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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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).

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Author notes

    • Faqiang Li

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


  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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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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