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CelEst:用于估计秀丽隐杆线虫中转录因子活性的统一基因调控网络。

CelEst: a unified gene regulatory network for estimating transcription factor activities in C. elegans.

作者信息

Perez Marcos Francisco

机构信息

Instituto de Biología Molecular de Barcelona (IBMB), CSIC, Parc Científic de Barcelona, C. Baldiri Reixac, 4-8, 08028 Barcelona, Spain.

出版信息

Genetics. 2025 Mar 17;229(3). doi: 10.1093/genetics/iyae189.

Abstract

Transcription factors (TFs) play a pivotal role in orchestrating critical intricate patterns of gene regulation. Although gene expression is complex, differential expression of hundreds of genes is often due to regulation by just a handful of TFs. Despite extensive efforts to elucidate TF-target regulatory relationships in Caenorhabditis elegans, existing experimental datasets cover distinct subsets of TFs and leave data integration challenging. Here, I introduce CelEst, a unified gene regulatory network designed to estimate the activity of 487 distinct C. elegans TFs-∼58% of the total-from gene expression data. To integrate data from ChIP-seq, DNA-binding motifs, and eY1H screens, optimal processing of each data type was benchmarked against a set of TF perturbation RNA-seq experiments. Moreover, I showcase how leveraging TF motif conservation in target promoters across genomes of related species can distinguish highly informative interactions, a strategy which can be applied to many model organisms. Integrated analyses of data from commonly studied conditions including heat shock, bacterial infection, and sex differences validates CelEst's performance and highlights overlooked TFs that likely play major roles in coordinating the transcriptional response to these conditions. CelEst can infer TF activity on a standard laptop computer within minutes. Furthermore, an R Shiny app with a step-by-step guide is provided for the community to perform rapid analysis with minimal coding required. I anticipate that widespread adoption of CelEsT will significantly enhance the interpretive power of transcriptomic experiments, both present and retrospective, thereby advancing our understanding of gene regulation in C. elegans and beyond.

摘要

转录因子(TFs)在协调基因调控的关键复杂模式中起着核心作用。尽管基因表达很复杂,但数百个基因的差异表达往往仅由少数几个转录因子调控所致。尽管人们为阐明秀丽隐杆线虫中转录因子与靶标的调控关系付出了巨大努力,但现有的实验数据集涵盖了不同的转录因子子集,使得数据整合颇具挑战性。在此,我介绍了CelEst,这是一个统一的基因调控网络,旨在从基因表达数据中估计487种不同的秀丽隐杆线虫转录因子的活性——约占总数的58%。为了整合来自ChIP-seq、DNA结合基序和eY1H筛选的数据,针对一组转录因子扰动RNA-seq实验对每种数据类型的最佳处理进行了基准测试。此外,我展示了如何利用相关物种基因组中靶标启动子中转录因子基序的保守性来区分高信息量的相互作用,这一策略可应用于许多模式生物。对热休克、细菌感染和性别差异等常见研究条件下的数据进行综合分析,验证了CelEst的性能,并突出了那些可能在协调对这些条件的转录反应中起主要作用但被忽视的转录因子。CelEst可以在标准笔记本电脑上几分钟内推断出转录因子的活性。此外,还为社区提供了一个带有逐步指南的R Shiny应用程序,以便在所需编码最少的情况下进行快速分析。我预计,CelEsT的广泛应用将显著增强当前和回顾性转录组实验的解释力,从而推动我们对秀丽隐杆线虫及其他生物中基因调控的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49dc/11912867/b2d16944a2e6/iyae189f1.jpg

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