Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.).
Ralstonia solanacearum
Bioinformatic analysis
Disease-resistance proteins
PPIs
Protein interactome
Journal
BMC plant biology
ISSN: 1471-2229
Titre abrégé: BMC Plant Biol
Pays: England
ID NLM: 100967807
Informations de publication
Date de publication:
20 Sep 2024
20 Sep 2024
Historique:
received:
23
01
2024
accepted:
09
09
2024
medline:
21
9
2024
pubmed:
21
9
2024
entrez:
20
9
2024
Statut:
epublish
Résumé
Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear. We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut. Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.
Sections du résumé
BACKGROUND
BACKGROUND
Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear.
RESULTS
RESULTS
We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut.
CONCLUSION
CONCLUSIONS
Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.
Identifiants
pubmed: 39304811
doi: 10.1186/s12870-024-05580-w
pii: 10.1186/s12870-024-05580-w
doi:
Substances chimiques
Plant Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
873Informations de copyright
© 2024. The Author(s).
Références
Cho DY, Kim YA, Przytycka TM, et al. Chapter 5: network biology approach to complex diseases. PLoS Comput Biol. 2012;8:e1002820.
pubmed: 23300411
pmcid: 3531284
doi: 10.1371/journal.pcbi.1002820
Cui J, Li P, Li G, et al. AtPID: Arabidopsis thaliana protein interactome database–an integrative platform for plant systems biology. Nucleic Acids Res. 2008;36:D999-1008.
pubmed: 17962307
doi: 10.1093/nar/gkm844
Tai YS. Interactome of signaling networks in wheat: the protein-protein interaction between TaRAR1 and TaSGT1. Mol Biol Rep. 2008;35:337–43.
pubmed: 17564813
doi: 10.1007/s11033-007-9091-5
Altmann M, Altmann S, Rodriguez PA, et al. Extensive signal integration by the phytohormone protein network. Nature. 2020;583:271–6.
pubmed: 32612234
doi: 10.1038/s41586-020-2460-0
Morsy M, Gouthu S, Orchard S, et al. Charting plant interactomes: possibilities and challenges. Trends Plant Sci. 2008;13:183–91.
pubmed: 18329319
doi: 10.1016/j.tplants.2008.01.006
Ding X, Richter T, Chen M, et al. A rice kinase-protein interaction map. Plant Physiol. 2009;149:1478–92.
pubmed: 19109415
pmcid: 2649385
doi: 10.1104/pp.108.128298
von Mering C, Krause R, Snel B, et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002;417:399–403.
doi: 10.1038/nature750
Xing S, Wallmeroth N, Berendzen KW, Grefen C. Techniques for the analysis of protein-protein interactions in vivo. Plant Physiol. 2016;171(2):727–58.
pubmed: 27208310
pmcid: 4902627
Snider J, Kotlyar M, Saraon P, Yao Z, Jurisica I, Stagljar I. Fundamentals of protein interaction network mapping. Mol Syst Biol. 2015;11(12):848.
pubmed: 26681426
pmcid: 4704491
doi: 10.15252/msb.20156351
Chang JW, Zhou YQ, Ul Qamar MT, Chen LL, Ding YD. Prediction of protein-protein interactions by evidence combining methods. Int J Mol Sci. 2016;17(11):1946.
pubmed: 27879651
pmcid: 5133940
doi: 10.3390/ijms17111946
Hu L, Wang X, Huang YA, Hu P, You ZH. A survey on computational models for predicting protein-protein interactions. Brief Bioinform. 2021;22(5):bbab036.
pubmed: 33693513
doi: 10.1093/bib/bbab036
Wang XW, Madeddu L, Spirohn K, et al. Assessment of community efforts to advance network-based prediction of protein-protein interactions. Nat Commun. 2023;14(1):1582.
pubmed: 36949045
pmcid: 10033937
doi: 10.1038/s41467-023-37079-7
Gao Z, Jiang C, Zhang J, et al. Hierarchical graph learning for protein-protein interaction. Nat Commun. 2023;14(1):1093.
pubmed: 36841846
pmcid: 9968329
doi: 10.1038/s41467-023-36736-1
Roslan R, Othman RM, Shah ZA, et al. Utilizing shared interacting domain patterns and gene ontology information to improve protein-protein interaction prediction. Comput Biol Med. 2010;40(6):555–64.
pubmed: 20417930
doi: 10.1016/j.compbiomed.2010.03.009
Zhang F, Liu S, Li L, Zuo K, Zhao L, Zhang L. Genome-wide inference of protein-protein interaction networks identifies crosstalk in abscisic acid signaling. Plant Physiol. 2016;171(2):1511–22.
pubmed: 27208273
pmcid: 4902594
Dong S, Lau V, Song R, et al. Proteome-wide, structure-based prediction of protein-protein interactions/new molecular interactions viewer. Plant Physiol. 2019;179(4):1893–907.
pubmed: 30679268
pmcid: 6446796
doi: 10.1104/pp.18.01216
Cooper B, Clarke JD, Budworth P, et al. A network of rice genes associated with stress response and seed development. Proc Natl Acad Sci USA. 2003;100:4945–50.
pubmed: 12684538
pmcid: 153660
doi: 10.1073/pnas.0737574100
Tardif G, Kane NA, Adam H, et al. Interaction network of proteins associated with abiotic stress response and development in wheat. Plant Mol Biol. 2007;63:703–18.
pubmed: 17211514
doi: 10.1007/s11103-006-9119-6
Singh G. Genome-wide interologous interactome map (TeaGPIN) of Camellia sinensis. Genomics. 2021;113:553–64.
pubmed: 33002625
doi: 10.1016/j.ygeno.2020.09.048
Petrakis S, Andrade-Navarro MA. Editorial: protein interaction networks in health and disease. Front Genet. 2016;7:111.
pubmed: 27379161
pmcid: 4905956
doi: 10.3389/fgene.2016.00111
Bertioli DJ, Cannon SB, Froenicke L, et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat Genet. 2016;48:438–46.
pubmed: 26901068
doi: 10.1038/ng.3517
Chen X, Li H, Pandey MK, et al. Draft genome of the peanut a-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens. Proc Natl Acad Sci USA. 2016;113:6785–90.
pubmed: 27247390
pmcid: 4914189
doi: 10.1073/pnas.1600899113
Lu Q, Li H, Hong Y, et al. Genome sequencing and analysis of the peanut B-Genome progenitor (Arachis ipaensis). Front Plant Sci. 2018;9: 604.
pubmed: 29774047
pmcid: 5943715
doi: 10.3389/fpls.2018.00604
Yin DM, Ji CM, Ma XL, et al. Genome of an allotetraploid wild peanut Arachis monticola: a de novo assembly. Gigascience. 2018;7:1.
doi: 10.1093/gigascience/giy066
Yin DM, Ji CM, Song QX, et al. Comparison of Arachis monticola with diploid and cultivated tetraploid genomes reveals asymmetric subgenome evolution and improvement of peanut. Adv Sci. 2020;7:1901672.
doi: 10.1002/advs.201901672
Bertioli DJ, Jenkins J, Clevenger J, et al. The genome sequence of segmental allotetraploid peanut Arachis hypogaea L. Nat Genet. 2019;51:877–84.
pubmed: 31043755
doi: 10.1038/s41588-019-0405-z
Zhuang WJ, Chen H, Yang M, et al. The genome of cultivated peanut provides insight into legume karyotypes, polyploid evolution and crop domestication. Nat Genet. 2019;51:865–76.
pubmed: 31043757
pmcid: 7188672
doi: 10.1038/s41588-019-0402-2
Zhao K, Ren R, Ma XL, et al. Genome-wide investigation of defensin genes in peanut (Arachis hypogaea L.) reveals AhDef2.2 conferring resistance to bacterial wilt. Crop J. 2022;10:809–19.
doi: 10.1016/j.cj.2021.11.002
Zhao K, Li Z, Ke Y, Ren R, et al. Dynamic N6 -methyladenosine RNA modification regulates peanut resistance to bacterial wilt. New Phytol. 2024;242(1):231–46.
pubmed: 38326943
doi: 10.1111/nph.19568
Szklarczyk D, Kirsch R, Koutrouli M, et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638–46.
pubmed: 36370105
doi: 10.1093/nar/gkac1000
Geisler-Lee J, O’Toole N, Ammar R, et al. A predicted interactome for Arabidopsis. Plant Physiol. 2007;145:317–29.
pubmed: 17675552
pmcid: 2048726
doi: 10.1104/pp.107.103465
Zhu P, Gu H, Jiao Y, et al. Computational identification of protein-protein interactions in rice based on the predicted rice interactome network. Genomics Proteomics Bioinformatics. 2011;9:128–37.
pubmed: 22196356
pmcid: 5054448
doi: 10.1016/S1672-0229(11)60016-8
Musungu B, Bhatnagar D, Brown RL, et al. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize. Front Genet. 2015;6:201.
pubmed: 26089837
pmcid: 4454876
doi: 10.3389/fgene.2015.00201
Ding Z, Kihara D. Computational identification of protein-protein interactions in model plant proteomes. Sci Rep. 2019;9:8740.
pubmed: 31217453
pmcid: 6584649
doi: 10.1038/s41598-019-45072-8
Du X, Sun S, Hu C, et al. DeepPPI: boosting prediction of protein-protein interactions with deep neural networks. J Chem Inf Model. 2017;57:1499–510.
pubmed: 28514151
doi: 10.1021/acs.jcim.7b00028
Li F, Zhu F, Ling X, et al. Protein interaction network reconstruction through ensemble deep learning with attention mechanism. Front Bioeng Biotechnol. 2020;8:390.
pubmed: 32432096
pmcid: 7215070
doi: 10.3389/fbioe.2020.00390
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24:282–4.
pubmed: 18006545
doi: 10.1093/bioinformatics/btm554
Gu H, Zhu P, Jiao Y, et al. PRIN: a predicted rice interactome network. BMC Bioinformatics. 2011;12:1–13.
doi: 10.1186/1471-2105-12-161
Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–2.
pubmed: 9623998
doi: 10.1038/30918
Wu H, Su Z, Mao F, et al. Prediction of functional modules based on comparative genome analysis and Gene Ontology application. Nucleic Acids Res. 2005;33:2822–37.
pubmed: 15901854
pmcid: 1130488
doi: 10.1093/nar/gki573
Stark C, Breitkreutz BJ, Reguly T, et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34:D535–9.
pubmed: 16381927
doi: 10.1093/nar/gkj109
Orchard S, Kerrien S, Abbani S, et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat Methods. 2012;9:345–50.
pubmed: 22453911
pmcid: 3703241
doi: 10.1038/nmeth.1931
Cantalapiedra CP, Hernandez-Plaza A, Letunic I, et al. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38:5825–9.
pubmed: 34597405
pmcid: 8662613
doi: 10.1093/molbev/msab293
Chen C, Chen H, Zhang Y, et al. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant. 2020;13:1194–202.
pubmed: 32585190
doi: 10.1016/j.molp.2020.06.009
Clevenger J, Chu Y, Scheffler B, et al. A developmental transcriptome map for allotetraploid Arachis hypogaea. Front Plant Sci. 2016;7:1446.
pubmed: 27746793
pmcid: 5043296
doi: 10.3389/fpls.2016.01446
Kim D, Paggi JM, Park C, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15.
pubmed: 31375807
pmcid: 7605509
doi: 10.1038/s41587-019-0201-4
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
Narayanan M, Vetta A, Schadt EE, et al. Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput Biol. 2010;6:e1000742.
pubmed: 20419151
pmcid: 2855327
doi: 10.1371/journal.pcbi.1000742
Du Z, Li L, Chen CF, et al. G-SESAME: web tools for GO-term-based gene similarity analysis and knowledge discovery. Nucleic Acids Res. 2009;37:W345–9.
pubmed: 19491312
pmcid: 2703883
doi: 10.1093/nar/gkp463
Wang JZ, Du Z, Payattakool R, et al. A new method to measure the semantic similarity of GO terms. Bioinformatics. 2007;23:1274–81.
pubmed: 17344234
doi: 10.1093/bioinformatics/btm087
Wu X, Zhu L, Guo J, Zhang DY, Lin K. Prediction of yeast protein-protein interaction network: insights from the gene ontology and annotations. Nucleic Acids Res. 2006;34:2137–50.
pubmed: 16641319
pmcid: 1449908
doi: 10.1093/nar/gkl219
Wu X, Zhu L, Guo J, Fu C, Zhou H, Dong D, Li Z, Zhang DY, Lin K. SPIDer: Saccharomyces protein-protein interaction database. BMC Bioinformatics. 2006;7(Suppl 5):S16.
pubmed: 17254300
pmcid: 1764472
doi: 10.1186/1471-2105-7-S5-S16