Unlocking the Genetic Basis of Abiotic Stress Tolerance in Wheat: In- sights from Differential Expression Analysis and Machine Learning

Saifeldeen M. Ibrahim, Manar S. Ibrahim, Radwa Khaled, Ahmed Fawzy Elkot, Shafik D. Ibrahim

Abstract


Abiotic stresses such as heat and cold temperatures, salinity, and drought are threatening global food security by affecting crop quality and reproductivity. Wheat is the most essential staple crop in the world, its complex genome is the main barrier to finding valuable genes responsive to different stresses. Thus, in our study we conducted differential RNA-seq analysis to identify Differentially Expressed Genes (DEGs) involved in 4 different stresses such as drought, heat, freeze resistance, and water-deficit stress, then applied two machine learning models; the "Extra-tree regressor" and LIME algorithms to accurately predict and select the highly significant genes. Our findings identified a set of 36 significant genes, many of which play important roles in various molecular functions, cellular components, and biological processes related to the response or resistance to abiotic stress in wheat. For example, Hsp101b is a member of the heat shock protein family, which protects cells against stress by stabilizing proteins. BADH, an enzyme involved in the synthesis of stress hormones, is important for the plant’s response to different stresses. AGL14 is a member of the AGL protein family, which regulates gene expression and is involved in the plant’s response to drought, cold, and salinity stresses. This study demonstrates the prospects of the integration of bioinformatics tools as well as machine learning models to assess the genes responsible for wheat stress resistance, genes’ regulatory networks, and their functions in order to save time and cost to improve wheat productivity.

Keywords


Wheat, Abiotic stress, Differential Gene Expression, Machine Learning

Full Text:

View Full Text

References


FAOSTAT. FAO Stat. Database. 2019. Food and Agriculture Organization of the United Nations Rome, Italy; 2019.

Mourad AM, Alomari DZ, Alqudah AM, Sallam A, Salem KF. Recent advances in wheat (Triticum spp.) breeding. Advances in Plant Breeding Strategies: Cereals: Volume 5. 2019:559-93.

Poole N, Donovan J, Erenstein O. Agri-nutrition research: revisiting the contribution of maize and wheat to human nutrition and health. Food Policy. 2021;100:101976.

Food, of the United Nations AO. Food Outlook: Biannual report on global food markets. Food and Agriculture Organization of the United Nations. 2021. Accessed December 22, 2021. Available from: http://www.fao.org/3/ca5162en/CA5162EN.pdf.

Golfam R, Kiarostami K, Lohrasebi T, Hasrak S, Razavi K. A review of drought stress on wheat (Triticum aestivum L.) starch. Farming and Management. 2021;6(1):47-57.

Olakanmi SJ, Jayas DS, Paliwal J. Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review. Foods. 2022;11(20):3287.

Halder T, Choudhary M, Liu H, Chen Y, Yan G, Siddique KH. Wheat proteomics for abiotic stress tolerance and root system architecture: current status and future prospects. Proteomes. 2022;10(2):17.

Mahmud AA, Upadhyay SK, Srivastava AK, Bhojiya AA. Biofertilizers: A Nexus between soil fertility and crop productivity under abiotic stress. Current Research in Environmental Sustainability. 2021;3:100063.

El-Esawi MA, Alayafi AA. Overexpression of rice Rab7 gene improves drought and heat tolerance and increases grain yield in rice (Oryza sativa L.). Genes. 2019;10(1):56.

Yaqoob U, Jan N, Raman PV, Siddique KH, John R. Crosstalk between brassinosteroid signaling, ROS signaling and phenylpropanoid pathway during abiotic stress in plants: Does it exist? Plant Stress. 2022:100075.

Rakkammal K, Priya A, Pandian S, Maharajan T, Rathinapriya P, Satish L, et al. Conventional and Omics Approaches for Understanding the Abiotic Stress Response in Cereal CropsAn Updated Overview. Plants. 2022;11(21):2852.

Mashabela MD, Piater LA, Steenkamp PA, Dubery IA, Tugizimana F, Mhlongo MI. Comparative metabolite profiling of wheat cultivars (Triticum aestivum) reveals signatory markers for resistance and susceptibility to stripe rust and aluminium (Al3+) toxicity. Metabolites. 2022;12(2):98.

Kaur B, Sandhu KS, Kamal R, Kaur K, Singh J, Röder MS, et al. Omics for the improvement of abiotic, biotic, and agronomic traits in major cereal crops: applications, challenges, and prospects. Plants. 2021;10(10):1989.

Moumeni A, Satoh K, Kondoh H, Asano T, Hosaka A, Venuprasad R, et al. Comparative analysis of root transcriptome profiles of two pairs of drought-tolerant and susceptible rice near-isogenic lines under different drought stress. BMC plant biology. 2011;11:1-17.

Tran LSP, Nakashima K, Sakuma Y, Osakabe Y, Qin F, Simpson SD, et al. Co-expression of the stress-inducible zinc finger homeodomain ZFHD1 and NAC transcription factors enhances expression of the ERD1 gene in Arabidopsis. The Plant Journal. 2007;49(1):46-63.

Rico-Chávez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, Guevara-González RG, Hernandez-Escobedo Q. Machine learning for plant stress modeling: A perspective towards hormesis management. Plants. 2022;11(7):970.

Zenda T, Liu S, Dong A, Duan H. Advances in cereal crop genomics for resilience under climate change. Life. 2021;11(6):502.

van IJzendoorn DG, Szuhai K, Briaire-de Bruijn IH, Kostine M, Kuijjer ML, Bovée JV. Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS computational biology. 2019;15(2):e1006826.

Hanczar B, Zehraoui F, Issa T, Arles M. Biological interpretation of deep neural network for phenotype prediction based on gene expression. BMC bioinformatics. 2020;21(1):1-18.

Osco LP, Ramos APM, Moriya ÉAS, Bavaresco LG, Lima BCd, Estrabis N, et al. Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks. Remote Sensing. 2019;11(23):2797.

Huang W, Lu J, Ye H, Kong W, Mortimer AH, Shi Y. Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis. International Journal of Agricultural and Biological Engineering. 2018;11(2):145-52.

Priya N, Amuthavalli A. Machine Learning Approaches to Predict the Abiotic and Biotic Stress Tolerance Genes in Plants-A Survey. Machine Learning;7(11):2020.

Sousa ICd, Nascimento M, Silva GN, Nascimento ACC, Cruz CD, Almeida DPd, et al. Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. Scientia Agricola. 2020;78.

N’Diaye A, Byrns B, Cory AT, Nilsen KT, Walkowiak S, Sharpe A, et al. Machine learning analyses of methylation profiles uncovers tissue-specific gene expression patterns in wheat. The Plant Genome. 2020;13(2):e20027.

Thanmalagan RR, Roy A, Jayaprakash A, Lakshmi P. Comprehensive meta-analysis and machine learning approaches identified the role of novel drought specific genes in Oryza sativa. Plant Gene. 2022;32:100382.

Illimoottil M. Analyzing the Differential Expression of OPTN during Herpes Simplex Virus-2 Infection.

Heberle GVdSFRTGPMR H ; Meirelles. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. 2022.

Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research. 2021;49(D1):D605-12.

Rambabu M, Ramakrishna N, Polamarasetty PK. Prediction and Analysis of Household Energy Consumption by Machine Learning Algo- rithms in Energy Management. In: E3S Web of Conferences. vol. 350. EDP Sciences; 2022. p. 02002.

Shi S, Du Y, Fan W. Kernel-based LIME with feature dependency sampling. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE; 2021. p. 9143-8.

Kobayashi F, Takumi S, Kume S, Ishibashi M, Ohno R, Murai K, et al. Regulation by Vrn-1/Fr-1 chromosomal intervals of CBF-mediated Cor/Lea gene expression and freezing tolerance in common wheat. Journal of Experimental Botany. 2005;56(413):887-95.

Sutton F, Chen DG, Ge X, Kenefick D. Cbfgenes of the Fr-A2 allele are differentially regulated between long-term cold acclimated crown tissue of freeze-resistant and–susceptible, winter wheat mutant lines. BMC Plant Biology. 2009;9(1):1-9.

Budak H, Hussain B, Khan Z, Ozturk NZ, Ullah N. From genetics to functional genomics: improvement in drought signaling and tolerance in wheat. Frontiers in plant Science. 2015;6:1012.

Kong Q, Mostafa HH, Yang W, Wang J, Nuerawuti M, Wang Y, et al. Comparative transcriptome profiling reveals that brassinosteroid- mediated lignification plays an important role in garlic adaption to salt stress. Plant Physiology and Biochemistry. 2021;158:34-42.

Kosová K, Urban MO, Vítámvás P, Prášil IT. Plant Abiotic Stress Proteomics: An Insight into Plant Stress Response at Proteome Level. In: Handbook of Plant and Crop Stress, Fourth Edition. CRC Press; 2019. p. 207-30.

Tsukuba T, Yamaguchi Y, Kadowaki T. Large Rab GTPases: Novel Membrane Trafficking Regulators with a Calcium Sensor and Functional Domains. International Journal of Molecular Sciences. 2021;22(14):7691.

Khalil MI, Singh V, King J, De Benedetti A. TLK1-mediated MK5-S354 phosphorylation drives prostate cancer cell motility and may signify distinct pathologies. Molecular Oncology. 2022.

Gharechahi J, Sharifi G, Komatsu S, Salekdeh GH. Proteomic analysis of crop plants under low temperature: A review of cold responsive proteins. Agricultural Proteomics Volume 2. 2016:97-127.

Sun H, Hu M, Li J, Chen L, Li M, Zhang S, et al. Comprehensive analysis of NAC transcription factors uncovers their roles during fiber development and stress response in cotton. BMC plant biology. 2018;18(1):1-15.

Garg G, Neha P. Plant transcription factors networking of pyrroline-5-carboxylate (p5c) enzyme under stress condition: A review. Plant Archives. 2019;19(2):562-9.

Biswal AK, Shamim M, Cruzado K, Soriano G, Ghatak A, Toleco M, et al. Role of biotechnology in rice production. In: Rice production worldwide. Springer; 2017. p. 487-547.

Muthusamy SK, Dalal M, Chinnusamy V, Bansal KC. Genome-wide identification and analysis of biotic and abiotic stress regulation of small heat shock protein (HSP20) family genes in bread wheat. Journal of plant physiology. 2017;211:100-13.

Liu W, Yuan JS, Stewart Jr CN. Advanced genetic tools for plant biotechnology. Nature Reviews Genetics. 2013;14(11):781-93.

Chen JH, Tang M, Jin XQ, Li H, Chen LS, Wang QL, et al. Regulation of Calvin–Benson cycle enzymes under high temperature stress. aBIOTECH. 2022:1-13.

Chen Y, Zhang JB, Wei N, Liu ZH, Li Y, Zheng Y, et al. A type-2C protein phosphatase (GhDRP1) participates in cotton (Gossypium hirsutum) response to drought stress. Plant Molecular Biology. 2021;107(6):499-517.

Lyu JI, Ramekar R, Kim JM, Hung NN, Seo JS, Kim JB, et al. Unraveling the complexity of faba bean (Vicia faba L.) transcriptome to reveal cold-stress-responsive genes using long-read isoform sequencing technology. Scientific reports. 2021;11(1):1-13.

Wang Y, Zhang X, Liu Y, Liu C, Gao J. Wheat grain softness protein gene family: Structure, function and regulation. Frontiers in Plant Science. 2019;10:818.

Ozturk M, Turkyilmaz Unal B, García-Caparrós P, Khursheed A, Gul A, Hasanuzzaman M. Osmoregulation and its actions during the drought stress in plants. Physiologia Plantarum. 2021;172(2):1321-35.

Zhou B, Wang J, Lou H, Wang H, Xu Q. Comparative transcriptome analysis of dioecious, unisexual floral development in Ribes diacanthum pall. Gene. 2019;699:43-53.

Tripathy MK, Deswal R, Sopory SK. Plant RABs: role in development and in abiotic and biotic stress responses. Current Genomics. 2021;22(1):26-40.

Li Z, Zhu M, Huang J, Jiang S, Xu S, Zhang Z, et al. Genome-Wide Comprehensive Analysis of the Nitrogen Metabolism Toolbox Reveals Its Evolution and Abiotic Stress Responsiveness in Rice (Oryza sativa L.). International Journal of Molecular Sciences. 2022;24(1):288.

Katiyar-Agarwal S, Agarwal M, Grover A. Heat-tolerant basmati rice engineered by over-expression of hsp101. Plant molecular biology. 2003;51:677-86.

Liu H, Yang L, Xin M, Ma F, Liu J. Gene-wide analysis of aquaporin gene family in Malus domestica and heterologous expression of the gene MpPIP2; 1 confers drought and salinity tolerance in Arabidposis thaliana. International Journal of Molecular Sciences. 2019;20(15):3710.

Zhao L, Li Y, Li Y, Chen W, Yao J, Fang S, et al. Systematical Characterization of the Cotton Di19 Gene Family and the Role of GhDi19-3 and GhDi19-4 as Two Negative Regulators in Response to Salt Stress. Antioxidants. 2022;11(11):2225.




DOI: https://doi.org/10.36462/H.BioSci.202307

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Ibrahim et al.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

...........................................................................................................................................................

Other "Highlights in" Journals

Highlights in Bioinformatics, Highlights in Chemistry, Highlights in Science, Highlights in Microbiology, Highlights in Plant Science

Free counters!


........................................................................................................................................

International Library of Science "HighlightsIn" is an Open Access Scientific Publishers, aiming to science and knowledge support