Exploring pathway interactions in insulin resistant mouse liver.
Exploring pathway interactions in insulin resistant mouse liver.
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DOI：
10.1186/1752-0509-5-127
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发表时间：
2011-08-15
影响因子：
--
通讯作者：
Evelo C
中科院分区：
文献类型：
--
作者：
Kelder T;Eijssen L;Kleemann R;van Erk M;Kooistra T;Evelo C
复杂的表型，如胰岛素抵抗，涉及不同的生物学途径，可能相互作用和相互影响。通过在数据集的背景下识别相关的途径相互作用，将有助于解释相关的实验数据。我们开发了一种分析方法，通过整合基因和蛋白质相互作用网络，生物途径信息和高通量数据来研究途径之间的相互作用。将该方法应用于转录组学数据集，以研究胰岛素抵抗小鼠肝脏中响应于葡萄糖挑战的途径相互作用。我们在葡萄糖挑战后的不同时间点确定了受调节的通路相互作用，并研究了潜在的蛋白质相互作用，以找到可能的机制和参与通路串扰的关键蛋白质。在t = 0时，两个饮食组之间的比较发现了大量的途径相互作用。对葡萄糖挑战的初始反应（t = 0.6）由急性应激反应和途径相互作用分型，两个饮食组之间存在很大的重叠，而后期反应的途径相互作用网络更不相似。研究途径相互作用提供了一个新的视角，补充了现有的途径分析方法，如富集分析。这项研究为胰岛素抵抗如何影响途径之间的相互作用提供了新的见解。此外，这里描述的分析方法通常可以应用于不同类型的高通量数据，因此也适用于其他复杂数据集的分析。
Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.
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影响因子：
3.4
作者：
Isserlin, Ruth;Merico, Daniele;Alikhani-Koupaei, Rasoul;Gramolini, Anthony;Bader, Gary D.;Emili, Andrew
通讯作者：
Emili, Andrew
影响因子：
14.9
作者：
Jensen LJ;Kuhn M;Stark M;Chaffron S;Creevey C;Muller J;Doerks T;Julien P;Roth A;Simonovic M;Bork P;von Mering C
通讯作者：
von Mering C
影响因子：
14.9
作者：
Kuhn M;Szklarczyk D;Franceschini A;Campillos M;von Mering C;Jensen LJ;Beyer A;Bork P
通讯作者：
Bork P
影响因子：
25.7
作者：
Garcia-Monzon, Carmelo;Lo Iacono, Oreste;Martin-Sanz, Paloma
通讯作者：
Martin-Sanz, Paloma
DOI：
10.1093/bioinformatics/btp101
发表时间：
2009-04-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bindea G;Mlecnik B;Hackl H;Charoentong P;Tosolini M;Kirilovsky A;Fridman WH;Pagès F;Trajanoski Z;Galon J
通讯作者：
Galon J