MindSpec, McLean, Virginia, United States of America.
PLoS Comput Biol. 2013;9(7):e1003128. doi: 10.1371/journal.pcbi.1003128. Epub 2013 Jul 25.
Autism spectrum disorder (ASD) is one of the most prevalent and highly heritable neurodevelopmental disorders in humans. There is significant evidence that the onset and severity of ASD is governed in part by complex genetic mechanisms affecting the normal development of the brain. To date, a number of genes have been associated with ASD. However, the temporal and spatial co-expression of these genes in the brain remain unclear. To address this issue, we examined the co-expression network of 26 autism genes from AutDB (http://mindspec.org/autdb.html), in the framework of 3,041 genes whose expression energies have the highest correlation between the coronal and sagittal images from the Allen Mouse Brain Atlas database (http://mouse.brain-map.org). These data were derived from in situ hybridization experiments conducted on male, 56-day old C57BL/6J mice co-registered to the Allen Reference Atlas, and were used to generate a normalized co-expression matrix indicating the cosine similarity between expression vectors of genes in this database. The network formed by the autism-associated genes showed a higher degree of co-expression connectivity than seen for the other genes in this dataset (Kolmogorov-Smirnov P = 5×10⁻²⁸). Using Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly enriched with autism genes (A Bonferroni corrected P<0.05). Genes in both these cliques were significantly over-expressed in the cerebellar cortex (P = 1×10⁻⁵) suggesting possible implication of this brain region in autism. In conclusion, our study provides a detailed profiling of co-expression patterns of autism genes in the mouse brain, and suggests specific brain regions and new candidate genes that could be involved in autism etiology.
自闭症谱系障碍（ASD）是人类最常见且高度遗传的神经发育障碍之一。有大量证据表明，ASD 的发病和严重程度部分受影响大脑正常发育的复杂遗传机制控制。迄今为止，已有许多基因与 ASD 相关。然而，这些基因在大脑中的时空共表达仍然不清楚。为了解决这个问题，我们在 Allen 鼠脑图谱数据库（http://mouse.brain-map.org）的冠状和矢状图像之间表达能量相关性最高的 3041 个基因的框架内，研究了 AutDB（http://mindspec.org/autdb.html）中 26 个自闭症基因的共表达网络。这些数据来自于在雄性、56 天大的 C57BL/6J 小鼠上进行的原位杂交实验，这些小鼠与 Allen 参考图谱共同注册，并用于生成归一化的共表达矩阵，该矩阵指示了该数据库中基因的表达向量之间的余弦相似度。与其他数据集相比，由自闭症相关基因形成的网络显示出更高的共表达连接度（Kolmogorov-Smirnov P=5×10⁻²⁸）。通过蒙特卡罗模拟，我们鉴定出两个与自闭症基因显著富集的共表达基因簇（Bonferroni 校正后 P<0.05）。这两个基因簇中的基因在小脑皮质中显著过表达（P=1×10⁻⁵），这表明该脑区可能与自闭症有关。总之，我们的研究提供了自闭症基因在小鼠大脑中的共表达模式的详细分析，并提出了可能涉及自闭症病因的特定脑区和新的候选基因。
