Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance
Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance
复制标题
DOI：
10.1073/pnas.0407114101
复制
发表时间：
2004-11-09
影响因子：
11.1
通讯作者：
Andersen, B
中科院分区：
文献类型：
--
作者：
Lin, KK;Chudova, D;Andersen, B
毛发生长周期是一个循环过程的例子，这种循环过程在形态上有很好的特征，但在分子水平上还不完全被理解。作为发现毛囊形态发生和周期调控的第一步，我们使用DNA微阵列对小鼠背部皮肤八个典型时间点的mRNA表达进行了分析。我们开发了一种统计算法来识别皮肤中表达的一组基因，这些基因与毛发生长周期特别相关。与同步周期相比，该方法在异步毛发周期中利用了更高的复制方差。在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表达变化，这表明与毛发生长周期相关的基因可能比文献中确定的要多得多。通过对重复测量使用概率聚类算法，这些基因被分成30个时序分布簇，这些簇分为四大类。不同的时间进程轮廓簇具有不同的遗传路径特征，为毛囊周期的调节提供了洞察力，并表明这种方法对于识别毛囊调节器是有用的。除了揭示已知的毛发相关基因外，我们还通过实时定量聚合酶链式反应和原位杂交，鉴定了以前未知的与毛发周期相关的基因，并确认了它们在毛发生长周期中的时空表达模式。同样的计算方法对于从复杂组织中识别与循环过程相关的基因通常也是有用的。
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 timecourse 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 hairfollicle 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.