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

Journal name:
Nature Biotechnology
Volume:
34,
Pages:
1198–1205
Year published:
DOI:
doi:10.1038/nbt.3681
Received
Accepted
Published online
Corrected online

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.

At a glance

Figures

  1. In-depth proteome sequencing reveals organ-specific proteins and post-translational modifications.
    Figure 1: In-depth proteome sequencing reveals organ-specific proteins and post-translational modifications.

    (a,b) Circular proteome maps depict the similarities and differences in the organ-specific proteomes acquired following proteome analysis of seven M. truncatula organs (apical meristem, flower, leaf, root, seed, stem, and nodules 10, 14, and 28 d past infection) (a) and nodule rhizobia (10, 14, and 28 d past infection) (b). The number of protein identifications associated with each organ is displayed by heat maps. The color gradient within these heat maps and respective links reflect the number of organs each protein identification is associated with, where the lightest green region represents the core proteome. The relative abundance of each protein within a given organ is represented by the black histograms. Organ-specific proteins, phosphorylation sites, and lysine acetylation sites are indicated by the red, blue, and purple histograms, respectively. Note that the length of each bar reflects the organ specificity of each protein or PTM site, i.e., longer bars represent a greater degree of specificity.

  2. Functional characterization of proteins and post-translational modifications in M. truncatula.
    Figure 2: Functional characterization of proteins and post-translational modifications in M. truncatula.

    Heat maps are composed of proteins, phospho-isoforms and lysine acetyl-isoforms significantly changing (FDR q < 0.01, ANOVA) between six M. truncatula organs (n = 4,765, n = 11,101 and n = 234, respectively). Expression data were grouped by hierarchical clustering, both on the organ level and on the protein/PTM-isoform level. For each data set, proteins/PTM-isoforms were grouped into 12 clusters, each of which was subjected to gene ontology (GO) enrichment (FDR q < 0.01, Fisher's exact test). Each heat map is associated with the top GO terms (right) significantly enriched within select clusters. Expression profiles (left) depict co-expressed and co-regulated (uncharacterized) proteins. Uncharacterized proteins and assigned PFAM domains are highlighted by WMG accession.

  3. Nodule-specific proteins and post-translational modifications provide evidence for key regulators in symbiosis.
    Figure 3: Nodule-specific proteins and post-translational modifications provide evidence for key regulators in symbiosis.

    (a) Volcano plots compare protein (top) and phospho-isoform (bottom) expression in nodules to root organs, indicating known proteins key to symbiosis, proteins of unknown function and candidates with a putative role in symbiosis. Known symbiosis proteins encompass processes such as nodule senescence, oxygen transport, and immune response, among others. (b) A nodule-specific sub-network was extracted from our global transcript co-expression network by mapping significantly (FDR q < 0.01, Student's t-test) upregulated proteins and motifs from (a) to the network. The sub-network is organized based on the classification of hub genes, which were either calmodulin-like/calmodulin-binding or kinases. The color of each node reflects the fold-change from (a). Select gene families have been illustrated by dashed boxes. Genes not identified by our protein analysis have been faded.

  4. Temporal stages of host-factor expression in M. truncatula and putative targets in S. meliloti.
    Figure 4: Temporal stages of host-factor expression in M. truncatula and putative targets in S. meliloti.

    (a) Host factor time-course profiles obtained from the deep sequencing (LFQ) analysis (n = 252) were grouped by hierarchical (left) and fuzzy c-means clustering (right). Expression profiles were pairwise correlated (Pearson), clustered (hierarchical) by correlation coefficients and reordered by fuzzy clusters. Darker trace colors reflect stronger membership to the given fuzzy cluster. (b) S. meliloti downregulated (more than twofold) proteins from 10–28 d past rhizobial infection were gene ontology enriched (FDR q ≤ 0.05, Fisher's exact test), extracted (shown along border) and mapped to their corresponding protein-protein interactions (STRING database, shown within inner network circle). The size of each protein node reflects its degree within the network and the color of each node reflects how its protein expression changed over the 18-day time course.

  5. Experimental design and workflow utilized to generate the WMG Protein Atlas.
    Supplementary Fig. 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.

  6. Correlation of LFQ and TMT quantification data.
    Supplementary Fig. 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.

  7. Protein identification and characterization within the WMG Protein Atlas.
    Supplementary Fig. 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.

  8. Organ-specific proteins and post-translational modifications.
    Supplementary Fig. 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.

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

  10. Global motif analysis of M. truncatula protein phosphorylation.
    Supplementary Fig. 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.

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

Change history

Corrected online 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.

References

  1. Timmers, A.C., Auriac, M.C. & Truchet, G. Refined analysis of early symbiotic steps of the Rhizobium-Medicago interaction in relationship with microtubular cytoskeleton rearrangements. Development 126, 36173628 (1999).
  2. Xiao, T.T. et al. Fate map of Medicago truncatula root nodules. Development 141, 35173528 (2014).
  3. Gibson, K.E., Kobayashi, H. & Walker, G.C. Molecular determinants of a symbiotic chronic infection. Annu. Rev. Genet. 42, 413441 (2008).
  4. Mergaert, P. et al. A novel family in Medicago truncatula consisting of more than 300 nodule-specific genes coding for small, secreted polypeptides with conserved cysteine motifs. Plant Physiol. 132, 161173 (2003).
  5. Vasse, J., de Billy, F., Camut, S. & Truchet, G. Correlation between ultrastructural differentiation of bacteroids and nitrogen fixation in alfalfa nodules. J. Bacteriol. 172, 42954306 (1990).
  6. Lauressergues, D. et al. Primary transcripts of microRNAs encode regulatory peptides. Nature 520, 9093 (2015).
  7. Oldroyd, G.E.D., Murray, J.D., Poole, P.S. & Downie, J.A. The rules of engagement in the legume-rhizobial symbiosis. Annu. Rev. Genet. 45, 119144 (2011).
  8. Van de Velde, W. et al. Aging in legume symbiosis. A molecular view on nodule senescence in Medicago truncatula. Plant Physiol. 141, 711720 (2006).
  9. Limpens, E. et al. cell- and tissue-specific transcriptome analyses of Medicago truncatula root nodules. PLoS One 8, e64377 (2013).
  10. Maunoury, N. et al. Differentiation of symbiotic cells and endosymbionts in Medicago truncatula nodulation are coupled to two transcriptome-switches. PLoS One 5, e9519 (2010).
  11. Lohar, D.P. et al. Transcript analysis of early nodulation events in Medicago truncatula. Plant Physiol. 140, 221234 (2006).
  12. El Yahyaoui, F. et al. Expression profiling in Medicago truncatula identifies more than 750 genes differentially expressed during nodulation, including many potential regulators of the symbiotic program. Plant Physiol. 136, 31593176 (2004).
  13. Grimsrud, P.A. et al. Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol. 152, 1928 (2010).
  14. Rose, C.M. et al. Medicago PhosphoProtein Database: a repository for Medicago truncatula phosphoprotein data. Front. Plant Sci. 3, 122 (2012).
  15. Rose, C.M. et al. Rapid phosphoproteomic and transcriptomic changes in the rhizobia-legume symbiosis. Mol. Cell. Proteomics 11, 724744 (2012).
  16. Volkening, J.D. et al. A proteogenomic survey of the Medicago truncatula genome. Mol. Cell. Proteomics 11, 933944 (2012).
  17. Clarke, V.C. et al. Proteomic analysis of the soybean symbiosome identifies new symbiotic proteins. Mol. Cell. Proteomics 14, 13011322 (2015).
  18. Durgo, H. et al. Identification of nodule-specific cysteine-rich plant peptides in endosymbiotic bacteria. Proteomics 15, 22912295 (2015).
  19. Benedito, V.A. et al. A gene expression atlas of the model legume Medicago truncatula. Plant J. 55, 504513 (2008).
  20. Young, N.D. et al. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480, 520524 (2011).
  21. Senko, M.W. et al. Novel parallelized quadrupole/linear ion trap/Orbitrap tribrid mass spectrometer improving proteome coverage and peptide identification rates. Anal. Chem. 85, 1171011714 (2013).
  22. Hebert, A.S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339347 (2014).
  23. Huttlin, E.L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 11741189 (2010).
  24. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 15831594 (2014).
  25. Nakagami, H. et al. Large-scale comparative phosphoproteomics identifies conserved phosphorylation sites in plants. Plant Physiol. 153, 11611174 (2010).
  26. Walley, J.W. et al. Reconstruction of protein networks from an atlas of maize seed proteotypes. Proc. Natl. Acad. Sci. USA 110, E4808E4817 (2013).
  27. Roitinger, E. et al. Quantitative phosphoproteomics of the ataxia telangiectasia-mutated (ATM) and ataxia telangiectasia-mutated and rad3-related (ATR) dependent DNA damage response in Arabidopsis thaliana. Mol. Cell. Proteomics 14, 556571 (2015).
  28. van Wijk, K.J., Friso, G., Walther, D. & Schulze, W.X. Meta-analysis of Arabidopsis thaliana phospho-proteomics data reveals compartmentalization of phosphorylation motifs. Plant Cell 26, 23672389 (2014).
  29. Deruyffelaere, C. et al. Ubiquitin-mediated proteasomal degradation of oleosins is involved in oil body mobilization during post-germinative seedling growth in Arabidopsis. Plant Cell Physiol. 56, 13741387 (2015).
  30. Lang, C. & Long, S.R. Transcriptomic analysis of sinorhizobium meliloti and Medicago truncatula symbiosis using nitrogen fixation-deficient nodules. Mol. Plant Microbe Interact. 28, 856868 (2015).
  31. Moreau, M. et al. EDS1 contributes to nonhost resistance of Arabidopsis thaliana against Erwinia amylovora. Mol. Plant Microbe Interact. 25, 421430 (2012).
  32. Udvardi, M. & Poole, P.S. Transport and metabolism in legume-rhizobia symbioses. Annu. Rev. Plant Biol. 64, 781805 (2013).
  33. Ott, T. et al. Symbiotic leghemoglobins are crucial for nitrogen fixation in legume root nodules but not for general plant growth and development. Curr. Biol. 15, 531535 (2005).
  34. Dixon, R. & Kahn, D. Genetic regulation of biological nitrogen fixation. Nat. Rev. Microbiol. 2, 621631 (2004).
  35. Bhar, K. et al. Phosphorylation of leghemoglobin at S45 is most effective to disrupt the molecular environment of its oxygen binding pocket. Protein J. 34, 158167 (2015).
  36. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249255 (2003).
  37. Gallardo, K., Le Signor, C., Vandekerckhove, J., Thompson, R.D. & Burstin, J. Proteomics of Medicago truncatula seed development establishes the time frame of diverse metabolic processes related to reserve accumulation. Plant Physiol. 133, 664682 (2003).
  38. Niebel, Fde.C., Lescure, N., Cullimore, J.V. & Gamas, P. The Medicago truncatula MtAnn1 gene encoding an annexin is induced by Nod factors and during the symbiotic interaction with Rhizobium meliloti. Mol. Plant Microbe Interact. 11, 504513 (1998).
  39. Barkan, A. & Small, I. Pentatricopeptide repeat proteins in plants. Annu. Rev. Plant Biol. 65, 415442 (2014).
  40. Kurihara, D., Matsunaga, S., Omura, T., Higashiyama, T. & Fukui, K. Identification and characterization of plant Haspin kinase as a histone H3 threonine kinase. BMC Plant Biol. 11, 73 (2011).
  41. Nallu, S. et al. Regulatory patterns of a large family of defensin-like genes expressed in nodules of Medicago truncatula. PLoS One 8, e60355 (2013).
  42. Tiricz, H. et al. Antimicrobial nodule-specific cysteine-rich peptides induce membrane depolarization-associated changes in the transcriptome of Sinorhizobium meliloti. Appl. Environ. Microbiol. 79, 67376746 (2013).
  43. Farkas, A. et al. Medicago truncatula symbiotic peptide NCR247 contributes to bacteroid differentiation through multiple mechanisms. Proc. Natl. Acad. Sci. USA 111, 51835188 (2014).
  44. Penterman, J. et al. Host plant peptides elicit a transcriptional response to control the Sinorhizobium meliloti cell cycle during symbiosis. Proc. Natl. Acad. Sci. USA 111, 35613566 (2014).
  45. Gong, Z.Y., He, Z.S., Zhu, J.B., Yu, G.Q. & Zou, H.S. Sinorhizobium meliloti nifA mutant induces different gene expression profile from wild type in Alfalfa nodules. Cell Res. 16, 818829 (2006).
  46. Marshall, E., Costa, L.M. & Gutierrez-Marcos, J. Cysteine-rich peptides (CRPs) mediate diverse aspects of cell-cell communication in plant reproduction and development. J. Exp. Bot. 62, 16771686 (2011).
  47. Poliakov, Anton et al. Large-scale label-free quantitative proteomics of the pea aphid-Buchnera symbiosis. Mol. Cell. Proteomics 10, M110.007039 (2011).
  48. Inoue, S. et al. Blue light-induced autophosphorylation of phototropin is a primary step for signaling. Proc. Natl. Acad. Sci. USA 105, 56265631 (2008).
  49. Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223226 (2014).
  50. Vizcaíno, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063D1069 (2013).
  51. Broughton, W.J. & Dilworth, M.J. Control of leghaemoglobin synthesis in snake beans. Biochem. J. 125, 10751080 (1971).
  52. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 13671372 (2008).
  53. Marx, H., Lemeer, S., Klaeger, S., Rattei, T. & Kuster, B. MScDB: a mass spectrometry-centric protein sequence database for proteomics. J. Proteome Res. 12, 23862398 (2013).
  54. Bairoch, A. et al. The Universal Protein Resource (UniProt). Nucleic Acids Res. 33, D154D159 (2005).
  55. Flicek, P. et al. Ensembl 2013. Nucleic Acids Res. 41, D48D55 (2013).
  56. Tatusova, T., Ciufo, S., Fedorov, B., O'Neill, K. & Tolstoy, I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553D559 (2014).
  57. Tang, H. et al. An improved genome release (version Mt4.0) for the model legume Medicago truncatula. BMC Genomics 15, 312 (2014).
  58. Stanke, M. & Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19 (Suppl. 2), ii215ii225 (2003).
  59. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 25132526 (2014).
  60. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731740 (2016).
  61. Phanstiel, D.H. et al. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Methods 8, 821827 (2011).
  62. Olsen, J.V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635648 (2006).
  63. Wenger, C.D., Phanstiel, D.H., Lee, M.V., Bailey, D.J. & Coon, J.J. COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA. Proteomics 11, 10641074 (2011).
  64. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 16391645 (2009).
  65. Schug, J. et al. Promoter features related to tissue specificity as measured by Shannon entropy. Genome Biol. 6, R33 (2005).
  66. Ihaka, R. & Gentleman, R.R. A language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299314 (1996).
  67. Roy, S. et al. Integrated module and gene-specific regulatory inference implicates upstream signaling networks. PLoS Comput. Biol. 9, e1003252 (2013).
  68. Meinshausen, N. & Bahlmann, P. Stability selection. J. R. Stat. Soc. Ser. A Stat. Soc. 72, 417473 (2010).
  69. He, J. et al. The Medicago truncatula gene expression atlas web server. BMC Bioinformatics 10, 441 (2009).
  70. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185193 (2003).
  71. Thain, D., Tannenbaum, T. & Livny, M. Distributed computing in practice: the Condor experience. Concurr. Comput. 17, 323356 (2005).

Download references

Author information

  1. These authors contributed equally to this work.

    • Harald Marx &
    • Catherine E Minogue

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

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.

Competing financial interests

The authors declare no competing financial interests.

Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Experimental design and workflow utilized to generate the WMG Protein Atlas. (222 KB)

    (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.

  2. Supplementary Figure 2: Correlation of LFQ and TMT quantification data. (166 KB)

    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.

  3. Supplementary Figure 3: Protein identification and characterization within the WMG Protein Atlas. (332 KB)

    (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.

  4. Supplementary Figure 4: Organ-specific proteins and post-translational modifications. (110 KB)

    (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.

  5. Supplementary Figure 5: Functional characterization of proteins and post-translational modifications (continued from Figure 2). (410 KB)

    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.

  6. Supplementary Figure 6: Global motif analysis of M. truncatula protein phosphorylation. (375 KB)

    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.

  7. Supplementary Figure 7: Novel translation start discovered for calmodulin-binding hub protein within the nodule-specific network. (395 KB)

    (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.

PDF files

  1. Supplementary Text and Figures (1,590 KB)

    Supplementary Figures 1–7

Excel files

  1. Supplementary Table 1 (12,653 KB)

    LFQ and TMT data from MaxQuant proteinGroups.txt and evidence.txt files

  2. Supplementary Table 2 (84 KB)

    Novel and refined gene models from Augustus gene predictions

  3. Supplementary Table 3 (786 KB)

    ANOVA analysis results for TMT protein, phospho and acetyl data

Zip files

  1. Supplementary Code (5,708 KB)

Additional data