- Research article
- Open Access
Identification of skin-expressed genes possibly associated with wool growth regulation of Aohan fine wool sheep
- Nan Liu†1,
- Hegang Li†2,
- Kaidong Liu2,
- Juanjuan Yu1,
- Ran Bu1,
- Ming Cheng2,
- Wei De4,
- Jifeng Liu1,
- Guangling He5 and
- Jinshan Zhao1, 2, 3Email author
© Liu et al.; licensee BioMed Central Ltd. 2014
- Received: 19 November 2013
- Accepted: 3 December 2014
- Published: 16 December 2014
Sheep are valuable resources for the animal fibre industry. Therefore, identifying genes which regulate wool growth would offer strategies for improving the quality of fine wool. In this study, we employed Agilent sheep gene expression microarray and proteomic technology to compare the gene expression patterns of the body side (hair-rich) and groin (hairless) skins of Aohan fine wool sheep (a Chinese indigenous breed).
Comparing the body side to the groin skins (S/G) of Aohan fine wool sheep, the microarray study revealed that 1494 probes were differentially expressed, including 602 more highly expressed and 892 less highly expressed probes. The microarray results were verified by means of quantitative PCR. Cluster analysis could distinguish the body side skin and the groin skin. Based on the Database for Annotation, Visualization and Integrated Discovery (DAVID), 38 of the differentially expressed genes were classified into four categories, namely regulation of receptor binding, multicellular organismal process, protein binding and macromolecular complex. Proteomic study revealed that 187 protein spots showed significant (p < 0.05) differences in their respective expression levels. Among them, 46 protein entries were further identified by MALDI-TOF/MS analyses.
Microarray analysis revealed thousands of differentially expressed genes, many of which were possibly associated with wool growth. Several potential gene families might participate in hair growth regulation. Proteomic analysis also indentified hundreds of differentially expressed proteins.
- Proteomic technology
- Wool growth
- Differential expression
Sheep are valuable resources for the animal fibre industry. Identifying genes which regulate wool growth offers the opportunity to improve the wool production efficiency, product quality and diversity in breeding programs. It can also offer the opportunity to develop transgenic lines and to develop therapeutic agents that can be used to tailor for desirable fibre attributes by altering gene expression . The genetic polymorphisms and their mechanisms of wool and cashmere growth and regulation have been thoroughly studied -. In mammals, several gene families, such as WNTs, tumor necrosis factors (TNFs), fibroblast growth factors (FGFs) and transforming growth factor(TGFs), have been implicated in hair follicle initiation, morphogenesis and cycling ,. Recently, the molecular characteristics of primary wool follicle initiation in Merino sheep have been reported recently .
Transcriptomic research such as microarray analysis, has been successfully applied to investigate the characteristics of hair follicle stem cells in mice -. Microarray studies have been reported for different traits in sheep and goats, such as resistance to parasites ,, mammary development and milk quality -, wool follicle development , natural fleece rot resistance  and pigmentation traits of skin and wool . A subset of skin-expressed microRNAs with possible roles in goat and sheep hair growth has also been reported . Several studies have demonstrated the usefulness of cDNA microarray for expression profiling of wool follicle growth cycles in whole skin -. More recently the RNA-seq method was also used to determine the genes differentially expressed among various tissues (including whole skin) of sheep .
The Aohan fine wool sheep, bred in Inner Mongolia, is an outstanding breed, providing both wool and meat. The major characteristics of this breed are high quality wool,high disease resistance, and high adaptability. According to figures, Aohan fine wool sheep can provide up to 9 kg of quality wool per year (fiber length up to 10.5 cm, fibre diameter less than 22 μm). Therefore, Aohan fine wool sheep are considered as a valuable genetic resource for fine wool production. Wang et al. demonstrated that seasonal factors significantly influenced the wool growth of Aohan fine wool sheep . The peak of the growth rate occurs in summer and the low in winter . The expression profiling of immune genes and type I inner root sheath (IRS) keratin genes in the whole skin of Aohan fine wool sheep has previously been reported by our laboratory team ,.
However, to our knowledge, as of yet no microarray or proteomic study at a genome-wide level has been reported on protein-coding genes which are possibly responsible for regulating hair growth of adult sheep so far. The aims of the present work are to investigate and compare the gene expression level of the body side skin and groin skin using microarray and proteomic technology, and to identify the possible genes and proteins responsible for the wool growth regulation of Aohan fine wool sheep.
Summary of microarray analysis
A total of 1494 probes were differentially expressed comparing the body side to groin skins (S/G) in Aohan fine wool sheep, including 602 up-regulated and 892 down-regulated probes, as shown in Additional file 1: Table S1. Most probes (1110) were not assigned to unique transcripts, due to the lack of information. The number of distinct genes/transcripts (annotated) was 331, of which 112 were up-regulated and 219 were down-regulated. In S/G, 7 genes (CYP1A1, LOC100137068, LOC443300, LOC101106865, Connexin 43, SCD and LOC101122398) were down-regulated by more than 10-fold.
Furthermore, many gene families which regulate different aspects of hair follicle growth showed differential expression in S/G (see Additional file 2: Table S3), such as growth factors, immune cytokines, Keratins (KRT) and Keratin-associated proteins (KAP), and so on.
Selective verification for microarray data by QPCR
Hierarchical cluster analysis, biological process gene ontology (GO) analyses and putative gene networks
Based on the Database for Annotation, Visualization and Integrated Discovery (DAVID), 38 of the differentially expressed genes were classified into four categories, many of which shared the same genes, according to their functional correlation (Additional file 3: Table S2). The majority of the genes possibly related to the wool growth control could be assigned into four categories including regulation of receptor binding, multicellular organismal process, protein binding and macromolecular complex.
Quantitative comparison and identification of protein spots on 2-DE gels
This study is a pilot to establish microarray methodologies in the wool growth field. Our results contrasted the gene expressed in the skin cells from hair-rich and hairless regions at transcriptional and translational levels. The number and fold-change of DE genes in December were much lower than that in August (5605 probes were differentially expressed in August; data not shown). That was compatible with reduced follicle activities in winter ,. And most of DE genes in December were also differentially expressed in August (data not shown).
The advantage of this study is that samples could be obtained easily while the weakness of this study is the low specificity in the samples because the skin samples compose not only wool follicle, but also other parts of the skin.
The limitations encountered during the study and how we determine the results
The small sample size is another weakness of this study. These three animals used in the study were half sibs (sharing the same father). The number of our resource population was not very large, so it was difficult to select more than three even-aged half sibs. It is unusual using only three animals, but this shortcoming could be partly made up for through using genetically related animals. Additionally, to confirm the reliability of the results from the microarray experiments, we conduct the qPCR validation for the expression levels of selected 10 genes, whose fold-changes between the two skin areas ranged from 2.15 to 19.42. Seven of these 10 genes displayed the same regulation directions compared to the microarray results, indicating our differential expression data had relatively reliable value in the field.
The technical approach (microarrays) is another limitation of the study. First, the number of the probes (15208) designed by Agilent is limited compared to the RNA-seq method . Most of the probes were inadequately annotated. The full utility of the microarray data depends on the complete annotation of the whole sheep genome. Second, the specificity of the probes is relatively lower than RNA-sequencing, although using 60-mers long probes in our study. False positive and false negative phenomena exsit unavoidably in the microarray data.
Functional implication of differentially expressed genes in the microarray study
In the present study, we investigated the molecular events possibly related to sheep hair growth control using microarray and proteomic technologies. Transcriptomic analysis identified hundreds of differentially expressed genes displaying over 2-fold difference between the two sampled skin regions of Aohan fine wool sheep at December time point, and the number of less highly expressed transcripts (892) in body side skin was greater than that of the more highly expressed ones (602). The qPCR results validated the reliability of our microarray data.
IL-1A and IL-1B inhibit hair growth in vitro as reported previously , but IL-1A has shown downregulation in body side skin in our study. One possibility is in vivo IL-1A controls wool growth in a time dependent or a negative feedback manner.
The skin of skin-specific SCD1 knock-out (SKO) mice exhibited variable orthokeratotic hyperkeratosis and parakeratotic hyperkeratosis, occasional mal-aligned hair follicles, and instances of protrusion of hair shafts out of the hair follicle and into the surrounding connective tissue, with an infiltration of inflammatory cells surrounding the exposed hair shaft and keratin material . In our study, SCD was less highly expressed at 11.91 fold change in body side skin. This maybe indicate that the SCD could play an important role in groin skin.
MMP2 activity is associated with the disappearance of collagen VII during the invasion of epithelial cords of hair follicles and sweat glands in human skin . MMP2 plays a role in hair growth-associated extracellular matrix remodeling and cell migration, and may be a downstream effector through which thymosin ß4 exerts its effect on hair growth . However, MMP2 showed significant lower expression in body side skin. This may suggest sheep has a different hair growth regulatory mechanism than human.
There is a log linear relationship between the relative level of beta 1 integrins (ITGB1) on the cell surface and proliferative capacity in keratinocytes . ITGB1-mediated signalling is also important in human hair growth control . Skin and hair follicle integrity is crucially dependent on ITGB1 expression in keratinocytes . In our study, however,ITGB1 expression in the body side skin was less highly expressed. The mechanism how it happens deserves further investigation.
Rowe et al. confirmed that predominantly CYP1A1 was located to the sebaceous gland surrounding the hair shaft . It is not known why CYP1A1 expression was less highly expressed more than 10 folds in the body side skin from our data. This interesting phenomenon is worth pursuing in future studies.
As development proceeds, GluD1 expression becomes restricted to the hippocampus, cochlear and vestibular hair cells, and spiral ganglion cells -. Hair follicles are also derived from ectoderm. So why GLUD1 was less highly expressed in body side skin really deserves further investigation.
Connexin 43 (Cx43) is a gap junction protein expressed in the follicular dermal papilla (DP) . A recent mouse model research suggested an important role for Cx43 in hair regeneration, growth, and cuticle formation . However, Connexin 43 was less highly expressed in body side skin in the microarray analysis of this study, and it was also confirmed by qPCR.
Several papers indicated that prostaglandin induced hair growth ,, but in our results, one prostaglandin synthase (PGFS) were less highly expressed in body side skin. This interesting contradiction remains to be further elucidated.
We have indeed tried our best to find out differentially expressed genes associated with LCE and MOGAT as reported in a Science paper . However, we could find only a few genes (DGAT2, LPL, ZO1, GJB2 and ITGB1) that have the possibility to play roles in epidermal development complex (EDC) or skin lipid metabolism. One reason for limited DE genes is that what we compared were different regions of sheep skin tissue (which might share similar principles in EDC regulation and lipid synthesis), not distinct tissues such as rumen and skin. That might also be caused by inadequate annotation of the probes or less gene coverage redundancy of the Agilent microarray.
Functional implication of differentially expressed proteins in the proteomic study
Most of the differentially expressed proteins were not listed above in the Discussion.
Cryab protein was detected in 2DE in groin skin but not in body side skin. But the Cryab transcripts was more highly expressed in body side skin by the microarray study, though the differential expression was not validated by qPCR. This gene, downregulated in the transcriptional level during the depilation-induced hair cycle , was considered to play a role in regulation of apoptosis.
FGF18 protein was less highly expressed in body side skin compared to the control. But the FGF18 transcripts was more highly expressed by the microarray study. Consistent to our microarray results, FGF18 is highly expressed in hair follicles and capable of inducing anagen from telogen stage hair follicles .
PADI3 played roles in the assembly of a globular S100A3 homotetramer,a putative Ca2+ modulator maturing human hair cuticle . But the PADI3 protein was less highly expressed in body side skin compared to the control in our study. This interesting contradiction remains to be further elucidated.
Concordance between the results of transcriptomic and proteomic levels
In the results of transcriptomic level (microarray), the number of more highly expressed genes was less than that of less highly expressed ones (602 versus 892). Consistently, more highly expressed protein spots were less than more lowly expressed ones (85 versus 102).
The concordance between these two results from microarray and proteomic experiments was shown in Additional file 4: Table S4. Of the 46 identified proteins, 13 corresponding transcripts also shown differential expression. Among these 13 transcripts, the regulation trends of only 6 (in green) were consistent with their protein counterparts. The inconsistency of the two levels of other 7 DE genes (in red) might be produced by post transcriptional regulation or for unknown reasons.
The confidence of the differentially expressed transcripts
For the Agilent microarray, the probe number of each gene is indefinite, from 1 to 5. There exists alternative splicing after gene transcription, so the inconsistency between different probes for the same gene is sometimes reasonable. Certainly the confidence is higher if all probes for the same gene were differentially expressed. For example, there are 4 probes for FGF7 and LOC443300(YWHAE) separately, of which 2 were both less highly expressed in S/G. So we speculated that the reliabilities of differential expression of FGF7 and LOC443300 were close to each other. While MMP2 differential expression was only detected in one of three probes, then its confidence might be lower than the former two genes (FGF7 and LOC443300). Nevertheless, because of the inadequate annotation of most probes, we found it difficult to calculate probe number of all the differentially expressed genes.
Figure 5 listed some of the factors which possibly play roles in wool growth regulation . We speculate that lower temperature and shorter day photoperiod were key causes of the downregulation of wool growth rate. So we could speculate that those 8 genes (IL1A,FGF7,TAC1,IGFBP3,CDKN1B,IL6,FGF18 and PRL) played roles in wool growth regulation during winter.
In summary, the data presented in this study suggested that the body side skin displays a differentially expressed pattern in comparison with the groin skin at December time point. The majority of these genes possibly related to the wool growth control, and they could be assigned into the categories including regulation of receptor binding, multicellular organismal process, protein binding and macromolecular complex. Several potential gene families might participate in hair growth regulation, including fibroblast growth factors, transforming growth factor-β, insulin-like growth factor, and so on. Proteomic analysis also identified hundreds of differentially expressed proteins. This systematic analysis could lead to a better understanding of the wool growth control mechanism in Aohan fine wool sheep.
Animals and sample preparation
All animals were treated in accordance with the animal protocols defined by national and local animal welfare bodies, and all animal work was approved by the Shandong Province Biological Studies Animal Care and Use Committee.
Sampling methods were described previously ,. One ram and two ewes of 16-month-old Aohan fine wool sheep were used in the microarray study. These animals were half sibs (sharing the same father). In December 2010, two areas of full-thickness skin were sampled from the same animal under local anaesthesia: body side skin (wool bearing) and groin skin (non-wool bearing) for microarray and proteomic experiments. The area of each sample was about 1 cm2. All samples were immediately put into collection tubes and stored in liquid nitrogen for RNA and protein extraction. A total of 15, 208 probes were spotted on this Agilent Sheep Gene Expression Microarray (Santa Clara, CA, USA).
RNA extraction and microarray hybridization
TRIzol (Invitrogen) was used for total RNA extractions according to the manufacturer’s protocol. RNA quantity and quality were measured by NanoDrop ND-1000. Its OD260/OD280 ratio was confirmed to be higher than 1.8. RNA integrity was assessed by standard denaturing agarose gel electrophoresis. The RNA samples were sent to Kangchen Biotechnology Limited Company (Shanghai, China) for hybridization to the Agilent Sheep Gene Expression Microarray (Santa Clara, CA, USA). Each RNA sample was hybridized to one microarray slide. 1 μg of total RNA from each sample was amplified and transcribed into fluorescent cRNA with using the manufacturer’s Agilent’s Quick Amp Labeling protocol (version 5.7, Agilent Technologies). The labeled cRNAs were hybridized onto the Whole Genome Oligo Array (4x44K, Agilent Technologies).
Microarrays data analysis
After hybridization and washing, the microarray slides were scanned with the Agilent Scanner G2505B. The resulting text files extracted from Agilent Feature Extraction Software (version 10.5.1.1) were imported into the Agilent GeneSpring GX software (version 11.0) for further analysis. Quantile normalization, probe annotation and subsequent data processing were performed using the GeneSpring GX v11.0 software package (Agilent Technologies). After Quantile normalization of the raw data, genes that at least 1 out of 6 samples have flags in Present (“All Targets Value”) were chosen for differentially expressed genes screening. Gene expression levels were quantified relative to the expression of GAPDH. Differentially expressed genes were identified through fold-change screening. The fold-change of 2.0 and a false discovery rate of approximately 5% were set as the threshold. All data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE62552 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc). Clustering analysis of all differentially expressed genes was performed using Cluster 3.0 , to analyze the similarity in the expression patterns among different skin sites. The functional annotation of differentially expressed genes was performed by the DAVID (The Database for Annotation, Visualization and Integrated Discovery) gene annotation tool (http://david.abcc.ncifcrf.gov/) . The KEGG pathway analysis was done manually (http://www.genome.jp/kegg/).
Particular differentially expressed genes, which also appeared in the Figure 1 of reference No. 29 summarizing selective factors with known hair growth regulatory roles , were included in the putative gene networks of wool growth regulation. The networks contained genes possibly playing roles in anagen promotion and maintenance, in catagen-telogen promotion and maintenance, or with function unknown.
Primers used for Q-PCR validation
Primer sequence (5’-3’)
Tm (°C) a
Target size (bp)
Tissue protein extraction
Lysis buffer preparation: 42% Urea, 15.2% Thiourea, 4% CHAPS, 1% DTT. Sampled tissues were homogenziated in lysis buffer (containing 1% cocktail and 2% IPG-buffer, added right before use) at the ratio of 1:7 (weight/volume). The tissues were cut into small pieces by ophthalmic scissors, and left at 4°C for 1 h, vortexed it every 15 min. Then, the tissue homogenate was centrifuge at 40,000 g for 30 min. Supernatants were collected and stored at −80°C. Protein concerntrations were determined by Bradford method.
0.5% IPG-buffer was added into each 150 μg protein sample (in a final volume of 400-600 μL), and was loaded in the One-dimensional electrophoresis instrument. The progamme is as follows: Step-n-hold (S1, 30 V for 6 h; S2, 60 V for 6 h); Gradient (S3, 500 V for 1 h;S4,1000 V for 1 h; S5, 3000 V for 3 h; S6, 8000 V for 3 h); Step-n-hold (S7, 8000 V for 20 h).
2-dimensional (2-D) SDS-PAGE preparation
Tris–HCl (PH = 8.8), Monomer storage (30% Acrylamid and 0.8% NN’-methy lenebisacry lamid), 10 × electrophoresis buffer (3.03% Tris-Base, 14.4% Glycine, 1% SDS), balanced solution (36.05% Urea, 5% Tris–HCl, 2% SDS, 34.5% Glycerine).
The second dimensional SDS-PAGE
The electrophoresis programme is as follows:
Transfer: Voltage 300v, Current 50 mA, Time 1 h.
Separation: Voltage 300v, Current 200 ~ 250 mA, Time 4 ~ 5 h.
Fixative preparation: 40% Ethanol and 10% Acetic acid.
Electrophoresis was carried out until the blue dye front had just disappeared from the bottom of the gel.
Fixation: take out the rubber strip and put it into Fixative for 1 h.
Staining and visualization
Sensitizing solution preparation: 30% Ethanol, 0.314% Na2S2O3, 6.8%NaAc.
Sensitizing: the gels were sensitized for 30 min.
Washing: the gels were washed for three times using ddH2O. Ten minutes each time.
Ilver staining: Silver staining solution was prepared (1.25 AgNO3 and 200 μL Formaldehyde in 500 mL ddH2O). The gels were stained by the solution for 20 min.
Washing: the gels were washed for 2 min using ddH2O.
Visualizing solution preparation: 12.5 g Na2CO3 and 100 μL Formaldehyde in 500 mL ddH2O.
Termination solution preparation: 2 g Glycine in 50 ml ddH2O.
Visualization until the solution became muddy, then terminating for 30 min.
Determination of relative protein expression
Gels were then scanned and analyzed using ImageMaster TM 2D platinum software (Version 5.0, GE Healthcare, San Francisco, CA, USA). The expression level was determined by the relative volume of each spot in the gel and expressed as %Vol (%Vol = [spot volume/Σvolumes of all spots resolved in the gel]). The means and standard deviations of both sample groups were calculated. Statistical significance with Student’s t-tests using ImageMaster TM 2D platinum software. P values <0.05 were considered statistically significant.
Identification of differentially expressed proteins by mass spectrometry (MS)
Protein spots with significant differences between the two groups were excised, dehydrated in acetonitrile, and dried at room temperature. Gel pieces were denatured, alkylated, trypsin digested and analyzed by an Ultraflex II MALDI-TOF-TOF mass spectrometer (Bruker Daltonics GmbH, Bremen, Germany) under the control of FlexControl TM 2.4 software (Bruker Daltonics GmbH). Acquired peptide mass fingerprint (PMF) were processed using the software FlexAnalysis™ 3.0 (Bruker Daltonics, Bremen, Germany). The peak detection algorithm was: SNAP (Sort Neaten Assign and Place); S/N threshold: 1.5; Quality Factor Threshold: 50. The tryptic auto-digestion ion picks (trypsin [108–115] 842.5094 Da, trypsin [58–77] 2211.104 Da) were used as internal standards. The resulting peptide mass lists were used to search the Matrixscience database (http://www.matrixscience.com). The following search parameter criteria were used: mass tolerance 100 ppm, miss cleavage≦1, modification comprises carbamidomethyl and methionine oxidation. Matched peptides number between experimental PMF and theoretical PMF≧5 . All MS data have been deposited in PeptideAtlas and are accessible through Dataset Identifier PASS00597 (http://www.peptideatlas.org/PASS/PASS00597).
All cDNA microarray data have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE62552 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62552).
Proteomic data have been deposited in PeptideAtlas and are accessible through Dataset Identifier PASS00597 (http://www.peptideatlas.org/PASS/PASS00597).
We are very grateful to Prof. Yinlin Ge for technical assistance. This research was supported by the Project of National Hair Sheep Industry Technology System (CARS-40), the Projects of Qingdao People’s Livelihood Science and Technology (13-1-3-88-nsh;14-2-3-45-nsh), and Open Research Fund Program of State key Laboratory of Hydroscience and Engineering (sklhse-2012-D-02).
- Purvis IW, Franklin IR: Major genes and QTL influencing wool production and quality: a review. Genet Sel Evol. 2005, 37 (S1): S97-S107. 10.1186/1297-9686-37-S1-S97.PubMedPubMed CentralView ArticleGoogle Scholar
- Beh KJ, Callaghan MJ, Leish Z, Hulme DJ, Lenane I, Maddox JF: A genome scan for QTL affecting fleece and wool traits in Merino sheep. Wool Technol Sheep Breed. 2001, 49: 88-89.Google Scholar
- Bidinost F, Roldan DL, Dodero AM, Cano EM, Taddeo HR, Mueller JP, Poli MA: Wool quantitative trait loci in Merino sheep. Small Ruminant Res. 2008, 74: 113-118. 10.1016/j.smallrumres.2007.04.005.View ArticleGoogle Scholar
- Cano EM, Marrube G, Roldan DL, Bidinost F, Abad M, Allain D, Vaiman D, Taddeo H, Poli MA, Bray AR: QTL affecting fleece traits in Angora goats. Small Ruminant Res. 2007, 71: 158-164. 10.1016/j.smallrumres.2006.06.002.View ArticleGoogle Scholar
- Itenge TO, Hickford JG, Forrest RH, McKenzie GW, Frampton CM: Improving the quality of wool through the use of gene markers. South Afr J Animal Sci. 2009, 39: 219-223.Google Scholar
- Galbraith H: Fundamental hair follicle biology and fine fibre production in animals. Animal. 2010, 4: 1490-1509. 10.1017/S175173111000025X.PubMedView ArticleGoogle Scholar
- Schmidt-Ullrich R, Paus R: Molecular principles of hair follicle induction and morphogenesis. Bioessays. 2005, 27: 247-261. 10.1002/bies.20184.PubMedView ArticleGoogle Scholar
- McGrice HA: Molecular Characterisation of Primary Wool Follicle Initiation in Merino Sheep. 2010, University of Adelaide, School of agriculture, food and wine, ᅟGoogle Scholar
- Janich P, Pascual G, Merlos-Suarez A, Batlle E, Ripperger J, Albrecht U, Obrietan K, Di Croce L, Benitah SA: The circadian molecular clock creates epidermal stem cell heterogeneity. Nature. 2011, 480: 209-214. 10.1038/nature10649.PubMedView ArticleGoogle Scholar
- Rhee H, Polak L, Fuchs E: Lhx2 maintains stem cell character in hair follicles. Science. 2006, 312: 1946-1949. 10.1126/science.1128004.PubMedPubMed CentralView ArticleGoogle Scholar
- Cotsarelis G: Gene expression profiling gets to the root of human hair follicle stem cells. J Clin Invest. 2006, 116: 19-22. 10.1172/JCI27490.PubMedPubMed CentralView ArticleGoogle Scholar
- Ohyama M, Terunuma A, Tock CL, Radonovich MF, Pise-Masison CA, Hopping SB, Brady JN, Udey MC, Vogel JC: Characterization and isolation of stem cell-enriched human hair follicle bulge cells. J Clin Invest. 2006, 116: 249-260. 10.1172/JCI26043.PubMedPubMed CentralView ArticleGoogle Scholar
- Keane OM, Zadissa A, Wilson T, Hyndman DL, Greer GJ, Baird DB, McCulloch AF, Crawford AM, McEwan JC: Gene expression profiling of naive sheep genetically resistant and susceptible to gastrointestinal nematodes. BMC Genomics. 2006, 7: 42-10.1186/1471-2164-7-42.PubMedPubMed CentralView ArticleGoogle Scholar
- MacKinnon KM, Burton JL, Zajac AM, Notter DR: Microarray analysis reveals difference in gene expression profiles of hair and wool sheep infected with Haemonchus contortus. Vet Immunol Immunopathol. 2009, 130: 210-220. 10.1016/j.vetimm.2009.02.013.PubMedView ArticleGoogle Scholar
- Bongiorni S, Chillemi G, Prosperini G, Bueno S, Valentini A, Pariset L: A tool for sheep product quality: custom microarrays from public databases. Nutrients. 2009, 1: 235-250. 10.3390/nu1020235.PubMedPubMed CentralView ArticleGoogle Scholar
- Faucon F, Rebours E, Bevilacqua C, Helbling JC, Aubert J, Makhzami S, Dhorne-Pollet S, Robin S, Martin P: Terminal differentiation of goat mammary tissue during pregnancy requires the expression of genes involved in immune functions. Physiol Genomics. 2009, 40: 61-82. 10.1152/physiolgenomics.00032.2009.PubMedView ArticleGoogle Scholar
- Ollier S, Robert-Granie C, Bernard L, Chilliard Y, Leroux C: Mammary transcriptome analysis of food-deprived lactating goats highlights genes involved in milk secretion and programmed cell death. J Nutr. 2007, 137: 560-567.PubMedGoogle Scholar
- Norris BJ, Bower NI, Smith WJ, Cam GR, Reverter A: Gene expression profiling of ovine skin and wool follicle development using a combined ovine-bovine skin cDNA microarray. Aust J Exp Agr. 2005, 45: 867-877. 10.1071/EA05050.View ArticleGoogle Scholar
- Smith WJ, Li Y, Ingham A, Collis E, McWilliam SM, Dixon TJ, Norris BJ, Mortimer SI, Moore RJ, Reverter A: A genomics-informed, SNP association study reveals FBLN1 and FABP4 as contributing to resistance to fleece rot in Australian Merino sheep. BMC Vet Res. 2010, 6: 27-10.1186/1746-6148-6-27.PubMedPubMed CentralView ArticleGoogle Scholar
- Penagaricano F, Zorrilla P, Naya H, Robello C, Urioste JI: Gene expression analysis identifies new candidate genes associated with the development of black skin spots in Corriedale sheep. J Appl Genet. 2012, 53: 99-106. 10.1007/s13353-011-0066-9.PubMedView ArticleGoogle Scholar
- Wenguang Z, Jianghong W, Jinquan L, Yashizawa M: A subset of skin-expressed microRNAs with possible roles in goat and sheep hair growth based on expression profiling of mammalian microRNAs. OMICS. 2007, 11: 385-396. 10.1089/omi.2006.0031.PubMedView ArticleGoogle Scholar
- Yu Z-D, Bawden CS, Henderson HV, Nixon AJ, Gordon SW, Pearson AJ: Micro-arrays as a discovery tool for wool genomics. Proc New Zeal SocAnimal Prod. 2006, 66: 129-133.Google Scholar
- Yu Z-D, Gordon SW, Pearson AJ, Henderson HV, Craven AJ, Nixon AJ: Gene expression profiling of wool follicle growth cycles by cDNA microarray. Proc New Zeal SocAnimal Prod. 2008, 68: 39-42.Google Scholar
- Liu N, Li H, Liu K, Yu J, Cheng M, De W, Liu J, Shi S, He Y, Zhao J: Differential expression of genes and proteins associated with wool follicle cycling. Mol Biol Rep 2014, published online in advance of the print edition.,Google Scholar
- Jiang Y, Xie M, Chen W, Talbot R, Maddox JF, Faraut T, Wu C, Muzny DM, Li Y, Zhang W, Stanton JA, Brauning R, Barris WC, Hourlier T, Aken BL, Searle SM, Adelson DL, Bian C, Cam GR, Chen Y, Cheng S, DeSilva U, Dixen K, Dong Y, Fan G, Franklin IR, Fu S, Fuentes-Utrilla P, Guan R, Highland MA, et al: The sheep genome illuminates biology of the rumen and lipid metabolism. Science. 2014, 344 (6188): 1168-1173. 10.1126/science.1252806.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang HR, Feng ZC, Du M, Ren JK, Li HR: Initial research for seasonal variation of wool growth of Aohan fine wool sheep. Inner Mongolia Animal Sci. 1994, 3: 1-3. (in Chinese)Google Scholar
- Yu JJ, Liu JF, Zhao JS, Cheng M, Liu KD, Liu N: Gene chip analysis of expression pattern of type I Inner Root Sheath (IRS) keratin in AoHan wool sheep. Agr Scie Technol. 2012, 13: 1171-1174.Google Scholar
- Zhao JS, Li HG, Liu KD, Liu N, Li JQ: Differential expression of immune genes between body side skin and groin skin of Aohan fine wool sheep. Agr Scie Technol. 2012, 13: 2475-2479.Google Scholar
- McElwee KJ, Sinclair R: Hair Physiology and its Disorders. Drug Discovery Today: Disease Mechanisms. 2008Google Scholar
- Kawano M, Komi-Kuramochi A: Comprehensive analysis of FGF and FGFR expression in skin. J Invest Dermatol. 2005, 124: 877-885. 10.1111/j.0022-202X.2005.23693.x.PubMedView ArticleGoogle Scholar
- Stenn KS, Paus R: Controls of hair follicle cycling. Physiol Rev. 2001, 81: 449-494.PubMedGoogle Scholar
- Sampath H, Flowers MT, Liu X, Paton CM, Sullivan R, Chu K, Zhao M, Ntambi JM: Skin-specific deletion of stearoyl-CoA desaturase-1 alters skin lipid composition and protects mice from high fat diet-induced obesity. J Biol Chem. 2009, 284: 19961-19973. 10.1074/jbc.M109.014225.PubMedPubMed CentralView ArticleGoogle Scholar
- Karelina TV, Bannikov GA, Eisen AZ: Basement membrane zone remodeling during appendageal development in human fetal skin: the absence of type VII collagen is associated with gelatinase-A (MMP2) activity. J Invest Dermatol. 2000, 114: 371-375. 10.1046/j.1523-1747.2000.00886.x.PubMedView ArticleGoogle Scholar
- Philp D, Nguyen M, Scheremeta B, St-Surin S, Villa AM, Orgel A, Kleinman HK, Elkin M: Thymosin beta4 increases hair growth by activation of hair follicle stem cells. FASEB J. 2004, 18: 385-387.PubMedGoogle Scholar
- Jones PH, Watt FM: Separation of human epidermal stem cells from transit amplifying cells on the basis of differences in integrin function and expression. Cell. 1993, 73: 713-724. 10.1016/0092-8674(93)90251-K.PubMedView ArticleGoogle Scholar
- Kloepper JE, Hendrix S, Bodo E, Tiede S, Humphries MJ, Philpott MP, Fassler R, Paus R: Functional role of beta 1 integrin-mediated signalling in the human hair follicle. Exp Cell Res. 2008, 314: 498-508. 10.1016/j.yexcr.2007.10.030.PubMedView ArticleGoogle Scholar
- Brakebusch C, Grose R, Quondamatteo F, Ramirez A, Jorcano JL, Pirro A, Svensson M, Herken R, Sasaki T, Timpl R, Werner S, Fassler R: Skin and hair follicle integrity is crucially dependent on beta 1 integrin expression on keratinocytes. EMBO J. 2000, 19: 3990-4003. 10.1093/emboj/19.15.3990.PubMedPubMed CentralView ArticleGoogle Scholar
- Rowe JM, Welsh C, Pena RN, Wolf CR, Brown K, Whitelaw CB: Illuminating role of CYP1A1 in skin function. J Invest Dermatol. 2008, 128: 1866-1868. 10.1038/sj.jid.5701236.PubMedView ArticleGoogle Scholar
- Ryu K, Yokoyama M, Yamashita M, Hirano T: Induction of excitatory and inhibitory presynaptic differentiation by GluD1. Biochem Biophys Res Commun. 2012, 417: 157-161. 10.1016/j.bbrc.2011.11.075.PubMedView ArticleGoogle Scholar
- Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, Boe AF, Boguski MS, Brockway KS, Byrnes EJ, Chen L, Chen TM, Chin MC, Chong J, Crook BE, Czaplinska A, Dang CN, Datta S, Dee NR, Desaki AL, Desta T, Diep E, Dolbeare TA, Donelan MJ, Dong HW, Dougherty JG, Duncan BJ, Ebbert AJ, Eichele G, Estin LK, et al: Genome-wide atlas of gene expression in the adult mouse brain. Nature. 2007, 445: 168-176. 10.1038/nature05453.PubMedView ArticleGoogle Scholar
- Magdaleno S, Jensen P, Brumwell CL, Seal A, Lehman K, Asbury A, Cheung T, Cornelius T, Batten DM, Eden C, Norland SM, Rice DS, Dosooye N, Shakya S, Mehta P, Curran T: BGEM: an in situ hybridization database of gene expression in the embryonic and adult mouse nervous system. PLoS Biol. 2006, 4: e86-10.1371/journal.pbio.0040086.PubMedPubMed CentralView ArticleGoogle Scholar
- Higgins CA, Richardson GD, Ferdinando D, Westgate GE, Jahoda CA: Modelling the hair follicle dermal papilla using spheroid cell cultures. Exp Dermatol. 2010, 19: 546-548. 10.1111/j.1600-0625.2009.01007.x.PubMedView ArticleGoogle Scholar
- Churko JM, Chan J, Shao Q, Laird DW: The G60S connexin43 mutant regulates hair growth and hair fiber morphology in a mouse model of human oculodentodigital dysplasia. J Invest Dermatol. 2011, 131: 2197-2204. 10.1038/jid.2011.183.PubMedView ArticleGoogle Scholar
- Colombe L, Vindrios A, Michelet JF, Bernard BA: Prostaglandin metabolism in human hair follicle. Exp Dermatol. 2007, 16: 762-769. 10.1111/j.1600-0625.2007.00586.x.PubMedView ArticleGoogle Scholar
- Johnstone MA, Albert DM: Prostaglandin-induced hair growth. Surv Ophthalmol. 2002, 47 (S1): S185-S202. 10.1016/S0039-6257(02)00307-7.PubMedView ArticleGoogle Scholar
- Umeda-Ikawa A: Time-course expression profiles of hair cycle-associated genes in male mini rats after depilation of telogen-phase hairs. Int J Mol Sci. 2009, 10: 1967-1977. 10.3390/ijms10051967.PubMedPubMed CentralView ArticleGoogle Scholar
- Powell BC, Beltrame JS: Characterization of a hair (wool) keratin intermediate filament gene domain. J Invest Dermatol. 1994, 102: 171-177. 10.1111/1523-1747.ep12371758.PubMedView ArticleGoogle Scholar
- Yu Z, Gordon SW, Nixon AJ, Bawden CS, Rogers MA, Wildermoth JE, Maqbool NJ, Pearson AJ: Expression patterns of keratin intermediate filament and keratin associated protein genes in wool follicles. Differentiation. 2009, 77: 307-316. 10.1016/j.diff.2008.10.009.PubMedView ArticleGoogle Scholar
- Goh BK, Common JE, Gan WH, Kumarasinghe P: A case of dermatopathia pigmentosa reticularis with wiry scalp hair and digital fibromatosis resulting from a recurrent KRT14 mutation. Clin Exp Dermatol. 2009, 34: 340-343. 10.1111/j.1365-2230.2008.02950.x.PubMedView ArticleGoogle Scholar
- Lugassy J, Itin P, Ishida-Yamamoto A, Holland K, Huson S, Geiger D, Hennies HC, Margarita I, Dani B, Jouni U, Bergman R, McGrath JA, Richard G, Sprecher E: Naegeli-Franceschetti-Jadassohn syndrome and dermatopathia pigmentosa reticularis: two allelic ectodermal dysplasias caused by dominant mutations in KRT14. Am J Hum Genet. 2006, 79: 724-730. 10.1086/507792.PubMedPubMed CentralView ArticleGoogle Scholar
- Horner ME, Parkinson KE, Kaye V, Lynch PJ: Dowling-Degos disease involving the vulva and back: case report and review of the literature. Dermatol Online J. 2011, 17: 1-PubMedGoogle Scholar
- Kizawa K, Takahara H, Troxler H, Kleinert P, Mochida U, Heizmann CW: Specific citrullination causes assembly of a globular S100A3 homotetramer: a putative Ca2+ modulator matures human hair cuticle. J Biol Chem. 2008, 283: 5004-5013. 10.1074/jbc.M709357200.PubMedView ArticleGoogle Scholar
- Lueking A, Huber O, Wirths C, Schulte K, Stieler KM, Blume-Peytavi U, Kowald A, Hensel-Wiegel K, Tauber R, Lehrach H, Meyer HE, Cahill DJ: Profiling of alopecia areata autoantigens based on protein microarray technology. Mol Cell Proteomics. 2005, 4: 1382-1390. 10.1074/mcp.T500004-MCP200.PubMedView ArticleGoogle Scholar
- Rosenquist TA, Martin GR: Fibroblast growth factor signalling in the hair growth cycle: expression of the fibroblast growth factor receptor and ligand genes in the murine hair follicle. Dev Dyn. 1996, 205: 379-386. 10.1002/(SICI)1097-0177(199604)205:4<379::AID-AJA2>3.0.CO;2-F.PubMedView ArticleGoogle Scholar
- Nacht M, Dracheva T, Gao Y, Fujii T, Chen Y, Player A, Akmaev V, Cook B, Dufault M, Zhang M, Zhang W, Guo M, Curran J, Han S, Sidransky D, Buetow K, Madden SL, Jen J: Molecular characteristics of non-small cell lung cancer. Proc Natl Acad Sci U S A. 2001, 98: 15203-15208. 10.1073/pnas.261414598.PubMedPubMed CentralView ArticleGoogle Scholar
- Tang Z, Li Y, Wan P, Li X, Zhao S, Liu B, Fan B, Zhu M, Yu M, Li K: LongSAGE analysis of skeletal muscle at three prenatal stages in Tongcheng and Landrace pigs. Genome Biol. 2007, 8: R115-10.1186/gb-2007-8-6-r115.PubMedPubMed CentralView ArticleGoogle Scholar
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003, 4: P3-10.1186/gb-2003-4-5-p3.PubMedView ArticleGoogle Scholar
- Berndt P, Hobohm U, Langen H: Reliable automatic protein identification from matrix-assisted laser desorption/ionization mass spectrometric peptide fingerprints. Electrophoresis. 1999, 20: 3521-3526. 10.1002/(SICI)1522-2683(19991201)20:18<3521::AID-ELPS3521>3.0.CO;2-8.PubMedView ArticleGoogle Scholar
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