Identification of gene expression signature for drought stress response in barley (Hordeum vulgare L.) using machine learning approach

IF 5.4 Q1 PLANT SCIENCES Current Plant Biology Pub Date : 2024-07-02 DOI:10.1016/j.cpb.2024.100370
Bahman Panahi, Saber Golkari
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Abstract

Barley (Hordeum vulgare L.) is an important cereal crop, playing a pivotal role in global agriculture and food systems. Drought has a significant impact on barley growth and yield productivity. In the current study, core drought stress responsive genes were investigated using an integrative approach. First, we determined the core differentially expressed genes (DEGs) in multiple RNA-seq experiments using a p-value combination approach. Then, machine learning approaches including four weighting algorithms were harnessed for prioritization and determination of signature genes. Moreover, predictive models were optimized using tree induction and naive Bayes algorithms. Finally, the functional importance of the core DEGs and signature genes and pathways were dissected using gene ontology, KEGG enrichment, and protein-protein interaction network analysis. Results showed that the core DEGs participate in carbon metabolism, biosynthesis of secondary metabolites, glyoxylate and dicarboxylate metabolism, carbon fixation, biosynthesis and degradation of amino acids, glycolysis/gluconeogenesis, pyruvate metabolism, starch and sucrose metabolism, glycerolipid metabolism, beta-alanine metabolism, ascorbate and aldarate metabolism, taurine and hypotaurine metabolism. Notably, the C4.5 algorithm, boasting a remarkable 100 % accuracy, pinpointed two genes of particular importance including HORVU.MOREX.R3.1HG0063740, encoding the endo-1, 3–1, 4-beta-D-glucanase, and HORVU.MOREX.R3.1HG0083720, which encodes the bifunctional inhibitor/lipid-transfer protein. This comprehensive analysis contributes significantly to understanding of the core drought responsive genes and pathways. Moreover, these findings lay the groundwork for further research aimed at developing drought-resistant barley varieties and utilizing predictive models in field screening programs.

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利用机器学习方法识别大麦(Hordeum vulgare L.)干旱胁迫响应的基因表达特征
大麦(Hordeum vulgare L.)是一种重要的谷类作物,在全球农业和粮食系统中发挥着举足轻重的作用。干旱对大麦的生长和产量有重大影响。本研究采用综合方法对大麦的核心干旱胁迫响应基因进行了研究。首先,我们使用 p 值组合方法确定了多个 RNA-seq 实验中的核心差异表达基因(DEGs)。然后,利用包括四种加权算法在内的机器学习方法确定特征基因的优先级。此外,还使用树归纳法和天真贝叶斯算法对预测模型进行了优化。最后,利用基因本体论、KEGG富集和蛋白-蛋白相互作用网络分析剖析了核心DEGs、特征基因和通路的功能重要性。结果表明,核心 DEGs 参与了碳代谢、次生代谢物的生物合成、乙醛酸和二羧酸代谢、碳固定、氨基酸的生物合成和降解、糖酵解/糖醛酸生成、丙酮酸代谢、淀粉和蔗糖代谢、甘油脂代谢、β-丙氨酸代谢、抗坏血酸和醛酸代谢、牛磺酸和低牛磺酸代谢。值得注意的是,C4.5 算法的准确率高达 100%,它确定了两个特别重要的基因,包括编码内-1,3-1,4-beta-D-葡聚糖酶的 HORVU.MOREX.R3.1HG0063740,以及编码双功能抑制剂/脂质转移蛋白的 HORVU.MOREX.R3.1HG0083720。这一全面分析极大地促进了对核心干旱响应基因和途径的了解。此外,这些发现还为进一步研究抗旱大麦品种和在田间筛选计划中利用预测模型奠定了基础。
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来源期刊
Current Plant Biology
Current Plant Biology Agricultural and Biological Sciences-Plant Science
CiteScore
10.90
自引率
1.90%
发文量
32
审稿时长
50 days
期刊介绍: Current Plant Biology aims to acknowledge and encourage interdisciplinary research in fundamental plant sciences with scope to address crop improvement, biodiversity, nutrition and human health. It publishes review articles, original research papers, method papers and short articles in plant research fields, such as systems biology, cell biology, genetics, epigenetics, mathematical modeling, signal transduction, plant-microbe interactions, synthetic biology, developmental biology, biochemistry, molecular biology, physiology, biotechnologies, bioinformatics and plant genomic resources.
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