Variance of gene expression identifies altered network constraints in neurological disease.
Variance of gene expression identifies altered network constraints in neurological disease.
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DOI：
10.1371/journal.pgen.1002207
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发表时间：
2011-08
期刊：
影响因子：
4.5
通讯作者：
Wells CA
中科院分区：
文献类型：
--
作者：
Mar JC;Matigian NA;Mackay-Sim A;Mellick GD;Sue CM;Silburn PA;McGrath JJ;Quackenbush J;Wells CA
基因表达分析已成为研究广泛的人类疾病的普遍工具。在一个典型的分析中，我们比较不同的表型组，并试图确定基因，平均而言，显着不同。在这里，我们描述了一种分析基因表达数据的创新方法，一种识别组间表达差异作为组表型信息度量的方法。我们发现具有不同表达变异谱的基因并不是随机分布在细胞信号网络中。低表达变异或高约束的基因与其他网络成员的联系明显更多，往往是信号转导通路的核心成员。表达差异较大的基因网络连接较少，也倾向于位于细胞的外围。使用来自精神分裂症（SZ）、帕金森病（PD）患者和健康对照组的神经干细胞，我们发现细胞信号通路表达变异的显著差异，为这些不同神经系统疾病相关的潜在机制提供了新的思路。特别是，我们发现SZ患者组核心网络的表达方差受到了相当大的限制，而PD患者组的表达方差则比预期的要大得多。一种假设是，SZ患者的变异减少对应于这些通路中约束程度的增加和干细胞网络稳健性的相应降低。这些结果强调了变异在生物系统中所起的作用，并表明表达变异分析在疾病中比以前认识到的要重要得多。此外，基因表达变异性的建模模式可以从根本上改变我们对细胞网络如何受到疾病过程影响的看法。基因是一个信息库，为细胞过程提供了框架，信息流从基因（DNA）到表型通过中间分子信使RNA。我们知道，基因序列的变化可能导致表型的变化，但不太清楚的是，信息流本身的变化也可能影响表型。在这项研究中，我们证明了疾病表型与表达变异相关。表达差异的变化可能推断出代表信息流的遗传网络不那么健壮——令人惊讶的是，我们发现，在神经系统疾病的背景下，太少和太多的差异同样有害。
Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance have fewer network connections and also tend to sit on the periphery of the cell. Using neural stem cells derived from patients suffering from Schizophrenia (SZ), Parkinson's disease (PD), and a healthy control group, we find marked differences in expression variance in cell signaling pathways that shed new light on potential mechanisms associated with these diverse neurological disorders. In particular, we find that expression variance of core networks in the SZ patient group was considerably constrained, while in contrast the PD patient group demonstrated much greater variance than expected. One hypothesis is that diminished variance in SZ patients corresponds to an increased degree of constraint in these pathways and a corresponding reduction in robustness of the stem cell networks. These results underscore the role that variation plays in biological systems and suggest that analysis of expression variance is far more important in disease than previously recognized. Furthermore, modeling patterns of variability in gene expression could fundamentally alter the way in which we think about how cellular networks are affected by disease processes. Genes are a repository of information that provides the framework for cellular processes, with the flow of information from gene (DNA) to phenotype via an intermediate molecule—the messenger RNA. We understand that sequence variations in a gene may lead to phenotypic variations, but less well understood is how variation in the information flow itself might also impact on phenotype. In this study we demonstrated that disease phenotypes were correlated with expression variance. A change in expression variance might infer that the genetic networks representing information flow were less robust—surprisingly, we found that too little and too much variance were equally detrimental in the context of neurological disease.
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影响因子：
64.8
作者：
Eichler, Evan E.;Nickerson, Deborah A.;Altshuler, David;Bowcock, Anne M.;Brooks, Lisa D.;Carter, Nigel P.;Church, Deanna M.;Felsenfeld, Adam;Guyer, Mark;Lee, Charles;Lupski, James R.;Mullikin, James C.;Pritchard, Jonathan K.;Sebat, Jonathan;Sherry, Stephen T.;Smith, Douglas;Valle, David;Waterston, Robert H.
通讯作者：
Waterston, Robert H.
DOI：
10.1073/pnas.0906183107
发表时间：
2010-01-26
影响因子：
11.1
作者：
Feinberg, Andrew P.;Irizarry, Rafael A.
通讯作者：
Irizarry, Rafael A.
影响因子：
6.6
作者：
Fowler, IL;Carr, VJ;Lewin, TJ
通讯作者：
Lewin, TJ
影响因子：
4.3
作者：
Matigian, Nicholas;Abrahamsen, Greger;Mackay-Sim, Alan
通讯作者：
Mackay-Sim, Alan
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影响因子：
44.1
作者：
Liao, Bing;Jin, Ying
通讯作者：
Jin, Ying