基于 RNA-seq 的木霉长链非编码 RNA 的生物信息学预测 及其重寄生相关性分析
In: Journal of Nanjing Agricultural University / Nanjuing Nongye Daxue Xuebao, Jg. 42 (2019-09-01), Heft 5, S. 877-886
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[Objectives J This study was to identify the long noncoding R\A(IncRNA) of Trichoderma guizhnuense NJAU 4742(NJAU 4742) in genome-wide and to explore the regulatory mechanism of IncRNA in mycoparasitism. [Methods] The transeriptome of the hypha from independently cultivated NJAU 4742, the hypha from NJAU 4742 before directly contacting with pathogenic fungi and the hypha from the zone of interaction of NJAU 4742 after contacting with pathogenic fungi were sequenced using strand-specific RNA-seq technology. Then a computational pipeline was designed to identify the IncRNA. RSEM and DF.Seq2 were used to analyze the expression levels of IncRNA during mycoparasitism. The regulation role of IncRNA during mycoparasitism was analyzed based on the function of targets and the expression profiles of IncRNA. [Results] A total of 1 676 IncRNA were identified in NJAU 4742, including 1 049 intergenic, 590 antisense, 32 sense and 5 intronic IncRNA. Compared to protein-coding genes, IncRNA had less exons, lower expression levels, shorter sequence length and shorter span distance in genome. The 1 496 IncRNA were predicted to targe! 2 269 protein-coding genes, including 1 492 IncRNA targeting 2 262 protein-coding genes by cis-form and 4 IncRNA targeting 7 protein-coding genes by trans-form. Metabolic process, catalytic activity and cellular process were the three most assigned GO subcategories. Signal transduction, transport and catabolism.and carbohydrate metabolism were the th: ree most assigned pathway categories. Furthermore, 147 IncRNA were predicted to target genes coding carbohydrate-active enzymes (CAZys), proteases and genes involved in the formation of secondary metabolites, which possibly associated with mycoparasitism. In the 147 IncRNA, there were 30 significantly differently expressed IncRNA during mycoparasitism. The significant correlations in the expression profiles were observed between 10 IncRNA and corresponding targets. [Conclusions] : I-ong-chain non-coding RNA exists in Trichoderma sp. NJAU 4742, and some members participate in the regulation of pathogenic bacterial parasitism. [ABSTRACT FROM AUTHOR]
[目的]本研究旨在全基因组范围内识别木霉( Trichoderma guizhouense) NJAU 4742( NJAU 4742) 的长链非编码RNA (long noncoding RNAꎬlncRNA)ꎬ探究lncRNA 在重寄生过程中可能参与的调控作用ꎮ [方法] 用链特异性RNA ̄seq 技术ꎬ对 重寄生过程中与病原菌互作接触前、后以及独立培养的木霉菌进行转录组测序ꎻ构建生物信息学流程ꎬ识别lncRNA 并用 RSEM 和DESeq2 软件分析lncRNA 在重寄生过程中的表达情况ꎻ对lncRNA 的靶标预测和表达情况进行分析ꎬ探索lncRNA 在重寄生过程中可能参与的调控作用ꎮ [结果]在木霉NJAU 4742 中识别了1 676 个lncRNAꎬ其中包含1 049 个基因间型 lncRNAꎬ590 个反义型lncRNAꎬ32 个正义型lncRNA 以及5 个内含子型lncRNAꎮ 与编码基因相比ꎬlncRNA 的外显子数量偏 少ꎬ序列长度偏短ꎬ表达量偏低ꎬ在基因组上的跨度偏短ꎮ 靶标预测结果显示:1 496 个lncRNA 能够靶向2 269 个蛋白编码 基因ꎬ其中1 492 个lncRNA 以顺式作用形式靶向2 262 个编码基因ꎬ4 个lncRNA 以反式作用形式靶向7 个编码基因ꎮ GO 功能分类结果显示:代谢过程(metabolic process)、催化活性(catalytic activity)和细胞过程(cellular process)是lncRNA 靶 标分布数量较多的3 个类别ꎮ KEGG 通路分析结果显示:信号转导( signal transduction)、转运与分解代谢( transport and catabolism)和碳水化合物代谢(carbohydrate metabolism)是lncRNA 靶标分布数量较多的3 类通路ꎮ 进一步分析发现:147 个 lncRNA 靶向编码碳水化合物活性酶和蛋白酶的基因以及次生代谢物合成相关的基因ꎬ其中30 个lncRNA 在重寄生过程中 表达水平发生显著变化ꎬ有10 个lncRNA 的表达和靶标基因显著相关ꎮ [ 结论] 木霉在NJAU 4742 中存在长链非编码 RNAꎬ部分成员参与对病原菌重寄生过程的调控ꎮ. [ABSTRACT FROM AUTHOR]
Titel: |
基于 RNA-seq 的木霉长链非编码 RNA 的生物信息学预测 及其重寄生相关性分析
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Autor/in / Beteiligte Person: | 刘金定 ; 王权 ; 黄水清 ; 沈其荣 |
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Zeitschrift: | Journal of Nanjing Agricultural University / Nanjuing Nongye Daxue Xuebao, Jg. 42 (2019-09-01), Heft 5, S. 877-886 |
Veröffentlichung: | 2019 |
Medientyp: | academicJournal |
ISSN: | 1000-2030 (print) |
DOI: | 10.7685/jnau.201812054 |
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