• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

表达模式决定调控逻辑。

Expression pattern determines regulatory logic.

机构信息

Evo-devo Helsinki community, Centre of Excellence in Experimental and Computational Developmental Biology, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.

出版信息

PLoS One. 2021 Jan 4;16(1):e0244864. doi: 10.1371/journal.pone.0244864. eCollection 2021.

DOI:10.1371/journal.pone.0244864
PMID:33395445
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7781484/
Abstract

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

摘要

大量的研究工作致力于理解单个基因组如何能够指定数百种细胞类型的身份。受秀丽隐杆线虫生物学某些方面的启发,我们实施了一种计算机模拟进化策略,生成了驱动细胞特异性基因表达模式的基因调控网络(GRNs),模拟了终末细胞分化的过程。基因调控网络的动态受基因表达的热力学模型控制,该模型将 DNA 序列和转录因子简并位置权重矩阵作为输入。在模型的一个版本中,我们纳入了染色质可及性。实验已经确定,细胞特异性和广泛表达的基因受到不同的调控。在我们的计算机模拟进化的 GRNs 中,广泛表达的基因受到非常冗余的调控,其顺式调控模块的结构也不同,这与秀丽隐杆线虫以及其他系统的发现一致。最后,我们发现这两类基因在 GRNs 中的拓扑位置存在差异,这有助于解释为什么广泛表达的基因对突变如此具有弹性。总的来说,我们的结果提供了一个关于为什么与细胞特异性基因相比,广泛表达的基因受到如此冗余调控的解释性假设,可以将其推广到 ChIP-seq HOT 区域等现象。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/16e9d11babb9/pone.0244864.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/7239320c9cb0/pone.0244864.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/06166aed38f4/pone.0244864.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/c240f664c97f/pone.0244864.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/8fd885de33bc/pone.0244864.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/cbd0ba71f664/pone.0244864.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/16e9d11babb9/pone.0244864.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/7239320c9cb0/pone.0244864.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/06166aed38f4/pone.0244864.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/c240f664c97f/pone.0244864.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/8fd885de33bc/pone.0244864.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/cbd0ba71f664/pone.0244864.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/99e7/7781484/16e9d11babb9/pone.0244864.g006.jpg

相似文献

1
Expression pattern determines regulatory logic.表达模式决定调控逻辑。
PLoS One. 2021 Jan 4;16(1):e0244864. doi: 10.1371/journal.pone.0244864. eCollection 2021.
2
Genomics: Hiding in plain sight.基因组学:隐匿于众目睽睽之下。
Nature. 2014 Aug 28;512(7515):374-5. doi: 10.1038/512374a.
3
Expression pattern analysis of regulatory transcription factors in Caenorhabditis elegans.秀丽隐杆线虫中调控转录因子的表达模式分析
Methods Mol Biol. 2012;786:21-50. doi: 10.1007/978-1-61779-292-2_2.
4
The comprehensive transcriptional analysis in Caenorhabditis elegans by integrating ChIP-seq and gene expression data.通过整合染色质免疫沉淀测序(ChIP-seq)和基因表达数据对线虫进行全面转录分析。
Genet Res (Camb). 2014;96:e005. doi: 10.1017/S0016672314000081.
5
Applying attractor dynamics to infer gene regulatory interactions involved in cellular differentiation.应用吸引子动力学来推断细胞分化过程中涉及的基因调控相互作用。
Biosystems. 2017 May;155:29-41. doi: 10.1016/j.biosystems.2016.12.004. Epub 2017 Feb 28.
6
Integrative analysis of C. elegans modENCODE ChIP-seq data sets to infer gene regulatory interactions.综合分析秀丽隐杆线虫 modENCODE ChIP-seq 数据集以推断基因调控相互作用。
Genome Res. 2013 Jun;23(6):941-53. doi: 10.1101/gr.152876.112. Epub 2013 Mar 26.
7
Gene-centered regulatory networks.基因中心调控网络。
Brief Funct Genomics. 2010 Jan;9(1):4-12. doi: 10.1093/bfgp/elp049. Epub 2009 Dec 13.
8
Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.网络基元模块在蠕虫和植物综合基因调控网络中的功能、动态和进化。
Nucleic Acids Res. 2018 Jul 27;46(13):6480-6503. doi: 10.1093/nar/gky468.
9
Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network.核激素受体在秀丽隐杆线虫代谢基因调控网络中的功能模块化。
Mol Syst Biol. 2010 May 11;6:367. doi: 10.1038/msb.2010.23.
10
Transcription factor redundancy and tissue-specific regulation: evidence from functional and physical network connectivity.转录因子冗余和组织特异性调节:来自功能和物理网络连通性的证据。
Genome Res. 2012 Oct;22(10):1907-19. doi: 10.1101/gr.133306.111. Epub 2012 Jun 22.

引用本文的文献

1
Range of chromatin accessibility configurations are permissive of GABAergic fate acquisition in developing mouse brain.在发育中的小鼠大脑中,染色质可及性构象的范围允许 GABA 能命运的获得。
BMC Genomics. 2023 Nov 30;24(1):725. doi: 10.1186/s12864-023-09836-x.
2
Early effects of gene duplication on the robustness and phenotypic variability of gene regulatory networks.基因复制对基因调控网络鲁棒性和表型可变性的早期影响。
BMC Bioinformatics. 2022 Nov 28;23(1):509. doi: 10.1186/s12859-022-05067-1.
3
Cell types as species: Exploring a metaphor.

本文引用的文献

1
Unique homeobox codes delineate all the neuron classes of C. elegans.独特的同源盒编码划定了秀丽隐杆线虫所有神经元的类型。
Nature. 2020 Aug;584(7822):595-601. doi: 10.1038/s41586-020-2618-9. Epub 2020 Aug 19.
2
Modular Organization of -regulatory Control Information of Neurotransmitter Pathway Genes in .神经递质通路基因的 -regulatory 控制信息的模块化组织。
Genetics. 2020 Jul;215(3):665-681. doi: 10.1534/genetics.120.303206. Epub 2020 May 22.
3
A lineage-resolved molecular atlas of embryogenesis at single-cell resolution.单细胞分辨率解析胚胎发生的谱系分辨分子图谱。
作为物种的细胞类型:探索一种隐喻。
Front Plant Sci. 2022 Aug 22;13:868565. doi: 10.3389/fpls.2022.868565. eCollection 2022.
Science. 2019 Sep 20;365(6459). doi: 10.1126/science.aax1971. Epub 2019 Sep 5.
4
Transcription factor autoregulation is required for acquisition and maintenance of neuronal identity.转录因子的自身调控对于神经元的身份获得和维持是必需的。
Development. 2019 Jun 21;146(13):dev177378. doi: 10.1242/dev.177378.
5
Phenotypic Convergence: Distinct Transcription Factors Regulate Common Terminal Features.表型趋同:不同转录因子调控共同的终末特征。
Cell. 2018 Jul 26;174(3):622-635.e13. doi: 10.1016/j.cell.2018.05.021. Epub 2018 Jun 18.
6
Cnidarian Cell Type Diversity and Regulation Revealed by Whole-Organism Single-Cell RNA-Seq.刺胞动物整体组织单细胞 RNA-Seq 揭示的细胞类型多样性和调控机制。
Cell. 2018 May 31;173(6):1520-1534.e20. doi: 10.1016/j.cell.2018.05.019.
7
A transcription factor collective defines the HSN serotonergic neuron regulatory landscape.转录因子集体定义了 HSN 血清素能神经元的调控景观。
Elife. 2018 Mar 22;7:e32785. doi: 10.7554/eLife.32785.
8
Comprehensive single-cell transcriptional profiling of a multicellular organism.多细胞生物的全面单细胞转录谱分析。
Science. 2017 Aug 18;357(6352):661-667. doi: 10.1126/science.aam8940.
9
Coordinated control of terminal differentiation and restriction of cellular plasticity.终末分化的协调控制与细胞可塑性的限制
Elife. 2017 Apr 19;6:e24100. doi: 10.7554/eLife.24100.
10
Single-cell mRNA quantification and differential analysis with Census.使用Census进行单细胞mRNA定量和差异分析。
Nat Methods. 2017 Mar;14(3):309-315. doi: 10.1038/nmeth.4150. Epub 2017 Jan 23.