Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.
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.
Lin
Kevin K
KK
Department of Biological Chemistry, Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA.
Chudova
Darya
D
Hatfield
G Wesley
GW
Smyth
Padhraic
P
Andersen
Bogi
B
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D018859:Hair Follicle / Q000254:growth & development* / Q000378:metabolism*; D017403:In Situ Hybridization; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D016133:Polymerase Chain Reaction; D012333:RNA, Messenger / Q000235:genetics; D013997:Time Factors
D012333:RNA, Messenger