Abstract

The turnover of cytoplasmic material by autophagic encapsulation and delivery to vacuoles is essential for recycling cellular constituents, especially under nutrient-limiting conditions. To determine how cells/tissues rely on autophagy, we applied in-depth multi-omic analyses to study maize (Zea mays) autophagy mutants grown under nitrogen-replete and -starvation conditions. Broad alterations in the leaf metabolome were evident in plants missing the core autophagy component ATG12, even in the absence of stress, particularly affecting products of lipid turnover and secondary metabolites, which were underpinned by substantial changes in the transcriptome and/or proteome. Cross-comparison of messenger RNA and protein abundances allowed for the identification of organelles, protein complexes and individual proteins targeted for selective autophagic clearance, and revealed several processes controlled by this catabolism. Collectively, we describe a facile multi-omic strategy to survey autophagic substrates, and show that autophagy has a remarkable influence in sculpting eukaryotic proteomes and membranes both before and during nutrient stress.

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Data availability

The raw values obtained from the metabolomic data sets are available in Supplementary Table 1 (associated figures: Figs. 2, 3 and 7, Supplementary Figs. 1, 2 and 3 and Supplementary Table 2). RNA-seq data sets for the transcriptome analysis are available at the NCBI Sequence Read Archive database under the submission number SRP139303 (associated figures: Figs. 1, 4 and 7, Supplementary Figs. 1, 4 and 8 and Supplementary Tables 3–7 and 15). The raw sequence, msf and xml files for the mass spectrometry data sets are available in the ProteomeXchange database under accession number PXD009627 within the PRIDE repository (http://www.proteomexchange.org/). Protein identifiers and the corresponding gene accession numbers for the catalogue of maize proteins identified here can be found in Supplementary Tables 10–13. If any data sets are unavailable through the links stated above, they can be obtained from the corresponding author on request.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Eguchi, M., Kimura, K., Makino, A. & Ishida, H. Autophagy is induced under Zn limitation and contributes to Zn-limited stress tolerance in Arabidopsis (Arabidopsis thaliana). Soil Sci. Plant Nutr. 63, 342–350 (2017).

  2. 2.

    Havé, M., Marmagne, A., Chardon, F. & Masclaux-Daubresse, C. Nitrogen re-mobilization during leaf senescence: lessons from Arabidopsis to crops. J. Exp. Bot. 68, 2513–2529 (2017).

  3. 3.

    Avin-Wittenberg, T. et al. Autophagy-related approaches for improving nutrient use efficiency and crop yield protection. J. Exp. Bot. 69, 1335–1353 (2018).

  4. 4.

    Marshall, R. S. & Vierstra, R. D. Autophagy: the master of bulk and selective recycling. Annu. Rev. Plant Biol. 69, 173–208 (2018).

  5. 5.

    Wang, P., Mugume, Y. & Bassham, D. C. New advances in autophagy in plants: regulation, selectivity and function. Sem. Cell Dev. Biol. 80, 113–122 (2017).

  6. 6.

    Masclaux-Daubresse, C., Chen, Q. & Havé, M. Regulation of nutrient recycling via autophagy. Cur. Opin. Plant Biol. 39, 8–17 (2017).

  7. 7.

    Li, F. et al. Autophagic recycling plays a central role in maize nitrogen re-mobilization. Plant Cell 27, 1389–1408 (2015).

  8. 8.

    Reggiori, F. & Klionsky, D. J. Autophagic processes in yeast: mechanism, machinery and regulation. Genetics 194, 341–361 (2013).

  9. 9.

    Farré, J. C. & Subramani, S. Mechanistic insights into selective autophagy pathways: lessons from yeast. Nat. Rev. Mol. Cell Biol. 17, 537–552 (2016).

  10. 10.

    Khaminets, A., Behl, C. & Dikic, I. Ubiquitin-dependent and -independent signals in selective autophagy. Trends Cell Biol. 26, 6–16 (2016).

  11. 11.

    Masclaux-Daubresse, C. et al. Stitching together the multiple dimensions of autophagy using metabolomics and transcriptomics reveals impacts on metabolism, development, and plant responses to the environment in Arabidopsis. Plant Cell 26, 1857–1877 (2014).

  12. 12.

    Avin-Wittenberg, T. et al. Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation. Plant Cell 27, 306–322 (2015).

  13. 13.

    Rabinowitz, J. D. & White, E. Autophagy and metabolism. Science 330, 1344–1348 (2010).

  14. 14.

    Galluzzi, L., Pietrocola, F., Levine, B. & Kroemer, G. Metabolic control of autophagy. Cell 159, 1263–1276 (2014).

  15. 15.

    Wada, S. et al. Autophagy supports biomass production and nitrogen use efficiency at the vegetative stage in rice. Plant Physiol. 168, 60–73 (2015).

  16. 16.

    Robertson, G. P. & Vitousek, P. M. Nitrogen in agriculture: balancing the cost of an essential resource. Annu. Rev. Environ. Resour. 34, 97–125 (2009).

  17. 17.

    Yang, X. S. et al. Gene expression biomarkers provide sensitive indicators of in planta nitrogen status in maize. Plant Physiol. 157, 1841–1852 (2011).

  18. 18.

    Curci, P. L. et al. Transcriptomic response of durum wheat to nitrogen starvation. Sci. Rep. 7, 1176 (2017).

  19. 19.

    Chung, T., Phillips, A. R. & Vierstra, R. D. ATG8 lipidation and ATG8-mediated autophagy in Arabidopsis require ATG12 expressed from the differentially controlled ATG12a and ATG12b loci. Plant J. 62, 483–493 (2010).

  20. 20.

    Thompson, A. R., Doelling, J. H., Suttangkakul, A. & Vierstra, R. D. Autophagic nutrient recycling in Arabidopsis directed by the ATG8 and ATG12 conjugation pathways. Plant Physiol. 138, 2097–2110 (2005).

  21. 21.

    Li, F., Chung, T. & Vierstra, R. D. AUTOPHAGY-RELATED11 plays a critical role in general autophagy and senescence-induced mitophagy in Arabidopsis. Plant Cell 26, 788–807 (2014).

  22. 22.

    Suttangkakul, A., Li, F., Chung, T. & Vierstra, R. D. The ATG1/ATG13 protein kinase complex is both a regulator and a target of autophagic recycling in Arabidopsis. Plant Cell 23, 3761–3779 (2011).

  23. 23.

    Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, non-targeted, ultra-high performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81, 6656–6667 (2009).

  24. 24.

    Ohta, T. et al. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol. Pathol. 37, 521–535 (2009).

  25. 25.

    Gerhardt, B. Fatty acid degradation in plants. Prog. Lipid Res. 31, 417–446 (1992).

  26. 26.

    Corbin, C. et al. Functional characterization of the pinoresinol-lariciresinol reductase-2 gene reveals its roles in yatein biosynthesis and flax defense response. Planta 246, 405–420 (2017).

  27. 27.

    Vogt, T. Phenylpropanoid biosynthesis. Mol. Plant 3, 2–20 (2010).

  28. 28.

    Dixon, D. P., Lapthorn, A. & Edwards, R. Plant glutathione transferases. Genome Biol. 3, reviews3004 (2002).

  29. 29.

    Zilic, S., Serpen, A., Akillioglu, G., Gokmen, V. & Vancetovic, J. Phenolic compounds, carotenoids, anthocyanins, and antioxidant capacity of colored maize (Zea mays L.) kernels. J. Agric. Food Chem. 60, 1224–1231 (2012).

  30. 30.

    Loreti, E. et al. Gibberellins, jasmonate and abscisic acid modulate the sucrose-induced expression of anthocyanin biosynthetic genes in Arabidopsis. New Phytol. 179, 1004–1016 (2008).

  31. 31.

    Doelling, J. H., Walker, J. M., Friedman, E. M., Thompson, A. R. & Vierstra, R. D. The APG8/12-activating enzyme APG7 is required for proper nutrient recycling and senescence in Arabidopsis thaliana. J. Biol. Chem. 277, 33105–33114 (2002).

  32. 32.

    Hanaoka, H. et al. Leaf senescence and starvation-induced chlorosis are accelerated by the disruption of an Arabidopsis autophagy gene. Plant Physiol. 129, 1181–1193 (2002).

  33. 33.

    Nishizawa, A., Yabuta, Y. & Shigeoka, S. Galactinol and raffinose constitute a novel function to protect plants from oxidative damage. Plant Physiol. 147, 1251–1263 (2008).

  34. 34.

    Yoshimoto, K. et al. Autophagy negatively regulates cell death by controlling NPR1-dependent salicylic acid signaling during senescence and the innate immune response in Arabidopsis. Plant Cell 21, 2914–2927 (2009).

  35. 35.

    Havé, M. et al. Increases in activity of proteasome and papain-like cysteine protease in Arabidopsis autophagy mutants: back-up compensatory effect or cell-death promoting effect? J. Exp. Bot. 69, 1369–1385 (2018).

  36. 36.

    Chen, G., Greer, M. S. & Weselake, R. J. Plant phospholipase A: advances in molecular biology, biochemistry, and cellular function. Biomol. Concepts 4, 527–532 (2013).

  37. 37.

    Hong, Y. et al. Plant phospholipases D and C and their diverse functions in stress responses. Prog. Lipid Res. 62, 55–74 (2016).

  38. 38.

    Izumi, M., Ishida, H., Nakamura, S. & Hidema, J. Entire photo-damaged chloroplasts are transported to the central vacuole by autophagy. Plant Cell 29, 377–394 (2017).

  39. 39.

    Sláviková, S. et al. The autophagy-associated Atg8 gene family operates during both favourable growth conditions and starvation stress in Arabidopsis. J. Exp. Bot. 56, 2839–2849 (2005).

  40. 40.

    Chung, T., Suttangkakul, A. & Vierstra, R. D. The ATG autophagic conjugation system in maize: ATG transcripts and abundance of the ATG8–lipid adduct are regulated by development and nutrient availability. Plant Physiol. 149, 220–234 (2009).

  41. 41.

    Shibata, M. et al. Highly oxidized peroxisomes are selectively degraded via autophagy in Arabidopsis. Plant Cell 25, 4967–4983 (2013).

  42. 42.

    Goto-Yamada, S. et al. Chaperone and protease functions of LON protease 2 modulate the peroxisomal transition and degradation with autophagy. Plant Cell Physiol. 55, 482–496 (2014).

  43. 43.

    Farmer, L. M. et al. Disrupting autophagy restores peroxisome function to an Arabidopsis lon2 mutant and reveals a role for the LON2 protease in peroxisomal matrix protein degradation. Plant Cell 25, 4085–4100 (2013).

  44. 44.

    Marshall, R. S., Li, F., Gemperline, D. C., Book, A. J. & Vierstra, R. D. Autophagic degradation of the 26S proteasome is mediated by the dual ATG8/ubiquitin receptor RPN10 in Arabidopsis. Mol. Cell 58, 1053–1066 (2015).

  45. 45.

    Liu, Y. et al. Degradation of the endoplasmic reticulum by autophagy during endoplasmic reticulum stress in Arabidopsis. Plant Cell 24, 4635–4651 (2012).

  46. 46.

    Guiboileau, A. et al. The autophagy machinery controls nitrogen re-mobilization at the whole plant level under both limiting and ample nitrate conditions in Arabidopsis. New Phytol. 194, 732–740 (2012).

  47. 47.

    Hu, J. et al. Plant peroxisomes: biogenesis and function. Plant Cell 24, 2279–2303 (2012).

  48. 48.

    Coradetti, S. T. et al. Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides. eLife 7, e32110 (2018).

  49. 49.

    Zhao, L., Dai, J. & Wu, Q. Autophagy-like processes are involved in lipid droplet degradation in Auxenochlorella protothecoides during the heterotrophy-autotrophy transition. Front. Plant Sci. 5, 400 (2014).

  50. 50.

    Singh, R. et al. Autophagy regulates lipid metabolism. Nature 458, 1131–1135 (2009).

  51. 51.

    Izumi, M., Hidema, J., Makino, A. & Ishida, H. Autophagy contributes to night-time energy availability for growth in Arabidopsis. Plant Physiol. 161, 1682–1693 (2013).

  52. 52.

    Kurusu, T. et al. OsATG7 is required for autophagy-dependent lipid metabolism in rice postmeiotic anther development. Autophagy 10, 878–888 (2014).

  53. 53.

    Kunz, H. H. et al. The ABC transporter PXA1 and peroxisomal β-oxidation are vital for metabolism in mature leaves of Arabidopsis during extended darkness. Plant Cell 21, 2733–2749 (2009).

  54. 54.

    Ishizaki, K. et al. The critical role of Arabidopsis ELECTRON-TRANSFER FLAVOPROTEIN:UBIQUINONE OXIDOREDUCTASE during dark-induced starvation. Plant Cell 17, 2587–2600 (2005).

  55. 55.

    Okazaki, Y. & Saito, K. Roles of lipids as signaling molecules and mitigators during stress response in plants. Plant J. 79, 584–596 (2014).

  56. 56.

    Elander, P. H., Minina, E. A. & Bozhkov, P. V. Autophagy in turnover of lipid stores: trans-kingdom comparison. J. Exp. Bot. 69, 1301–1311 (2018).

  57. 57.

    Pyc, M. et al. Turning over a new leaf in lipid droplet biology. Trends Plant Sci. 22, 596–609 (2017).

  58. 58.

    Brehelin, C., Kessler, F. & van Wijk, K. J. Plastoglobules: versatile lipoprotein particles in plastids. Trends Plant Sci. 12, 260–266 (2007).

  59. 59.

    Michaeli, S., Honig, A., Levanony, H., Peled-Zehavi, H. & Galili, G. Arabidopsis ATG8-INTERACTING PROTEIN1 is involved in autophagy-dependent vesicular trafficking of plastid proteins to the vacuole. Plant Cell 26, 4084–4101 (2014).

  60. 60.

    Yoshimoto, K. et al. Processing of the ubiquitin-like ATG8 proteins by ATG4 is essential for plant autophagy. Plant Cell 16, 2967–2983 (2004).

  61. 61.

    Kim, J. et al. Autophagy-related proteins are required for degradation of peroxisomes in Arabidopsis hypocotyls during seedling growth. Plant Cell 25, 4956–4966 (2013).

  62. 62.

    Settles, A. M. et al. Sequence-indexed mutations in maize using the UniformMu transposon-tagging population. BMC Genomics 8, 116 (2007).

  63. 63.

    Arnon, D. I. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol. 24, 1–15 (1949).

  64. 64.

    Reyes, F. C. et al. Delivery of prolamins to the protein storage vacuole in maize aleurone cells. Plant Cell 23, 769–784 (2011).

  65. 65.

    Subbaiah, C. C. et al. Mitochondrial localization and putative signaling function of sucrose synthase in maize. J. Biol. Chem. 281, 15625–15635 (2006).

  66. 66.

    Smalle, J. et al. Cytokinin growth responses in Arabidopsis involve the 26S proteasome subunit RPN12. Plant Cell 14, 17–32 (2002).

  67. 67.

    Dehaven, C. D., Evans, A. M., Dai, H. & Lawton, K. A. Organization of GC/MS and LC/MS metabolomics data into chemical libraries. J. Cheminform. 2, 9 (2010).

  68. 68.

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

  69. 69.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

  70. 70.

    Ziegler, G. et al. Ionomic screening of field-grown soybean identifies mutants with altered seed elemental composition. Plant Genome 6, 2 (2013).

  71. 71.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  72. 72.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

  73. 73.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  74. 74.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  75. 75.

    Leng, N. et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035–1043 (2013).

  76. 76.

    Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res. 29, e45 (2001).

  77. 77.

    Manoli, A., Sturaro, A., Trevisan, S., Quaggiotti, S. & Nonis, A. Evaluation of candidate reference genes for qPCR in maize. J. Plant Physiol. 169, 807–815 (2012).

  78. 78.

    Aguilar-Hernandez, V. et al. Mass spectrometric analyses reveal a central role for ubiquitylation in remodeling the Arabidopsis proteome during photomorphogenesis. Mol. Plant 10, 846–865 (2017).

  79. 79.

    Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

  80. 80.

    Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell Proteomics. 5, 144–156 (2006).

  81. 81.

    Du, Z., Zhou, X., Ling, Y., Zhang, Z. & Su, Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 38, W64–W70 (2010).

  82. 82.

    Tian, T. et al. agriGOv2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 45, W122–W129 (2017).

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Acknowledgements

We thank J. Barrett, G. Ziegler and I. Baxter in the USDA-ARS Plant Genetics Facility at the Donald Danforth Plant Science Center for performing the ionomic analyses; M. Dyer at Washington University in St. Louis for help with the greenhouse studies; D. Alexander and L. Guo at Metabolon for the metabolomic analyses; and A. W. Lomax at the University of Wisconsin-Madison for the NBR1 antibody. This work was supported by a grant from the NSF Plant Genome Research Program (IOS-1329956).

Author information

Author notes

    • Faqiang Li

    Present address: College of Life Sciences, South China Agricultural University, Guangzhou, China

Affiliations

  1. Department of Biology, Washington University in St. Louis, St. Louis, MO, USA

    • Fionn McLoughlin
    • , Robert C. Augustine
    • , Richard S. Marshall
    • , Liam D. Kirkpatrick
    •  & Richard D. Vierstra
  2. Department of Genetics, University of Wisconsin, Madison, WI, USA

    • Faqiang Li
    • , Marisa S. Otegui
    •  & Richard D. Vierstra
  3. Department of Botany, University of Wisconsin, Madison, WI, USA

    • Marisa S. Otegui
  4. Laboratory of Cell and Molecular Biology, University of Wisconsin, Madison, WI, USA

    • Marisa S. Otegui

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Contributions

F.L. and R.D.V. conceived of and designed the project. F.L. grew the plant material for the omics work. F.M., F.L., R.S.M. and L.D.K. conducted the experiments with help from the staff at Metabolon for the metabolite analyses. F.M., R.C.A., R.S.M., L.D.K., M.S.O. and R.D.V. analysed data. F.M. and R.D.V. wrote the manuscript with input from all of the co-authors.

Competing interests

The authors declare that they have no competing financial interests as defined by Nature Publishing Group, or other interests that might be perceived to influence the results and/or discussion reported in this article.

Corresponding author

Correspondence to Richard D. Vierstra.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–9.

  2. Reporting Summary

  3. Dataset 1

    Raw values metabolomics both leaves.

  4. Dataset 2

    Metabolomics both leaves.

  5. Dataset 3

    Transcriptomics Fragment length and CV values.

  6. Dataset 4

    Transcriptomics 2nd leaf raw data.

  7. Dataset 5

    Transcriptomics 4th leaf raw data.

  8. Dataset 6

    Transcriptomics 2nd leaf.

  9. Dataset 7

    Transcriptomics 4th leaf.

  10. Dataset 8

    Ionomics Raw data both leaves.

  11. Dataset 9

    Values used for protein normalization.

  12. Dataset 10

    Proteomics 2nd leaf raw data.

  13. Dataset 11

    Proteomics 4th leaf raw data.

  14. Dataset 12

    Proteomics 2nd leaf.

  15. Dataset 13

    Proteomics 4th leaf.

  16. Dataset 14

    Modified gene ontology list.

  17. Dataset 15

    Merged mRNA and Protein responses.

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DOI

https://doi.org/10.1038/s41477-018-0299-2

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