Liao Ben-Yang, Zhang Jianzhi
Department of Ecology and Evolutionary Biology, University of Michigan, USA.
Mol Biol Evol. 2006 Mar;23(3):530-40. doi: 10.1093/molbev/msj054. Epub 2005 Nov 9.
Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.
小鼠模型常被用于研究人类基因，因为人们认为这两个物种之间大多数直系同源基因的表达和功能相似。然而，最近对数千个人类和小鼠直系同源基因的微阵列数据比较表明，在最小或无选择约束下基因表达谱快速进化。这些发现似乎与许多单个基因基于非阵列的观察结果相矛盾，并暗示小鼠模型在研究人类基因方面毫无用处。由于当数据由物种特异性微阵列生成时，物种间基因表达的绝对水平不可比，因此组织间相对mRNA丰度（RA）的使用优于绝对表达信号。因此，我们重新分析了由寡核苷酸微阵列生成的人类和小鼠全基因组基因表达数据。我们表明，同一基因不同探针集检测到的表达谱之间的平均相关系数，人类为0.38，小鼠为0.28，这表明当前的表达差异测量存在缺陷，因为大的估计误差（同一基因不同探针集检测到的表达信号差异）被错误地包含在物种间差异中。当减去这个误差时，84%的人类 - 小鼠直系同源基因对显示出比随机基因对显著更低的表达差异。与之前的发现相反，但与常识一致的是，物种间直系同源组织的表达谱彼此之间比与非直系同源组织的表达谱更相似。此外，当使用RA和欧几里得距离来测量表达谱差异时，发现表达差异的进化速率与编码序列差异的进化速率呈弱但显著的正相关。这些结果突出了在比较物种间微阵列数据时适当考虑各种估计误差的重要性。
