Skip to main content

Advertisement

Log in

Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini–Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene–gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Figueroa, M., Hammond-Kosack, K. E., & Solomon, P. S. (2018). A review of wheat diseases-a field perspective. Molecular Plant Pathology, 19, 1523–1536.

    Article  PubMed  Google Scholar 

  2. Bhalla, P. L. (2006). Genetic engineering of wheat–current challenges and opportunities. TRENDS in Biotechnology, 24, 305–311.

    Article  CAS  PubMed  Google Scholar 

  3. Coram, T. E., Huang, X., Zhan, G., Settles, M. L., & Chen, X. (2010). Meta-analysis of transcripts associated with race-specific resistance to stripe rust in wheat demonstrates common induction of blue copper-binding protein, heat-stress transcription factor, pathogen-induced WIR1A protein, and ent-kaurene synthase transcripts. Functional & Integrative Genomics, 10, 383–392.

    Article  CAS  Google Scholar 

  4. Wang, L., Xiang, L., Hong, J., Xie, Z., & Li, B. (2019). Genome-wide analysis of bHLH transcription factor family reveals their involvement in biotic and abiotic stress responses in wheat (Triticum aestivum L.). 3 Biotech, 9, 1–12.

    Article  Google Scholar 

  5. Perochon, A., Vary, Z., Malla, K. B., Halford, N. G., Paul, M. J., & Doohan, F. M. (2019). The wheat SnRK1alpha family and its contribution to Fusarium toxin tolerance. Plant Science, 288, 110217.

    Article  CAS  PubMed  Google Scholar 

  6. Lv, S., Guo, H., Zhang, M., Wang, Q., Zhang, H., & Ji, W. (2020). Large-scale cloning and comparative analysis of TaNAC genes in response to stripe rust and powdery mildew in wheat (Triticum aestivum L.). Genes (Basel), 11, 1073.

    Article  CAS  PubMed  Google Scholar 

  7. Bhatta, M., Morgounov, A., Belamkar, V., Wegulo, S. N., Dababat, A. A., Erginbas-Orakci, G., Bouhssini, M. E., Gautam, P., Poland, J., Akci, N., Demir, L., Wanyera, R., & Baenziger, P. S. (2019). Genome-wide association study for multiple biotic stress resistance in synthetic hexaploid wheat. International Journal of Molecular Sciences, 20, 3667.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Golkari, S., Gilbert, J., Ban, T., & Procunier, J. D. (2009). QTL-specific microarray gene expression analysis of wheat resistance to Fusarium head blight in Sumai-3 and two susceptible NILs. Genome, 52, 409–418.

    Article  CAS  PubMed  Google Scholar 

  9. Bozkurt, T. O., McGrann, G. R., MacCormack, R., Boyd, L. A., & Akkaya, M. S. (2010). Cellular and transcriptional responses of wheat during compatible and incompatible race-specific interactions with Puccinia striiformis f. sp. tritici. Molecular Plant Pathology, 11, 625–640.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Bolton, M. D., Kolmer, J. A., Xu, W. W., & Garvin, D. F. (2008). Lr34-mediated leaf rust resistance in wheat: Transcript profiling reveals a high energetic demand supported by transient recruitment of multiple metabolic pathways. Molecular Plant-Microbe Interactions, 21, 1515–1527.

    Article  CAS  PubMed  Google Scholar 

  11. Xin, M., Wang, X., Peng, H., Yao, Y., Xie, C., Han, Y., Ni, Z., & Sun, Q. (2012). Transcriptome comparison of susceptible and resistant wheat in response to powdery mildew infection. Genomics, Proteomics & Bioinformatics, 10, 94–106.

    Article  CAS  Google Scholar 

  12. Erayman, M., Turktas, M., Akdogan, G., Gurkok, T., Inal, B., Ishakoglu, E., Ilhan, E., & Unver, T. (2015). Transcriptome analysis of wheat inoculated with Fusarium graminearum. Frontiers in Plant Science, 6, 867.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gurevitch, J., Koricheva, J., Nakagawa, S., & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555, 175–182.

    Article  CAS  PubMed  Google Scholar 

  14. Adie, B. A., Pérez-Pérez, J., Pérez-Pérez, M. M., Godoy, M., Sánchez-Serrano, J.-J., Schmelz, E. A., & Solano, R. J. T. P. C. (2007). ABA is an essential signal for plant resistance to pathogens affecting JA biosynthesis and the activation of defenses in Arabidopsis. The Plant Cell, 19, 1665–1681.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ehlting, J., Chowrira, S. G., Mattheus, N., Aeschliman, D. S., Arimura, G., & Bohlmann, J. (2008). Comparative transcriptome analysis of Arabidopsis thaliana infested by diamond back moth (Plutella xylostella) larvae reveals signatures of stress response, secondary metabolism, and signalling. BMC Genomics, 9, 154.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ransbotyn, V., Yeger-Lotem, E., Basha, O., Acuna, T., Verduyn, C., Gordon, M., Chalifa-Caspi, V., Hannah, M. A., & Barak, S. (2015). A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes. Plant Biotechnology Journal, 13, 501–513.

    Article  CAS  PubMed  Google Scholar 

  17. Shaik, R., & Ramakrishna, W. (2014). Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiology, 164, 481–495.

    Article  CAS  PubMed  Google Scholar 

  18. Sirohi, P., Yadav, B. S., Afzal, S., Mani, A., & Singh, N. K. (2020). Identification of drought stress-responsive genes in rice (Oryza sativa) by meta-analysis of microarray data. Journal of Genetics, 99, 1–10.

    Article  Google Scholar 

  19. Tahmasebi, A., Ashrafi-Dehkordi, E., Shahriari, A. G., Mazloomi, S. M., & Ebrahimie, E. (2019). Integrative meta-analysis of transcriptomic responses to abiotic stress in cotton. Progress in Biophysics and Molecular Biology, 146, 112–122.

    Article  CAS  PubMed  Google Scholar 

  20. Shen, P. C., Hour, A. L., & Liu, L. D. (2017). Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis. Botanical Studies, 58, 22.

    Article  PubMed  PubMed Central  Google Scholar 

  21. de Abreu Neto, J. B., & Frei, M. (2015). Microarray meta-analysis focused on the response of genes involved in redox homeostasis to diverse abiotic stresses in rice. Frontiers in Plant Science, 6, 1260.

    PubMed  Google Scholar 

  22. Zinati, Z., Sazegari, S., Tahmasebi, A., & Delavari, A. (2020). A comprehensive meta-analysis to identify transcriptional signatures of abiotic stress responses in barley (Hordeum vulgare). Cereal Research Communications, 49, 1–7.

    Google Scholar 

  23. Cohen, S. P., & Leach, J. E. (2019). Abiotic and biotic stresses induce a core transcriptome response in rice. Science and Reports, 9, 6273.

    Article  Google Scholar 

  24. Balan, B., Marra, F. P., Caruso, T., & Martinelli, F. (2018). Transcriptomic responses to biotic stresses in Malus x domestica: A meta-analysis study. Science and Reports, 8, 1970.

    Article  Google Scholar 

  25. Ashrafi-Dehkordi, E., Alemzadeh, A., Tanaka, N., & Razi, H. (2018). Meta-analysis of transcriptomic responses to biotic and abiotic stress in tomato. PeerJ, 6, e4631.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bilgin, D. D., Zavala, J. A., Zhu, J., Clough, S. J., Ort, D. R., & DeLucia, E. H. (2010). Biotic stress globally downregulates photosynthesis genes. Plant, Cell and Environment, 33, 1597–1613.

    Article  CAS  PubMed  Google Scholar 

  27. Osmani, Z., Sabet, M. S., Shams-Bakhsh, M., Moieni, A., & Vahabi, K. (2019). Virus-specific and common transcriptomic responses of potato (Solanum tuberosum) against PVY, PVA and PLRV using microarray meta-analysis. Plant Breeding, 138, 216–228.

    Article  CAS  Google Scholar 

  28. Rodrigo, G., Carrera, J., Ruiz-Ferrer, V., del Toro, F. J., Llave, C., Voinnet, O., & Elena, S. F. (2012). A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS ONE, 7, e40526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ostlund, G., & Sonnhammer, E. L. (2014). Avoiding pitfalls in gene (co)expression meta-analysis. Genomics, 103, 21–30.

    Article  PubMed  Google Scholar 

  30. Tseng, G. C., Ghosh, D., & Feingold, E. (2012). Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Research, 40, 3785–3799.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chang, L. C., Lin, H. M., Sibille, E., & Tseng, G. C. (2013). Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline. BMC Bioinformatics, 14, 368.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Christie, N., Myburg, A. A., Joubert, F., Murray, S. L., Carstens, M., Lin, Y. C., Meyer, J., Crampton, B. G., Christensen, S. A., Ntuli, J. F., Wighard, S. S., Van de Peer, Y., & Berger, D. K. (2017). Systems genetics reveals a transcriptional network associated with susceptibility in the maize-grey leaf spot pathosystem. The Plant Journal, 89, 746–763.

    Article  CAS  PubMed  Google Scholar 

  33. Sircar, S., & Parekh, N. (2015). Functional characterization of drought-responsive modules and genes in Oryza sativa: A network-based approach. Frontiers in Genetics, 6, 256.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sircar, S., & Parekh, N. (2019). Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach. PLoS ONE, 14, e0216068.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1128

    Article  PubMed  Google Scholar 

  36. Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., & Speed, T. P. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249–264.

    Article  PubMed  Google Scholar 

  38. Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004). affy—Analysis of affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307–315.

    Article  CAS  PubMed  Google Scholar 

  39. Kauffmann, A., Gentleman, R., & Huber, W. (2009). arrayQualityMetrics—A bioconductor package for quality assessment of microarray data. Bioinformatics, 25, 415–416.

    Article  CAS  PubMed  Google Scholar 

  40. Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127.

    Article  PubMed  Google Scholar 

  41. Xia, J., Gill, E. E., & Hancock, R. E. (2015). NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nature Protocols, 10, 823–844.

    Article  CAS  PubMed  Google Scholar 

  42. Piras, I. S., Manchia, M., Huentelman, M. J., Pinna, F., Zai, C. C., Kennedy, J. L., & Carpiniello, B. (2019). Peripheral biomarkers in Schizophrenia: A meta-analysis of microarray gene expression datasets. International Journal of Neuropsychopharmacology, 22, 186–193.

    Article  CAS  PubMed  Google Scholar 

  43. Zhang, L., Zhang, Z., Zhang, X., Yao, Y., Wang, R., Duan, B., & Fan, S. (2019). Comprehensive meta-analysis and co-expression network analysis identify candidate genes for salt stress response in Arabidopsis. Plant Biosystems, 153, 367–377.

    Article  Google Scholar 

  44. Wang, X., Kang, D. D., Shen, K., Song, C., Lu, S., Chang, L. C., Liao, S. G., Huo, Z., Tang, S., Ding, Y., Kaminski, N., Sibille, E., Lin, Y., Li, J., & Tseng, G. C. (2012). An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics, 28, 2534–2536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zaykin, D. V. (2011). Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. Journal of Evolutionary Biology, 24, 1836–1841.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Choi, J. K., Yu, U., Kim, S., & Yoo, O. J. (2003). Combining multiple microarray studies and modeling interstudy variation. Bioinformatics, 19(Suppl 1), i84-90.

    Article  PubMed  Google Scholar 

  47. Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., & Lopez, R. (2007). Clustal W and Clustal X version 2.0. Bioinformatics, 23, 2947–2948.

    Article  CAS  PubMed  Google Scholar 

  48. Guindon, S., Lethiec, F., Duroux, P., & Gascuel, O. (2005). PHYML online—A web server for fast maximum likelihood-based phylogenetic inference. Nucleic Acids Research, 33, W557–W559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ramirez-Gonzalez, R. H., Borrill, P., Lang, D., Harrington, S. A., Brinton, J., Venturini, L., Davey, M., Jacobs, J., van Ex, F., Pasha, A., Khedikar, Y., Robinson, S. J., Cory, A. T., Florio, T., Concia, L., Juery, C., Schoonbeek, H., Steuernagel, B., Xiang, D., … International Wheat Genome Sequencing C. (2018). The transcriptional landscape of polyploid wheat. Science, 361, 6403.

    Article  Google Scholar 

  50. Borrill, P., Ramirez-Gonzalez, R., & Uauy, C. (2016). expVIP: A customizable RNA-seq data analysis and visualization platform. Plant Physiology, 170, 2172–2186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yang, F., Li, W., & Jorgensen, H. J. (2013). Transcriptional reprogramming of wheat and the hemibiotrophic pathogen Septoria tritici during two phases of the compatible interaction. PLoS ONE, 8, e81606.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Cantu, D., Segovia, V., MacLean, D., Bayles, R., Chen, X., Kamoun, S., Dubcovsky, J., Saunders, D. G., & Uauy, C. (2013). Genome analyses of the wheat yellow (stripe) rust pathogen Puccinia striiformis f. sp. tritici reveal polymorphic and haustorial expressed secreted proteins as candidate effectors. BMC Genomics, 14, 1–18.

    Article  Google Scholar 

  53. Kugler, K. G., Siegwart, G., Nussbaumer, T., Ametz, C., Spannagl, M., Steiner, B., Lemmens, M., Mayer, K. F., Buerstmayr, H., & Schweiger, W. (2013). Quantitative trait loci-dependent analysis of a gene co-expression network associated with Fusarium head blight resistance in bread wheat (Triticum aestivum L.). BMC Genomics, 14, 1–15.

    Article  Google Scholar 

  54. Zhang, H., Yang, Y., Wang, C., Liu, M., Li, H., Fu, Y., Wang, Y., Nie, Y., Liu, X., & Ji, W. (2014). Large-scale transcriptome comparison reveals distinct gene activations in wheat responding to stripe rust and powdery mildew. BMC Genomics, 15, 898.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Powell, J. J., Carere, J., Fitzgerald, T. L., Stiller, J., Covarelli, L., Xu, Q., Gubler, F., Colgrave, M. L., Gardiner, D. M., Manners, J. M., Henry, R. J., & Kazan, K. (2017). The Fusarium crown rot pathogen Fusarium pseudograminearum triggers a suite of transcriptional and metabolic changes in bread wheat (Triticum aestivum L.). Annals of Botany, 119, 853–867.

    CAS  PubMed  Google Scholar 

  56. Gou, L., Hattori, J., Fedak, G., Balcerzak, M., Sharpe, A., Visendi, P., Edwards, D., Tinker, N., Wei, Y. M., & Chen, G. Y. (2016). Development and validation of Thinopyrum elongatum—expressed molecular markers specific for the long arm of chromosome 7E. Crop Science, 56, 354–364.

    Article  CAS  Google Scholar 

  57. Ma, J., Stiller, J., Zhao, Q., Feng, Q., Cavanagh, C., Wang, P., Gardiner, D., Choulet, F., Feuillet, C., Zheng, Y. L., Wei, Y., Yan, G., Han, B., Manners, J. M., & Liu, C. (2014). Transcriptome and allele specificity associated with a 3BL locus for Fusarium crown rot resistance in bread wheat. PLoS ONE, 9, e113309.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., & Vilo, J. (2019). g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Research, 47, W191–W198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hong, F., & Breitling, R. (2008). A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics, 24, 374–382.

    Article  CAS  PubMed  Google Scholar 

  60. de Abreu Neto, J. B., & Frei, M. (2016). Microarray meta-analysis focused on the response of genes involved in redox homeostasis to diverse abiotic stresses in rice. Frontiers in Plant Science, 6, 1260.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Zhang, L., Zhang, Z., Zhang, X., Yao, Y., Wang, R., Duan, B., & Fan, S. (2019). Comprehensive meta-analysis and co-expression network analysis identify candidate genes for salt stress response in Arabidopsis. Plant Biosystems—An International Journal Dealing with all Aspects of Plant Biology, 153, 367–377.

    Article  Google Scholar 

  62. Zhu, L., Liu, X., Wang, H., Khajuria, C., Reese, J. C., Whitworth, R. J., Welti, R., & Chen, M. S. (2012). Rapid mobilization of membrane lipids in wheat leaf sheaths during incompatible interactions with Hessian fly. Molecular Plant-Microbe Interactions, 25, 920–930.

    Article  CAS  PubMed  Google Scholar 

  63. Evangelou, E., & Ioannidis, J. (2013). Meta-analysis methods for genome-wide association studies and beyond. Nature Reviews Genetics, 14, 379–389.

    Article  CAS  PubMed  Google Scholar 

  64. Chang, L.-C., Lin, H.-M., Sibille, E., & Tseng, G. C. (2013). Meta-analysis methods for combining multiple expression profiles: Comparisons, statistical characterization and an application guideline. BMC Bioinformatics, 14, 1–15.

    Article  CAS  Google Scholar 

  65. Hahn, A., Vonck, J., Mills, D. J., Meier, T., & Kuhlbrandt, W. (2018). Structure, mechanism, and regulation of the chloroplast ATP synthase. Science, 360, 6389.

    Article  Google Scholar 

  66. Gong, C., Cheng, M. Z., Li, J. F., Chen, H. Y., Zhang, Z. Z., Qi, H. N., Zhang, Y., Liu, J., Chen, X. L., & Wang, A. X. (2021). The alpha-subunit of the chloroplast ATP synthase of tomato reinforces resistance to gray mold and broad-spectrum resistance in transgenic tobacco. Phytopathology, 111, 485–495.

    Article  CAS  PubMed  Google Scholar 

  67. Schmelz, E. A., LeClere, S., Carroll, M. J., Alborn, H. T., & Teal, P. E. (2007). Cowpea chloroplastic ATP synthase is the source of multiple plant defense elicitors during insect herbivory. Plant physiology, 144, 793–805.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Farahani, A. S., & Taghavi, S. (2015). Expression profiling of malate dehydrogenase, superoxide dismutase and polygalacturonase-inhibiting protein in common bean in response to host and non-host pathogens. Journal of Plant Pathology, 97, 491.

    Google Scholar 

  69. Guo, Y., Song, Y., Zheng, H., Zhang, Y., Guo, J., & Sui, N. (2018). NADP-malate dehydrogenase of sweet sorghum improves salt tolerance of Arabidopsis thaliana. Journal of Agriculture and Food Chemistry, 66, 5992–6002.

    Article  CAS  Google Scholar 

  70. Kandoi, D., Mohanty, S., & Tripathy, B. C. (2018). Overexpression of plastidic maize NADP-malate dehydrogenase (ZmNADP-MDH) in Arabidopsis thaliana confers tolerance to salt stress. Protoplasma, 255, 547–563.

    Article  CAS  PubMed  Google Scholar 

  71. Ahn, H. K., Yoon, J. T., Choi, I., Kim, S., Lee, H. S., & Pai, H. S. (2019). Functional characterization of chaperonin containing T-complex polypeptide-1 and its conserved and novel substrates in Arabidopsis. Journal of Experimental Botany, 70, 2741–2757.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Srivastava, D., Shamim, M., Kumar, M., Mishra, A., Maurya, R., Sharma, D., Pandey, P., & Singh, K. (2019). Role of circadian rhythm in plant system: An update from development to stress response. Environmental Experimental Botany, 162, 256–271.

    Article  Google Scholar 

  73. Tiwari, S., Rahul, S. N., Sehrawat, A., & Rawat, B. (2020). Circadian redox rhythms play an important role in plant-pathogen interaction. Plant microbiome paradigm (pp. 147–162). Springer.

    Chapter  Google Scholar 

  74. Grundy, J., Stoker, C., & Carre, I. A. (2015). Circadian regulation of abiotic stress tolerance in plants. Frontiers in Plant Science, 6, 648.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Bhattacharya, A., Khanale, V., & Char, B. (2017). Plant circadian rhythm in stress signaling. Indian Journal of Plant Physiology, 22, 147–155.

    Article  CAS  Google Scholar 

  76. Hildebrandt, T., Knuesting, J., Berndt, C., Morgan, B., & Scheibe, R. (2015). Cytosolic thiol switches regulating basic cellular functions: GAPDH as an information hub? Biological Chemistry, 396, 523–537.

    Article  CAS  PubMed  Google Scholar 

  77. Kim, S. C., Guo, L., & Wang, X. (2020). Nuclear moonlighting of cytosolic glyceraldehyde-3-phosphate dehydrogenase regulates Arabidopsis response to heat stress. Nature Communications, 11, 3439.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Zeng, L., Deng, R., Guo, Z., Yang, S., & Deng, X. (2016). Genome-wide identification and characterization of glyceraldehyde-3-phosphate dehydrogenase genes family in wheat (Triticum aestivum). BMC Genomics, 17, 240.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Li, X., Wei, W., Li, F., Zhang, L., Deng, X., Liu, Y., & Yang, S. (2019). The plastidial glyceraldehyde-3-phosphate dehydrogenase is critical for abiotic stress response in wheat. International Journal of Molecular Sciences, 20, 1104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Hashimoto, M., Neriya, Y., Yamaji, Y., & Namba, S. (2016). Recessive resistance to plant viruses: Potential resistance genes beyond translation initiation factors. Frontiers in Microbiology, 7, 1695.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Moin, M., Bakshi, A., Saha, A., Dutta, M., Madhav, S. M., & Kirti, P. (2016). Rice ribosomal protein large subunit genes and their spatio-temporal and stress regulation. Frontiers in Plant Science, 7, 1284.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Cheng, Z., Dong, K., Ge, P., Bian, Y., Dong, L., Deng, X., Li, X., & Yan, Y. (2015). Identification of leaf proteins differentially accumulated between wheat cultivars distinct in their levels of drought tolerance. PLoS ONE, 10, e0125302.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Gietler, M., Nykiel, M., Orzechowski, S., Fettke, J., & Zagdanska, B. (2016). Proteomic analysis of S-nitrosylated and S-glutathionylated proteins in wheat seedlings with different dehydration tolerances. Plant Physiology and Biochemistry, 108, 507–518.

    Article  CAS  PubMed  Google Scholar 

  84. Zang, X., & Komatsu, S. (2007). A proteomics approach for identifying osmotic-stress-related proteins in rice. Phytochemistry, 68, 426–437.

    Article  CAS  PubMed  Google Scholar 

  85. Begum, Y., & Mondal, S. K. (2020). Comprehensive study of the genes involved in chlorophyll synthesis and degradation pathways in some monocot and dicot plant species. Journal of Biomolecular Structure Dynamics, 39, 1–28.

    Google Scholar 

  86. Komatsu, S., Kamal, A. H., & Hossain, Z. (2014). Wheat proteomics: Proteome modulation and abiotic stress acclimation. Frontiers in Plant Science, 5, 684.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Li, X.-W., Zhu, Y.-L., Chen, C.-Y., Geng, Z.-J., Li, X.-Y., Ye, T.-T., Mao, X.-N., & Du, F. (2020). Cloning and characterization of two chlorophyll A/B binding protein genes and analysis of their gene family in Camellia sinensis. Scientific Reports, 10, 1–9.

    Google Scholar 

  88. Gao, J., Liu, Z., Zhao, B., Liu, P., & Zhang, J.-W. (2020). Physiological and comparative proteomic analysis provides new insights into the effects of shade stress in maize (Zea mays L.). BMC Plant Biology, 20, 60.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Xu, Y. H., Liu, R., Yan, L., Liu, Z. Q., Jiang, S. C., Shen, Y. Y., Wang, X. F., & Zhang, D. P. (2012). Light-harvesting chlorophyll a/b-binding proteins are required for stomatal response to abscisic acid in Arabidopsis. Journal of Experimental Botany, 63, 1095–1106.

    Article  PubMed  Google Scholar 

  90. Liu, R., Xu, Y.-H., Jiang, S.-C., Lu, K., Lu, Y.-F., Feng, X.-J., Wu, Z., Liang, S., Yu, Y.-T., & Wang, X.-F. (2013). Light-harvesting chlorophyll a/b-binding proteins, positively involved in abscisic acid signalling, require a transcription repressor, WRKY40, to balance their function. Journal of Experimental Botany, 64, 5443–5456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Li, P., Liu, W., Zhang, Y., Xing, J., Li, J., Feng, J., Su, X., & Zhao, J. (2019). Fungal canker pathogens trigger carbon starvation by inhibiting carbon metabolism in poplar stems. Science and Reports, 9, 10111.

    Article  Google Scholar 

  92. Ye, C., Zhou, Q., Wu, X., Ji, G., & Li, Q. Q. (2019). Genome-wide alternative polyadenylation dynamics in response to biotic and abiotic stresses in rice. Ecotoxicology and Environmental Safety, 183, 109485.

    Article  CAS  PubMed  Google Scholar 

  93. Pitino, M., Armstrong, C. M., & Duan, Y. (2017). Molecular mechanisms behind the accumulation of ATP and H2O2 in citrus plants in response to ‘Candidatus Liberibacter asiaticus’ infection. Horticulture Research. https://doi.org/10.1038/hortres.2017.40

    Article  PubMed  PubMed Central  Google Scholar 

  94. Casassola, A., Brammer, S. P., Chaves, M. S., Martinelli, J. A., Stefanato, F., & Boyd, L. A. (2015). Changes in gene expression profiles as they relate to the adult plant leaf rust resistance in the wheat cv. Toropi. Physiological and Molecular Plant Pathology, 89, 49–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Laxalt, A. M., Cassia, R. O., Sanllorenti, P. M., Madrid, E. A., Andreu, A. B., Daleo, G. R., Conde, R. D., & Lamattina, L. (1996). Accumulation of cytosolic glyceraldehyde-3-phosphate dehydrogenase RNA under biological stress conditions and elicitor treatments in potato. Plant Molecular Biology, 30, 961–972.

    Article  CAS  PubMed  Google Scholar 

  96. Berger, S., Sinha, A. K., & Roitsch, T. (2007). Plant physiology meets phytopathology: Plant primary metabolism and plant–pathogen interactions. Journal of Experimental Botany, 58, 4019–4026.

    Article  CAS  PubMed  Google Scholar 

  97. Jones, J. D., & Dangl, J. L. (2006). The plant immune system. Nature, 444, 323–329.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nasser Zare.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nemati, M., Zare, N., Hedayat-Evrigh, N. et al. Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis. Mol Biotechnol 65, 453–465 (2023). https://doi.org/10.1007/s12033-022-00541-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12033-022-00541-w

Keywords

Navigation