Project description:Hair follicles undergo recurrent cycling of controlled growth (anagen), regression (catagen), and relative quiescence (telogen) with a defined periodicity. Taking a genomics approach to study gene expression during synchronized mouse hair follicle cycling, we discovered that, in addition to circadian fluctuation, CLOCK-regulated genes are also modulated in phase with the hair growth cycle. During telogen and early anagen, circadian clock genes are prominently expressed in the secondary hair germ, which contains precursor cells for the growing follicle. Analysis of Clock and Bmal1 mutant mice reveals a delay in anagen progression, and the secondary hair germ cells show decreased levels of phosphorylated Rb and lack mitotic cells, suggesting that circadian clock genes regulate anagen progression via their effect on the cell cycle. Consistent with a block at the G1 phase of the cell cycle, we show a significant upregulation of p21 in Bmal1 mutant skin. While circadian clock mechanisms have been implicated in a variety of diurnal biological processes, our findings indicate that circadian clock genes may be utilized to modulate the progression of non-diurnal cyclic processes. To investigate the molecular control of hair follicle cycling, we profiled mRNA expression in mouse dorsal skin at multiple representative time points in the synchronized second postnatal hair growth cycle and in a depilation-induced hair growth cycle. For profiling of second synchronized and depilation-induced hair growth cycle, the same upper-mid region of dorsal skin was excised from C57BL/6 mice at representative postnatal days (P). The time points for second hair growth cycle are classified into different phases of the hair growth cycle based on established morphological guidelines as follow: early anagen (P23, P25), mid anagen (P27), late anagen (P29, P34), early catagen (P37, P39), mid catagen (P41), and telogen (P44). Depilation-induced hair growth cycle by applying wax/rosin mixture on the dorsal skin of seven-week old mice (all follicles in telogen) was performed on mice. The corresponding phases of the hair growth cycle at number of days following depilation (D) is as follow: early anagen (D3), mid anagen (D5), late anagen (D8, D12), and early catagen (D17). For each time point, multiple biological replicates were profiled, with each mouse dorsal skin separately hybridized to an Affymetrix array.

Project description:Inner Mongolia Cashmere Goat is a local excellent breed of cashmere and meat dual-purpose, which is a typical heterogeneous indumentum. The hair follicles cycle through periods of vigorous growth (anagen), a regression caused by apoptosis (catagen), and relative rest (telogen). At present, it is not clear which genes affect the cycle transformation of hair follicles and unclear how proteins impact the creation and expansion of hair follicles. In this work, we investigated the possible pathways of transformation and apoptosis in goat hair follicles using multi-omics joint analysis methodologies. The results showed that 917 , 1187 and 716 proteins were specifically expressed in anagen, catagen and telogen. The result of gene ontology (GO) annotation showed that differentially expressed proteins（DEPs) are in different growth cycle periods, and enriched GO items are mostly related to the transformation of cells and proteins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment result indicated that the apoptosis process has a great impact on hair follicle's growth cycle. The results of the protein interaction network of differential proteins showed that the Ribosomal Protein family ( RPL4, RPL8, RPS16, RPS18, RPS2, RPS27A, RPS3 ) was the core protein in the network. The results of combined transcriptome and proteomics analysis showed that there were 16,34, and 26 overlapped DEGs and DEPs in the comparison of anagen VS catagen, catagen VS telogen and anagen VS telogen, of which API5 plays an important role in regulating protein and gene expression levels. We focused on API5 and Ribosomal protein and found that API5 affected the apoptosis process of hair follicles, and Ribosomal Protein was highly expressed in the resting stage of hair follicles. They are both useful as molecular marker candidate genes to study hair follicle growth and apoptosis, and they both have an essential function in the cycle transition process of hair follicles. The results of this study may provide a theoretical basis for further research on the growth and development of hair follicles in Inner Mongolian Cashmere goats.

Project description:This a model from the article: The follicular automaton model: effect of stochasticity and of synchronization of hair cycles. Halloy J, Bernard BA, Loussouarn G, Goldbeter A. J Theor Biol 2002 Feb 7;214(3):469-79 11846603 , Abstract: Human scalp hair consists of a set of about 10(5)follicles which progress independently through developmental cycles. Each hair follicle successively goes through the anagen (A), catagen (C), telogen (T) and latency (L) phases that correspond, respectively, to growth, arrest and hair shedding before a new anagen phase is initiated. Long-term experimental observations in a group of ten male, alopecic and non-alopecic volunteers allowed determination of the characteristics of hair follicle cycles. On the basis of these observations, we previously proposed a follicular automaton model to simulate the dynamics of human hair cycles and the development of different patterns of alopecia [Halloy et al. (2000) Proc. Natl Acad. Sci. U.S.A.97, 8328-8333]. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states A, T, L and the subsequent return to A. These transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a standard deviation. The follicular automaton model was shown to account both for the dynamical transitions observed in a single follicle, and for the behaviour of an ensemble of independently cycling follicles. Here, we extend these results and investigate additional properties of the model. We present a deterministic version of the follicular automaton. We show that numerical simulations of the stochastic version of the automaton yield steady-state level of follicles in the different phases which approach the levels predicted by the deterministic equations as the number of follicles progressively increases. Only the stochastic version can successfully reproduce the fluctuations of the fractions of follicles in each of the three phases, observed in small follicle populations. When the standard deviation is reduced or when the follicles become otherwise synchronized, e.g. by a periodic external signal inducing the transition of anagen follicles into telogen phase, large-amplitude oscillations occur in the fractions of follicles in the three phases. These oscillations are not observed in humans but are reminiscent of the phenomenon of moulting observed in a number of mammalian species. Copyright 2002 Elsevier Science Ltd. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Halloy J, Bernard BA, Loussouarn G, Goldbeter A. (2002) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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