Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 101, Issue 45, Pages 15955-15960Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0407114101
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Funding
- NIAMS NIH HHS [R01 AR044882, AR 02080, AR 44882] Funding Source: Medline
- NLM NIH HHS [5T15 LM 00744] Funding Source: Medline
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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.
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