Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.
Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.
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DOI：
10.1371/journal.pone.0007249
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发表时间：
2009-10-01
期刊：
影响因子：
3.7
通讯作者：
Dalrymple BP
中科院分区：
文献类型：
--
作者：
Hudson NJ;Reverter A;Wang Y;Greenwood PL;Dalrymple BP
尽管有现代技术和新的计算方法，解码因果转录调控仍然具有挑战性。这对于研究较少的生物体和只有基因表达数据可用时尤其如此。在肌肉中，提出了少数充分表征的转录因子来调节发育。因此，肌肉似乎是一个易于处理的系统，提出新的计算方法。在这里，我们报告了一个简单的算法，问“转录调控具有最高的平均绝对共表达相关的基因在一个共表达模块？”它正确地推断了一些已知的基本生物学过程的因果调节因子，包括细胞周期活性（E2F1），糖酵解（HLF），线粒体转录（TFB2M），脂肪生成（PIAS 1），神经元发育（TLX3），免疫功能（IRF 1）和血管生成（SOX 17），在骨骼肌背景下。然而，没有一个典型的促肌生成转录因子（MYOD 1、MYOG、MYF 5、MYF 6和MEF 2C）与肌肉结构基因表达模块相关联。共表达值的计算使用发育牛肌肉从60天后的概念（早期胎儿）到30个月后纳塔尔（成年）的两个品种的牛，除了营养比较与第三品种。构建了一些转录景观，并整合成一个总是相关的景观。一个显著的特征是一端由糖酵解基因形成的“代谢轴”，另一端由核编码的线粒体蛋白基因形成，并由线粒体编码的线粒体蛋白基因集中束缚。新的模块到监管器算法补充了我们最近描述的监管影响因子分析。再加上一个简单的检查共表达模块的内容，这三个基因表达的方法开始照亮骨骼肌发育的体内转录调控。
Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Here we report a simple algorithm that asks “which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?” It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a ‘metabolic axis’ formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.
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影响因子：
3.7
作者：
Finocchietto P;Barreyro F;Holod S;Peralta J;Franco MC;Méndez C;Converso DP;Estévez A;Carreras MC;Poderoso JJ
通讯作者：
Poderoso JJ
DOI：
10.1126/science.1162327
发表时间：
2009-06-26
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Badis G;Berger MF;Philippakis AA;Talukder S;Gehrke AR;Jaeger SA;Chan ET;Metzler G;Vedenko A;Chen X;Kuznetsov H;Wang CF;Coburn D;Newburger DE;Morris Q;Hughes TR;Bulyk ML
通讯作者：
Bulyk ML
DOI：
10.1073/pnas.212532399
发表时间：
2002-10-29
影响因子：
11.1
作者：
Chen, L;Segal, D;Westphal, H
通讯作者：
Westphal, H
影响因子：
4.1
作者：
BRAND, MD;COUTURE, P;HULBERT, AJ
通讯作者：
HULBERT, AJ
影响因子：
21.3
作者：
De La Fuente, Rabindranath;Baumann, Claudia;Muegge, Kathrin
通讯作者：
Muegge, Kathrin