Dataset Information

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

ABSTRACT: 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.

SUBMITTER: Lin KK

PROVIDER: S-EPMC524696 | biostudies-literature | 2004 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

Lin Kevin K KK Lin Kevin K KK Chudova Darya D Hatfield G Wesley GW Smyth Padhraic P Andersen Bogi B

Proceedings of the National Academy of Sciences of the United States of America 20041101 45

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. Th ...[more]

PMID: 15520371

Similar Datasets

Project description:Cashmere goat from Inner Mongolia is an excellent local breed in China, and the related cashmere product is a kind of precious textile raw material with high price. Cashmere is generated from secondary hair follicles, which has obvious annual periodicity and includes three different stages: anagen, catagen, and telogen. Therefore, we investigated skin transcriptome data for 12 months using weighted gene co-expression network analysis (WGCNA) to explore essential modules, pathways, and genes responsible for the periodic growth and development of secondary hair follicles. A total of 17 co-expression modules were discovered by WGCNA, and there is a strong correlation between steelblue module and month (0.65, p = 3E-09), anagen (0.52, p = 1E-05), telogen (-0.6, p = 8E-08). Gene expression was generally high during late anagen to catagen (June to December), while expression was downregulated from telogen to early anagen (January-May), which is similar to the growth rule of hair follicle cycle. KEGG pathway enrichment analyses of the genes of steelblue module indicated that genes are mainly enriched in Cell cycle, Wnt signaling pathway, p53 signaling pathway and other important signal pathways. These genes were also significantly enriched in GO functional annotation of the cell cycle, microtubule movement, microtubule binding, tubulin binding, and so on. Ten genes (WIF1, WNT11, BAMBI, FZD10, NKD1, LEF1, CCND3, E2F3, CDC6, and CDC25A) were selected from these modules, and further identified as candidate biomarkers to regulate periodic development of hair follicles using qRT-PCR. The Wnt signaling pathway and Cell cycle play an important role in the periodic development of hair follicles. Ten genes were identified as essential functional molecules related to periodic development of hair follicle. These findings laid a foundation for understanding molecular mechanisms in biological functions such as hair follicle development and hair growth in cashmere goats.

Project description:BackgroundCashmere goat is famous for its high-quality fibers. The growth of cashmere in secondary hair follicles exhibits a seasonal pattern arising from circannual changes in the natural photoperiod. Although several studies have compared and analyzed the differences in gene expression between different hair follicle growth stages, the selection of samples in these studies relies on research experience or morphological evidence. Distinguishing hair follicle growth cycle according to gene expression patterns may help to explore the regulation mechanisms related to cashmere growth and the effect of melatonin from a molecular level more accurately.ResultsIn this study, we applied RNA-sequencing to the hair follicles of three normal and three melatonin-treated Inner Mongolian cashmere goats sampled every month during a whole hair follicle growth cycle. A total of 3559 and 988 genes were subjected as seasonal changing genes (SCGs) in the control and treated groups, respectively. The SCGs in the normal group were divided into three clusters, and their specific expression patterns help to group the hair follicle growth cycle into anagen, catagen and telogen stages. Some canonical pathways such as Wnt, TGF-beta and Hippo signaling pathways were detected as promoting the hair follicle growth, while Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Jak-STAT, Fc epsilon RI, NOD-like receptor, Rap1, PI3K-Akt, cAMP, NF-kappa B and many immune-related pathways were detected in the catagen and telogen stages. The PI3K-Akt signaling, ECM-receptor interaction and Focal adhesion were found in the transition stage between telogen to anagen, which may serve as candidate biomarkers for telogen-anagen regeneration. A total of 16 signaling pathways, 145 pathway mRNAs, and 93 lncRNAs were enrolled to construct the pathway-mRNA-lncRNA network, which indicated the function of lncRNAs through interacting with their co-expressed mRNAs. Pairwise comparisons between the control and melatonin-treated groups also indicated 941 monthly differentially expressed genes (monthly DEGs). These monthly DEGs were mainly distributed from April and September, which revealed a potential signal pathway map regulating the anagen stage triggered by melatonin. Enrichment analysis showed that Wnt, Hedgehog, ECM, Chemokines and NF-kappa B signaling pathways may be involved in the regulation of non-quiescence and secondary shedding under the influence of melatonin.ConclusionsOur study decoded the key regulators of the whole hair follicle growth cycle, laying the foundation for the control of hair follicle growth and improvement of cashmere yield.

Project description:BackgroundThe cell cycle has long been an important model to study the genome-wide transcriptional regulation. Although several methods have been introduced to identify cell cycle regulated genes from microarray data, they can not be directly used to investigate cell cycle regulated transcription factors (CCRTFs), because for many transcription factors (TFs) it is their activities instead of expressions that are periodically regulated across the cell cycle. To overcome this problem, it is useful to infer TF activities across the cell cycle by integrating microarray expression data with ChIP-chip data, and then examine the periodicity of the inferred activities. For most species, however, large-scale ChIP-chip data are still not available.ResultsWe propose a two-step method to identify the CCRTFs by integrating microarray cell cycle data with ChIP-chip data or motif discovery data. In S. cerevisiae, we identify 42 CCRTFs, among which 23 have been verified experimentally. The cell cycle related behaviors (e.g. at which cell cycle phase a TF achieves the highest activity) predicted by our method are consistent with the well established knowledge about them. We also find that the periodical activity fluctuation of some TFs can be perturbed by the cell synchronization treatment. Moreover, by integrating expression data with in-silico motif discovery data, we identify 8 cell cycle associated regulatory motifs, among which 7 are binding sites for well-known cell cycle related TFs.ConclusionOur method is effective to identify CCRTFs by integrating microarray cell cycle data with TF-gene binding information. In S. cerevisiae, the TF-gene binding information is provided by the systematic ChIP-chip experiments. In other species where systematic ChIP-chip data is not available, in-silico motif discovery and analysis provide us with an alternative method. Therefore, our method is ready to be implemented to the microarray cell cycle data sets from different species. The C++ program for AC score calculation is available for download from URL http://leili-lab.cmb.usc.edu/yeastaging/projects/project-base/.

Dataset Information

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

Publications

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets