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Prioritization of biological processes based on the reconstruction and analysis of associative gene networks describing the response of plants to adverse environmental factors

https://doi.org/10.18699/VJ21.065

Abstract

Methods for prioritizing or ranking candidate genes according to their importance based on specific criteria via the analysis of gene networks are widely used in biomedicine to search for genes associated with diseases and to predict biomarkers, pharmacological targets and other clinically relevant molecules. These methods have also been used in other fields, particularly in crop production. This is largely due to the development of technologies to solve problems in marker-oriented and genomic selection, which requires knowledge of the molecular genetic mechanisms underlying the formation of agriculturally valuable traits. A new direction for the study of molecular genetic mechanisms is the prioritization of biological processes based on the analysis of associative gene networks. Associative gene networks are heterogeneous networks whose vertices can depict both molecular genetic objects (genes, proteins, me tabolites, etc.) and the higher-level factors (biological processes, diseases, external environmental factors, etc.) related to regulatory, physicochemical or associative interactions. Using a previously developed method, biological processes involved in plant responses to increased cadmium content, saline stress and drought conditions were prioritized according to their degree of connection with the gene networks in the SOLANUM TUBEROSUM knowledge base. The prioritization results indicate that fundamental processes, such as gene expression, post-translational modifications, protein degradation, programmed cell death, photosynthesis, signal transmission and stress response play important roles in the common molecular genetic mechanisms for plant response to various adverse factors. On the other hand, a group of processes related to the development of seeds (“seeding development”) was revealed to be drought specific, while processes associated with ion transport (“ion transport”) were included in the list of responses specific to salt stress and processes associated with the metabolism of lipids were found to be involved specifically in the response to cadmium.

About the Authors

P. S. Demenkov
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University
Russian Federation

Novosibirsk



E. A. Oshchepkova
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



T. V. Ivanisenko
Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences
Russian Federation

Novosibirsk



V. A. Ivanisenko
Novosibirsk State University; Kurchatov Genomic Center of ICG SB RAS
Russian Federation

Novosibirsk



References

1. Agurla S., Gahir S., Munemasa S., Murata Y., Raghavendra A.S. Mechanism of stomatal closure in plants exposed to drought and cold stress. In: Iwaya-Inoue M., Sakurai M., Uemura M. (Eds.). Survival Strategies in Extreme Cold and Desiccation. Advances in Experimental Medicine and Biology. Vol. 1081. Singapore: Springer, 2018;215-232. DOI 10.1007/978-981-13-1244-1_12.

2. Allu A.D., Soja A.M., Wu A., Szymanski J., Balazadeh S. Salt stress and senescence: identification of cross-talk regulatory components. J. Exp. Bot. 2014;65(14):3993-4008. DOI 10.1093/jxb/eru173.

3. Arruda M.P., Lipka A.E., Brown P.J., Krill A.M., Thurber C., BrownGuedira G., Dong Y., Foresman B.J., Kolb F.L. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol. Breed. 2016;36(7):84. DOI 10.1007/s11032-016-0508-5.

4. Bargsten J.W., Nap J.P., Sanchez-Perez G.F., van Dijk A.D. Prioritization of candidate genes in QTL regions based on associations between traits and biological processes. BMC Plant Biol. 2014;14:330. DOI 10.1186/s12870-014-0330-3.

5. Benjamini Y., Yekutieli D. The сontrol of the false discovery rate in multiple testing under dependency. Ann. Statist. 2001;29(4):1165-1188. DOI 10.1214/aos/1013699998.

6. Cai Z., Guldbrandtsen B., Lund M.S., Sahana G. Prioritizing candidate genes for fertility in dairy cows using gene-based analysis, functional annotation and differential gene expression. BMC Genomics. 2019;20(1):255. DOI 10.1186/s12864-019-5638-9.

7. Cesur M.F., Siraj B., Uddin R., Durmuş S., Çakır T. Network-based metabolism-centered screening of potential drug targets in Klebsiella pneumoniae at genome scale. Front. Cell. Infect. Microbiol. 2020;9:447. DOI 10.3389/fcimb.2019.00447.

8. Chen Y., Jiang T., Jiang R. Uncover disease genes by maximizing information flow in the phenome–interactome network. Bioinformatics. 2011;27(13):i167-i176. DOI 10.1093/bioinformatics/btr213.

9. Cho A., Shim J.E., Kim E., Supek F., Lehner B., Lee I. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. Genome Biol. 2016;17(1):1-16. DOI 10.1186/s13059-016-0989-x.

10. Crossa J., Pérez-Rodríguez P., Cuevas J., Montesinos-López O., Jarquín D., de Los Campos G., Burgueño J., González-Camacho J.M., Pérez-Elizalde S., Beyene Y., Dreisigacker S., Singh R., Zhang X., Gowda M., Roorkiwal M., Rutkoski J., Varshney R.K. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 2017;22(11):961-975. DOI 10.1016/j.tplants.2017.08.011.

11. Demenkov P.S., Ivanisenko T.V., Kolchanov N.A., Ivanisenko V.A. ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem. In Silico Biol. 2012;11(3):149-161. DOI 10.3233/ISB-2012-0449.

12. Demenkov P.S., Saik O.V., Ivanisenko T.V., Kolchanov N.A., Kochetov A.V., Ivanisenko V.A. Prioritization of potato genes involved in the formation of agronomically valuable traits using the SOLANUM TUBEROSUM knowledge base. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2019; 23(3):312-319. DOI 10.18699/VJ19.501.

13. Djebali W., Zarrouk M., Brouquisse R., El Kahoui S., Limam F., Ghorbel M.H., Chaïbi W. Ultrastructure and lipid alterations induced by cadmium in tomato (Lycopersicon esculentum) chloroplast membranes. Plant Biol. (Stuttg). 2005;7(4):358-368. DOI 10.1055/s-2005-837696.

14. Du Y.T., Zhao M.J., Wang C.T., Gao Y., Wang Y.X., Liu Y.W., Chen M., Chen J., Zhou Y.B., Xu Z.S., Ma Y.Z. Identification and characterization of GmMYB118 responses to drought and salt stress. BMC Plant Biol. 2018;18(1):320. DOI 10.1186/s12870-018-1551-7.

15. Freeman L.C. A set of measures of centrality based on betweenness. Sociometry. 1977;40:35-41. DOI 10.2307/3033543.

16. Freeman L.C. Centrality in social networks conceptual clarification. Social Networks. 1978;1(3):215-239. DOI 10.1016/0378-8733(78)90021-7.

17. Genchi G., Sinicropi M.S., Lauria G., Carocci A., Catalano A. The effects of cadmium toxicity. Int. J. Environ. Res. Public Health. 2020; 17(11):3782. DOI 10.3390/ijerph17113782.

18. Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330-D338. DOI 10.1093/nar/gky1055.

19. Hwang K., Susila H., Nasim Z., Jung J.Y., Ahn J.H. Arabidopsis ABF3 and ABF4 transcription factors act with the NF-YC complex to regulate SOC1 expression and mediate drought-accelerated flowering. Mol. Plant. 2019;12(4):489-505. DOI 10.1016/j.molp.2019.01.002.

20. Iakimova E.T., Woltering E.J., Kapchina-Toteva V.M., Harren F.J., Cristescu S.M. Cadmium toxicity in cultured tomato cells – role of ethylene, proteases and oxidative stress in cell death signaling. Cell Biol. Int. 2008;32(12):1521-1529. DOI 10.1016/j.cellbi.2008.08.021.

21. Ivanisenko T.V., Saik O.V., Demenkov P.S., Ivanisenko N.V., Savostianov A.N., Ivanisenko V.A. ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature. BMC Bioinformatics. 2020;21(Suppl.11):228. DOI 10.1186/s12859-020-03557-8.

22. Ivanisenko T.V., Saik O.V., Demenkov P.S., Khlestkin V.K., Khlestkina E.K., Kolchanov N.A., Ivanisenko V.A. The SOLANUM TUBEROSUM knowledge base: the section on molecular-genetic regulation of metabolic pathways. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2018;22(1): 8-17. DOI 10.18699/VJ18.325. (in Russian)

23. Ivanisenko V.A., Demenkov P.S., Ivanisenko T.V., Mishchenko E.L., Saik O.V. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019; 20(Suppl.1):34. DOI 10.1186/s12859-018-2567-6.

24. Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology. BMC Syst. Biol. 2015;9(Suppl.2):S2. DOI 10.1186/1752-0509-9-S2-S2.

25. Jeong H., Mason S.P., Barabási A.L., Oltvai Z.N. Lethality and centrality in protein networks. Nature. 2001;411(6833):41-42. DOI 10.1038/35075138.

26. Jha M., Roy S., Kalita J.K. Prioritizing disease biomarkers using functional module based network analysis: a multilayer consensus driven scheme. Comput. Biol. Med. 2020;126:104023. DOI 10.1016/j.compbiomed.2020.104023.

27. Jia P.L., Zheng S.Y., Long J.R., Zheng W., Zhao Z.M. dmGWAS: dense module searching for genome-wide association studies in proteinprotein interaction networks. Bioinformatics. 2011;27:95-102. DOI 10.1093/bioinformatics/btq615.

28. Jiang H., Tang B., Xie Z., Nolan T., Ye H., Song G.Y., Walley J., Yin Y. GSK3-like kinase BIN2 phosphorylates RD26 to potentiate drought signaling in Arabidopsis. Plant J. 2019;100(5):923-937. DOI 10.1111/tpj.14484.

29. Kaya C., Ashraf M., Alyemeni M.N., Ahmad P. Responses of nitric oxide and hydrogen sulfide in regulating oxidative defence system in wheat plants grown under cadmium stress. Physiol. Plant. 2020;168(2):345-360. DOI 10.1111/ppl.13012.

30. Kochetov A.V., Glagoleva A.Y., Strygina K.V., Khlestkina E.K., Gerasimova S.V., Ibragimova S.M., Shatskaya N.V., Vasilyev G.V., Afonnikov D.A., Shmakov N.A., Antonova O.Y., Gavrilenko T.A., Alpatyeva N.V., Khiutti A., Afanasenko O.S. Differential expression of NBS-LRR-encoding genes in the root transcriptomes of two Solanum phureja genotypes with contrasting resistance to Globodera rostochiensis. BMC Plant Biol. 2017;17(Suppl.2):251. DOI 10.1186/s12870-017-1193-1.

31. Kolchanov N.A., Kochetov A.V., Salina E.A., Pershina L.A., Khlestkina E.K., Shumny V.K. Status and prospects of marker-assisted and genomic plant breeding. Herald of the Russian Academy of Sciences. 2017;87(2):125-131. DOI 10.1134/S1019331617020113.

32. Küpper H., Leitenmaier B. Cadmium-accumulating plants. In: Sigel A., Sigel H., Sigel R. (Eds.). Cadmium: From Toxicity to Essentiality. Metal Ions in Life Sciences. Vol. 11. Dordrecht: Springer, 2013;373-393. DOI 10.1007/978-94-007-5179-8_12.

33. Le D.H., Pham V.H. HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network. BMC Syst. Biol. 2017;11(1):61. DOI 10.1186/s12918-017-0437-x.

34. Leng P., Zhao J. Transcription factors as molecular switches to regulate drought adaptation in maize. Theor. Appl. Genet. 2020;133(5):1455-1465. DOI 10.1007/s00122-019-03494-y.

35. Leung A., Bader G.D., Reimand J. Hypermodules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery. Bioinformatics. 2014;30:2230-2232. DOI 10.1093/bioinformatics/btu172.

36. Lin F., Fan J., Rhee S.Y. QTG-Finder: a machine-learning based algorithm to prioritize causal genes of quantitative trait loci in Arabidopsis and rice. G3: Genes, Genomes, Genetics. (Bethesda). 2019; 9(10):3129-3138. DOI 10.1534/g3.119.400319.

37. Lysenko A., Boroevich K.A., Tsunoda T. Arete – candidate gene prioritization using biological network topology with additional evidence types. BioData Min. 2017;10:22. DOI 10.1186/s13040-017-0141-9.

38. Magwanga R.O., Lu P., Kirungu J.N., Cai X., Zhou Z., Wang X., Diouf L., Xu Y., Hou Y., Hu Y., Dong Q., Wang K., Liu F. Whole genome analysis of cyclin dependent kinase (CDK) gene family in cotton and functional evaluation of the role of CDKF4 gene in drought and salt stress tolerance in plants. Int. J. Mol. Sci. 2018;19(9):2625. DOI 10.3390/ijms19092625.

39. Munns R., Tester M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 2008;59:651-681. DOI 10.1146/annurev.arplant.59.032607.092911.

40. Pushpakom S., Iorio F., Eyers P.A., Escott K.J., Hopper S., Wells A., Doig A., Guilliams T., Latimer J., McNamee C., Norris A., Sanseau P., Cavalla D., Pirmohamed M. Drug repurposing: progress, challenges and recommendations. Nat. Rev. Drug Discov. 2019; 18(1):41-58. DOI 10.1038/nrd.2018.168.

41. Raj M.R., Sreeja A. Analysis of computational gene prioritization approaches. Procedia Comput. Sci. 2018;143:395-410. DOI 10.1016/j.procs.2018.10.411.

42. Ramšak Ž., Coll A., Stare T., Tzfadia O., Baebler Š., Van de Peer Y., Gruden K. Network modeling unravels mechanisms of crosstalk between ethylene and salicylate signaling in potato. Plant Physiol. 2018;178(1):488-499. DOI 10.1104/pp.18.00450.

43. Sabidussi G. The centrality index of a graph. Psychometrika. 1966;31: 581-603. DOI 10.1007/BF02289527.

44. Saik O.V., Demenkov P.S., Ivanisenko T.V., Bragina E.Y., Freidin M.B., Goncharova I.A., Dosenko V.E., Zolotareva O.I., Hofestaedt R., Lavrik I.N., Rogaev E.I., Ivanisenko V.A. Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks. BMC Med. Genomics. 2018;11(Suppl.1): 15. DOI 10.1186/s12920-018-0331-4.

45. Saik O.V., Demenkov P.S., Ivanisenko T.V., Kolchanov N.A., Ivanisenko V.A. Development of methods for automatic extraction of knowledge from texts of scientific publications for the creation of a knowledge base SOLANUM TUBEROSUM. Selskokhozyaystvennaya Biologiya = Agricultural Biology. 2017;52(1):63-74. DOI 10.15389/agrobiology.2017.1.63eng.

46. Saik O.V., Nimaev V.V., Usmonov D.B., Demenkov P.S., Ivanisenko T.V., Lavrik I.N., Ivanisenko V.A. Prioritization of genes involved in endothelial cell apoptosis by their implication in lymphedema using an analysis of associative gene networks with ANDSystem. BMC Med. Genomics. 2019;12(Suppl.2):47. DOI 10.1186/s12920-019-0492-9.

47. Schaefer R.J., Michno J.M., Jeffers J., Hoekenga O., Dilkes B., Baxter I., Myers C.L. Integrating coexpression networks with GWAS to prioritize causal genes in maize. Plant Cell. 2018;30(12):2922-2942. DOI 10.1105/tpc.18.00299.

48. Sehgal A., Sita K., Bhandari K., Kumar S., Kumar J., Vara Prasad P.V., Siddique K.H.M., Nayyar H. Influence of drought and heat stress, applied independently or in combination during seed development, on qualitative and quantitative aspects of seeds of lentil (Lens culinaris Medikus) genotypes, differing in drought sensitivity. Plant Cell Environ. 2019;42(1):198-211. DOI 10.1111/pce.13328.

49. Shim J.E., Lee I. Network-assisted approaches for human disease research. Animal Cells Syst. 2015;19:231-235.

50. Shim J.E., Lee T., Lee I. From sequencing data to gene functions: cofunctional network approaches. Animal Cells Syst. 2017;21(2):77-83. DOI 10.1080/19768354.2017.1284156.

51. Silva N.C.Q., de Souza G.A., Pimenta T.M., Brito F.A.L., Picoli E.A.T., Zsögön A., Ribeiro D.M. Salt stress inhibits germination of Stylosanthes humilis seeds through abscisic acid accumulation and associated changes in ethylene production. Plant Physiol. Biochem. 2018;130:399-407. DOI 10.1016/j.plaphy.2018.07.025.

52. Souza V.L., de Almeida A.A., Lima S.G., de M. Cascardo J.C., da C. Silva D., Mangabeira P.A., Gomes F.P. Morphophysiological responses and programmed cell death induced by cadmium in Genipa americana L. (Rubiaceae). Biometals. 2011;24(1):59-71. DOI 10.1007/s10534-010-9374-5.

53. Subramanian A., Tamayo P., Mootha V.K., Mukherjee S., Ebert B.L., Gillette M.A., Paulovich A., Pomeroy S.L., Golub T.R., Lander E.S., Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA. 2005;102(43):15545-15550. DOI 10.1073/pnas.0506580102.

54. Sun C., Dong Z., Zhao L., Ren Y., Zhang N., Chen F. The Wheat 660K SNP array demonstrates great potential for marker‐assisted selection in polyploid wheat. Plant Biotechnol. J. 2020;18(6):1354-1360. DOI 10.1111/pbi.13361.

55. Takahashi F., Kuromori T., Sato H., Shinozaki K. Regulatory gene networks in drought stress responses and resistance in plants. Adv. Exp. Med. Biol. 2018;1081:189-214. DOI 10.1007/978-981-13-1244-1_11.

56. Tranchevent L.C., Ardeshirdavani A., ElShal S., Alcaide D., Aerts J., Auboeuf D., Moreau Y. Candidate gene prioritization with Endeavour. Nucleic Acids Res. 2016;44(W1):W117-W121. DOI 10.1093/nar/gkw365.

57. van Dongen S., Abreu-Goodger C. Using MCL to extract clusters from networks. Methods Mol. Biol. 2012;804:281-295. DOI 10.1007/978-1-61779-361-5_15.

58. Voss-Fels K.P., Cooper M., Hayes B.J. Accelerating crop genetic gains with genomic selection. Theor. Appl. Genet. 2019;132(3):669-686. DOI 10.1007/s00122-018-3270-8.

59. Wang J.Z., Du Z., Payattakool R., Yu P.S., Chen C.F. A new method to measure the semantic similarity of GO terms. Bioinformatics. 2007; 23(10):1274-1281. DOI 10.1093/bioinformatics/btm087.

60. Wang L., Jin X., Li Q., Wang X., Li Z., Wu X. Comparative proteomics reveals that phosphorylation of β carbonic anhydrase 1 might be important for adaptation to drought stress in Brassica napus. Sci. Rep. 2016;6:39024. DOI 10.1038/srep39024.

61. Wijewardana C., Reddy K.R., Krutz L.J., Gao W., Bellaloui N. Drought stress has transgenerational effects on soybean seed germination and seedling vigor. PLoS One. 2019;14(9):e0214977. DOI 10.1371/journal.pone.0214977.

62. Wu J., Jiang Y., Liang Y., Chen L., Chen W., Cheng B. Expression of the maize MYB transcription factor ZmMYB3R enhances drought and salt stress tolerance in transgenic plants. Plant Physiol. Biochem. 2019;137:179-188. DOI 10.1016/j.plaphy.2019.02.010.


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