Skip to main content
SpringerLink
Log in

Identification of Rice Drought-Resistant Gene Based on Gene Expression Profiles and Network Analysis Algorithm

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

Included in the following conference series:

  • 927 Accesses

Abstract

Drought is a primary cause of grain yield reduction that reduces agricultural production and seriously damage food security worldwide. Drought tolerance has a complex quantitative characteristic with a complicated phenotype that affects different developmental stages of plants. The level of susceptibility or tolerance of rice to several drought conditions is coordinated by the action of different drought-resistant genes. This study presents a bioinformatics approach to identify candidate rice drought-resistant genes based on other known related rice genes. By using the sub-network extraction algorithm with gene co-expression profile, we obtained the integrated network comprising of the known rice drought-resistant related genes (denoted as seed genes) and putative genes (denoted as linker genes). These genes are ranked according to the newly proposed rating scores. Some of the discovered candidate genes were validated by the previous scientific literature and gene set enrichment analysis. The results offer useful gene information that serve as guidance for the researchers and rice breeders. In addition, the proposed approach is sufficiently effective to be applied on other crops via biological network analysis.

Y. Gao and Y. Chen—These authors contributed equally to this work

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sahebi, M., Hanafi, M.M., Rafii, M.Y., Mahmud, T.M.M., Azizi, P., Osman, M., et al.: Improvement of drought tolerance in rice (Oryza sativa L.): genetics, genomic tools, and the WRKY gene family. Biomed. Res. Int. 2018, 3158474 (2018)

    Google Scholar 

  2. Ashraf, M.: Inducing drought tolerance in plants: recent advances. Biotechnol. Adv. 28(1), 169–183 (2010)

    Article  Google Scholar 

  3. Ji, K., Wang, Y., Sun, W., Lou, Q., Mei, H., Shen, S., et al.: Drought-responsive mechanisms in rice genotypes with contrasting drought tolerance during reproductive stage. J. Plant Physiol. 169(4), 336–344 (2012)

    Article  Google Scholar 

  4. van Noort, V., Snel, B., Huynen, M.A.: The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Rep. 5(3), 280–284 (2004)

    Article  Google Scholar 

  5. Xiong, L.M., Schumaker, K.S., Zhu, J.K.: Cell signaling during cold, drought, and salt stress. Plant Cell. 14, S165–S183 (2002)

    Article  Google Scholar 

  6. Li, X., Chang, Y., Ma, S., Shen, J., Hu, H., Xiong, L.: Genome-wide identification of SNAC1-targeted genes involved in drought response in rice. Front. Plant Sci. 10, 982 (2019)

    Article  Google Scholar 

  7. Hu, H.H., Xiong, L.Z.: Genetic engineering and breeding of drought-resistant crops. In: Merchant, S.S. (ed.) Annual Review of Plant Biology, vol. 65, pp. 715–741 (2014)

    Google Scholar 

  8. Ganie, S.A., Pani, D.R., Mondal, T.K.: Genome-wide analysis of DUF221 domain containing gene family in Oryza species and identification of its salinity stress-responsive members in rice. PLoS ONE 12(8) (2017)

    Google Scholar 

  9. Li, Y., Yuan, F., Wen, Z., Li, Y., Wang, F., Zhu, T., et al.: Genome-wide survey and expression analysis of the OSCA gene family in rice. BMC Plant Biol. 15, 261 (2015)

    Article  Google Scholar 

  10. Swamy, B.P.M., Kumar, A.: Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotechnol. Adv. 31(8), 1308–1318 (2013)

    Article  Google Scholar 

  11. Ulitsky, I., Shamir, R.: Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25(9), 1158–1164 (2009)

    Article  Google Scholar 

  12. Vandin, F., Clay, P., Upfal, E., Raphael, B.J.: Discovery of mutated subnetworks associated with clinical data in cancer. In: Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, pp. 55–66 (2012)

    Google Scholar 

  13. Scott, M.S., Perkins, T., Bunnell, S., Pepin, F., Thomas, D.Y., Hallett, M.: Identifying regulatory subnetworks for a set of genes. Mol. Cellul. Proteom. 4(5), 683–692 (2005)

    Article  Google Scholar 

  14. Noirel, J., Ow, S.Y., Sanguinetti, G., Jaramillo, A., Wright, P.C.: Automated extraction of meaningful pathways from quantitative proteomics data. Brief. Funct. Genom. Proteom. 7(2), 136–146 (2008)

    Article  Google Scholar 

  15. Smita, S., Katiyar, A., Lenka, S.K., Dalal, M., Kumar, A., Mahtha, S.K., et al.: Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis. Funct. Integr. Genom. 20(1), 29–49 (2020)

    Article  Google Scholar 

  16. Lv, Y., Xu, L., Dossa, K., Zhou, K., Zhu, M., Xie, H., et al.: Identification of putative drought-responsive genes in rice using gene co-expression analysis. Bioinformation 15(7), 480–489 (2019)

    Article  Google Scholar 

  17. Wang, X., Wu, Y.J., Wang, R.J., Wei, Y.Y., Gui, Y.M.: Gray BP neural network based prediction of rice protein interaction network. Cluster Comput. J. Netw. Softw. Tools Appl. 22(2), S4165-S4171 (2019)

    Google Scholar 

  18. Kudo, T., Terashima, S., Takaki, Y., Tomita, K., Saito, M., Kanno, M., et al.: PlantExpress: a database integrating OryzaExpress and ArthaExpress for single-species and cross-species gene expression network analyses with microarray-based transcriptome data. Plant Cell Physiol. 58(1) (2017). https://doi.org/10.1093/pcp/pcw208

  19. Serin, E.A.R., Nijveen, H., Hilhorst, H.W.M., Ligterink, W.: Learning from co-expression networks: possibilities and challenges. Front. Plant Sci. 7, 444 (2016)

    Article  Google Scholar 

  20. Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J., Pavlidis, P.: Coexpression analysis of human genes across many microarray data sets. Genome Res. 14(6), 1085–1094 (2004)

    Article  Google Scholar 

  21. Wolfe, C.J., Kohane, I.S., Butte, A.J.: Systematic survey reveals general applicability of “guilt-by-association” within gene coexpression networks. BMC Bioinf. 6, 227 (2005)

    Article  Google Scholar 

  22. Zhuang, D.-Y., Jiang, L., He, Q.-Q., Zhou, P., Yue, T.: Identification of hub subnetwork based on topological features of genes in breast cancer. Int. J. Mol. Med. 35(3), 664–674 (2015)

    Article  Google Scholar 

  23. Chin, C.H., Chen, S.H., Wu, H.H., Ho, C.W., Ko, M.T., Lin, C.Y.: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8, S11 (2014)

    Article  Google Scholar 

  24. Klein, P.N., Ravi, R.J.J.A.: A nearly best-possible approximation algorithm for node-weighted Steiner trees. J. Algorithms 19(1), 104–115 (1995)

    Article  MathSciNet  Google Scholar 

  25. Ravi, R., Marathe, M.V., Ravi, S.S., Rosenkrantz, D.J., Hunt, H.B.: Approximation algorithms for degree-constrained minimum-cost network-design problems. Algorithmica 31(1), 58–78 (2001)

    Article  MathSciNet  Google Scholar 

  26. Zheng, S., Zhao, Z.: GenRev: exploring functional relevance of genes in molecular networks. Genomics 99(3), 183–188 (2012)

    Article  Google Scholar 

  27. Huang, D.W., Sherman, B.T., Lempicki, R.A.: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4(1), 44–57 (2009)

    Article  Google Scholar 

  28. Wang, H., Niu, Q.-W., Wu, H.-W., Liu, J., Ye, J., Yu, N., et al.: Analysis of non-coding transcriptome in rice and maize uncovers roles of conserved lncRNAs associated with agriculture traits. Plant J. 84(2), 404–416 (2015)

    Article  Google Scholar 

  29. Yamada, N., Theerawitaya, C., Kageyama, H., Cha-um, S., Takabe, T.: Expression of developmentally regulated plasma membrane polypeptide (DREPP2) in rice root tip and interaction with Ca2+/CaM complex and microtubule. Protoplasma 252(6), 1519–1527 (2015)

    Article  Google Scholar 

  30. Soranzo, N., Gorla, M.S., Mizzi, L., De Toma, G., Frova, C.: Organisation and structural evolution of the rice glutathione S-transferase gene family. Mol. Genet. Genom. 271(5), 511–521 (2004)

    Article  Google Scholar 

  31. Wang, C., Chen, S., Dong, Y., Ren, R., Chen, D., Chen, X.: Chloroplastic Os3BGlu6 contributes significantly to cellular ABA pools and impacts drought tolerance and photosynthesis in rice. New Phytol. 226(4), 1042–1054 (2020)

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Key Research and Development Project (2017YFD0301303), the Natural Science Young Foundation of Anhui Agricultural University (2019zd12) and the Introduction and Stabilization of Talent Project of Anhui Agricultural University (yj2019-32).

Author information

Authors and Affiliations

  1. School of Information and Computer, Laboratory of Anhui Beidou Precision Agriculture Information Engineering, Anhui Agricultural University, Hefei, 230036, Anhui, China

    Yujia Gao, Yiqiong Chen, Zhiyu Ma, Tao Zeng, Iftikhar Ahmad, Youhua Zhang & Zhenyu Yue

Authors
  1. Yujia Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiqiong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiyu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iftikhar Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Youhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhenyu Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Youhua Zhang or Zhenyu Yue .

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, Y. et al. (2020). Identification of Rice Drought-Resistant Gene Based on Gene Expression Profiles and Network Analysis Algorithm. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60802-6_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60801-9

  • Online ISBN: 978-3-030-60802-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation