Weighted gene co-expression analysis for identification of key genes regulating heat stress in wheat

Cereal Research Communications volume 49, pages73–81(2021)Cite this article

Abstract

Wheat is an important cereal crop, which holds the second rank globally in terms of production after maize. However, its productivity is highly sensitive to heat stress, which is one of the most serious threats due to global warming. Therefore, development of heat tolerance variety of wheat through molecular breeding approach is an urgent need of the hour for not only reducing productivity loss but also improving crop yield for feeding growing population. In this context, identification of heat-related genes is the first step for this molecular breeding. In this regard, several studies have been conducted in the past, but due to identification of large number of genes, it was found to be practically difficult to use these in molecular breeding programs. In order to address this issue, in this study, system biology approach has been followed to identify set of key genes related to heat stress in wheat which contributes significantly to regulating this entire process. Here, high-throughput RNAseq data were generated using control and treated samples of two contrasting wheat varieties, namely HD2967 (thermo-tolerant) and BT-Schomburgk (thermo-susceptible). Further, in order to identify important key genes, an advanced statistical framework called weighted gene co-expression network analysis (WGCNA) has been used. Moreover, functional annotation of these identified key genes has also been carried out, which confirms their association with the heat stress. These results will provide important lead to experimenters involved in the development of new heat-stress-tolerant wheat cultivars to mitigate effects of global warming.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

US$ 39.95

Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Altuhaish AAK, Yahya S (2014) Field adaptation of some introduced wheat (Triticum aestivum L.) genotypes in two altitudes of tropical agro-ecosystem environment of Indonesia. Hayati J Biosci 21(1):31–38

    Google Scholar 

  2. Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc

  3. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Calderini DF, Abeledo LG, Savin R, Slafer GA (1999) Effect of temperature and carpel size during pre-anthesis on potential grain weight in wheat. J Agric Sci 132(4):453–459

    Google Scholar 

  5. Chen Z (2001) A superfamily of proteins with novel cysteine-rich repeats. Plant Physiol 126(2):473–476

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18):3674–3676

    CAS  Google Scholar 

  7. Das K, Roychoudhury A (2014) Reactive oxygen species (ROS) and response of antioxidants as ROS-scavengers during environmental stress in plants. Front Environ Sci 2:53

    Google Scholar 

  8. Das S, Meher PK, Rai A, Bhar LM et al (2017) Statistical approaches for gene selection, hub gene identification and module interaction in gene co-expression network analysis: an application to aluminum stress in soybean (Glycine max L.). PLoS ONE 12(1):e0169605

    PubMed  PubMed Central  Google Scholar 

  9. Gaiteri C, Ding Y, French B, Tseng GC et al (2014) Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav 13(1):13–24

    CAS  PubMed  Google Scholar 

  10. Girousse C, Roche J, Guerin C, Le Gouis J et al (2018) Coexpression network and phenotypic analysis identify metabolic pathways associated with the effect of warming on grain yield components in wheat. PLoS ONE 13(6):e0199434

    PubMed  PubMed Central  Google Scholar 

  11. Heyer LJ, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9(11):1106–1115

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Hudson NJ, Dalrymple BP, Reverter A (2012) Beyond differential expression: the quest for causal mutations and effector molecules. BMC Genom 13(1):356

    CAS  Google Scholar 

  13. Jarsch IK, Ott T (2011) Perspectives on remorin proteins, membrane rafts, and their role during plant–microbe interactions. Mol Plant Microbe Interact 24(1):7–12

    CAS  PubMed  Google Scholar 

  14. Kumar RM, Cahan P, Shalek AK, Satija R et al (2014) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516(7529):56

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Kumar M, Kumar RR, Goswami S, Verma P et al (2017a) miR430: the novel heat-responsive microRNA identified from miRNome analysis in wheat (Triticum aestivum L.). Indian J Plant Physiol 22(4):566–576

    CAS  Google Scholar 

  16. Kumar RR, Goswami S, Shamim M, Mishra U et al (2017b) Biochemical defense response: characterizing the plasticity of source and sink in spring wheat under terminal heat stress. Front Plant Sci 8:1603

    PubMed  PubMed Central  Google Scholar 

  17. Kumar RR, Goswami S, Dubey K, Singh K, Singh JP, Kumar A, Rai GK, Singh SD, Bakshi S, Singh B, Pathak H, Chnnusamy V, Rai RD, Parveen S (2019a) RuBisCo activase: a catalytic chaperone involved in modulating the RuBisCo activity and heat stress-tolerance in wheat. J Plant Biochem Biotechnol 28:63–75

    Google Scholar 

  18. Kumar RR, Tasleem M, Jain M, Ahuja S, Goswami S, Bakshi S, Jambhulkar S, Singh SD, Singh GP, Pathak H, Viswanathan C, Praveen S (2019b) Nitric oxide triggered defense network in wheat: augmenting tolerance and grain-quality related traits under heat-induced oxidative damage. Environ Exp Bot 158:189–204

    CAS  Google Scholar 

  19. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9(1):559

    PubMed  PubMed Central  Google Scholar 

  20. Langfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24(5):719–720

    PubMed  Google Scholar 

  21. Lefebvre B, Timmers T, Mbengue M, Moreau S, Hervé C, Tóth K, Bittencourt-Silvestre J, Klaus D, Deslandes L, Godiard L, Murray JD (2010) A remorin protein interacts with symbiotic receptors and regulates bacterial infection. Proc Natl Acad Sci 107(5):2343–2348

    CAS  PubMed  Google Scholar 

  22. Leff B, Ramankutty N, Foley JA (2004) Geographic distribution of major crops across the world. Global Biogeochem Cycles 18(1):GB1009

    Google Scholar 

  23. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12(1):323

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Li X, Liu N, You L, Ke X et al (2016) Patterns of cereal yield growth across China from 1980 to 2010 and their implications for food production and food security. PLoS ONE 11(7):e0159061

    PubMed  PubMed Central  Google Scholar 

  25. Li Q, Wang W, Wang W, Zhang G et al (2018) Wheat F-box protein gene TaFBA1 is involved in plant tolerance to heat stress. Front Plant Sci 9:521

    PubMed  PubMed Central  Google Scholar 

  26. Liang YH, Cai B, Chen F, Wang G et al (2014) Construction and validation of a gene co-expression network in grapevine (Vitis vinifera L.). Hortic Res 1:14040

    PubMed  PubMed Central  Google Scholar 

  27. Luo R, Liu B, Xie Y, Li Z et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1(1):18

    PubMed  PubMed Central  Google Scholar 

  28. Matsui T, Omasa K, Horie T (2000) High temperature at flowering inhibits swelling of pollen grains, a driving force for thecae dehiscence in rice (Oryza sativa L.). Plant Prod Sci 3(4):430–434

    Google Scholar 

  29. Ni W, Xu SL, Tepperman JM, Stanley DJ, Maltby DA, Gross JD, Burlingame AL, Wang ZY, Quail PH (2014) A mutually assured destruction mechanism attenuates light signaling in Arabidopsis. Science 344(6188):1160–1164

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ni Z, Li H, Zhao Y, Peng H et al (2017) Genetic improvement of heat tolerance in wheat: recent progress in understanding the underlying molecular mechanisms. Crop J 6(1):32–41

    Google Scholar 

  31. Pfaffl MW, Horgan GW, Dempfle L (2002) Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30(9):e36

    PubMed  PubMed Central  Google Scholar 

  32. Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos Trans R Soc B Biol Sci 360(1463):2021–2035

    Google Scholar 

  33. Raffaele S, Bayer E, Mongrand S (2009) Up regulation of the plant protein remorin correlates with dehiscence and cell maturation; a link with the maturation of plasmodesmata? A link with the maturation of plasmodesmata? Plant Signal Behav 4(10):915–919

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Saini HS, Aspinall D (1982) Abnormal sporogenesis in wheat (Triticum aestivum L.) induced by short periods of high temperature. Ann Bot 49(6):835–846

    Google Scholar 

  35. Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41(1):1–14

    Google Scholar 

  36. Serin EA, Nijveen H, Hilhorst HW, Ligterink W (2016) Learning from co-expression networks: possibilities and challenges. Front Plant Sci 7:444

    PubMed  PubMed Central  Google Scholar 

  37. Shannon P, Markiel A, Ozier O, Baliga NS et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Shi K, Bing ZT, Cao GQ, Guo L et al (2015) Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis. Int J Ophthalmol 8(2):269

    PubMed  PubMed Central  Google Scholar 

  39. Slafer GA, Abeledo LG, Miralles DJ, Gonzalez FG, Whitechurch EM (2001) Photoperiod sensitivity during stem elongation as an avenue to raise potential yield in wheat. Euphytica 119:191–197

    Google Scholar 

  40. Smith DL, Almaraz JJ (2010) Climate change and crop production: contributions, impacts, and adaptations. Can J Plant Pathol 26(3):253–266

    Google Scholar 

  41. Streck NA (2005) Climate change and agroecosystems: the effect of elevated atmospheric CO2 and temperature on crop growth, development, and yield. Ciencia Rural 35:730–740

    Google Scholar 

  42. Sun AZ, Guo FQ (2016) Chloroplast retrograde regulation of heat stress responses in plants. Front Plant Sci 7:398

    PubMed  PubMed Central  Google Scholar 

  43. Tripathy BC, Oelmüller R (2012) Reactive oxygen species generation and signaling in plants. Plant Signal Behav 7(12):1621–1633

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Van Breusegem F, Dat JF (2006) Reactive oxygen species in plant cell death. Plant Physiol 141(2):384–390

    PubMed  PubMed Central  Google Scholar 

  45. Wang QL, Chen JH, He NY, Guo FQ (2018) Metabolic reprogramming in chloroplasts under heat stress in plants. Int J Mol Sci 19(3):849

    PubMed Central  Google Scholar 

  46. Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921

    CAS  PubMed  Google Scholar 

  47. Wu WS, Li WH (2008) Identifying gene regulatory modules of heat shock response in yeast. BMC Genom 9(1):439

    CAS  Google Scholar 

  48. Xue GP, Drenth J, McIntyre CL (2015) TaHsfA6f is a transcriptional activator that regulates a suite of heat stress protection genes in wheat (Triticum aestivum L.) including previously unknown Hsf targets. J Exp Bot 66(3):1025–1039

    CAS  PubMed  Google Scholar 

  49. Yadeta KA, Elmore JM, Creer AY, Feng B, Franco JY, Rufian JS, He P, Phinney B, Coaker G (2017) A cysteine-rich protein kinase associates with a membrane immune complex and the cysteine residues are required for cell death. Plant Physiol 173(1):771–787

    CAS  PubMed  Google Scholar 

  50. Yang K, Rong W, Qi L, Li J, Wei X, Zhang Z (2013) Isolation and characterization of a novel wheat cysteine-rich receptor-like kinase gene induced by Rhizoctonia cerealis. Sci Rep 3:3021

    PubMed  PubMed Central  Google Scholar 

  51. Zecevic V, Boskovic J, Knezevic D, Micanovic D (2014) Effect of seeding rate on grain quality of winter wheat. Chil J Agric Res 74(1):23–28

    Google Scholar 

  52. Zhao S, Guo Y, Sheng Q, Shyr Y (2014) Advanced heat map and clustering analysis using heatmap3. Biomed Res Int 2014:986048

    PubMed  PubMed Central  Google Scholar 

  53. Zou C, Lehti-Shiu MD, Thomashow M, Shiu SH (2009) Evolution of stress-regulated gene expression in duplicate genes of Arabidopsis thaliana. PLoS Genet 5(7):e1000581

    PubMed  PubMed Central  Google Scholar 

Download references

Funding

Partial support was received from the ICAR Network Project on Computational Biology and Agricultural Bioinformatics under CABin Scheme.

Author information

Affiliations

  1. Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India

    Dwijesh Chandra Mishra, Devender Arora, Shivangi Varshney, Neeraj Budhlakoti, Sanjeev Kumar, Krishna Kumar Chaturvedi, Anu Sharma & Anil Rai

  2. ICAR-Indian Agricultural Research Institute, New Delhi, India

    Ranjeet Ranjan Kumar, Suneha Goswami & Viswanathan Chinnusamy

Authors
  1. Dwijesh Chandra Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devender Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ranjeet Ranjan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suneha Goswami
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shivangi Varshney
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Neeraj Budhlakoti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sanjeev Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Krishna Kumar Chaturvedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Anu Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Viswanathan Chinnusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Anil Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DM and DA initiated research, performed analysis, interpreted the results and drafted the manuscript. RR, SG and VC conducted wet laboratory experimentation and results interpretation. NB, KKC, AS, SK, AR and SV interpreted the results and finalized the manuscript.

Corresponding author

Correspondence to Krishna Kumar Chaturvedi.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Communicated by S. Gottwald.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2384 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mishra, D.C., Arora, D., Kumar, R.R. et al. Weighted gene co-expression analysis for identification of key genes regulating heat stress in wheat. CEREAL RESEARCH COMMUNICATIONS 49, 73–81 (2021). https://doi.org/10.1007/s42976-020-00072-7

Download citation

Keywords

  • Co-expression analysis
  • Global warming
  • Heat stress
  • Transcriptome
  • Gene regulatory network
  • Key genes