Maize multi-omics reveal roles for autophagic recycling in proteome remodelling and lipid turnover

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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Fig. 1: Maize atg12 mutants are hypersensitive to nitrogen stress and have elevated protein content.
Fig. 2: Maize atg12 mutants have substantially altered metabolomes in nitrogen-rich and -starvation conditions, especially with respect to lipids and secondary products.
Fig. 3: A survey of metabolic processes identifies points in maize lipid and secondary metabolism that are severely affected in atg12 plants.
Fig. 4: The maize transcriptome is substantially altered in the atg12 mutants under both nitrogen-rich and -starvation conditions.
Fig. 5: The maize proteome is substantially altered in the atg12 mutants under both nitrogen-rich and -starvation conditions.
Fig. 6: The impact of the atg12 mutations on the abundances of cellular compartments, metabolic processes and protein complexes in maize leaves.
Fig. 7: Comparisons between transcript and protein profiles identify processes impacted by transcription and/or autophagy and describe how various aspects of lipid and secondary metabolism are impacted by mRNA abundance and/or autophagy.

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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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).

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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.

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Correspondence to Richard D. Vierstra.

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Supplementary information

Supplementary Information

Supplementary Figures 1–9.

Reporting Summary

Dataset 1

Raw values metabolomics both leaves.

Dataset 2

Metabolomics both leaves.

Dataset 3

Transcriptomics Fragment length and CV values.

Dataset 4

Transcriptomics 2nd leaf raw data.

Dataset 5

Transcriptomics 4th leaf raw data.

Dataset 6

Transcriptomics 2nd leaf.

Dataset 7

Transcriptomics 4th leaf.

Dataset 8

Ionomics Raw data both leaves.

Dataset 9

Values used for protein normalization.

Dataset 10

Proteomics 2nd leaf raw data.

Dataset 11

Proteomics 4th leaf raw data.

Dataset 12

Proteomics 2nd leaf.

Dataset 13

Proteomics 4th leaf.

Dataset 14

Modified gene ontology list.

Dataset 15

Merged mRNA and Protein responses.

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McLoughlin, F., Augustine, R.C., Marshall, R.S. et al. Maize multi-omics reveal roles for autophagic recycling in proteome remodelling and lipid turnover. Nature Plants 4, 1056–1070 (2018). https://doi.org/10.1038/s41477-018-0299-2

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