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

The DOI matches its Crossref record; 1 of the 1 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., Fernando, P.C.·2024·BMC Plant Biology
DOI 10.1101/2024.11.03.621786PMC PMC12060574

Preprint — published as 10.1186/s12870-025-06595-7 · Paper page

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 unravelling 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 (MV), Hishigaki, Functional flow, and Random Walk with Restart (RWR). The predictions were validated using enrichment analysis, and the rice seed development subnetwork 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 crucial role in coordinating interactions during rice seed development. 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 improving rice cultivars for better yield and quality through genetic engineering and breeding. 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 · 10.1101/2024.11.03.621786

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 The bioinformatics pipeline used for prediction and validation of protein candidates for seed development in rice. The hub protein and sub-pathway analysis procedures are also represented
Fig. 1 The bioinformatics pipeline used for prediction and validation of protein candidates for seed development in rice. The hub protein and sub-pathway analysis procedures are also representedCC BY-NC-ND · PubMed Central
Fig. 2 Precision-Recall Curves (A) and Receiver Operator Characteristic Curves (B) for the five algorithms, including Majority Voting (MV), Hishigaki Algorithm (HA), Random Walk with Restart (RWR), and Functional flow (FF), and the ensemble model. The ensemble model outperforms all the other algorithms on both metrics based on its higher area under the curve values
Fig. 2 Precision-Recall Curves (A) and Receiver Operator Characteristic Curves (B) for the five algorithms, including Majority Voting (MV), Hishigaki Algorithm (HA), Random Walk with Restart (RWR), and Functional flow (FF), and the ensemble model. The ensemble model outperforms all the other algorithms on both metrics based on its higher area under the curve valuesCC BY-NC-ND · PubMed Central
Fig. 3 Modified precision top-N curve for protein predictions related to rice seed development, obtained from the rice protein–protein interaction (PPI) network. The x-axis represents the number of top-ranked predicted proteins (N), while the y-axis shows the modified precision, calculated as the percentage of known differentially expressed proteins (DEPs) among the top N predictions. The curve peaks at N = 454, where a maximum precision of 43.17% was achieved
Fig. 3 Modified precision top-N curve for protein predictions related to rice seed development, obtained from the rice protein–protein interaction (PPI) network. The x-axis represents the number of top-ranked predicted proteins (N), while the y-axis shows the modified precision, calculated as the percentage of known differentially expressed proteins (DEPs) among the top N predictions. The curve peaks at N = 454, where a maximum precision of 43.17% was achievedCC BY-NC-ND · PubMed Central
Fig. 4 A visual representation of the seed development protein–protein interaction (PPI) sub-network of rice, with different colors assigned to the detected sub-modules, based on the Louvain community detection algorithm. The sub-modules are numbered and the most relevant Gene Ontology-Biological Process (GO-BP) term related to seed development according to enrichment analysis results are listed in the legend
Fig. 4 A visual representation of the seed development protein–protein interaction (PPI) sub-network of rice, with different colors assigned to the detected sub-modules, based on the Louvain community detection algorithm. The sub-modules are numbered and the most relevant Gene Ontology-Biological Process (GO-BP) term related to seed development according to enrichment analysis results are listed in the legendCC BY-NC-ND · PubMed Central
Fig. 5 Network visualization of hub proteins (highly connected proteins within the network) in the seed development protein–protein interaction (PPI) sub-network of rice. Node size and color indicate the type of hub classification: larger green nodes, such as Ehd2, represent intra-modular hubs (proteins highly connected within a single module), identified using the Z-score method; larger blue nodes, such as SODCP, represent inter-modular hubs (proteins with high connectivity between modules), identified using the partition coefficient (PC). The SDH1 protein, which functions as both an intra- and inter-modular hub, is shown as a larger orange node. Smaller white nodes represent non-hub proteins (proteins with low connectivity). Sub-modules, identified through the Louvain community detection algorithm, are numbered, and the most significantly enriched Gene Ontology-Biological Process (GO-BP) terms derived from functional enrichment analysis are listed in the legend
Fig. 5 Network visualization of hub proteins (highly connected proteins within the network) in the seed development protein–protein interaction (PPI) sub-network of rice. Node size and color indicate the type of hub classification: larger green nodes, such as Ehd2, represent intra-modular hubs (proteins highly connected within a single module), identified using the Z-score method; larger blue nodes, such as SODCP, represent inter-modular hubs (proteins with high connectivity between modules), identified using the partition coefficient (PC). The SDH1 protein, which functions as both an intra- and inter-modular hub, is shown as a larger orange node. Smaller white nodes represent non-hub proteins (proteins with low connectivity). Sub-modules, identified through the Louvain community detection algorithm, are numbered, and the most significantly enriched Gene Ontology-Biological Process (GO-BP) terms derived from functional enrichment analysis are listed in the legendCC BY-NC-ND · PubMed Central

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

Research works printed but not linked: 87

  1. Huang R , Jiang L , Zheng J , Wang T , Wang H , Huang Y , et al. Genetic bases of rice grain shape: So many genes, so little known. Trends Plant Sci. 2013. p. 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. 10.1016/j.tifs.2017.08.014 · DOI
  4. Sadigov R . Rapid Growth of the World Population and Its Socioeconomic Results. Scientific World Journal. 2022;2022. · DOI
  5. Deng ZY , Gong CY , Wang T . Use of proteomics to understand seed development in rice. Proteomics. 2013. p. 1784–800. · DOI
  6. An L , Tao Y , Chen H , He M , Xiao F , Li G , et al. Embryo-Endosperm Interaction and Its Agronomic Relevance to Rice Quality. Front Plant Sci. 2020. · DOI
  7. Kozaki A , Aoyanagi T . Molecular Aspects of Seed Development Controlled by Gibberellins and Abscisic Acids. Int J Mol Sci. 2022. · DOI
  8. 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. · DOI
  9. 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. · DOI
  10. 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 Bioinformatics. 2020;21. · DOI
  11. Sharan R , Ulitsky I , Shamir R . Network-based prediction of protein function. Mol Syst Biol. Nature Publishing Group; 2007. p. 1–13. · DOI
  12. Fernando PC , Mabee PM , Zeng E . Protein–protein interaction network module changes associated with the vertebrate fin-to-limb transition. Sci Rep. 2023;13. · DOI
  13. Roy A , Kucukural A , Zhang Y . I-TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc. 2010;5. · DOI
  14. Fortunato S. Community detection in graphs. 2009; Available from: http://arxiv.org/abs/0906.0612 · arXiv
  15. Blondel VD , Guillaume J-L , Lambiotte R , Lefebvre E. Fast unfolding of communities in large networks. 2008; Available from: http://arxiv.org/abs/0803.0476 · DOI · Paper page
  16. 10.1039/C6MB00104A · DOI
  17. 2006, Why do hubs tend to be essential in protein networks?, PLoS Genet, 2, 0826
  18. 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. 2022;28. · DOI
  19. Wu B , Xi S . Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer. BMC Cancer. 2021;21. · DOI
  20. 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. · DOI
  21. 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). Journal of Genetic Engineering and Biotechnology. 2023;21. · DOI
  22. 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. · DOI
  23. Deng JL , Xu YH , Wang G . Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet. 2019;10. · DOI
  24. Chen L , Gao W , Chen S , Wang L , Zou J , Liu Y , et al. High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice. Rice. 2016;9. · DOI
  25. 10.3390/ijms22031010 · DOI
  26. Mahto A , Mathew IE , Agarwal P . Decoding the Transcriptome of Rice Seed During Development. Advances in Seed Biology. InTech; 2017. · DOI
  27. 10.9787/PBB.2015.3.1.011 · DOI
  28. Yang Y , Dai L , Xia H , Zhu K , Liu H , Chen K. Protein profile of rice (Oryza sativa) seeds [Internet]. 2013. Available from: www.sbg.org.br · DOI
  29. You C , Chen L , He H , Wu L , Wang S , Ding Y , et al. iTRAQ-based proteome profile analysis of superior and inferior Spikelets at early grain filling stage in japonica Rice. BMC Plant Biol. 2017;17. · DOI
  30. Xue LJ , Zhang JJ , Xue HW . Genome-wide analysis of the complex transcriptional networks of rice developing seeds. PLoS One. 2012;7. · DOI
  31. Lee J , Koh HJ . A label-free quantitative shotgun proteomics analysis of rice grain development. Proteome Sci. 2011;9. · DOI
  32. Cao M , Pietras CM , Feng X , Doroschak KJ , Schaffner T , Park J , et al. New directions for diffusion-based network prediction of protein function: Incorporating pathways with confidence. Bioinformatics. 2014;30. · DOI
  33. 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. · DOI
  34. 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. · DOI
  35. Cowen L , Ideker T , Raphael BJ , Sharan R. Network propagation: A universal amplifier of genetic associations. Nat Rev Genet. Nature Publishing Group; 2017. p. 551–62. · DOI
  36. Fu J , Gao J , Liang Z , Yang D . PDI-regulated disulfide bond formation in protein folding and biomolecular assembly. Molecules. MDPI AG; 2021. · DOI
  37. 10.1016/j.cmpb.2017.09.005 · DOI
  38. Schwikowski B , Uetz P , Fields S . A network of protein-protein interactions in yeast. Nat Biotechnol. 2000;18. · DOI
  39. 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. · DOI
  40. 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. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018. · DOI
  41. Zhang Z , Zhang J . A big world inside small-world networks. PLoS One. 2009;4. · DOI
  42. Wu TY , Müller M , Gruissem W , Bhullar NK . Genome Wide Analysis of the Transcriptional Profiles in Different Regions of the Developing Rice Grains. Rice. 2020;13. · DOI
  43. Li X , Lv J , Yi Z . Outlier Detection Using Structural Scores in a High-Dimensional Space. IEEE Trans Cybern. 2020;50. · DOI
  44. 10.33963/KP.15339 · DOI
  45. Alcalá-Corona SA , Sandoval-Motta S , Espinal-Enríquez J , Hernández-Lemus E . Modularity in Biological Networks. Front Genet. 2021. · DOI
  46. 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. · DOI
  47. McGarry K , Daniel U. Computational techniques for identifying networks of interrelated diseases. 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc.; 2014. · DOI
  48. 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. · DOI
  49. Vértes PE , Rittman T , Whitaker KJ , Romero-Garcia R , Váša F , Kitzbichler MG , et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philosophical Transactions of the Royal Society B: Biological Sciences. 2016;371. · DOI
  50. 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. 2017. · DOI
  51. Varadi M , Anyango S , Deshpande M , Nair S , Natassia C , Yordanova G , et al. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50. · DOI · Paper page
  52. Zhou Y-F , Qing T , Shu X-L , Liu J-X . Unfolded protein response and storage product accumulation in rice grains. Seed Biology. 2022;1. · DOI
  53. 2020, 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, 9, 1
  54. 10.1270/jsbbs.62.133 · DOI
  55. 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. · DOI
  56. 10.1016/j.molp.2017.12.006 · DOI
  57. 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. · DOI
  58. Huang XY , Deng F , Yamaji N , Pinson SRM , Fujii-Kashino M , Danku J , et al. A heavy metal P-type ATPase OsHMA4 prevents copper accumulation in rice grain. Nat Commun. 2016;7. · DOI
  59. 10.1093/jxb/erv469 · DOI
  60. 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. · DOI
  61. 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. · DOI
  62. 10.1016/j.jprot.2015.12.003 · DOI
  63. 10.1016/j.molp.2022.12.013 · DOI
  64. Zhang Y , Fernie AR. On the role of the tricarboxylic acid cycle in plant productivity. J Integr Plant Biol. Blackwell Publishing Ltd; 2018. p. 1199–216. · DOI
  65. 10.1038/ng.2923 · DOI
  66. 10.1007/s00425-013-1862-8 · DOI
  67. Erales J , Coffino P . Ubiquitin-independent proteasomal degradation. Biochim Biophys Acta Mol Cell Res. 2014. p. 216–21. · DOI
  68. 10.1016/j.jprot.2015.12.003 · DOI
  69. 10.1093/jxb/erw167 · DOI
  70. 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. · DOI
  71. Kyosseva S V. MITOGEN-ACTIVATED PROTEIN KINASE SIGNALING. 2004. · DOI
  72. 10.1111/tpj.13399 · DOI
  73. Zhao H , Yin CC , Ma B , Chen SY , Zhang JS. Ethylene signaling in rice and Arabidopsis: New regulators and mechanisms. J Integr Plant Biol. Blackwell Publishing Ltd; 2021. p. 102– 25. · DOI
  74. 10.1111/jipb.12488 · DOI
  75. 10.1111/pbi.13279 · DOI
  76. Ali F , Qanmber G , Li F , Wang Z . Updated role of ABA in seed maturation, dormancy, and germination. J Adv Res. Elsevier B.V.; 2022. p. 199–214. · DOI
  77. Divi UK , Krishna P . Brassinosteroid: a biotechnological target for enhancing crop yield and stress tolerance. N Biotechnol. Elsevier; 2009. p. 131–6. · DOI
  78. 10.1105/tpc.105.030973 · DOI
  79. 10.1016/j.plantsci.2011.04.009 · DOI
  80. Liao JL , Zhou HW , Peng Q , Zhong PA , Zhang HY , He C , et al. Transcriptome changes in rice (Oryza sativa L.) in response to high night temperature stress at the early milky stage. BMC Genomics. 2015;16. · DOI
  81. Cao H , Duncan O , Millar AH . The molecular basis of cereal grain proteostasis. Essays Biochem. 2022. · DOI
  82. 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. · DOI
  83. 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. · DOI
  84. 10.1002/cbf.3545 · DOI
  85. Guimerà R , Mossa S , Turtschi A , Amaral LAN, Wachter KW. The worldwide air transportation network: Anomalous centrality, community structure, and cities’ global roles [Internet]. 2005. Available from: https://www.pnas.org · DOI
  86. Joyce KE , Laurienti PJ , Burdette JH , Hayasaka S . A new measure of centrality for brain networks. PLoS One. 2010;5. · DOI
  87. 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. 2017. · DOI

Metadata

Authors
De Silva, M. R. P., Weeraman, J. W. J. K., & Fernando, P. C.
Affiliations
University of Colombo (LK)
Corresponding author
Fernando, P.C.
ORCID
Venue
BMC Plant Biology
Publisher
openRxiv
Published
2024-11-06
Volume
25
Issue
1
Pages
604-604
ISSN
1471-2229 (ISSN-L)
DOI
10.1101/2024.11.03.621786
PMC
PMC12060574
OpenAlex
W4410187128
Cited by
3
References
1
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