Autophagy mediates temporary reprogramming and dedifferentiation in plant somatic cells
2020; Springer Nature; Volume: 39; Issue: 4 Linguagem: Inglês
10.15252/embj.2019103315
ISSN1460-2075
AutoresEleazar Rodriguez, Jonathan Chevalier, Jakob Vesterlund Olsen, Jeppe Ansbøl, Vaitsa Kapousidou, Zhangli Zuo, Steingrim Svenning, Christian Loefke, Stefanie Koemeda, Pedro Serrano Drozdowskyj, Jakub Jeż, Gerhard Dürnberger, Fabian Kuenzl, Michael Schutzbier, Karl Mechtler, Elise Nagel Ebstrup, Signe Lolle, Yasin Dagdas, Morten Petersen,
Tópico(s)Seed Germination and Physiology
ResumoArticle13 January 2020free access Source DataTransparent process Autophagy mediates temporary reprogramming and dedifferentiation in plant somatic cells Eleazar Rodriguez orcid.org/0000-0002-3641-4980 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jonathan Chevalier Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jakob Olsen Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jeppe Ansbøl Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Vaitsa Kapousidou Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Zhangli Zuo orcid.org/0000-0003-0924-8830 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Steingrim Svenning Molecular Cancer Research Group, Department of Medical Biology, University of Tromsø, Tromsø, Norway Search for more papers by this author Christian Loefke Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Stefanie Koemeda Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Pedro Serrano Drozdowskyj Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Jakub Jez orcid.org/0000-0002-6481-4383 Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Gerhard Durnberger Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Fabian Kuenzl Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Michael Schutzbier Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Karl Mechtler orcid.org/0000-0002-3392-9946 Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Elise Nagel Ebstrup Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Signe Lolle Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Yasin Dagdas Corresponding Author [email protected] orcid.org/0000-0002-9502-355X Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Morten Petersen Corresponding Author [email protected] orcid.org/0000-0002-3035-5991 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Eleazar Rodriguez orcid.org/0000-0002-3641-4980 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jonathan Chevalier Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jakob Olsen Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jeppe Ansbøl Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Vaitsa Kapousidou Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Zhangli Zuo orcid.org/0000-0003-0924-8830 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Steingrim Svenning Molecular Cancer Research Group, Department of Medical Biology, University of Tromsø, Tromsø, Norway Search for more papers by this author Christian Loefke Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Stefanie Koemeda Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Pedro Serrano Drozdowskyj Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Jakub Jez orcid.org/0000-0002-6481-4383 Vienna Biocenter Core Facilities (VBCF), Vienna, Austria Search for more papers by this author Gerhard Durnberger Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Fabian Kuenzl Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Michael Schutzbier Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Karl Mechtler orcid.org/0000-0002-3392-9946 Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Elise Nagel Ebstrup Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Signe Lolle Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Yasin Dagdas Corresponding Author [email protected] orcid.org/0000-0002-9502-355X Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria Search for more papers by this author Morten Petersen Corresponding Author [email protected] orcid.org/0000-0002-3035-5991 Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Author Information Eleazar Rodriguez1,‡, Jonathan Chevalier1,‡, Jakob Olsen1, Jeppe Ansbøl1, Vaitsa Kapousidou1, Zhangli Zuo1, Steingrim Svenning2, Christian Loefke3, Stefanie Koemeda4, Pedro Serrano Drozdowskyj4, Jakub Jez4, Gerhard Durnberger3, Fabian Kuenzl3, Michael Schutzbier3, Karl Mechtler3, Elise Nagel Ebstrup1, Signe Lolle1,†, Yasin Dagdas *,3 and Morten Petersen *,1 1Functional Genomic Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark 2Molecular Cancer Research Group, Department of Medical Biology, University of Tromsø, Tromsø, Norway 3Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Vienna, Austria 4Vienna Biocenter Core Facilities (VBCF), Vienna, Austria †Present address: Department of Plant Pathology, University of California, Davis, CA, USA ‡These authors contributed equally to this work. *Corresponding author. Tel: +431790449850; E-mail: [email protected] *Corresponding author. Tel: +4531757660; E-mail: [email protected] EMBO J (2020)39:e103315https://doi.org/10.15252/embj.2019103315 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Somatic cells acclimate to changes in the environment by temporary reprogramming. Much has been learned about transcription factors that induce these cell-state switches in both plants and animals, but how cells rapidly modulate their proteome remains elusive. Here, we show rapid induction of autophagy during temporary reprogramming in plants triggered by phytohormones, immune, and danger signals. Quantitative proteomics following sequential reprogramming revealed that autophagy is required for timely decay of previous cellular states and for tweaking the proteome to acclimate to the new conditions. Signatures of previous cellular programs thus persist in autophagy-deficient cells, affecting cellular decision-making. Concordantly, autophagy-deficient cells fail to acclimatize to dynamic climate changes. Similarly, they have defects in dedifferentiating into pluripotent stem cells, and redifferentiation during organogenesis. These observations indicate that autophagy mediates cell-state switches that underlie somatic cell reprogramming in plants and possibly other organisms, and thereby promotes phenotypic plasticity. Synopsis Adjustment to new environmental signals requires temporary and rapid modulation of the cellular proteome. Proteome adjustments are found to require autophagy, which erases previous cellular states in plant somatic cells to allow new programs to unfold. Multiple external stimuli rapidly activate autophagy in plant cells. Proteome adjustments during cell-state switching are disrupted in autophagy mutants. Autophagy-deficient plants exhibit decreased phenotypic plasticity. Autophagy-deficient somatic cells cannot de-differentiate efficiently into pluripotent stem cells. Exit from pluripotency and organ formation are dysregulated in autophagy-deficient cells. Introduction Somatic cells in multicellular eukaryotes are relentlessly exposed to diverse physiological and environmental stimuli including changes in temperature, nutrients, hormones, and pathogen load (Cherkasov et al, 2013; Chovatiya & Medzhitov, 2014). At certain levels, such stimuli become stressful and provoke adaptive cellular responses (Galluzzi et al, 2018). To survive, eukaryotes have evolved sophisticated acclimation mechanisms that mediate temporary reprogramming of somatic cells. In both animals and plants, these mechanisms include alterations in transcriptional activities and epigenetic signatures (Davière & Achard, 2016; Xu et al, 2017; Koo & Guan, 2018; Zhang et al, 2018; Hafner et al, 2019). Somatic cells can also undergo directional reprogramming through dedifferentiation and can form pluripotent cells. This allows somatic cells to redifferentiate into other cell types, organs, and even whole organisms in plants (Takahashi & Yamanaka, 2006; Papp & Plath, 2013; Ikeuchi et al, 2015; Li & Belmonte, 2017; Li et al, 2017). Similar to temporary reprogramming, reprogramming into other cell types is orchestrated by evolutionarily conserved processes and involves major changes in the transcriptome and epigenetic landscape (Roche et al, 2017; Sang et al, 2018; Iwafuchi-Doi, 2019). Despite the wealth of knowledge on initial transcriptional and epigenetic changes driving somatic reprogramming events, how proteostasis mechanisms delete current cellular states to allow installment of new programs remains largely unknown. Macroautophagy (hereafter autophagy) is a conserved quality-control pathway that facilitates cellular adaptation by removing superfluous or damaged macromolecules and organelles (Popovic & Dikic, 2014; Liu & Klionsky, 2015; Ho et al, 2017). Although initially discovered as a starvation-induced survival mechanism in yeast (Yang & Klionsky, 2013), many studies have now shown that autophagy plays crucial roles in a variety of stress responses (Bassham et al, 2006; Mizushima et al, 2008; Munch et al, 2014; Rui et al, 2015; Katheder et al, 2017; Kumsta et al, 2017; Dikic & Elazar, 2018) and may act as both positive and negative regulator of programmed cell death (Gutierrez et al, 2004; Nakagawa et al, 2004; Liu et al, 2005; Berry & Baehrecke, 2007; Hofius et al, 2009). Autophagy has also been implicated in induced pluripotent stem cell (iPSC) formation, cellular regeneration and stem cell survival in mammals (Saera-Vila et al, 2016; Boya et al, 2018; Calvo-Garrido et al, 2019), and cell fate determination of embryo suspensor in plants (Minina et al, 2013). However, some of these studies have contrasting conclusions. For example, autophagy was shown to have opposite functions in mammalian cells during reprogramming into pluripotency (Wang et al, 2013; Wu et al, 2015) and stem cell maintenance in mice (Mortensen et al, 2011; Ho et al, 2017). So, how can we reconcile these functions and discrepancies regarding the function of autophagy? And is autophagy involved in iPSC formation in plants? Unlike reprogramming in stem cells, temporary reprogramming events in somatic cells are reversible and provide phenotypic plasticity in response to various stimuli (Fusco & Minelli, 2010; Pfennig et al, 2010; Kelly et al, 2012; Oostra et al, 2018). Although autophagy possesses the degratory capacity to mediate rapid cell-state switches, whether it is involved in temporary reprogramming of somatic cells remains unknown. Here, we find that autophagy functions in various cellular reprogramming events in plants. Stimuli as diverse as phytohormones, danger signals, and microbial elicitors all trigger rapid and robust activation of autophagy. Using quantitative proteomics, we show that autophagy mediates the switch between somatic cell programs by removing cellular components that are no longer required. At the same time, autophagic mechanisms ensure a controlled execution of the newly established programs. Accordingly, autophagic dysfunction leads to defects in organismal fitness, dedifferentiation of somatic cells into pluripotency, and redifferentiation of pluripotent cells into other cell types in plants. Results Autophagy is rapidly engaged upon perception of diverse stimuli To examine whether temporary reprogramming engages autophagy, we exposed young seedlings of the model plant Arabidopsis thaliana expressing the autophagic markers GFP-ATG8a or YFP-mCherry-NBR1 (Svenning et al, 2011) to an array of treatments. We evaluated autophagic flux in response to a selection of microbial elicitors, danger signals, and hormones known to induce temporary reprogramming: peptide-1 (PEP1, a small peptide produced during wounding) and ATP, which are perceived as danger-associated molecular patterns (DAMPs); abscisic acid (ABA, a hormone commonly associated with abiotic stress responses); 1-aminocyclopropane-1-carboxylic acid (ACC, precursor of the gaseous hormone ethylene involved in development and senescence); brassinolide (BL, a steroid hormone involved in growth); 1-naphthalene acetic acid (NAA, a synthetic auxin involved in growth modulation); and 6-benzylaminopurine (6-BA, a synthetic cytokinin involved in cytokinesis and growth). All of these treatments induced rapid accumulation of GFP-ATG8a (Fig 1A and B) and YFP-mCherry-NBR1 foci (Fig EV1A and B). GFP-ATG8a vacuolar degradation produces free GFP fragments that can be detected by immunoblotting to measure autophagic flux (Mizushima et al, 2010). All of the treatments induced accumulation of free GFP, pointing to increased autophagic flux (Fig 1C). Further corroboration of autophagic flux increase came from immunoblotting against native NBR1, a well-known autophagy receptor (Svenning et al, 2011) (Fig 1D). Because high NBR1 turnover complicates this analysis in wild-type plants, we used atg2-2 (Wang et al, 2011) instead and observed increased levels of NBR1 in all treatments (Fig 1D), further confirming the induction of autophagy during temporary reprogramming events. Taken together, our results indicate that regardless of the nature of the signal, autophagy is rapidly induced and may function as an intrinsic component in temporary reprogramming of somatic cells. Figure 1. Autophagy is rapidly engaged upon perception of diverse stimuliGFP-ATG8a expressing seedlings in Murashige and Skoog (MS) growth medium or 30 min after treatment with MS containing ACC, ABA, ATP, BL, 6-BA, Flg22, NAA, or PEP1. Representative maximum intensity projection images of 10 Z-stacks per image. Scale bar: 10 μm. Quantification of GFP foci per 0.0025 mm2. Values are presented as mean ± standard deviation of the mean and were calculated from at least three independent experiments with three individuals per replicate. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to the t-test. GFP-ATG8a cleavage immunoblot for plants exposed to the same treatments as in (A). Numbers below the blots represent ratio for given sample normalized to input and relative to non-treated control. Experiments were repeated minimum 3 times with similar results. NBR1 immunoblot for atg2-2 samples for given treatments. Numbers below the blots represent ratio for given sample normalized to input and relative to non-treated control. Experiments were repeated minimum three times with similar results. Source data are available online for this figure. Source Data for Figure 1 [embj2019103315-sup-0004-SDataFig1.tiff] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Autophagy is rapidly induced upon recognition of a wide range of stimuliYFP-mCherry NBR1 accumulation before or 30 min after treatment with ACC, ABA, ATP, BL, 6-BA, Flg22, NAA, and PEP1. Representative maximum intensity projection images of 10 Z-stacks per image. Scale bar: 10 μm. Quantification of YFP/mCherry foci for given treatments per 0.0025 mm2. Values given are mean ± standard deviation of the mean and are based on three independent experiments, with three individuals per condition. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to the t-test. Download figure Download PowerPoint Autophagy facilitates temporary reprogramming We hypothesized that the primary function of autophagy in temporary reprogramming is to assist cellular “clean-up” to allow a new program to unfold before returning to basal levels. If so, (i) a second reprogramming stimulus should re-activate autophagy, and (ii) establishment of the second program should be concurrent with a rapid decay of the first program. To test this, we applied consecutive stimuli and examined reprogramming from ABA (abiotic stress proxy) to flg22 (immunity stress proxy) and NAA (growth and development) to 6-BA (growth and development; Figs 2A and D, and EV2A and C). We quantified GFP-ATG8a foci (Fig 2B and E), YFP-mCherry-NBR1 foci (Fig EV2B and D), and free GFP via the cleavage assay (Fig 2C and F). All of these assays demonstrated that autophagic flux resets to basal levels after 16 h of ABA or NAA treatment, contrasting with the rapid induction seen before (Fig 1). Transferring those seedlings to flg22- or 6-BA-containing medium caused reactivation of autophagy as demonstrated by significant accumulation of GFP and YFP-mCherry-positive foci (P < 0.05, Figs 2B and E, and EV2B and D) and free GFP (Fig 2C and F), in comparison with the control treatment. Hence, our data indicate that autophagy is engaged to clean-up, is reset after the clean-up, and can be reactivated upon perception of new stimuli. Figure 2. Autophagy is reactivated upon contrasting stimulus perception Seedlings were acclimated for 16 h in MS containing ABA and then imaged 30 min after being swapped to MS containing ABA/NAA (control), MS, or MS containing flg22. Images are representative maximum intensity projection of 10 Z-stacks per condition. Experiments were repeated three times independently with similar results. Scale bar: 10 μm. Quantification of GFP foci per 0.0025 mm2, for samples treated as described in (A). Values are presented as mean ± standard deviation of the mean and are based on three independent experiments, with three individuals per condition. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to the t-test. GFP-ATG8a cleavage immunoblot for given treatments. The ratio of free GFP to loading control, normalized to the 16-h pre-treated sample (set to 1), is provided below each band. Seedlings were acclimated for 16 h in MS containing NAA and then imaged 30 min after being swapped to MS containing ABA/NAA (control), MS, or MS containing 6-BA. Images are representative maximum intensity projection of 10 Z-stacks per condition. Experiments were repeated 3 times independently with similar results. Scale bar: 10 μm. Quantification of GFP foci per 0.0025 mm2, for samples treated as described in (D). Values are presented as mean ± standard deviation of the mean and are based on three independent experiments, with three individuals per condition. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to the t-test. GFP-ATG8a cleavage immunoblot for given treatments. The ratio of free GFP to loading control, normalized to the 16-h pre-treated sample (set to 1), is provided below each band. Source data are available online for this figure. Source Data for Figure 2 [embj2019103315-sup-0005-SDataFig2.tiff] Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Autophagy is reactivated upon contrasting stimulus perception Seedlings were acclimated for 16 h in MS containing ABA and then imaged 30 min after being swapped to MS containing ABA/NAA (control), MS, or MS containing flg22. Images are representative maximum intensity projection of 10 Z-stacks per condition. Experiments were repeated 3 times independently with similar results. Scale bar: 10 μm. Foci quantification for given treatments per 0.0025 mm2. Values given are mean ± standard deviation of the mean and are based on three independent experiments, with three individuals per condition. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to a t-test. Seedlings were acclimated for 16 h in MS containing NAA and then imaged 30 min after being swapped to MS containing ABA/NAA (control), MS, or MS containing 6-BA. Images are representative maximum intensity projection of 10 Z-stacks per condition. Experiments were repeated 3 times independently with similar results. Scale bar: 10 μm. Foci quantification for given treatments per 0.0025 mm2. Values given are mean ± standard deviation of the mean and are based on three independent experiments, with three individuals per condition. Bars marked with an asterisk (*) are statistically significant (P < 0.05) according to a t-test. Download figure Download PowerPoint To further support our observations, we performed comparative proteomics using Tandem Mass Tag labeling (TMT) Mass Spectrometry (MS/MS) on wild-type (WT) and the autophagy-deficient mutant atg2-2 upon consecutive, temporary reprogramming inducing stimuli ABA and flg22 (Fig 3A). We detected 11,300 proteins, of which 1,241 responded to the treatments (Table EV1). Based on their behavior, we divided these proteins into fifty clusters. Validating our proteome profiling approach, various ATG8 isoforms and NBR1 clustered together and accumulated to higher levels in atg2-2 (Fig EV3A). We then searched for proteins induced by ABA that decreased when switched to flg22 treatment in WT plants but failed to decrease in atg2-2 (Fig 3B, correlation = 0.86, 10.5% of all responding proteins). Importantly, most proteins with this profile also decreased faster in WT plants when switched from ABA to flg22 than to control media (Fig 3C). Several proteins fitting this profile have been previously associated with ABA responses, among them TSPO, which is degraded through autophagy upon completion of the ABA program (Vanhee et al, 2011). Using a TSPO antibody (Guillaumot et al, 2009), we confirmed that TSPO follows the same pattern in another autophagy-deficient mutant, atg5-1 (Thompson et al, 2005), during consecutive reprogramming from ABA to flg22 (Fig EV3B), as well as during reprogramming from ABA to NAA (Fig EV3C). These results indicate that autophagy is activated to rapidly remove components of previous cellular programs. Figure 3. Autophagy facilitates temporary reprogramming during perception of contrasting stimuli by removing old components and modulating the intensity of new responses A. Schematic representation of the strategy used for consecutive stress treatment. B. Pattern correlation used to find proteins that accumulate upon ABA/NAA treatment and are removed in WT but not in atg2 after swapping to flg22/6-BA. C–F. Protein clusters obtained after quantitative proteomics of WT (green) and atg2-2 (magenta) samples treated as described in (A). (C) Protein cluster fitting the pattern displayed in (B). (D) Protein cluster for proteins that accumulate to higher levels in atg2 than WT upon treatment with flg22. (E) Protein cluster of proteins which accumulate upon NAA treatment and are removed in WT but not in atg2 after swapping to 6-BA. (F) Protein cluster for proteins that accumulate to higher levels in atg2 than WT upon treatment with 6-BA. Source data are available online for this figure. Source Data for Figure 3 [embj2019103315-sup-0006-SDataFig3.tiff] Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Protein level change upon treatments with consecutive stresses Proteins that accumulate in atg2 in comparison with WT. TSPO immunoblot for WT, atg2-2, and atg5-1 samples treated as described in Fig 2A for the ABA to flg22 consecutive stress set. TSPO immunoblot for WT, atg2-2, and atg5-1 samples treated as described in Fig 2A for the ABA to NAA consecutive stress set. CAT immunoblot for WT, atg2-2, and atg5-1 samples treated as described in Fig 2A for the NAA to 6-BA consecutive stress set. Download figure Download PowerPoint Our clustering analyses revealed that some stress-related proteins peaked to much higher levels in atg2-2 upon switching from ABA to flg22 (Fig 3D, 4.6% of all responding proteins). As expected, many of these such as ATPXG2, PDF2.1, and its close homolog AT1G47540 have previously been associated with immune responses (Petersen et al, 2000; Tsiatsiani et al, 2013; Zhao, 2015). This indicates that autophagy also modulates the intensity of a new cellular program when it is being installed. To confirm that autophagy functions as an intrinsic component in cellular reprogramming, we extended our proteomic analysis and examined reprogramming between the contrasting developmental phytohormones auxin (NAA) and cytokinin (6-BA; Table EV2). Here we also observed major proteostatic dysregulation in atg2-2 plants and identified a major cluster (Fig 3E, 10.2% of all responding proteins), comparable to our previous observations (Fig 3A). We identified catalase 2 (CAT2) in this cluster and used a catalase antibody to confirm the same pattern in both atg5-1 and atg2-2 (Fig EV3D). Interestingly, we observed that auxin-responsive proteins accumulate in untreated atg2-2 (Fig 3E), unlike ABA-responsive proteins (Fig 3A). Since stress programs (ABA) are normally “off” under normal growth conditions, while growth and development programs (auxin) are recruited continuously, gradual accumulation of auxin-responsive proteins may not be surprising in autophagy-deficient backgrounds. Similar to our observations above (Fig 3D), these results again show that autophagy is also needed to modulate the intensity of new cellular programs when switched from auxin to cytokinin (Fig 3F, 3% of all responding proteins). Importantly, our proteomic data (Tables EV1 and EV2) also show that other proteostasis mechanisms may also function during temporary reprogramming. When plants are moved from ABA to flg22, the level of several proteins declines in the absence of autophagy, including ABF3 (Table EV1, cluster 1), an ABA-inducible protein known to be degraded by the proteasome (Chen et al, 2013). Similarly, among proteins that decline in both WT and atg2-2 when transferred from NAA to 6-BA, we detected PIN2/EIR1 (Table EV2, cluster 1), also previously reported as a proteasomal target (Abas et al, 2006). Together, these results indicate that the proteasome, like autophagy, also participates in removing components from previous programs during temporary reprogramming. Autophagy deficiencies lead to reduced phenotypic plasticity and increased heterogeneity The above results indicate that cells lacking autophagic activity lose cellular homeostasis and accumulate signatures of different cellular programs and states. If so, autophagy deficiency may lead to increased heterogeneity during acclimatization to fluctuating environmental conditions. To assess this at an organismal level, we used a high-throughput phenotyping chamber to compare the development of WT, atg2-2, and atg5-1 mutant plants grown in standard, stable conditions versus plants grown in highly variable conditions recorded for the Swedish spring of 2013 (Fig 4A). Data dispersion for dry weight was higher in atg plants than in WT, regardless of the conditions tested, albeit with more outliers for atg2 grown under variable conditions (Figs 4B and C, and EV4A and B). This indicates that the loss of cellular homeostasis in atg mutants translates into higher heterogeneity, and this increased heterogeneity might stem from their decreased ability to cope with daily variations that involve dynamic, temporary reprogramming events. Figure 4. Autophagy deficiency leads to reduced phenotypic plasticity and increased heterogeneity Weather pattern recorded for the Swedish spring of 2013. Dry weight of WT, atg5-1, or atg2-2 plants grown under stable (21/16°C 16/8-h photoperiod) or following the Swedish Spring of 2013. Box plots: Centerlines show the medians; box limits indicate the 25th and 75th percentiles; and whiskers extend to the minimum and maximum. S
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