A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
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DOI：
10.1371/journal.pcbi.1000382
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发表时间：
2009-05
影响因子：
4.3
通讯作者：
Dalrymple BP
中科院分区：
文献类型：
--
作者：
Hudson NJ;Reverter A;Dalrymple BP
转录因子（TF）的调节通常是翻译后的。TF修饰，如可逆磷酸化和错义突变，可以独立于TF表达水平发挥作用，被差异表达分析所忽略。使用牛皮埃蒙特肌肉生长抑制素突变体作为概念验证，我们提出了一种新的算法，正确地识别基因含有因果突变的微阵列数据。肌肉生长抑制素突变通过翻译一种功能失调的蛋白质来释放皮埃蒙特肌肉生长的刹车。与肌肉较少的非突变品种相比，我们发现肌肉生长抑制素在十个发育时间点的任何一个都没有差异表达。尽管存在这一挑战，该算法通过协调、同时、加权整合三个来源的微阵列信息（转录丰度、差异表达和差异布线）来识别肌生长抑制素“确凿证据”。通过提出一个新的问题“哪种调节因子与丰富的差异表达基因的累积差异最大？”它产生正确的答案“肌肉生长抑制素”。我们的新方法通过全球对比共表达网络动态来识别因果监管变化。完全数据驱动的“加权”程序强调相对于网络表型相关部分的监管运动。与其他公开的比较共表达网络的方法相比，显著性检验不用于消除连接。进化、发育和癌症都是由调节回路控制的，其中的中心节点是转录因子。因此，有很大的兴趣，在方法，可以确定因果突变/扰动负责任何电路重新布线。最广泛使用的高通量技术，微阵列，测定转录组。然而，许多调控扰动是转录后的。这意味着它们被传统的差异基因表达分析所忽视。我们假设，通过将生物系统视为网络，可以通过检查网络中位置变化最大的调节器来识别因果突变和扰动。使用肌肉肌生长抑制素突变牛作为概念验证，我们提出了一项仅基于27只动物的微阵列表达数据就成功的分析。我们的分析与竞争网络方法的不同之处在于，我们不使用显著性测试来消除连接。所有的联系都是对比的，无论多么微弱。此外，在整个分析过程中保持靶基因的同一性。最后，对分析进行“加权”，以强调相对于网络表型最相关部分的运动。通过确定肌肉生长抑制素是答案的问题，我们提出了一个比较的网络连接，这是潜在的概括。
Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin ‘smoking gun’ through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question “which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?” it yields the correct answer, “myostatin”. Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven ‘weighting’ procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections. Evolution, development, and cancer are governed by regulatory circuits where the central nodes are transcription factors. Consequently, there is great interest in methods that can identify the causal mutation/perturbation responsible for any circuit rewiring. The most widely available high-throughput technology, the microarray, assays the transcriptome. However, many regulatory perturbations are post-transcriptional. This means that they are overlooked by traditional differential gene expression analysis. We hypothesised that by viewing biological systems as networks one could identify causal mutations and perturbations by examining those regulators whose position in the network changes the most. Using muscular myostatin mutant cattle as a proof-of-concept, we propose an analysis that succeeds based solely on microarray expression data from just 27 animals. Our analysis differs from competing network approaches in that we do not use significance testing to eliminate connections. All connections are contrasted, no matter how weak. Further, the identity of target genes is maintained throughout the analysis. Finally, the analysis is ‘weighted’ such that movement relative to the phenotypically most relevant part of the network is emphasised. By identifying the question to which myostatin is the answer, we present a comparison of network connectivity that is potentially generalisable.
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