生物技术通报 ›› 2025, Vol. 41 ›› Issue (12): 177-189.doi: 10.13560/j.cnki.biotech.bull.1985.2025-0451

• 研究报告 • 上一篇    下一篇

利用WGCNA 筛选鉴定花花柴耐盐核心基因

许聪聪1,2(), 郑美1, 李翠1, 赵春桥1, 何玮2, 侯新村1(), 郭强1()   

  1. 1.北京市农林科学院草业花卉与景观生态研究所,北京 100097
    2.西北大学生命科学院,西安 710069
  • 收稿日期:2025-05-01 出版日期:2025-12-26 发布日期:2026-01-06
  • 通讯作者: 侯新村,男,副研究员,研究方向 :饲草抗逆遗传育种;E-mail: houxincun@baafs.net.cn
    郭强,男,研究员,研究方向 :饲草抗逆遗传育种;E-mail: guoqiang@baafs.net.cn
  • 作者简介:许聪聪,女,硕士研究生,研究方向 :植物逆境生理与生物育种;E-mail: 202332680@stumail.nwu.edu.cn
  • 基金资助:
    国家自然科学基金项目(32371758);北京市农林科学院科技创新能力建设专项(KJCX20251303);北京市农林科学院科技创新能力建设专项(KJCX20240409)

Screening and Identification of Salt-tolerant Hub Genes in Karelinia caspia Using WGCNA

XU Cong-cong1,2(), ZHENG Mei1, LI Cui1, ZHAO Chun-qiao1, HE Wei2, HOU Xin-cun1(), GUO Qiang1()   

  1. 1.Institute of Grassland, Flowers, and Ecology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097
    2.Northwest University, College of Life Sciences, Xi’an 710069
  • Received:2025-05-01 Published:2025-12-26 Online:2026-01-06

摘要:

目的 通过分析盐处理下花花柴转录组数据,挖掘与之耐盐性相关的特异性基因模块,旨在为深入理解花花柴耐盐适应性分子机制提供理论依据。 方法 以不同盐浓度处理的花花柴叶片为研究对象,基于转录组和加权基因共表达网络分析(Weighted Gene Co-expression Network Analysis, WGCNA)鉴定与其耐盐相关的特异性基因模块,以此筛选关键基因。 结果 转录组测序产生了103.66 GB的数据,组装得到66 823个Unigene。其中,45.15%(30 171个)在6个公共蛋白质数据库中获得10 243个差异表达基因(DEGs)注释。同时,通过WGCNA分析,从其显著上调的DEGs中识别出10个共表达模块。值得注意的是,Brown模块、Pink模块、Yellow模块分别与400、300、200 mmol/L NaCl处理呈正相关。进一步,对这些特异性模块内的基因进行KEGG分析,发现它们主要在次生代谢物合成、植物激素信号转导和MAPK信号通路等生物学过程中显著富集。因此,基于模块内基因的连接度和注释信息,筛选出PILS6、REM4.1、DOF21、MAPKKK18、GATA8-like、SAUR76、ABH、CIPK6、DIR22、4CL2等核心基因。由此表明,这些基因可能在参与花花柴耐盐适应性中起重要的作用。 结论 通过转录组和WGCNA分析,筛选出与花花柴耐盐相关的核心基因和特异性模块。为花花柴耐盐基因资源挖掘与利用奠定基础。

关键词: 花花柴, 耐盐性, 转录组测序, 加权基因共表达网络分析, 核心基因

Abstract:

Objective Salt-tolerance-specific gene modules were explored by investigating RNA-seq, which elucidated the molecular mechanisms underlying salt adaptation in Karelinia caspia. Method Leaves of K. caspia subjected to different NaCl concentrations (200, 300, and 400 mmol/L) were analyzed using RNA-seq and weighted gene co-expression network analysis (WGCNA). Key salt-responsive modules and genes were identified by functional annotation and network topology. Result RNA-seq generated 103.66 GB of data, yielding 66 823 unigenes, of which 45.15% (30 171) were annotated across six public protein databases. A total of 10 243 differentially expressed genes (DEGs) were identified, with 10 co-expression modules extracted via WGCNA. Notably, the brown, pink, and yellow modules demonstrated strong positive correlations with the 400, 300, and 200 mmol/L NaCl treatments, respectively. KEGG enrichment analysis revealed that genes within these modules were significantly involved in secondary metabolite biosynthesis, plant hormone signal transduction, and MAPK signaling pathways. Hub genes, such as PILS6, REM4.1, DOF21, MAPKKK18, GATA8-like, SAUR76, ABH, CIPK6, DIR22, and 4CL2, were screened based on connectivity and functional annotation. These suggested that they might play a pivotal role in adaptation of K. caspia to salt. Conclusion This study identified key salt-tolerance-specific gene modules and hub genes in K. caspia via integrated transcriptome and WGCNA analyses, thereby providing a theoretical foundation for exploration and utilization of salt-tolerant genetic resources in K. caspia.

Key words: Karelinia caspia, tolerance to salt, RNA-Seq, weighted gene co-expression network analysis, hub gene