The Human Blood Metabolome-Transcriptome Interface.
The Human Blood Metabolome-Transcriptome Interface.
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DOI：
10.1371/journal.pgen.1005274
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发表时间：
2015-06
期刊：
影响因子：
4.5
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Bartel J;Krumsiek J;Schramm K;Adamski J;Gieger C;Herder C;Carstensen M;Peters A;Rathmann W;Roden M;Strauch K;Suhre K;Kastenmüller G;Prokisch H;Theis FJ
生物系统由多个组织层次组成，它们彼此紧密地相互作用，以确保系统的功能和灵活性。同时分析来自大群体研究的横截面多组学数据是在生理尺度上全面表征潜在分子机制的有力工具。在这项研究中，我们系统地分析了来自德国KORA F4队列的712名个体的空腹血清代谢组学和全血转录组学数据之间的关系。基于相关性的分析确定了522个转录物和114个代谢物之间的1,109个显著关联，这些代谢物总结在一个综合网络中，即“人类血液代谢组-转录组界面”（BMTI）。使用孟德尔随机化的双向因果关系分析没有在转录本和代谢物之间产生任何统计学显著的因果关系。基于知识的解释和整合与基因组规模的人类代谢重建揭示了信号传导，运输和代谢过程的系统特征，即主要属于脂质，能量和氨基酸代谢的代谢反应。此外，基于功能类别的网络的构建说明了在通路水平上生物层之间的串扰。使用转录因子结合位点富集分析，在调控水平上进一步证实了该途径串扰。最后，我们展示了构建的网络如何用于获得与中间临床特征相关的分子机制的新见解。总的来说，我们的研究结果表明，多组学综合方法的效用，以了解正常生理和疾病的分子机制。生物系统在多个相互交织的组织层上运行，这些组织层现在可以通过高通量测量方法（所谓的“组学”技术）进行访问。系统生物学领域的一个主要目标是在健康和疾病的系统水平上了解不同层次之间的生物信息流。为了解开这些分子过程背后的复杂机制，并了解不同的功能水平如何相互作用，需要多层次的综合分析，即“多组学”方法。在我们目前的研究中，我们调查了血清中的循环代谢物与来自基于人群的队列的个体血液中测量的全血基因表达之间的关系。为此，我们构建了一个相关网络，显示哪些转录物和代谢物显示相同的上调和下调趋势。我们通过开发一种新的计算分析得出了网络的功能特征。该分析揭示了在调节和途径水平上的信号传导、运输和代谢过程的系统特征。此外，整合网络与临床标志物如HDL-胆固醇，LDL-胆固醇和TG的关联，确定了协同激活的途径或模块，这可能有助于评估这种中间表型背后的分子机制。
Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease. Biological systems operate on multiple, intertwined organizational layers that can nowadays be accesses by high-throughput measurement methods, the so-called ‘omics’ technologies. A major aim in the field of systems biology is to understand the flow of biological information between the different layers at a systems level in both health and disease. To unravel the complex mechanisms underlying those molecular processes and to understand how the different functional levels interact with each other, an integrated analysis of multiple layers, i.e. a ‘multi-omics‘ approach is required. In our present study, we investigate the relationship between circulating metabolites in serum and whole-blood gene expression measured in the blood of individuals from a population-based cohort. To this end, we constructed a correlation network that displays which transcript and metabolite show the same trend of up- and down-regulation. We derived a functional characterization of the network by developing a novel computational analysis. The analysis revealed systematic signatures of signaling, transport and metabolic processes on both a regulatory and a pathway level. Moreover, integrating the network with associations to clinical markers such as HDL-cholesterol, LDL-cholesterol and TG identified coordinately activated pathways or modules which might help to assess the molecular machinery behind such an intermediate phenotype.
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影响因子：
3
作者：
Defrance M;Touzet H
通讯作者：
Touzet H
影响因子：
14.9
作者：
Bresnick EH;Katsumura KR;Lee HY;Johnson KD;Perkins AS
通讯作者：
Perkins AS
影响因子：
37.8
作者：
Cheng S;Rhee EP;Larson MG;Lewis GD;McCabe EL;Shen D;Palma MJ;Roberts LD;Dejam A;Souza AL;Deik AA;Magnusson M;Fox CS;O'Donnell CJ;Vasan RS;Melander O;Clish CB;Gerszten RE;Wang TJ
通讯作者：
Wang TJ
影响因子：
4.5
作者：
Ferrara CT;Wang P;Neto EC;Stevens RD;Bain JR;Wenner BR;Ilkayeva OR;Keller MP;Blasiole DA;Kendziorski C;Yandell BS;Newgard CB;Attie AD
通讯作者：
Attie AD
影响因子：
3.7
作者：
Charmandari, Evangelia;Chrousos, George P.;Kino, Tomoshige
通讯作者：
Kino, Tomoshige