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A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti

Nature Biotechnology volume 34, pages 1198–1205 (2016)Cite this article

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Abstract

Legumes are essential components of agricultural systems because they enrich the soil in nitrogen and require little environmentally deleterious fertilizers. A complex symbiotic association between legumes and nitrogen-fixing soil bacteria called rhizobia culminates in the development of root nodules, where rhizobia fix atmospheric nitrogen and transfer it to their plant host. Here we describe a quantitative proteomic atlas of the model legume Medicago truncatula and its rhizobial symbiont Sinorhizobium meliloti, which includes more than 23,000 proteins, 20,000 phosphorylation sites, and 700 lysine acetylation sites. Our analysis provides insight into mechanisms regulating symbiosis. We identify a calmodulin-binding protein as a key regulator in the host and assign putative roles and targets to host factors (bioactive peptides) that control gene expression in the symbiont. Further mining of this proteomic resource may enable engineering of crops and their microbial partners to increase agricultural productivity and sustainability.

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Figure 1: In-depth proteome sequencing reveals organ-specific proteins and post-translational modifications.
Figure 2: Functional characterization of proteins and post-translational modifications in M. truncatula.
Figure 3: Nodule-specific proteins and post-translational modifications provide evidence for key regulators in symbiosis.
Figure 4: Temporal stages of host-factor expression in M. truncatula and putative targets in S. meliloti.

Change history

  • 20 October 2016

    In the version of this article initially published, Alireza F. Siahpirani's name was misspelled as Sihapirani. The error has been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

We thank T. Rhoads, K. Overmyer, and A. Hebert for fruitful discussions. This work was supported mainly by funds from the NSF IOS-PGRP Grants 1237936 and 1546742. S.R. gratefully acknowledges support from an NSF CAREER award (DBI 1350677). C.E.M. was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM T15LM007359). A.L.R. gratefully acknowledges support from a National Institutes of Health-funded Genomic Sciences Training Program (5T32HG002760), the ACS Division of Analytical Chemistry, and the Society of Analytical Chemists of Pittsburgh (SACP).

Author information

Author notes

  1. Harald Marx and Catherine E Minogue: These authors contributed equally to this work.

  2. jeanmichel.ane@wisc.edu

  3. jcoon@chem.wisc.edu

Authors and Affiliations

  1. Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Harald Marx, Catherine E Minogue, Alicia L Richards, Nicholas W Kwiecien, Michael S Westphall & Joshua J Coon

  2. Department of Agronomy, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Dhileepkumar Jayaraman, Shanmugam Rajasekar & Jean-Michel Ané

  3. Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Alireza F Siahpirani

  4. Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Junko Maeda, Kevin Garcia, Angel R Del Valle-Echevarria & Jean-Michel Ané

  5. Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Jeremy D Volkening & Michael R Sussman

  6. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Sushmita Roy

  7. Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Sushmita Roy

  8. Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA

    Joshua J Coon

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  1. Harald Marx
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Contributions

H.M., C.E.M., D.J., J.-M.A., and J.J.C. conceived and designed the study. D.J., S. Rajasekar, and J.M., provided the plant material. C.E.M., A.L.R., and M.S.W. performed the proteomics experiments. H.M. and C.E.M. analyzed the data. H.M., C.E.M., D.J., K.G., and A.R.D.V. interpreted the data. N.W.K. and H.M. built the website. A.F.S., J.D.V., and S. Roy generated the regulatory network. The paper was written by H.M., C.E.M., D.J., M.R.S., K.G., A.R.D.V., J.-M.A., and J.J.C. and was edited by all authors.

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Integrated supplementary information

Supplementary Figure 1 Experimental design and workflow utilized to generate the WMG Protein Atlas.

(a) Illustration of the plant organs and nodule infection time points analyzed. (b) Proteomic workflow employed for the identification and quantification of proteins and PTMs.

Supplementary Figure 2 Correlation of LFQ and TMT quantification data.

Scatter plots illustrate the quantitative values obtained for proteins identified in both the deep sequencing (LFQ) and multiplexed (TMT) datasets for all plant organs analyzed. Note that for comparative purposes, quantitative values in both the LFQ and TMT datasets have been mean-normalized across all organs identified for a given protein. R2 values reflect the relative correlation of the data.

Supplementary Figure 3 Protein identification and characterization within the WMG Protein Atlas.

(a) Frequency of proteins to the number of organs each protein is associated with. (b) Heat maps illustrate the most significantly enriched gene ontology biological processes carried out by the core proteins within each organ. (c) Pair-wise Pearson correlation coefficients were calculated using protein abundance measurements obtained from the deep sequencing analysis (LFQ). Note that correlation analysis was performed twice: once using only those core proteins (bottom triangle) identified in every organ and once using only proteins not identified in every organ (top triangle). The color and size of each circle reflect the strength of the correlation (the darker and larger the circle, the greater the similarity between the two organs). Organ samples were clustered using hierarchical clustering.

Supplementary Figure 4 Organ-specific proteins and post-translational modifications.

(a) Distribution of inverted Shannon entropy scores obtained for proteins and post-translational modifications. Dashed lines indicate the 10% quantile cut-off for organ specificity. (b) Venn diagrams display protein identifications, indicating the number of protein groups observed with and without phosphorylated and/or acetylated residues in both M. truncatula and S. meliloti.

Supplementary Figure 5 Functional characterization of proteins and post-translational modifications (continued from Figure 2).

Bar plots illustrate the complete GO terms significantly enriched within the clusters in Figure 2. P-values associated with each cluster have been corrected for multiple hypotheses and are shown on the x-axes.

Supplementary Figure 6 Global motif analysis of M. truncatula protein phosphorylation.

Motif analysis performed within motif-x (v1.2) using all phospho-isoforms identified in the M. truncatula Protein Atlas. Sequences were manually aligned and all M. truncatula proteins identified in our study were used as background. A width of 13 residues, the minimum number of 50 (pSer and pThr) or 25 (pTyr) occurrences, and a significance of 1e-06 were specified before running motif-x. For each motif listed, the localized phosphorylated residue is indicated in lowercase underlined letters (s, t, or y), and x indicates any amino acid residue.

Supplementary Figure 7 Novel translation start discovered for calmodulin-binding hub protein within the nodule-specific network.

(a) Sequence alignment for the four most highly-scored protein identifications within the protein group representing the gene hub (performed using JalView). (b) MS/MS spectrum resulting in the peptide spectral match that maps to the new translation start of the calmodulin-binding protein.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1553 kb)

Supplementary Table 1

LFQ and TMT data from MaxQuant proteinGroups.txt and evidence.txt files (XLSX 12356 kb)

Supplementary Table 2

Novel and refined gene models from Augustus gene predictions (XLSX 82 kb)

Supplementary Table 3

ANOVA analysis results for TMT protein, phospho and acetyl data (XLSX 767 kb)

Supplementary Code (ZIP 5 kb)

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Marx, H., Minogue, C., Jayaraman, D. et al. A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti. Nat Biotechnol 34, 1198–1205 (2016). https://doi.org/10.1038/nbt.3681

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