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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.
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Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

机译：通过使用复制方差从时程基因表达谱数据中鉴定与毛发循环相关的基因。

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摘要

The hair-growth cycle is an example of a cyclic process that is well characterized morphologically but understood incompletely at the molecular level. As an initial step in discovering regulators in hair-follicle morphogenesis and cycling, we used DNA microarrays to profile mRNA expression in mouse back skin from eight representative time points. We developed a statistical algorithm to identify the set of genes expressed within skin that are associated specifically with the hair-growth cycle. The methodology takes advantage of higher replicate variance during asynchronous hair cycles in comparison with synchronous cycles. More than one-third of genes with detectable skin expression showed hair-cycle-related changes in expression, suggesting that many more genes may be associated with the hair-growth cycle than have been identified in the literature. By using a probabilistic clustering algorithm for replicated measurements, these genes were grouped into 30 time-course profile clusters, which fall into four major classes. Distinct genetic pathways were characteristic for the different time-course profile clusters, providing insights into the regulation of hair-follicle cycling and suggesting that this approach is useful for identifying hair follicle regulators. In addition to revealing known hair-related genes, we identified genes that were not previously known to be hair cycle-associated and confirmed their temporal and spatial expression patterns during the hair-growth cycle by quantitative real-time PCR and in situ hybridization. The same computational approach should be generally useful for identifying genes associated with cyclic processes from complex tissues.
机译：毛发生长循环是循环过程的一个例子，该循环过程在形态学上得到了很好的表征，但是在分子水平上却不完全被理解。作为在毛囊形态发生和循环中发现调节剂的第一步，我们使用DNA微阵列从八个代表性的时间点分析了小鼠背部皮肤中mRNA的表达。我们开发了一种统计算法来识别在皮肤内表达的与毛发生长周期特别相关的基因集。与同步周期相比，该方法在异步头发周期中利用了较高的复制方差。具有可检测到的皮肤表达的基因中，超过三分之一的基因表现出与毛发周期相关的变化，这表明与毛发生长周期相关的基因可能比文献中所确定的更多。通过使用概率聚类算法进行重复测量，这些基因被分组为30个时程分布图聚类，分为四个主要类别。独特的遗传途径是不同时程谱簇的特征，为深入研究毛囊循环提供了见识，并表明该方法可用于鉴定毛囊调节剂。除了揭示已知的与头发相关的基因外，我们还鉴定了以前不知道与毛发循环相关的基因，并通过定量实时PCR和原位杂交证实了它们在毛发生长周期中的时空表达模式。通常，相同的计算方法应可用于从复杂组织中识别与循环过程相关的基因。

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