Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis.
Integration of transcriptomics and metabonomics: improving diagnostics, biomarker identification and phenotyping in ulcerative colitis.
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DOI：
10.1007/s11306-013-0580-3
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发表时间：
2014
期刊：
影响因子：
3.6
通讯作者：
Nielsen, Ole Haagen
中科院分区：
文献类型：
--
作者：
Bjerrum, Jacob Tveiten;Rantalainen, Mattias;Wang, Yulan;Olsen, Jorgen;Nielsen, Ole Haagen
关键词：
多方面疾病的系统生物学方法提供了一个机会，建立一个整体的理解过程中发挥作用。因此，目前的研究合并了转录组学和代谢组学数据，以改善诊断，生物标志物鉴定和探索溃疡性结肠炎（UC）患者分子表型的可能性。从43例UC患者（22例活动期UC和21例静止期UC）和15例对照组的降结肠获得活检。全基因组基因表达分析使用Affyssin GeneChip Human Genome U133 Plus 2.0进行。使用1H核磁共振光谱法（Bruker 600 MHz，Bruker BioSpin，莱茵施泰滕，德国）生成代谢谱。数据分析采用正交投影潜在结构判别分析和多元Logistic回归模型拟合拉索。使用嵌套蒙特卡罗交叉验证评估预测性能。合并数据集和相对较小（<20个变量）的多变量生物标志物组的预测性能表明，有可能区分活动性UC、静止期UC和对照组;区分有或无类固醇依赖的患者，以及区分早期或晚期疾病发作。因此，这项研究表明，整合代谢组学和转录组学的新方法结合了两个世界中的更好的一个，并为我们提供了临床适用的候选生物标志物面板。这些组合面板改善了UC的诊断，更重要的是还改善了UC的分子表型，并提供了对病理生理过程的深入了解，使优化和个性化药物成为可能。本文的在线版本（doi：10.1007/s11306-013-0580-3）包含补充材料，可供授权用户使用。
A systems biology approach to multi-faceted diseases has provided an opportunity to establish a holistic understanding of the processes at play. Thus, the current study merges transcriptomics and metabonomics data in order to improve diagnostics, biomarker identification and to explore the possibilities of a molecular phenotyping of ulcerative colitis (UC) patients. Biopsies were obtained from the descending colon of 43 UC patients (22 active UC and 21 quiescent UC) and 15 controls. Genome-wide gene expression analyses were performed using Affymetrix GeneChip Human Genome U133 Plus 2.0. Metabolic profiles were generated using 1H Nuclear magnetic resonance spectroscopy (Bruker 600 MHz, Bruker BioSpin, Rheinstetten, Germany). Data were analyzed with the use of orthogonal-projection to latent structure-discriminant analysis and a multivariate logistic regression model fitted by lasso. Prediction performance was evaluated using nested Monte Carlo cross-validation. The prediction performance of the merged data sets and that of relative small (<20 variables) multivariate biomarker panels suggest that it is possible to discriminate between active UC, quiescent UC, and controls; between patients with or without steroid dependency, as well as between early or late disease onset. Consequently, this study demonstrates that the novel approach of integrating metabonomics and transcriptomics combines the better of the two worlds, and provides us with clinical applicable candidate biomarker panels. These combined panels improve diagnostics and more importantly also the molecular phenotyping in UC and provide insight into the pathophysiological processes at play, making optimized and personalized medication a possibility. The online version of this article (doi:10.1007/s11306-013-0580-3) contains supplementary material, which is available to authorized users.
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