• 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

定量绝对基因表达谱揭示了酵母中心碳代谢基因的不同调控。

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast.

机构信息

Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.

Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.

出版信息

Elife. 2021 Mar 15;10:e65722. doi: 10.7554/eLife.65722.

DOI:10.7554/eLife.65722
PMID:33720010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8016476/
Abstract

In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies.

摘要

除了通过特定的调控回路来控制基因的表达外,蛋白质和转录本的丰度也会受到细胞生理状态的影响,如生长速度和代谢。在这里,我们通过分析由 22 种稳定酵母培养物的转录组、蛋白质组和氨基酸的绝对定量丰度组成的多组学数据集,研究了基因表达受生长速度和代谢的控制。我们发现,转录和翻译通过 RNA 聚合酶 II 和核糖体的丰度被细胞生长速度协同控制,但通过氨基酸和核苷酸的可用性被氮代谢独立控制。然而,中心碳代谢基因的调节方式明显不同,它们不响应细胞生长速度或氮代谢,而其他所有基因都响应。了解这些影响可以在基因表达谱研究中考虑到生长速度和代谢的混杂因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/96fbac6662df/elife-65722-fig9-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/74e8fee86d32/elife-65722-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/a50629c47477/elife-65722-fig1-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/484cfb0a713d/elife-65722-fig1-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/76f7f5fa4522/elife-65722-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/d8bdb7576b40/elife-65722-fig2-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/47299beeadd9/elife-65722-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/75384f337b34/elife-65722-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/cd03e75ae29c/elife-65722-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/2e1dbc69c1fd/elife-65722-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/765fc311161a/elife-65722-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/6ded170e5208/elife-65722-fig6-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/cc725450f50c/elife-65722-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/272d3b9389fc/elife-65722-fig7-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/b40301126942/elife-65722-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/acedc3480e80/elife-65722-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/96fbac6662df/elife-65722-fig9-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/74e8fee86d32/elife-65722-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/a50629c47477/elife-65722-fig1-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/484cfb0a713d/elife-65722-fig1-figsupp2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/76f7f5fa4522/elife-65722-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/d8bdb7576b40/elife-65722-fig2-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/47299beeadd9/elife-65722-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/75384f337b34/elife-65722-fig3-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/cd03e75ae29c/elife-65722-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/2e1dbc69c1fd/elife-65722-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/765fc311161a/elife-65722-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/6ded170e5208/elife-65722-fig6-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/cc725450f50c/elife-65722-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/272d3b9389fc/elife-65722-fig7-figsupp1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/b40301126942/elife-65722-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/acedc3480e80/elife-65722-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/93d9/8016476/96fbac6662df/elife-65722-fig9-figsupp1.jpg

相似文献

1
Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast.定量绝对基因表达谱揭示了酵母中心碳代谢基因的不同调控。
Elife. 2021 Mar 15;10:e65722. doi: 10.7554/eLife.65722.
2
Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen.酿酒酵母中响应环境氮变化的稳态和动态基因表达程序
Mol Biol Cell. 2016 Apr 15;27(8):1383-96. doi: 10.1091/mbc.E14-05-1013. Epub 2016 Mar 3.
3
Integrated analysis, transcriptome-lipidome, reveals the effects of INO-level (INO2 and INO4) on lipid metabolism in yeast.整合分析,转录组-脂质组,揭示了INO水平(INO2和INO4)对酵母脂质代谢的影响。
BMC Syst Biol. 2013 Oct 16;7 Suppl 3(Suppl 3):S7. doi: 10.1186/1752-0509-7-S3-S7.
4
Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: a three factor design.酿酒酵母中生长速率依赖性基因的转录因子调控:一种三因素设计
BMC Genomics. 2008 Jul 18;9:341. doi: 10.1186/1471-2164-9-341.
5
Targeted proteome analysis of single-gene deletion strains of Saccharomyces cerevisiae lacking enzymes in the central carbon metabolism.酿酒酵母单基因缺失菌株的靶向蛋白质组分析,这些菌株在中心碳代谢中缺乏酶。
PLoS One. 2017 Feb 27;12(2):e0172742. doi: 10.1371/journal.pone.0172742. eCollection 2017.
6
Quantitative proteomics and transcriptomics of anaerobic and aerobic yeast cultures reveals post-transcriptional regulation of key cellular processes.厌氧和好氧酵母培养物的定量蛋白质组学和转录组学揭示了关键细胞过程的转录后调控。
Microbiology (Reading). 2007 Nov;153(Pt 11):3864-3878. doi: 10.1099/mic.0.2007/009969-0.
7
Carbon- and nitrogen-quality signaling to translation are mediated by distinct GATA-type transcription factors.碳和氮质量信号向翻译的传导由不同的GATA型转录因子介导。
Proc Natl Acad Sci U S A. 2001 Jun 19;98(13):7283-8. doi: 10.1073/pnas.121186898.
8
Determination of the Global Pattern of Gene Expression in Yeast Cells by Intracellular Levels of Guanine Nucleotides.通过细胞内鸟嘌呤核苷酸水平测定酵母细胞中的全球基因表达模式。
mBio. 2019 Jan 22;10(1):e02500-18. doi: 10.1128/mBio.02500-18.
9
Nutrient control of eukaryote cell growth: a systems biology study in yeast.真核细胞生长的营养素控制:酵母的系统生物学研究。
BMC Biol. 2010 May 24;8:68. doi: 10.1186/1741-7007-8-68.
10
Role of nitrogen and carbon transport, regulation, and metabolism genes for Saccharomyces cerevisiae survival in vivo.氮和碳的转运、调控及代谢基因对酿酒酵母体内存活的作用。
Eukaryot Cell. 2006 May;5(5):816-24. doi: 10.1128/EC.5.5.816-824.2006.

引用本文的文献

1
Interaction of genetic variants activates latent metabolic pathways in yeast.基因变异的相互作用激活了酵母中潜在的代谢途径。
Nat Commun. 2025 Aug 27;16(1):8014. doi: 10.1038/s41467-025-63306-4.
2
Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.基于模型推导的代谢表型对代谢物-蛋白质相互作用进行机器学习。
NAR Genom Bioinform. 2024 Sep 3;6(3):lqae114. doi: 10.1093/nargab/lqae114. eCollection 2024 Sep.
3
Yeast9: a consensus genome-scale metabolic model for S. cerevisiae curated by the community.

本文引用的文献

1
Proteome reallocation from amino acid biosynthesis to ribosomes enables yeast to grow faster in rich media.蛋白质组从氨基酸生物合成到核糖体的重新分配使酵母能够在丰富的培养基中更快地生长。
Proc Natl Acad Sci U S A. 2020 Sep 1;117(35):21804-21812. doi: 10.1073/pnas.1921890117. Epub 2020 Aug 17.
2
Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast.氮限制揭示了酵母在代谢和翻译能力方面的巨大储备。
Nat Commun. 2020 Apr 20;11(1):1881. doi: 10.1038/s41467-020-15749-0.
3
Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.
酵母 9:由社区精心整理的酿酒酵母综合基因组代谢模型。
Mol Syst Biol. 2024 Oct;20(10):1134-1150. doi: 10.1038/s44320-024-00060-7. Epub 2024 Aug 12.
4
siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape.siqRNA-seq 是一种用于定量绘制 mRNA 图谱的 Spike-in 独立技术。
BMC Genomics. 2024 Jul 30;25(1):743. doi: 10.1186/s12864-024-10650-2.
5
Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress.全面的翻译组学特征分析和 STE AI 揭示了细胞应激过程中蛋白质生物合成的快速调控。
Nucleic Acids Res. 2024 Jul 22;52(13):7925-7946. doi: 10.1093/nar/gkae365.
6
PARROT: Prediction of enzyme abundances using protein-constrained metabolic models.利用蛋白约束代谢模型预测酶丰度。
PLoS Comput Biol. 2023 Oct 19;19(10):e1011549. doi: 10.1371/journal.pcbi.1011549. eCollection 2023 Oct.
7
A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes.单细胞微生物中碳氮代谢的粗粒度资源分配模型
J R Soc Interface. 2023 Sep;20(206):20230206. doi: 10.1098/rsif.2023.0206. Epub 2023 Sep 27.
8
Growth-dependent Gene Expression Variation Influences the Strength of Codon Usage Biases.生长依赖性基因表达变化影响密码子使用偏好的强度。
Mol Biol Evol. 2023 Sep 1;40(9). doi: 10.1093/molbev/msad189.
9
The proteomic landscape of genome-wide genetic perturbations.全基因组遗传扰动的蛋白质组学全景。
Cell. 2023 Apr 27;186(9):2018-2034.e21. doi: 10.1016/j.cell.2023.03.026. Epub 2023 Apr 19.
10
Growth-dependent gene expression variation influences the strength of codon usage biases.生长依赖性基因表达变异影响密码子使用偏好的强度。
bioRxiv. 2023 Jul 13:2023.03.14.532645. doi: 10.1101/2023.03.14.532645.
药物组合的紧急基因表达反应可预测更高阶的药物相互作用。
Cell Syst. 2019 Nov 27;9(5):423-433.e3. doi: 10.1016/j.cels.2019.10.004. Epub 2019 Nov 13.
4
Big data in yeast systems biology.酵母系统生物学中的大数据。
FEMS Yeast Res. 2019 Nov 1;19(7). doi: 10.1093/femsyr/foz070.
5
Production of Protein-Complex Components Is Stoichiometric and Lacks General Feedback Regulation in Eukaryotes.真核生物中蛋白质复合物组分的产生是计量的,且缺乏普遍的反馈调节。
Cell Syst. 2018 Dec 26;7(6):580-589.e4. doi: 10.1016/j.cels.2018.11.003. Epub 2018 Dec 12.
6
The Gene Ontology Resource: 20 years and still GOing strong.《基因本体论资源:20 年,持续强大》
Nucleic Acids Res. 2019 Jan 8;47(D1):D330-D338. doi: 10.1093/nar/gky1055.
7
The PRIDE database and related tools and resources in 2019: improving support for quantification data.PRIDE 数据库及相关工具和资源在 2019 年的进展:提高定量数据支持。
Nucleic Acids Res. 2019 Jan 8;47(D1):D442-D450. doi: 10.1093/nar/gky1106.
8
Dilution and titration of cell-cycle regulators may control cell size in budding yeast.细胞周期调控因子的稀释和滴定可能控制出芽酵母的细胞大小。
PLoS Comput Biol. 2018 Oct 24;14(10):e1006548. doi: 10.1371/journal.pcbi.1006548. eCollection 2018 Oct.
9
PomBase 2018: user-driven reimplementation of the fission yeast database provides rapid and intuitive access to diverse, interconnected information.PomBase 2018:用户驱动的裂殖酵母数据库重新实现,提供快速直观的访问多样化、相互关联的信息。
Nucleic Acids Res. 2019 Jan 8;47(D1):D821-D827. doi: 10.1093/nar/gky961.
10
Cooperative STAT/NF-κB signaling regulates lymphoma metabolic reprogramming and aberrant GOT2 expression.协同的 STAT/NF-κB 信号调节淋巴瘤代谢重编程和 GOT2 的异常表达。
Nat Commun. 2018 Apr 17;9(1):1514. doi: 10.1038/s41467-018-03803-x.