Modeling the dynamics of human hair cycles by a follicular automaton

The hair follicle cycle successively goes through the anagen, catagen, telogen, and latency phases, which correspond, respectively, to hair growth, arrest, shedding, and absence before a new anagen phase is initiated. Experimental observations collected over a period of 14 years in a group of 10 male volunteers, alopecic and nonalopecic, allowed us to determine the characteristics of scalp hair follicle cycles. On the basis of these observations, we propose a follicular automaton model to simulate the dynamics of human hair cycles. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states anagen, telogen, and latency, and the subsequent return to anagen. The transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a variance. The follicular automaton model accounts both for the dynamical transitions observed in a single follicle and for the behavior of an ensemble of independently cycling follicles. Thus, the model successfully reproduces the evolution of the fractions of follicle populations in each of the three phases, which fluctuate around steady-state or slowly drifting values. We apply the follicular automaton model to the study of spatial patterns of follicular growth that result from a spatially heterogeneous distribution of parameters such as the mean duration of anagen phase. When considering that follicles die or miniaturize after going through a critical number of successive cycles, the model can reproduce the evolution to hair patterns similar to well known types of diffuse or androgenetic alopecia.

Accumulation of a Clock-Regulated Transcript during Flower-Inductive Darkness in Pharbitis nil1

To clarify the molecular basis of the photoperiodic induction of flowering in the short-day plant Pharbitis nil cv Violet, we examined changes in the level of mRNA in cotyledons during the flower-inductive photoperiod using the technique of differential display by the polymerase chain reaction. A transcript that accumulated during the inductive dark period was identified and a cDNA corresponding to the transcript, designated PnC401 (P. nil C401), was isolated. RNA-blot hybridization verified that levels of PnC401 mRNA fluctuated with a circadian rhythm, with maxima between 12 and 16 h after the beginning of the dark period) and minima of approximately 0. This oscillation continued even during an extended dark period but was damped under continuous light. Accumulation of PnC401 mRNA was reduced by a brief exposure to red light at the 8th h of the dark period (night-break treatment) or by exposure to far-red light at the end of the light period (end-of-day far-red treatment). These results suggest that fluctuations in levels of PnC401 mRNA are regulated by phytochrome(s) and a circadian clock and that they are associated with photoperiodic events that include induction of flowering.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Use of an oocyte expression assay to reconstitute inductive signaling.

We have developed a paracrine signaling assay capable of mimicking inductive events in the early vertebrate embryo. RNA encoding one or more secreted proteins is microinjected into a Xenopus laevis oocyte. After a brief incubation to allow translation, a piece of embryonic tissue competent to respond to the signaling protein is grafted onto the oocyte. The secreted protein's effect on the grafted explant is then scored by assaying expression of tissue-specific markers. Explants of ectodermal tissue from blastula or gastrula stage embryos were grafted onto oocytes that had been injected with RNA encoding activin or noggin. We found that the paracrine assay faithfully reconstitutes mesoderm induction by activin and neural induction by noggin. Blastula-stage explants grafted onto activin-expressing oocytes expressed the mesodermal marker genes brachyury, goosecoid, and muscle actin. Gastrula-stage explants grafted onto noggin-expressing oocytes expressed neural cell adhesion molecule (NCAM) and formed cement gland. By injecting pools of RNA synthesized from a cDNA expression library into the oocyte, we also used the assay to screen for secreted neural-inducing proteins. We assayed 20,000 independent transformants of a library constructed from LiCl-dorsalized Xenopus laevis embryos, and we identified two cDNAs that induced neural tissue in ectodermal explants from gastrula-stage embryos. Both cDNAs encode noggin. These results suggest that the paracrine assay will be useful for the cloning of novel signaling proteins as well as for the analysis of known factors.

Basic fibroblast growth factor can mediate the early inductive events in renal development.

The earliest characterized events during induction of tubulogenesis in renal anlage include the condensation or compaction of metanephrogenic mesenchyme with the concurrent upregulation of WT1, the gene encoding the Wilms tumor transcriptional activator/suppressor. We report that basic fibroblast growth factor (FGF2) can mimic the early effects of an inductor tissue by promoting the condensation of mesenchyme and inhibiting the tissue degeneration associated with the absence of an inductor tissue. By in situ hybridization, FGF2 was also found to mediate the transcriptional activation of WT1 and of the hepatocyte growth factor receptor gene, c-met. Although FGF2 can induce these early events of renal tubulogenesis, it cannot promote the epithelial conversion associated with tubule formation in metanephrogenic mesenchyme. For this, an undefined factor(s) from pituitary extract in combination with FGF2 can cause tubule formation in uninduced mesenchyme. These findings support the concept that induction in kidney is a multiphasic process that is mediated by more than a single comprehensive inductive factor and that soluble molecules can mimic these inductive activities in isolated uninduced metanephrogenic mesenchyme.