CoChat
Sign Up
Cited by 3Bioinformatics and Genomic NetworksPlant Molecular Biology ResearchGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach

Open access
De Silva, M. R. P., Weeraman, J. W. J. K., Piyatissa, S., Fernando, P. C.·2025·BMC Plant Biology
DOI 10.1186/s12870-025-06595-7PMC PMC12060574

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Rice is a critical global food source, but it faces challenges due to nutritional deficiencies and the pressures of a growing population. Understanding the molecular mechanisms and protein functions in rice seed development is essential to improve yield and grain quality. However, there is still a significant knowledge gap regarding the key proteins and their interactions that govern rice seed development. Protein-protein interaction (PPI) analysis is a powerful tool for studying developmental processes like seed development, though its potential in rice research is yet to be fully realized. With the aim of unraveling the protein interaction landscape associated with rice seed development, this systems biology study conducted a PPI network-based analysis. Using a list of known seed development proteins from the Gene Ontology (GO) knowledgebase and literature, novel candidate proteins for seed development were predicted using an ensemble of network-based algorithms, including Majority Voting, Hishigaki Algorithm, Functional Flow, and Random Walk with Restart, which were selected based on their popularity and usability. The predictions were validated using enrichment analysis and cross-checked with independent transcriptomic analysis results. The rice seed development sub-network was further analyzed for community and hub detection. RESULTS: The study predicted 196 new proteins linked to rice seed development and identified 14 sub-modules within the network, each representing different developmental pathways, such as endosperm development and seed growth regulation. Of these, 17 proteins were identified as intra-modular hubs and 6 as inter-modular hubs. Notably, the protein SDH1 emerged as a dual hub, acting as both an intra-modular and inter-modular hub, highlighting its importance in seed development PPI network stability. CONCLUSIONS: These findings, including the identified hub proteins and sub-modules, provide a better understanding of the PPI interaction landscape governing seed development in rice. This information is useful for achieving a systems biology understanding of seed development. This study implements an ensemble of algorithms for the analysis and showcases how systems biology techniques can be applied in developmental biology.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12870-025-06595-7

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 2 references

Research works printed but not linked: 132

  1. Huang R, Jiang L, Zheng J, Wang T, Wang H, Huang Y. Genetic bases of rice grain shape: So many genes, so little known. Trends Plant Sci. 2013;18:218–26. · DOI
  2. Waterworth WM, Bray CM, West CE. Seeds and the art of genome maintenance. Front Plant Sci. Frontiers Media S.A.; 2019. · DOI
  3. King T, Cole M, Farber JM, Eisenbrand G, Zabaras D, Fox EM. Food safety for food security: Relationship between global megatrends and developments in food safety. Trends Food Sci Technol. 2017;68:160–75. · DOI
  4. Sadigov R. Rapid Growth of the World Population and Its Socioeconomic Results. Sci World J. 2022;2022(1):8110229. · DOI
  5. Deng ZY, Gong CY, Wang T. Use of proteomics to understand seed development in rice. Proteomics. 2013;13(12–13):1784–800. · DOI
  6. An L, Tao Y, Chen H, He M, Xiao F, Li G. Embryo-Endosperm Interaction and Its Agronomic Relevance to Rice Quality. Front Plant Sci. 2020;11:587641. · DOI
  7. Kozaki A, Aoyanagi T. Molecular Aspects of Seed Development Controlled by Gibberellins and Abscisic Acids. Int J Mol Sci. 2022;23(3):1876. · DOI
  8. Fitzgerald MA, McCouch SR, Hall RD. Not just a grain of rice: the quest for quality. Trends Plant Sci. 2009;14(3):133–9. · DOI
  9. Tappiban P, Ying Y, Xu F, Bao J. Proteomics and post-translational modifications of starch biosynthesis-related proteins in developing seeds of rice. Int J Mol Sci. 2021;22(11):5901. · DOI
  10. Wimalagunasekara SS, Weeraman JWJK, Tirimanne S, Fernando PC. Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa). J Genet Eng Biotechnol. 2023;21(1):69. · DOI
  11. Vella D, Marini S, Vitali F, Di Silvestre D, Mauri G, Bellazzi R. MTGO: PPI Network Analysis Via Topological and Functional Module Identification. Sci Rep. 2018;8(1):5499. · DOI
  12. Fernando PC, Mabee PM, Zeng E. Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinf. 2020;21:1–26. · DOI
  13. Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol. 2007;3(1):88. · DOI
  14. Fernando PC, Mabee PM, Zeng E. Protein–protein interaction network module changes associated with the vertebrate fin-to-limb transition. Sci Rep. 2023;13(1):22594. · DOI
  15. Roy A, Kucukural A, Zhang Y. I-TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc. 2010;5(4):725–38. · DOI
  16. Zheng LL, Li YX, Ding J, Guo XK, Feng KY, Wang YJ. A comparison of computational methods for identifying virulence factors. PLoS One. 2012;7(8):e42517. · DOI
  17. Lin B, Luo X, Liu Y, Jin X. A comprehensive review and comparison of existing computational methods for protein function prediction. Brief. Bioinform: Oxford University Press; 2024. · DOI
  18. Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T. Structure-based protein function prediction using graph convolutional networks. Nat Commun. 2021;12(1):3168. · DOI
  19. Song F V., Su J, Huang S, Zhang N, Li K, Ni M. DeepSS2GO: protein function prediction from secondary structure. Brief Bioinform. 2024;25(3):bbae196. · DOI · Paper page
  20. Avery C, Patterson J, Grear T, Frater T, Jacobs DJ. Protein Function Analysis through Machine Learning. Biomol. 2022;12(9):1246. · DOI
  21. Alzubaidi L, Bai J, Al-Sabaawi A, Santamaría J, Albahri AS, Al-dabbagh BSN. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. J Big Data. 2023;10(1):46. · DOI
  22. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput Surv. 2018;51(5):1–36. · DOI
  23. Fortunato S. Community detection in graphs. Phys Rep. 2010;486(3–5):75–174. · DOI · Paper page
  24. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech Theor Exp. 2008;2008(10):P10008. · DOI · Paper page
  25. Kiran M, Nagarajaram HA. Interaction and localization diversities of global and local hubs in human protein-protein interaction networks. Mol Biosyst. 2016;12:2875–82. · DOI
  26. He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet. 2006;2:0826–34. · DOI
  27. Hasan MI, Rahman MH, Islam MB, Islam MZ, Hossain MA, Moni MA. Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19. Inform Med Unlocked. 2021;28:100840. · DOI
  28. Wu B, Xi S. Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer. BMC Cancer. 2021;21(1):733. · DOI
  29. Zhuang DY, Jiang LI, He QQ, Zhou P, Yue T. Identification of hub subnetwork based on topological features of genes in breast cancer. Int J Mol Med. 2015;35(3):664–74. · DOI
  30. Xu Z, Zhou Y, Cao Y, Dinh TLA, Wan J, Zhao M. Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis. Med Oncol. 2016;33:1–8. · DOI
  31. Hozhabri H, Dehkohneh RSG, Razavi SM, Razavi SM, Salarian F, Rasouli A. Comparative analysis of protein-protein interaction networks in metastatic breast cancer. PLoS One. 2022;17(1):e0260584. · DOI
  32. Backiyarani S, Sasikala R, Sharmiladevi S, Uma S. Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network. Sci Rep. 2021;11(1):14592. · DOI
  33. Dua S, Chowriappa P. Data Mining for Bioinformatics. 1st ed. CRC Press; 2012. · DOI
  34. Folador EL, Hassan SS, Lemke N, Barh D, Silva A, Ferreira RS. An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage. Integr Biol. 2014;6(11):1080–7. · DOI
  35. Von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M. STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 2005;33(suppl_1):D433–7. · DOI
  36. Chen L, Gao W, Chen S, Wang L, Zou J, Liu Y. High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice. Rice. 2016;9:1–17. · DOI
  37. Mahto A, Mathew I, Agarwal P. Decoding the transcriptome of rice seed during development. In: Jimenez-Lopez JC, editor. Advances in Seed Biology. Spain: InTech; 2017. p. 25. · DOI
  38. Kim YJ, Kim S-I, Kesavan M, Kwak JS, Song JT, Seo HS. Ascorbate Peroxidase OsAPx1 is Involved in Seed Development in Rice. Plant Breed Biotech. 2015;3:11–20. · DOI
  39. Yang Y, Dai L, Xia H, Zhu K, Liu H, Chen K. Protein profile of rice (Oryza sativa) seeds. Genet Mol Biol. 2013;36:87–92. · DOI
  40. You C, Chen L, He H, Wu L, Wang S, Ding Y. iTRAQ-based proteome profile analysis of superior and inferior Spikelets at early grain filling stage in japonica Rice. BMC Plant Biol. 2017;17:1–20. · DOI
  41. Lee J, Koh HJ. A label-free quantitative shotgun proteomics analysis of rice grain development. Proteome Sci. 2011;9:1–10. · DOI
  42. Xue LJ, Zhang JJ, Xue HW. Genome-wide analysis of the complex transcriptional networks of rice developing seeds. PLoS One. 2012;7(2):e31081. · DOI
  43. Kotu V, Deshpande B. Predictive analytics and data mining : concepts and practice with RapidMiner. Burlington: Morgan Kaufmann; 2014.
  44. Lee I, Nam H. Identification of drug-target interaction by a random walk with restart method on an interactome network. BMC Bioinf. 2018;19:9–18. · DOI
  45. Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nat Biotechnol. 2000;18(12):1257–61. · DOI
  46. Hishigaki H, Nakai K, Ono T, Tanigami A, Takagi T. Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast. 2001;18(6):523–31. · DOI
  47. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005;21(suppl_1):i302–10. · DOI
  48. Cowen L, Ideker T, Raphael BJ, Sharan R. Network propagation: A universal amplifier of genetic associations. Nat Rev Genet. 2017;18:551–62. · DOI
  49. Xiao Y, Wu J, Lin Z, Zhao X. A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed. 2018;153:1–9. · DOI
  50. Li JR, Chen L, Wang SP, Zhang YH, Kong XY, Huang T. A computational method using the random walk with restart algorithm for identifying novel epigenetic factors. Mol Genet Genomics. 2018;293(1):293–301. · DOI
  51. Zhu L, Su F, Xu YC, Zou Q. Network-based method for mining novel HPV infection related genes using random walk with restart algorithm. Biochim Biophys Acta Mol Basis Dis. 2018;1864(6):2376–83. · DOI
  52. Carlin DE, Demchak B, Pratt D, Sage E, Ideker T. Network propagation in the cytoscape cyberinfrastructure. PLoS Comput Biol. 2017;13(10):e1005598. · DOI
  53. Jiang Z, Liu H, Fu B, Wu Z, Zhang T. Recommendation in heterogeneous information networks based on generalized random walk model and Bayesian Personalized Ranking. In: Yi C, Chengxiang Z, editors. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. New York: Association for Computing Machinery; 2018. p. 288–96. · DOI
  54. Zhang Z, Zhang J. A big world inside small-world networks. PLoS One. 2009;4(5):e5686. · DOI
  55. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics. 1988;44:837–45. · DOI · Paper page
  56. Li X, Lv J, Yi Z. Outlier Detection Using Structural Scores in a High-Dimensional Space. IEEE Trans Cybern. 2020;50(5):2302–10. · DOI
  57. Fan G, Wei J. Identification of potential novel biomarkers and therapeutic targets involved in human atrial fibrillation based on bioinformatics analysis. Kardiol Pol. 2020;78:694–702. · DOI
  58. Alcalá-Corona SA, Sandoval-Motta S, Espinal-Enríquez J, Hernández-Lemus E. Modularity in Biological Networks. Front Genet. 2021;12:701331. · DOI
  59. Tang D, Zhao X, Zhang L, Wang Z, Wang C. Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses. J Cell Biochem. 2019;120(6):9522–31. · DOI
  60. McGarry K, Daniel U. Computational techniques for identifying networks of interrelated diseases. In: Neagu D, editor. 2014 14th UK Workshop on Computational Intelligence (UKCI). Bradford: Institute of Electrical and Electronics Engineers (IEEE); 2014. p. 1–8.
  61. Zilidou VI, Frantzidis CA, Romanopoulou ED, Paraskevopoulos E, Douka S, Bamidis PD. Functional Re-organization of Cortical Networks of Senior Citizens After a 24-Week Traditional Dance Program. Front Aging Neurosci. 2018;10:422. · DOI
  62. Ghalmane Z, El Hassouni M, Cherifi C, Cherifi H. Centrality in modular networks. EPJ Data Sci. 2019;8(1):15. · DOI
  63. Vértes PE, Rittman T, Whitaker KJ, Romero-Garcia R, Váša F, Kitzbichler MG. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos Trans R Soc B. 2016;371(1705):20150362. · DOI
  64. Power JD, Schlaggar BL, Lessov-Schlaggar CN, Petersen SE. Evidence for hubs in human functional brain networks. Neuron. 2013;79(4):798–813. · DOI · Paper page
  65. Liu Y, Hong X, Bengson JJ, Kelley TA, Ding M, Mangun GR. Deciding Where to Attend: Large-scale Network Mechanisms Underlying Attention and Intention Revealed by Graph-theoretic Analysis. Neuroimage. 2017;157:45–60. · DOI
  66. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50(D1):D439–44. · DOI · Paper page
  67. Sabelli PA, Larkins BA, editors. Advances in Seed Biology. Advances in Seed Biology. Lausanne: Frontiers Media SA; 2015. · DOI
  68. Wang W, Li Y, Dang P, Zhao S, Lai D, Zhou L. Rice secondary metabolites: Structures, roles, biosynthesis, and metabolic regulation. Molecules. 2018;23(12):3098. · DOI
  69. Adom KK, Liu RH. Antioxidant activity of grains. J Agric Food Chem. 2002;50(21):6182–7. · DOI
  70. Shu K, Zhou W, Chen F, Luo X, Yang W. Abscisic acid and gibberellins antagonistically mediate plant development and abiotic stress responses. Front Plant Sci. 2018;9:416. · DOI
  71. Kusano H, Arisu Y, Nakajima J, Yaeshima M, She KC, Shimada H. Implications of the gene for F1-ATPase β subunit (AtpB) for the grain quality of rice matured in a high-temperature environment. Plant Biotechnol. 2016;33:169–75. · DOI
  72. Borisjuk L, Rolletschek H, Radchuk R, Weschke W, Wobus U, Weber H. Seed development and differentiation: A role for metabolic regulation. Plant Biol. 2004;6(04):375–86. · DOI
  73. Muench SP, Trinick J, Harrison MA. Structural divergence of the rotary ATPases. Q Rev Biophys. 2011;44:311–56. · DOI
  74. Zhu M, Zang Y, Zhang X, Shang S, Xue S, Chen J. Insights into the regulation of energy metabolism during the seed-to-seedling transition in marine angiosperm Zostera marina L.: Integrated metabolomic and transcriptomic analysis. Front Plant Sci. 2023;14:1130292. · DOI
  75. Ali F, Qanmber G, Li F, Wang Z. Updated role of ABA in seed maturation, dormancy, and germination. J Adv Res. 2022;35:199–214. · DOI
  76. Gampala SSL, Finkelstein RR, Sun SSM, Rock CD. ABI5 interacts with abscisic acid signaling effectors in rice protoplasts. J Biol Chem. 2002;277(3):1689–94. · DOI
  77. Zheng M, Wang Y, Liu X, Sun J, Wang Y, Xu Y. The RICE MINUTE-LIKE1 (RML1) gene, encoding a ribosomal large subunit protein L3B, regulates leaf morphology and plant architecture in rice. J Exp Bot. 2016;67:3457–69. · DOI
  78. Huang XY, Deng F, Yamaji N, Pinson SRM, Fujii-Kashino M, Danku J. A heavy metal P-type ATPase OsHMA4 prevents copper accumulation in rice grain. Nat Commun. 2016;7(1):12138. · DOI
  79. Walker EL, Waters BM. The role of transition metal homeostasis in plant seed development. Curr Opin Plant Biol. 2011;14(3):318–24. · DOI
  80. Troncoso-Ponce MA, Kruger NJ, Ratcliffe G, Garcés R, Martínez-Force E. Characterization of glycolytic initial metabolites and enzyme activities in developing sunflower (Helianthus annuus L.) seeds. Phytochemistry. 2009;70(9):1117–22. · DOI
  81. Dirk LMA, Downie AB. An examination of Job’s rule: Protection and repair of the proteins of the translational apparatus in seeds. Seed Sci Res. 2018;28(3):168–81. · DOI
  82. Luan X, Ke S, Liu S, Tang G, Huang D, Wei M. OsPEX1, a leucine-rich repeat extensin protein, functions in the regulation of caryopsis development and quality in rice. Crop J. 2022;10:704–15. · DOI
  83. Wu X, Liu J, Li D, Liu CM. Rice caryopsis development II: Dynamic changes in the endosperm. J Integr Plant Biol. 2016;58:786–98. · DOI
  84. Patrick JW, Offler CE. Compartmentation of transport and transfer events in developing seeds. J Exp Bot. 2001;52(356):551–64. · DOI
  85. Hacham Y, Shitrit O, Nisimi O, Friebach M, Amir R. Elucidating the importance of the catabolic enzyme, methionine-gamma-lyase, in stresses during Arabidopsis seed development and germination. Front Plant Sci. 2023;14:1143021. · DOI
  86. Ben-Nissan G, Sharon M. Regulating the 20S proteasome ubiquitin-independent degradation pathway. Biomolecules. 2014;4(3):862–84. · DOI
  87. Wang WQ, Liu SJ, Song SQ, Møller IM. Proteomics of seed development, desiccation tolerance, germination and vigor. Plant Physiol Biochem. 2015;86:1–5. · DOI
  88. Yin CC, Zhao H, Ma B, Chen SY, Zhang JS. Diverse roles of ethylene in regulating agronomic traits in rice. Front Plant Sci. 2017;8:1676. · DOI
  89. Dante RA, Larkins BA, Sabelli PA. Cell cycle control and seed development. Front Plant Sci. 2014;5:493. · DOI
  90. Zhang C, Bai M yi, Chong K. Brassinosteroid-mediated regulation of agronomic traits in rice. Plant Cell Rep. 2014;33:683–96. · DOI
  91. Usman B, Nawaz G, Zhao N, Liu Y, Li R. Generation of high yielding and fragrant rice (Oryza sativa l.) lines by CRISPR/Cas9 targeted mutagenesis of three homoeologs of cytochrome p450 gene family and osbadh2 and transcriptome and proteome profiling of revealed changes triggered by mutations. Plants. 2020;9:1–28. · DOI
  92. Heang D, Sassa H. An atypical bHLH protein encoded by POSITIVE REGULATOR OF GRAIN LENGTH 2 is involved in controlling grain length and weight of rice through interaction with a typical bHLH protein APG. Breed Sci. 2012;62:133–41. · DOI
  93. Nallamilli BRR, Zhang J, Mujahid H, Malone BM, Bridges SM, Peng Z. Polycomb Group Gene OsFIE2 Regulates Rice (Oryza sativa) Seed Development and Grain Filling via a Mechanism Distinct from Arabidopsis. PLoS Genet. 2013;9(3):e1003322. · DOI
  94. Wang L, Yuan J, Ma Y, Jiao W, Ye W, Yang DL. Rice Interploidy Crosses Disrupt Epigenetic Regulation, Gene Expression, and Seed Development. Mol Plant. 2018;11:300–14. · DOI
  95. Yamaji N, Xia J, Mitani-Ueno N, Yokosho K, Ma JF. Preferential delivery of zinc to developing tissues in rice is mediated by P-type heavy metal ATPase OsHMA2. Plant Physiol. 2013;162:927–39. · DOI
  96. Zhang L, Ren Y, Lu B, Yang C, Feng Z, Liu Z. FLOURY ENDOSPERM7 encodes a regulator of starch synthesis and amyloplast development essential for peripheral endosperm development in rice. J Exp Bot. 2016;67:633–47. · DOI
  97. Gayen D, Ghosh S, Paul S, Sarkar SN, Datta SK, Datta K. Metabolic regulation of carotenoid-enriched golden rice line. Front Plant Sci. 2016;7:1622. · DOI
  98. He W, Wang L, Lin Q, Yu F. Rice seed storage proteins: Biosynthetic pathways and the effects of environmental factors. J Integr Plant Biol. 2021;63(12):1999–2019. · DOI
  99. Timabud T, Yin X, Pongdontri P, Komatsu S. Gel-free/label-free proteomic analysis of developing rice grains under heat stress. J Proteomics. 2016;133:1–19. · DOI
  100. Shi Y, Zhang Y, Sun Y, Xie Z, Luo Y, Long Q. Natural variations of OsAUX5, a target gene of OsWRKY78, control the neutral essential amino acid content in rice grains. Mol Plant. 2023;16:322–36. · DOI
  101. Sikdar MSI, Kim JS. Isolation of a gene encoding 3-isopropylmalate dehydrogenase from rice. Russ J Plant Physiol. 2011;58:190–6. · DOI
  102. Li Y, Fan C, Xing Y, Yun P, Luo L, Yan B. Chalk5 encodes a vacuolar H + -translocating pyrophosphatase influencing grain chalkiness in rice. Nat Genet. 2014;46:398–404. · DOI
  103. Kim YS, Kim IS, Bae MJ, Choe YH, Kim YH, Park HM. Homologous expression of cytosolic dehydroascorbate reductase increases grain yield and biomass under paddy field conditions in transgenic rice (Oryza sativa L. japonica). Planta. 2013;237:1613–25. · DOI
  104. Erales J, Coffino P. Ubiquitin-independent proteasomal degradation. Biochim Biophys Acta, Mol Cell Res. 2014;1843:216–21. · DOI
  105. Kyosseva SV. Mitogen-Activated Protein Kinase Signaling. Int Rev Neurobiol. 2004;59:201–20. · DOI
  106. Minkenberg B, Xie K, Yang Y. Discovery of rice essential genes by characterizing a CRISPR-edited mutation of closely related rice MAP kinase genes. Plant J. 2017;89:636–48. · DOI
  107. Zhao H, Yin CC, Ma B, Chen SY, Zhang JS. Ethylene signaling in rice and Arabidopsis: New regulators and mechanisms. J Integr Plant Biol. 2021;63(1):102–25. · DOI
  108. Yang BJ, Wendrich JR, De Rybel B, Weijers D, Xue HW. Rice microtubule-associated protein IQ67-DOMAIN14 regulates grain shape by modulating microtubule cytoskeleton dynamics. Plant Biotechnol J. 2020;18:1141–52. · DOI
  109. Divi UK, Krishna P. Brassinosteroid: a biotechnological target for enhancing crop yield and stress tolerance. New Biotechnol. 2009;26(3–4):131–6. · DOI
  110. Hong Z, Ueguchi-Tanaka M, Fujioka S, Takatsuto S, Yoshida S, Hasegawa Y. The rice brassinosteroid-deficient dwarf2 mutant, defective in the rice homolog of arabidopsis DIMINUTO/DWARF1, is rescued by the endogenously accumulated alternative bioactive brassinosteroid, dolichosterone. Plant Cell. 2005;17:2243–54. · DOI
  111. Ito Y, Thirumurugan T, Serizawa A, Hiratsu K, Ohme-Takagi M, Kurata N. Aberrant vegetative and reproductive development by overexpression and lethality by silencing of OsHAP3E in rice. Plant Sci. 2011;181:105–10. · DOI
  112. Huang S, Millar AH. Succinate dehydrogenase: the complex roles of a simple enzyme. Curr Opin Plant Biol. 2013;16:344–9. · DOI
  113. Liao JL, Zhou HW, Peng Q, Zhong PA, Zhang HY, He C. Transcriptome changes in rice (Oryza sativa L.) in response to high night temperature stress at the early milky stage. BMC Genomics. 2015;16:1–4. · DOI
  114. Zhang Y, Fernie AR. On the role of the tricarboxylic acid cycle in plant productivity. J Integr Plant Biol. 2018;60(12):1199–216. · DOI
  115. Cao H, Duncan O, Millar AH. The molecular basis of cereal grain proteostasis. Essays Biochem. 2022;66(2):243–53. · DOI
  116. Gorji-bahri G, Moghimi HR, Hashemi A. RAB5A is associated with genes involved in exosome secretion: Integration of bioinformatics analysis and experimental validation. J Cell Biochem. 2021;122(3–4):425–41. · DOI
  117. Zhu L, Zhang H, Cao D, Xu Y, Li L, Ning Z. Drought Stress-Related Gene Identification in Rice by Random Walk with Restart on Multiplex Biological Networks. Agriculture. 2022;13(1):53. · DOI
  118. Yang P, Li X, Wu M, Kwoh CK, Ng SK. Inferring Gene-Phenotype associations via global protein complex network propagation. PLoS One. 2011;6(7):e21502. · DOI
  119. Zamanian-Azodi M, Rezaei-Tavirani M, Rahmati-Rad S, Hasanzadeh H, Tavirani MR, Seyyedi SS. Protein-protein interaction network could reveal the relationship between the breast and colon cancer. GHFBB. 2015;8(3):215.
  120. Rahman MM, Vadrev SM, Magana-Mora A, Levman J, Soufan O. A novel graph mining approach to predict and evaluate food-drug interactions. Sci Rep. 2022;12(1):1061. · DOI
  121. Xiong Y, You W, Wang R, Peng L, Fu Z. Prediction and validation of hub genes associated with colorectal cancer by integrating PPI network and gene expression data. Biomed Res Int. 2017;2017(1):2421459. · DOI
  122. Wang W, Shen J, Qi C, Pu J, Chen H, Zuo Z. The key candidate genes in tubulointerstitial injury of chronic kidney diseases patients as determined by bioinformatic analysis. Cell Biochem Funct. 2020;38:761–72. · DOI
  123. Guimerà R, Mossa S, Turtschi A, Amaral LAN, Wachter KW. The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles. PNAS. 2005;102:7794–9. · DOI
  124. Joyce KE, Laurienti PJ, Burdette JH, Hayasaka S. A new measure of centrality for brain networks. PLoS One. 2010;5(8):e12200. · DOI
  125. Deng JL, Xu YH, Wang G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet. 2019;10:695. · DOI
  126. Flórez AF, Park D, Bhak J, Kim BC, Kuchinsky A, Morris JH. Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection. BMC Bioinf. 2010;11:1–9. · DOI
  127. Huang S, Zhong J, Qi Q, Liu G, Gong M. CircRNA expression profile and potential role of hsa_circ_0040039 in intervertebral disc degeneration. Medicine. 2022;101(32):e30035. · DOI
  128. Kelder TP, Penning ME, Uh HW, Cohen D, Bloemenkamp KWM, Bruijn JA. Quantitative polymerase chain reaction-based analysis of podocyturia is a feasible diagnostic tool in preeclampsia. Hypertension. 2012;60(6):1538–44. · DOI
  129. Jiao SY, Yang YH, Chen SR. Molecular genetics of infertility: Loss-of-function mutations in humans and corresponding knockout/mutated mice. Hum Reprod Update. 2021;27(1):154–89. · DOI
  130. Ghavami S, Pandi A. CRISPR interference and its applications. Prog Mol Biol Transl Sci. 2021;180:123–40. · DOI
  131. Geng S, Sohail H, Cao H, Sun J, Chen Z, Zhou L. An efficient root transformation system for CRISPR/Cas9-based analyses of shoot-root communication in cucurbit crops. Hortic Res. 2022;9:uhab082. · DOI
  132. Watts GF, Schwabe C, Scott R, Gladding PA, Sullivan D, Baker J. RNA interference targeting ANGPTL3 for triglyceride and cholesterol lowering: phase 1 basket trial cohorts. Nat Med. 2023;29(9):2216–23. · DOI · Paper page

Metadata

Authors
De Silva, M. R. P., Weeraman, J. W. J. K., Piyatissa, S., & Fernando, P. C.
Affiliations
University of Colombo (LK)
Corresponding author
Fernando, P. C.
ORCID
Venue
BMC Plant Biology
Publisher
Springer Science and Business Media LLC
Published
2025-05-08
Volume
25
Issue
1
Pages
604-604
ISSN
1471-2229 (ISSN-L)
DOI
10.1186/s12870-025-06595-7
PMC
PMC12060574
OpenAlex
W4410187128
Cited by
3
References
2
Citations by year
2026: 1 · 2025: 2
FWCI
0.90
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Plant Biology (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Keywords
rice seed development, protein-protein interaction network, hub proteins, community detection, endosperm development, rice
MeSH terms
Plant Proteins, Oryza, Seeds, Gene Expression Regulation, Plant, Systems Biology, Protein Interaction Maps

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review