Regulation of regeneration in Arabidopsis thaliana

IF 4.6 4区 农林科学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Md Khairul Islam, Sai Teja Mummadi, Sanzhen Liu, Hairong Wei
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Abstract

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs—WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1—were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

拟南芥的再生调节。
我们采用了几种高效算法来分析公共转录组数据,目的是找出调控拟南芥再生的关键转录因子(TFs)。最初,我们利用 CollaborativeNet(又称 TF-Cluster)构建了所有 TFs 的协作网络,随后利用 Triple-Link 和 Compound Spring Embedder(CoSE)算法将其分解为许多子网络。通过对这些子网络进行功能分析,确定了九个与再生密切相关的子网络。我们进一步应用主成分分析和基因本体(GO)富集分析,将子网络从九个减少到三个,即子网络 1、12 和 17。搜索这三个子网络中所有 TF 的共表达和共调控(CCGs)基因启动子中的 TF 结合位点,并对这三个子网络中的 TF 与参与再生的 CCGs 进行三基因互作分析,从而对每个子网络中的 TF 进行排序。最后,我们确定了子网络 1 中的六个潜在候选 TF--WOX9A、LEC2、PGA37、WIP5、PEI1 和 AIL1,并讨论了它们在体细胞胚胎发生(GO:0010262)和再生(GO:0031099)中的作用,以及子网络 12 和 17 中与再生相关的 TF。我们还利用 CIS-BP 数据库和表达图谱对所发现的 TFs 进行了评估。我们的分析提示了一些可能在再生和胚胎发生过程中具有调控作用的新型 TFs,并为了解与再生相关的调控机制提供了宝贵的数据和见解。这里使用的工具和程序有助于分析高通量转录组数据,推进我们对各种相关生物过程调控的理解:在线版本包含补充材料,可查阅 10.1007/s42994-023-00121-9。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.70
自引率
2.80%
发文量
0
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