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Extensive signal integration by the phytohormone protein network

A Publisher Correction to this article was published on 29 July 2020

This article has been updated


Plant hormones coordinate responses to environmental cues with developmental programs1, and are fundamental for stress resilience and agronomic yield2. The core signalling pathways underlying the effects of phytohormones have been elucidated by genetic screens and hypothesis-driven approaches, and extended by interactome studies of select pathways3. However, fundamental questions remain about how information from different pathways is integrated. Genetically, most phenotypes seem to be regulated by several hormones, but transcriptional profiling suggests that hormones trigger largely exclusive transcriptional programs4. We hypothesized that protein–protein interactions have an important role in phytohormone signal integration. Here, we experimentally generated a systems-level map of the Arabidopsis phytohormone signalling network, consisting of more than 2,000 binary protein–protein interactions. In the highly interconnected network, we identify pathway communities and hundreds of previously unknown pathway contacts that represent potential points of crosstalk. Functional validation of candidates in seven hormone pathways reveals new functions for 74% of tested proteins in 84% of candidate interactions, and indicates that a large majority of signalling proteins function pleiotropically in several pathways. Moreover, we identify several hundred largely small-molecule-dependent interactions of hormone receptors. Comparison with previous reports suggests that noncanonical and nontranscription-mediated receptor signalling is more common than hitherto appreciated.

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Fig. 1: Phytohormone network mapping and analysis.
Fig. 2: Validation of pathway contact points.
Fig. 3: Hormone–receptor interactions.

Data availability

All functional, genetic and interaction data generated here are available as Supplementary Information. The genes selected for interactome mapping (the search space) are presented in Supplementary Table 1. All protein–protein interaction data can be found in Supplementary Table 2. The data for genetic validation assays can be found in Supplementary Table 5. The preliminary edge scores for all interactions identified here are in Supplementary Table 6. Additionally, all protein interactions identified here have been submitted to IMEx ( through IntAct5 with identification code IM-27834. Source data are provided with this paper.

Code availability

Custom scripts used here are available at

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  1. 1.

    Krouk, G. et al. A framework integrating plant growth with hormones and nutrients. Trends Plant Sci. 16, 178–182 (2011).

    CAS  PubMed  Google Scholar 

  2. 2.

    Peleg, Z. & Blumwald, E. Hormone balance and abiotic stress tolerance in crop plants. Curr. Opin. Plant Biol. 14, 290–295 (2011).

    CAS  PubMed  Google Scholar 

  3. 3.

    Lumba, S. et al. A mesoscale abscisic acid hormone interactome reveals a dynamic signaling landscape in Arabidopsis. Dev. Cell 29, 360–372 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Nemhauser, J. L., Hong, F. & Chory, J. Different plant hormones regulate similar processes through largely nonoverlapping transcriptional responses. Cell 126, 467–475 (2006).

    CAS  Google Scholar 

  5. 5.

    Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).

    CAS  PubMed  Google Scholar 

  6. 6.

    Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Altmann, M., Altmann, S., Falter, C. & Falter-Braun, P. High-quality yeast-2-hybrid interaction network mapping. Curr. Protoc. Plant Biol. 3, e20067 (2018).

    PubMed  Google Scholar 

  8. 8.

    Weßling, R. et al. Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. Cell Host Microbe 16, 364–375 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Pruneda-Paz, J. L. et al. A genome-scale resource for the functional characterization of Arabidopsis transcription factors. Cell Rep. 8, 622–632 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Arabidopsis Interactome Mapping Consortium. Evidence for network evolution in an Arabidopsis interactome map. Science 333, 601–607 (2011).

    PubMed Central  Google Scholar 

  11. 11.

    Braun, P. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat. Methods 6, 91–97 (2009).

    CAS  Google Scholar 

  12. 12.

    Braun, P. Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays. Proteomics 12, 1499–1518 (2012).

    CAS  PubMed  Google Scholar 

  13. 13.

    Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47 (D1), D529–D541 (2019).

    CAS  PubMed  Google Scholar 

  14. 14.

    Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99, 7821–7826 (2002).

    ADS  MathSciNet  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  16. 16.

    Cutler, S. R., Rodriguez, P. L., Finkelstein, R. R. & Abrams, S. R. Abscisic acid: emergence of a core signaling network. Annu. Rev. Plant Biol. 61, 651–679 (2010).

    CAS  PubMed  Google Scholar 

  17. 17.

    Deikman, J. & Hammer, P. E. Induction of anthocyanin accumulation by cytokinins in Arabidopsis thaliana. Plant Physiol. 108, 47–57 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Guzmán, P. & Ecker, J. R. Exploiting the triple response of Arabidopsis to identify ethylene-related mutants. Plant Cell 2, 513–523 (1990).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Vlot, A. C., Dempsey, D. A. & Klessig, D. F. Salicylic acid, a multifaceted hormone to combat disease. Annu. Rev. Phytopathol. 47, 177–206 (2009).

    CAS  PubMed  Google Scholar 

  20. 20.

    Lumba, S., Cutler, S. & McCourt, P. Plant nuclear hormone receptors: a role for small molecules in protein–protein interactions. Annu. Rev. Plant Biol. 26, 445–469 (2010).

    CAS  Google Scholar 

  21. 21.

    Tischer, S. V. et al. Combinatorial interaction network of abscisic acid receptors and coreceptors from Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 114, 10280–10285 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Aleman, F. et al. An ABA-increased interaction of the PYL6 ABA receptor with MYC2 transcription factor: a putative link of ABA and JA signaling. Sci. Rep. 6, 28941 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Zhao, Y. et al. The ABA receptor PYL8 promotes lateral root growth by enhancing MYB77-dependent transcription of auxin-responsive genes. Sci. Signal. 7, ra53 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Née, G. et al. DELAY OF GERMINATION1 requires PP2C phosphatases of the ABA signalling pathway to control seed dormancy. Nat. Commun. 8, 72 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Kuai, X., MacLeod, B. J. & Després, C. Integrating data on the Arabidopsis NPR1/NPR3/NPR4 salicylic acid receptors; a differentiating argument. Front. Plant Sci. 6, 235 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Ding, Y. et al. Opposite roles of salicylic acid receptors NPR1 and NPR3/NPR4 in transcriptional regulation of plant immunity. Cell 173, 1454–1467 (2018).

    CAS  PubMed  Google Scholar 

  27. 27.

    Hermann, M. et al. The Arabidopsis NIMIN proteins affect NPR1 differentially. Front Plant Sci 4, 88 (2013).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Waters, M. T., Gutjahr, C., Bennett, T. & Nelson, D. C. Strigolactone signaling and evolution. Annu. Rev. Plant Biol. 68, 291–322 (2017).

    CAS  PubMed  Google Scholar 

  29. 29.

    Scaffidi, A. et al. Strigolactone hormones and their stereoisomers signal through two related receptor proteins to induce different physiological responses in Arabidopsis. Plant Physiol. 165, 1221–1232 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Villaécija-Aguilar, J. A. et al. SMAX1/SMXL2 regulate root and root hair development downstream of KAI2-mediated signalling in Arabidopsis. PLoS Genet. 15, e1008327 (2019).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Jiang, Z. et al. AHD2.0: an update version of Arabidopsis Hormone Database for plant systematic studies. Nucleic Acids Res. 39, D1123–D1129 (2011).

    CAS  PubMed  Google Scholar 

  32. 32.

    Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006).

    CAS  PubMed  Google Scholar 

  33. 33.

    Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 83–90 (2009).

    CAS  PubMed  Google Scholar 

  34. 34.

    Barabási, A. L. & Oltvai, Z. N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    PubMed  Google Scholar 

  35. 35.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Cheng, C. Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).

    CAS  PubMed  Google Scholar 

  37. 37.

    Csárdi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).

    Google Scholar 

  38. 38.

    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    CAS  Google Scholar 

  39. 39.

    Chatr-aryamontri, A. et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45 (D1), D369–D379 (2017).

    CAS  PubMed  Google Scholar 

  40. 40.

    Edwards, K., Johnstone, C. & Thompson, C. A simple and rapid method for the preparation of plant genomic DNA for PCR analysis. Nucleic Acids Res. 19, 1349 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Longair, M. H., Baker, D. A. & Armstrong, J. D. Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27, 2453–2454 (2011).

    CAS  PubMed  Google Scholar 

  43. 43.

    Nakata, M. & Ohme-Takagi, M. Quantification of anthocyanin content. Bio Protoc. 4, e1098 (2014).

    Google Scholar 

  44. 44.

    Wenig, M. et al. Systemic acquired resistance networks amplify airborne defense cues. Nat. Commun. 10, 3813 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Belda-Palazón, B. et al. Aminopropyltransferases involved in polyamine biosynthesis localize preferentially in the nucleus of plant cells. PLoS ONE 7, e46907 (2012).

    ADS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Gehl, C. et al. Quantitative analysis of dynamic protein-protein interactions in planta by a floated-leaf luciferase complementation imaging (FLuCI) assay using binary Gateway vectors. Plant J. 67, 542–553 (2011).

    CAS  PubMed  Google Scholar 

  47. 47.

    Klepikova, A. V., Kasianov, A. S., Gerasimov, E. S., Logacheva, M. D. & Penin, A. A. A high resolution map of the Arabidopsis thaliana developmental transcriptome based on RNA-seq profiling. Plant J. 88, 1058–1070 (2016).

    CAS  PubMed  Google Scholar 

  48. 48.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016); erratum 34, 888 (2016).

    CAS  Google Scholar 

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We thank all INET members for helpful discussions. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) Emmy Noether program (grant GU1423/1-1) to C.G.; by DFG grant SFB924 to A.C.V., C.G., E.G. and P.F.-B.; by the Bundesministerium für Bildung und Forschung (BMBF, Federal Ministry for Education, Science, Research and Technology) grant 031L0141 e:Bio-Modul III: ChlamyInt to P.F.-B.; and by the European Research Council’s Horizon 2020 Research and Innovation Programme (grant agreement 648420) to P.F.-B.

Author information




The authors contributed as follows: project conception, P.F.-B.; ORF selection and cloning, M.A., S.A., P.F.-B., G.W.B., S.C. and C.G.; Y2H screening, M.A., P.A.R., L.E.V., M.S., V.Y., R.P. and N.M.R.; hormone-dependent Y2H screens, M.A. and J.P.; PRSPhI/RRSPhI curation, M.A., S.A., N.M.R., A.G.-M and P.F.-B.; network analyses, S.A., P.F.-B., M.A., K.G.K. and K.F.X.M.; edge-score calculation, C.-W.L., S.A. and P.F.-B; pulldown experiment, J.P. and N.M.R.; systematic validation assay, B.W., P.A.R., M.S., M.A., A.S., V.Y. and P.F.-B.; BiFC validations, Fig. 2h and Extended Data Fig. 7b, P.A.R., M.A., N.M.R. and J.P.; genetic validation assays, for ABA, M.A., E.G., L.G., S.A. and P.F.-B.; for cytokinins, M.A. and L.E.V.; for ethylene, M.A. and L.E.V; for salicylic acid, A.C.V., M.W., J.S. and M.A.; for jasmonate, N.M.R., M.A. and P.A.R.; for gibberellic acid, M.A.; for karrikin/GR24 vignette and Fig. 3h, j, J.A.V.-A. and C.G.; figures, M.A., S.A., C.F. and P.F.-B.; manuscript writing, P.F.-B., C.F., M.A., S.A., C.G. and A.C.V.

Corresponding author

Correspondence to Pascal Falter-Braun.

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The authors declare no competing interests.

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Peer review information Nature thanks Peter McCourt, Ulrich Stelzl and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Network analyses.

a, Network map showing binary protein–protein interactions among search space proteins derived from IntAct (LCIIntA). The colour code indicates existing hormone pathway annotations, as indicated in the key. b, Degree distribution and clustering coefficient distribution (on a log–log scale) for the network in a. c, Network map showing binary protein–protein interactions among search space proteins derived from BioGRID (LCIBioG). d, As in b, but for network shown in c. e, Number of proteins and interactions in the PhI interactome subsets. f, Number of total and new interactions found in PhI for all proteins belonging to each pathway, and the non-redundant total for PhI. g, Fraction of positive scoring pairs in PRSPhI (78), PRSunc (hormone-independent PRS interactions) (69), RRSPhI (85), combined PhI subsets (green; 285) and the individual subsets from the single Y2H screens: PhIEXT (110), PhIMAIN (115) and Rep-TF (60). Error bars indicate standard errors of proportions. Individual results for all pairs are provided in Supplementary Table 2.

Source data

Extended Data Fig. 2 Communities and validation.

a, Communities with three or more nodes identified in PhIMAIN. Community numbers (1–33) correspond to the numbering in Supplementary Table 3. The colour code indicates existing hormone pathway annotations as indicated in the legend. Node labels are gene symbols when available, and otherwise are locus identification numbers. b, BiFC analysis of CIPK14–MYC2. Nicotiana benthamiana epidermal leaves that are transiently coexpressing cYFP–MYC2 (cYFP: C-terminal amino-acid residues 154–240 of YFP) and nYFP–CIPK14 (nYFP: N-terminal amino-acid residues 1–154 of YFP) restore YFP fluorescence, whereas coexpression of the noninteracting cYFP–JAZ1 and nYFP–CIPK14 does not. c, Maltose-binding-protein (MBP) pulldown of MBP–MYC2 and of CIPK14 tagged with glutathione-S-reductase (GST) shows specific copurification of the latter. b, c, Representative results of two experiments with similar results are shown.

Source data

Extended Data Fig. 3 Enrichment of pathway contact points.

Number of PCPI and PCPII per hormone combination. Asterisks indicate a significantly higher number of PCPs compared with LCIIntA (*P ≤ 0.001), as obtained by bootstrap subsampling analysis (n = 1,000) of 100 interactions followed by two-sided Welch’s two-sample t-test. Precise P values for PCPI and PCPII and pathway combinations are listed in Supplementary Table 2. The key at the bottom illustrates PCPI (counts in brown bars) as interactions between proteins with strictly different pathway annotations, and PCPII (counts in blue bars) as interactions of proteins with both shared and different pathway annotations.

Source data

Extended Data Fig. 4 Hormone-response assays I.

a, Rate of ABA-induced germination for Col-0 (wild-type), cbl9, gai, myc2, ibr5, rcar1 and rcn1 (positive controls), ahp2, as1, bee1, bee2, bim1, ddl, eds1, jaz3, myb77 and wrky54 in the absence (mock) or presence of 0.3 μM ABA. b, Root elongation in absence (mock) or presence of 30 μM ABA for the same plant lines as in a. c, Brassinolide (BL)-mediated inhibition of root length in the absence (mock) or presence of indicated concentrations of BL for Col-0 (wild type), bee1, bee2, bim2 (control), ddl, rcn1 and ttl (candidate) lines. d, Gibberellic acid (GA)-mediated inhibition of root length in the presence of the indicated concentrations of paclobutrazol (Pac) for Col-0 (wild type), as1, gai, gi, rga, rga-28 (control), hub1, jaz3, nia2 and rcn1 (candidate) lines. bd, Boxes represent IQRs; bold black lines represent medians; whiskers indicate the highest and lowest data points within 1.5 IQRs; outliers are plotted individually. ad, Two-sided t-test; *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. Precise n values for each repeat and precise P values are provided in Supplementary Table 5.

Source data

Extended Data Fig. 5 Hormone-response assays II.

a, Salicylic acid (SA)-associated phenotypes, showing Pst titres four days after leaves were inoculated with Pst by syringe infiltration. In planta Pst titres were elevated in mature plants of the indicated genotypes relative to wild-type Col-0 plants. b, Jasmonate (JA)-induced root growth in the absence (mock) or presence of 25 μM Me-JA. cf, Ethylene-induced triple response in control conditions compared with Col-0 plants. c, Apical hook formation in the absence or presence of 10 μM ACC. Shown are representative results underlying the quantitation in d. Scale bars, 5 mm. d, Proportion of apical loop formation (rather than hook formation) following treatment with 10 μM ACC, for the same lines as in c. e, Hypocotyl length in the absence or presence of 10 μM ACC for the same lines as in d. f, Root elongation in the absence or presence of 10 μM ACC for same lines as in d. Two-sided t-test; *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. b, e, f, Boxes represent IQRs; black lines represent medians; whiskers indicate highest and lowest data points within 1.5 IQRs; outliers are plotted individually. a, b, df, Two-sided t-test; *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. Precise n values for each repeat and exact P values are provided in Supplementary Table 5.

Source data

Extended Data Fig. 6 Ethylene-induced triple response assays (negative controls).

The ethylene-mediated triple response in negative control lines is compared with that in Col-0 and ein3 lines. a, Proportion of apical loop formation in response to 10 μM ACC. b, Hypocotyl length in the absence or presence of 10 μM ACC. c, Root elongation in the absence or presence of 10 μM ACC. Two-sided t-test; *P ≤ 0.05, **P ≤ 0.01. b, c, Boxes represent IQRs; black lines represent medians; whiskers indicate highest and lowest data points within 1.5 IQRs; outliers are plotted individually. Precise n values for each repeat and P values are in Supplementary Table 5.

Source data

Extended Data Fig. 7 PCP validation.

a, Summary of hormone-assay results for 27 candidate genes. Light colours indicate previously known hormone pathway annotations. Bright colours indicate significant (Fig. 2, Extended Data Figs. 47 and Supplementary Table 5) new phenotypes observed in validation assays. b, BiFC analysis in N. benthamiana of two PCPI pairs (AHP2–MYC2, MYB77–RCAR1) and five PCPII pairs (CBL9–IBR5, PP2CA–IBR5, TT4–COS1, AS1–NIA2, EDS1–HUB1). PCP pairs were also tested with one or two negative controls in the BiFC assay. Each construct was tested in duplicate and in two independent assays; one representative result is shown. Scale bars, 10 μm.

Extended Data Fig. 8 ABA Y2H interactions.

a, ABA-dependent and -independent interactions of the ABA receptors RCAR1–14. All identified interactors were systematically tested against all receptors in the presence or absence of 30 μM ABA. Excluding PP2Cs, single RCAR-specific interactors are displayed above the receptors; interactors common to several RCARs are shown below. Node colours represent hormone annotations. bf, One representative set of Y2H results, out of four repeats, showing yeast growth on selective media in the presence or absence of 30 μM ABA. All candidate interactors identified in primary screens were tested systematically against all receptors in the shown representative verification experiments. g, Plate layout of candidate interactors tested with the indicated RCARs in bf.

Source data

Extended Data Fig. 9 Hormone-dependent Y2H interactions.

a, SA-dependent interactors of NPR1,3,4 in the presence or absence of 100 μM SA. b, Evidence for NPR4 interactor functions in plant defence. c, MAX2, D14 and KAI2 interactions in the presence or absence of 5 μM rac–GR24. a, c, One representative set of Y2H results, out of four, showing yeast growth in the presence or absence of hormone. All candidate interactors identified in a primary screen were tested against all receptors in the shown representative verification experiments.

Extended Data Fig. 10 Pathway convergence on transcription factors.

Y2H-derived map showing interactions of repressors and non-DNA-binding transcriptional regulators (boxed and colour-coded for involvement in the relevant main pathway) with Arabidopsis transcription factors. Above the repressors/regulators are transcription factors that interact specifically with regulators from one pathway. Lower layers show transcription factors that interact with regulators from several pathways. Node annotations are represented by the indicated colour codes.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

This table contains all candidate genes selected for Phytohormone network analysis and associated annotations. This file can be used as a node attribute file for Cytoscape.

Supplementary Table 2

This table contains all interactions identified in this manuscript, in which screen which interactions were identified and whether the data were already part of literature curated datasets at IntAct or BioGRID. Interactions mediating pathway contact points are identified and curation data for PRS/RRS provided along with experimental benchmarking and validation results.

Supplementary Table 3

This table contains information related to the identified network communities including the underlying betweenness values, community identity for all proteins as well as detailed statistical values underlying the enrichment analysis.

Supplementary Table 4

This table provides GO-enrichment analysis for all communities with more than three constituting proteins.

Supplementary Table 5

This table contains all plant phenotyping related information including the used cell lines, primers used for line validation and results, as well as detailed results of the conducted experiments, including number of repeats, n, and precise P values.

Supplementary Table 6

This table contains the edge-score values for all PhI interactions based on the Kleptikova48 transcriptome map.

Source data

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Altmann, M., Altmann, S., Rodriguez, P.A. et al. Extensive signal integration by the phytohormone protein network. Nature 583, 271–276 (2020).

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