glycolysis AT1G65930.1 cICDH AT3G52300.1 ATPQ

1 Fig 4. Specific proteins that degrade more slowly in atg5 and atg11 roots and 2 shoots compared with wild-type Arabidopsis. (A) A heatmap of 43 root proteins 3 with significant slower degrada�on rate (rela�ve ΔKD) in both atg5 and atg11. (B) A 4 heatmap of 31 shoot proteins with significant slower ΔKD in atg5 or atg11. Proteins 5 with significance differences in both atg5 and atg11 and shown in bold font. Proteins 6 are group according to the top three func�onal categories in root and top 3 7 organelles in shoot �ssues. Specific protein degrada�on rates in WT, atg5 and atg11 8 can be found in Data S3. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Root A atg11/Wt B atg5/Wt C atg5&11/Wt 0.6 ER (10) 37% 8% 47% 11% Cytosol (121)

D 0.3 Nucleus (4) K

Peroxisome (4)

0 Mitochondrion (42)

Relative ∆ Vacuole (7)

PM (11) -0.3 Plastid (41) 9% 46% 11% 32% Extracellular (26) -0.6 0 20 40 60 80 100% -0.8 -0.4 0 0.4 0.8-0.8 -0.4 0 0.4 0.8

Shoot D E F 1.5 Peroxisome (6) 29% 6% 36% 28% Cytosol (38)

0.75 Mitochondrion (18)

D Nucleus (5) K Extracellular (17) 0 Plastid (139)

Vacuole (7) Relative ∆ -0.75 0 20 40 60 80 100% 49% 16% 19% 17% Abundance Degradation More Slower -1.5 -1 . 5 -0 . 75 0 0.75 1 . 5 -1 . 5 -0 . 75 0 0 . 75 1 . 5 Less Faster More Faster Relative ∆Abundance Relative ∆Abundance Less Slower

1 Fig 5. Combina�on of changes in abundance and degrada�on rate for proteins in 2 atg5 and atg11 from different cellular compartments. Matching sets of protein 3 degrada�on rate changes (Rela�ve ΔKD) and protein abundance changes (Rela�ve 4 ΔAbundance) were graphed orthogonally to iden�fy puta�ve autophagy cargo. 140 5 root proteins and 116 shoot proteins with significant changes in abund ance or 6 degrada�on (Student’s T -test, P<0.05) in atg11 are plo�ed in A and D. 200 root 7 proteins and 187 shoot proteins with significant changes in abundance/degrada�on 8 (Student’s T-test, P<0.05) in atg5 are plo�ed in B and E. The propor�on of proteins 9 shown in each quadrant that reside in a par�cular subcellular loca�on in either mutant 10 is shown for root data in C and shoot data in F. The four colors represent the four 11 quadrants. Group 1 (green ) represents proteins with slower degrada�on rates and 12 greater abundance, consistent with direct changes driven by deficient autophagy 13 substrate degrada�on; Group 2 (red) represents proteins with faster degrada�on rates 14 and a lower steady -state abundance, poten�ally driven by alterna�ve degrada�on 15 pathways compensa�ng for defects in autophagy; Group 3 (blue ) contains proteins 16 with faster degrada�on rates and greater abundance (likely driven by enhanced 17 protein synthesis); and Group 4 (gray) contains proteins with slower degrada�on rates 18 and lower abundance, puta�ve examples of feedback regulated response to impaired 19 autophagy degrada�on triggering decreasing protein synthesis. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A B root E Wt atg11 atg5 8 control **

control -1 40 Pi starv ** 4 ** ** ** 20 [Pi] μ mol•g spx1 ** 0 0 Pi starv shoot

8 3 ** -1 **

2 4 ** ** ** atg8h

[Pi] μ mol•g 1 control Pi starv Pi starv (zoom-in) 0 C 0

GFP-ATG8a 2 D 0.8 * E64d

0.4 1

10 µm 10 µm 10 µm atg7 Fv/Fm

250 ** 200

150 0 DIC Wt atg11 atg5 Wt atg11 atg5 100

50 AO per z-stack 0 control Pi starv

1 Fig 6. Pi limita�on induces changes in atg5, agt11 and WT Arabidopsis plants. (A) 2 Arabidopsis plants grown in hydroponics for 21 days were transferred to fresh growth 3 media with/without Pi for three days. (B) Free inorganic Pi concentra�on per fresh 4 weight of root and shoot �ssues was measured by a colorimetric assay method. (C) 5 Root �ps of a GFP-ATG8a line under control and Pi starva�on condi�ons were treated 6 with E64d (protease inhibitor) overnight before confocal imaging. Confocal image of 7 elonga�on region are presented as well as diﬀeren�al interference contrast (DIC) 8 images. Number of autophagic organelles (AO) per z-stack in roots grown under 9 control or Pi limita�on condi�ons (Kolmogorov-Smirnov two distribu�on test, 10 **P<0.01). (D) Shoot �ssue quantum eﬃciency of photosystem II (Fv/Fm) in Wt, atg5 11 and atg11 lines. (E) Transcript abundance of SPX1, ATG8H and ATG7 in Wt, atg5 and 12 atg11 under both control and Pi starva�on condi�ons. Student’s T-test, *P<0.05, 13 **P<0.01. Error bars show standard devia�ons of four biological replicates. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Root Shoot T0 T3 T3-Pi log2FC (vs Wt T0) T0 T3 T3-Pi -3 3 Wt Wt Wt Wt Wt Wt atg11 atg5 atg11 atg5 atg11 atg5 atg11 atg5 atg11 atg5 atg11 atg5 0 Malate # # Succinate # ** ** Fumarate # ** Isocitrate ** # Citrate # Organic acids Pyruvate # 2-oxoglutarate # # D-2-Hydroxyglutarate ** # GABA ** # Alanine ** # ** ** Serine # * * ** ** Asparagine # * * Glutamine # Threonine ** ** # ** ** ** # Glutamate # Proline ** Amino acids Valine ** ** ** ** # Methionine ** ** Aspartate * ** Leucine-Isoleucine ** ** ** ** ** # Tyrosine ** ** ** # Phenylalanine ** ** ** * # ** ** Tryptophan ** ** ** ** # Glucose Sucrose ** * Sugars and Glucose 6-phosphate * Fructose 6-phosphate Derivatives UDP-glucose Salicylic acid ** # 3-methylsulfinylbutyl glucosinolate # ** 4-methylsulfinylbutyl glucosinolate # ** 5-methylsulfinylpentyl glucosinolate ** # ** * 7-methylsulfinylheptyl glucosinolate ** ** # ** ** ** ** * 8-methylsulfinyloctyl glucosinolate # * ** ** * Secondary 4-methylthiobutyl glucosinolate ** ** # * ** 2,3-dihydroxybenzoate glucoside ** ** # metabolites 2,3-dihydroxybenzoate xyloside ** ** ** ** # ** 2,5-dihydroxybenzoate xyloside ** ** ** ** # indole-3-methylglucosinolate ** ** # sinapoyl-glucoside kaempferol-3-glucoside-2-rhamnoside-7-rhamnoside

1 Fig 7. Primary and secondary metabolite profile changes under control condi�ons 2 and Pi limita�on in atg5 and atg11. Heatmaps of changes in metabolite abundance 3 in shoots and roots are shown. Metabolites were determined by LC/MS. Two-way 4 ANOVA analyses were carried out to determine the genotype and Pi starva�on 5 effects on metabolite abundance changes. Metabolite content significantly altered 6 due to Pi starva�on, regardless of genotype, was labelled by a hashtag (#) (p< 0.05 7 for both T0 vs T3-P and T3-P vs T3+P). Metabolite content significantly altered due to 8 genotype was determined by Tukey Ad hoc analysis for Col-0 vs either atg5 or atg11 9 and labelled by an asterisk (*); *p< 0.05 or ** p < 0.01. Metabolites with 10 unquan�fiable abundance in a given sample are shown in grey. bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A atg5 B 15 atg5 atg11 y-axis y-axis PC2 13% 150 PC2 10% Wt Wt 10 atg11

5 x-axis 50 x-axis PC1 15% PC1 44% -150 -100 -50 50 100 150 -25 -15 -5 5 15 25 -50 atg11-Pi starv -5 atg5-Pi starv Wt-Pi starv Wt-Pi starv atg11-Pi starv -10 -150 atg5-Pi starv -15 C D control Pi Starv atg11/Wt 2 0.7 * ** 1.5 0.5 1 D 0.3 K 0.5 0.1 Abundance

∆ 0 -0.1

-0.5 Relative ∆ -0.3 Relative Relative -1 -1.5 -0.5 (63) (5) (18) (1) (44) (7) (3) (17） (30) (6) (50) (5) (15) (17) (3) (1) (6） (9) (5) Cytosol ER Extracellular Golgi Mito Nucleus Pero PM Pt Vacuole Cytosol ER Extracellular Mito Nucleus Pero PM Pt Vacuole E atg5/Wt F 2 0.7 * 1.5 0.5 1 0.3 D

0.5 K 0.1 Abundance 0 ∆ -0.1 -0.5 Relative ∆ -1 -0.3 Relative Relative -1.5 -0.5 (79) (14) (22) (5) (23) (9) (9) (19） (47) (6) (54) (7) (12) (1) (12) (3) (2) (5） (14) (5) Cytosol ER Extracellular Golgi Mito Nucleus Pero PM Pt Vacuole Cytosol ER Extracellular Golgi Mito Nucleus Pero PM Pt Vacuole

G Relative ∆Abundance Relative ∆KD -0.6 0.6 -0.6 0.6 Wt atg11 atg5 Wt atg11 atg5 AT1G56070.1 Elongation factor G/III/V AT1G79550.1 Phosphoglycerate kinase AT1G09780.1 Phosphoglycerate mutase AT2G36530.1 Enolase AT3G55440.1 Triosephosphate isomerase AT3G52930.1 Fructose biphosphate aldolase AT1G66280.1 BGLU22 Pi Starv vs Control AT1G35720.1 Annexin 1 AT2G47730.1 ATGSTF8

Fig 8. Pi limita�on effects on changes of root protein abundance and degrada�on in atg5 and atg11. (A,B) PCA analysis was applied to evaluate Pi limita�on effects on protein abundance and degrada�on changes in Wt, atg11 and atg5 using 1045 and 476 proteins, respec�vely. Protein abundance data were LN transformed before being used for PCA analysis. Principle components 1 and 2 (x and y axis) for all genotypes under both control and Pi starva�on condi�ons are shown for protein abundance (A) and protein degrada�on (B). Rela�ve changes of protein abundance and degrada�on between Wt and autophagy mutant lines were plo�ed to visualize Pi limita�on effects on specific proteins of known loca�on in root cells. Rela�ve changes in protein abundance from 194 proteins in atg11/Wt comparisons (C) and from 2 33 proteins in atg5/Wt comparisons (E) are shown as sca�ergrams. Rela�ve changes in protein degrada�on rates from 111 proteins in atg11/Wt comparisons (D) and from 115 proteins in atg5/Wt comparisons (F) are also shown as sca�ergrams. A nonparametric Kolmogorov-Smirno test was u�lized for comparison of control and Pi starva�on on distribu�on of rela�ve changes in protein abundance and degrada�on rate of cellular localisa�ons to evaluate the Pi limita�on effect (** P<0.01, *P<0.05). Nine proteins show significantly faster degrada�on rates under Pi starva�on condi�ons compared with control in Wt root. Rela�ve degrada�on rate changes (rela�ve ΔKD) and rela�ve abundance changes (rela�ve Δabundance) of these proteins in Wt, atg5 and atg11 between control and Pi limi�ng condi�ons are shown as heatmaps (G). bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Wt B atg11-Pi starv 30 PC2 8% 100 atg11-Pi starv PC2 13% atg5-Pi starv 20 50 PC1 17% PC1 38% atg11 -300 -100 100 200 300 -30 -20 -10 10 20 30 40 atg11 -50 Wt-Pi starv atg5 atg5 Wt atg5-Pi starv -100 -20 Wt-Pi starv C D -30 atg11/Wt atg11/Wt 2 1.5 ** * ** 1.5 1 0.5 1 D K

Abundance 0

∆ 0.5 -0.5 0

Relative ∆ -1 Relative Relative -0.5 -1.5 -1 -2 (41) (2) (12) (1) (21) (3) (7) (3） (140) (4) (12) (4) (1) (5） (2) (25） (1) Cytosol ER Extracellular Golgi Mito Nucleus Pero PM Pt Vacuole Cytosol Extracellular Golgi Mito Pero Pt Vacuole

atg5/Wt atg5/Wt E 2 F 1.5 ** ** 1 1.5

D 0.5 1 K 0

Abundance 0.5

∆ -0.5 Relative ∆ 0 -1 control

Relative Relative -0.5 -1.5

-1 Pi starv -2 (37) (1) (13) (26) (5) (8) (2） (142) (4) (11) (5) (1) (1） (1) (32） (1) Cytosol ER Extracellular Mito Nucleus Pero PM Pt Vacuole Cytosol Extracellular Golgi Nucleus Pero Pt Vacuole

Relative ∆Abundance G Relative ∆KD -0.6 0.6 -0.6 0.6 Wt atg11 atg5 Wt atg11 atg5 AT5G66570.1 PSBO-1 AT3G47070.1 unkown AT1G03680.1 Thioredoxin ATCG01060.1 PSAC AT1G61520.1 LHCA3

Pi Starv vs Control AT1G76100.1 plastocyanin 1

Fig 9. Pi limita�on effects on changes of shoot protein abundance and degrada�on in atg5 and atg11. (A,B) PCA analysis was applied to evaluate Pi limita�on effects on protein abundance and degrada�on changes in Wt, atg11 and atg5 using 782 and 505 proteins, respec�vely. Protein abundance data were LN transformed before being used for PCA analysis. Principle components 1 and 2 (x an d y axis) for all genotypes under both control and Pi starva�on condi�ons are shown for protein abundance (A) and protein degrada�on (B). Rela�ve changes of protein abundance and degrada�on between Wt and autophagy mutant lines were plo�ed to visualize Pi limita�on effects on specific proteins of known loca�on in shoot cells. Rela�ve changes in protein abundance from 238 proteins in atg11/Wt comparisons (C) and from 234 proteins in atg5/Wt comparisons (E) are shown as sca�ergrams. Rela�ve changes in protein degrada�on rates from 50 proteins in atg11/Wt comparisons (D) and from 52 proteins in atg5/Wt comparisons (F) are also shown as sca�ergrams. A nonparametric Kolmogorov-Smirno test was u�lized for comparison of control and Pi starva�on on distribu�on of rela�ve changes in protein abundance and degrada�on rate of cellular localisa�ons to evaluate the Pi limita�on effect (** P<0.01, *P<0.05). Six proteins show significantly faster degrada�on rates under Pi limi�ng condi�ons compared with control in Wt shoot. Their rela�ve degrada�on rate changes (rela�ve ΔKD) and rela�ve abundance changes (rela�ve Δabundance) in Wt, atg5 and atg11 between control and Pi limi�ng condi�ons are shown as heatmaps (G). bioRxiv preprint doi: https://doi.org/10.1101/2021.04.29.441983; this version posted April 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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