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本文引用的文献

1
A subcellular map of the human proteome.人类蛋白质组的亚细胞图谱。
Science. 2017 May 26;356(6340). doi: 10.1126/science.aal3321. Epub 2017 May 11.
2
Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions.调和大鼠和人类代谢网络进行比较毒代动力学和生物标志物预测。
Nat Commun. 2017 Feb 8;8:14250. doi: 10.1038/ncomms14250.
3
Design of efficient computational workflows for in silico drug repurposing.用于计算机辅助药物重新利用的高效计算工作流程设计
Drug Discov Today. 2017 Feb;22(2):210-222. doi: 10.1016/j.drudis.2016.09.019. Epub 2016 Sep 28.
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Recon 2.2: from reconstruction to model of human metabolism.Recon 2.2:从重建到人类代谢模型
Metabolomics. 2016;12:109. doi: 10.1007/s11306-016-1051-4. Epub 2016 Jun 7.
5
Repurposing of FDA-approved drugs against cancer - focus on metastasis.重新利用美国食品药品监督管理局(FDA)批准的抗癌药物——聚焦于转移
Aging (Albany NY). 2016 Apr;8(4):567-8. doi: 10.18632/aging.100941.
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Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease.全基因组规模研究揭示非酒精性脂肪性肝病患者代谢适应性降低。
Nat Commun. 2016 Feb 3;7:8994. doi: 10.1038/ncomms9994.
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Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production.2000例乳腺癌代谢图谱的生成揭示了一个血清素产生活跃的预后不良组。
Sci Rep. 2016 Jan 27;6:19771. doi: 10.1038/srep19771.
8
Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond.迈向改进的基因组规模代谢网络重建:统一、转录特异性及其他。
Brief Bioinform. 2016 Nov;17(6):1060-1069. doi: 10.1093/bib/bbv100. Epub 2015 Nov 28.
9
MetaNetX/MNXref--reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks.MetaNetX/MNXref——代谢物与生化反应的整合,以汇集基因组规模的代谢网络。
Nucleic Acids Res. 2016 Jan 4;44(D1):D523-6. doi: 10.1093/nar/gkv1117. Epub 2015 Nov 2.
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The gut microbiota modulates host amino acid and glutathione metabolism in mice.肠道微生物群调节小鼠体内宿主的氨基酸和谷胱甘肽代谢。
Mol Syst Biol. 2015 Oct 16;11(10):834. doi: 10.15252/msb.20156487.

人类代谢中超过 11000 个基因-转录本-蛋白质-反应关联的框架和资源。

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism.

机构信息

Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea.

出版信息

Proc Natl Acad Sci U S A. 2017 Nov 7;114(45):E9740-E9749. doi: 10.1073/pnas.1713050114. Epub 2017 Oct 24.

DOI:10.1073/pnas.1713050114
PMID:29078384
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5692585/
Abstract

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

摘要

可变剪接在从一个基因产生不同的转录本,进而产生各种蛋白质异构体方面发挥着重要作用。然而,目前还没有一种系统的方法可以方便地描述蛋白质异构体在整个人类代谢中的功能作用。在这里,我们提出了一个在人类代谢中生成基因-转录本-蛋白质-反应关联(GeTPRA)的系统框架。该框架共生成了 11415 个 GeTPRA,对应于代谢基因的主要和非主要转录本(PTs 和 NPTs)的 1106 个代谢基因。该框架进一步评估了 GeTPRA,使用了与生化一致且与转录水平数据兼容的人类基因组规模代谢模型(GEM),并随后更新了人类 GEM。为此目的开发了一个通用的人类 GEM,Recon 2M.1,并通过该框架更新到 Recon 2M.2。该框架基于对 446 个人 RNA-Seq 数据和使用 Recon 2M.1 重建的 1784 个人 GEM 的先前分析,考虑了代谢基因的 PTs 和 NPTs。该框架和 GeTPRA 将有助于更好地理解系统水平的人类代谢,并能够实现进一步的医学应用。