Unveiling functional module associated with fungal disease stress in barley (Hordeum vulgare)

IF 2.2 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Biochemistry and Biophysics Reports Pub Date : 2025-02-20 DOI:10.1016/j.bbrep.2025.101958
Bahman Panahi , Rasmieh Hamid
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Abstract

Fungal infections pose a considerable threat to the cultivation of barley (Hordeum vulgare) and often limit the crop yield. During infection, the transcriptome undergoes extensive reprogramming involving several regulatory pathways. To address this complexity, we performed a comprehensive meta-analysis and co-expression network analysis using rigorously curated RNA-seq datasets from three different fungal diseases. Pre-processing of the data, including batch effect correction, ensured high-quality integration of the datasets. Module-trait relationship (MTR) analysis identified functional modules associated with fungal disease response. Hub genes within these modules were prioritized by multi-model centrality analyses using Cytoscape, which considered the metrics Degree, Closeness, Betweenness and Maximum Clique Centrality together with the MCODE algorithm to detect densely connected subclusters. These hub genes were further validated by cross-validation and receiver operating characteristic (ROC) curve analysis and achieved AUC values greater than 0.7, confirming their robustness. A total of 6688 consistently expressed genes were identified, including 879 upregulated and 701 downregulated genes. Co-expression networks revealed 19 different gene modules, six of which were significantly associated with the response of barley to fungal infection. The blue module in particular was associated with immune responses such as activation of the MAPK cascade and pathogen recognition, while the green module correlated with defence mechanisms and secondary metabolism. The hub genes within these modules showed high predictive power for fungal resistance, as shown by the AUC values of the ROC curve of over 0.7, emphasizing their potential as biomarkers. This study uniquely integrates multiple RNA-seq datasets to identify novel regulatory networks and hub genes, including 345 transcription factors (TFs) from different families, with MYB and bHLH being particularly abundant. The results provide valuable insights into regulatory networks associated with fungal disease response in barley. These results can support genomic selection and marker-assisted breeding programs and accelerate the development of resistant varieties.
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揭示大麦真菌病害胁迫相关功能模块
真菌感染对大麦(Hordeum vulgare)的种植构成相当大的威胁,往往限制作物产量。在感染过程中,转录组经历了涉及多种调控途径的广泛重编程。为了解决这种复杂性,我们使用严格整理的来自三种不同真菌疾病的RNA-seq数据集进行了全面的荟萃分析和共表达网络分析。数据的预处理,包括批量效果校正,确保了数据集的高质量集成。模块-性状关系(MTR)分析确定了与真菌疾病反应相关的功能模块。使用Cytoscape进行多模型中心性分析,对这些模块中的枢纽基因进行优先排序,该分析考虑了度、亲密度、间度和最大团簇中心性等指标,并结合MCODE算法检测紧密连接的子簇。通过交叉验证和受试者工作特征(ROC)曲线分析对这些枢纽基因进行进一步验证,AUC值均大于0.7,证实了其稳健性。共鉴定出6688个一致表达基因,其中上调基因879个,下调基因701个。共表达网络揭示了19个不同的基因模块,其中6个与大麦对真菌感染的反应显著相关。特别是蓝色模块与免疫反应相关,如MAPK级联的激活和病原体识别,而绿色模块与防御机制和次级代谢相关。这些模块内的枢纽基因对真菌抗性具有较高的预测能力,ROC曲线的AUC值超过0.7,强调了它们作为生物标志物的潜力。本研究独特地整合了多个RNA-seq数据集,以鉴定新的调控网络和枢纽基因,包括来自不同家族的345个转录因子(tf),其中MYB和bHLH尤其丰富。这些结果为大麦真菌病害反应的调控网络提供了有价值的见解。这些结果可以支持基因组选择和标记辅助育种计划,并加速抗性品种的开发。
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来源期刊
Biochemistry and Biophysics Reports
Biochemistry and Biophysics Reports Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
4.60
自引率
0.00%
发文量
191
审稿时长
59 days
期刊介绍: Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.
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