Identification of Crucial Genes and Regulatory Pathways in Alfalfa against Fusarium Root Rot.
Shengze WangHaibin HanBo ZhangLe WangJie WuZhengqiang ChenKejian LinJianjun J HaoRuifang JiaYuan-Yuan ZhangPublished in: Plants (Basel, Switzerland) (2023)
Fusarium root rot, caused by Fusarium spp. in alfalfa ( Medicago sativa L.), adversely impacts alfalfa by diminishing plant quality and yield, resulting in substantial losses within the industry. The most effective strategy for controlling alfalfa Fusarium root rot is planting disease-resistant varieties. Therefore, gaining a comprehensive understanding of the mechanisms underlying alfalfa's resistance to Fusarium root rot is imperative. In this study, we observed the infection process on alfalfa seedling roots infected by Fusarium acuminatum strain HM29-05, which is labeled with green fluorescent protein (GFP). Two alfalfa varieties, namely, the resistant 'Kangsai' and the susceptible 'Zhongmu No. 1', were examined to assess various physiological and biochemical activities at 0, 2, and 3 days post inoculation (dpi). Transcriptome sequencing of the inoculated resistant and susceptible alfalfa varieties were conducted, and the potential functions and signaling pathways of differentially expressed genes (DEGs) were analyzed through gene ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Meanwhile, a DEG co-expression network was constructed though the weighted gene correlation network analysis (WGCNA) algorithm. Our results revealed significant alterations in soluble sugar, soluble protein, and malondialdehyde (MDA) contents in both the 'Kangsai' and 'Zhongmu No. 1' varieties following the inoculation of F. acuminatum . WGCNA analysis showed the involvement of various enzyme and transcription factor families related to plant growth and disease resistance, including cytochrome P450, MYB, ERF, NAC, and bZIP. These findings not only provided valuable data for further verification of gene functions but also served as a reference for the deeper explorations between plants and pathogens.
Keyphrases
- transcription factor
- genome wide identification
- genome wide
- network analysis
- dna binding
- machine learning
- genome wide analysis
- single cell
- copy number
- bioinformatics analysis
- dna methylation
- plant growth
- signaling pathway
- deep learning
- poor prognosis
- binding protein
- gene expression
- magnetic resonance imaging
- oxidative stress
- long non coding rna
- big data
- risk assessment
- cell proliferation
- computed tomography