INTRODUCTION

The number of cosmeceutical products on the market which claim a variety of beneficial effects on skin structure and function is growing rapidly with new product introductions occurring almost daily. Products which claim effectiveness in stimulating collagen and elastin production, blocking activity of matrix metalloproteinases, and slowing down the aging process are widely available and most advertise that ‘scientific research’ is behind their development. In reality, few ingredients in cosmetic products have been shown, by rigorous laboratory analysis, to have specific antiaging effects. Note-worthy exceptions are retinoic acid and its derivatives, vitamin C, and Matrixyl (palmitoyl-L-lysyl-L-threonyl-L-threonyl-L-lysyl-L-serine), which are three compounds for which credible scientific data exists to support antiaging claims. The development of truly efficacious cosmeceuticals involves:

Since the first step in developing an effective cosmeceutical product is to demonstrate that the putative ‘active’ ingredient not only produces the desired biologic action but also does not have any deleterious effect on skin structure or function, it would be advantageous to have access to a single biologic screening tool that could accomplish both needs simultaneously. Such a screening method would allow one to predict a compound’s efficacy prior to undertaking any laborious formulation development and before conducting expensive clinical studies. The use of gene array technology fulfills these requirements.

BASIC PRINCIPLES OF GENE ARRAY ANALYSIS

All cells in the body continuously produce a specific set of proteins that defines the structure and function of that particular cell type. For example, liver cells produce unique hormone receptors for glucagon and insulin, while kidney cells produce proteins for the vasopressin receptor and for those involved in ion transport. These proteins are coded for by genes that produce unique mRNAs and, thus, each cell type expresses a unique ‘footprint’ of these mRNAs. Under certain conditions such as ultraviolet radiation (UVR), hormone influence, and aging, this profile of mRNA expression changes as do the proteins coded for by these ‘messengers’. Thus, for example, in young skin, dermal fibroblasts express mRNA for the proteins collagen I, III, and VII, whereas in aged skin the fibroblasts produce less mRNA for the collagens but more mRNA for the enzyme MMP-1 (matrix metalloproteinase 1; collagenase 1) which destroys collagen. With the advent of modern molecular biology gene arrays, it is now possible to isolate a ‘pool’ of mRNA from cells expressing different phenotypes (e.g. young and old human fibroblasts) and, from an analysis of these mRNAs, determine which genes are being expressed or repressed in different cell types or in cells exposed to different conditions.

Gene arrays are filters or glass slides to which are bound small pieces of known and unknown (EST—expressed sequence tags) human genes. Typical nylon gene array filters may contain over 5000 different gene sequences on a single filter and some arrays have been designed with specific tissues or diseases in mind. For example, a gene filter has been designed to which over 4000 ‘skin specific’ genes have been bound, allowing one to assess the effects of biologic modifiers such as hormones, cytokines, and UVR on the expression of genes important in skin.

The sequence of steps involved in a gene array analysis is shown in Figure 31.1. The first step involves isolating mRNA from cells that represent the ‘control’ group, and from cells exposed to some experimental condition, such as UVR (‘experimental’ group). The mRNA preparation from each group is then reverse transcribed into ‘complementary DNA’ (cDNA), which is more stable and hybridizes better to DNA than mRNA. This cDNA is then labeled with either a radioisotope or a fluorescent tag so that each unique cDNA can be detected and identified at the conclusion of the experiment. Once the cDNAs have been tagged, they are incubated with the gene array filter (e.g. the ‘skin specific’ array) so that hybridization between a given cDNA and its complementary DNA on the array can occur. Once hybridization is complete, any unbound cDNA is washed away and the hybridized cDNA is detected and quantified. Since the location and identity of each gene on the filter is known, by comparing the quantified spots on the array produced from the ‘control’ group to those spots that are produced in the ‘experimental’ array, one can determine if a particular gene in the experimental group is upregulated or downregulated relative to the control group. Given the complexity of gene arrays, a computer software program is used to aid in the quantification and analysis of the large amount of data that is obtained. The software produces an ‘overlay’ image of both gene array filters, calculates the difference in expression level for each gene between the control and experimental groups, and then converts this relative expression data into a color image. Typically, a gene that is upregulated in the experimental group relative to the control group is shown as a green spot on the computer-generated image while genes that are downregulated are shown in red. An example of the use of this technology in the identification of a novel antiaging and anti-inflammatory active is discussed below.

Fig. 31.1 Sequence of steps in gene array analysis. Yellow stars on the cDNAs represent radioactive or fluorescent label. Colored circles on the control and experimental arrays represent mRNAs that are expressed, while white circles represent mRNAs that are not expressed

APPLICATION OF GENE ARRAYS TO THE IDENTIFICATION AND CHARACTERIZATION OF ANTIAGING AND ANTI-INFLAMMATORY BIOACTIVE MOLECULES

As results from microarray technology have become more reliable and reproducible over the last few years, it has become possible to determine the effects of candidate ‘bioactive’ molecules on skin cells with increased confidence. Furthermore, the use of microarrays has expanded from basic research studies for candidate compound identification to screening tissue samples in a clinical setting to determine an individual’s susceptibility to certain diseases such as cancer. Due to the vast amount of data obtained from one particular experiment (e.g. 4000 genes of interest on one DNA filter), it is advantageous to only select a highly restricted set of ‘critical’ genes of interest for investigation. For example, if anti-inflammatory activity is desired one might investigate the regulation of genes such as COX-2 (PGE₂ producing gene), IL-1α, IL-6, IL-8, and TNF-α. Alternatively, if an antiaging bioactive was desired one would focus on the expression of extracellular matrix genes such as collagens, elastin, and proteoglycans combined with the inhibition of matrix degrading proteases such as collagenase and the gelatinases. Microarray technology also offers the opportunity to identify new beneficial effects that would not have been discovered otherwise using typical single gene experiments.

We have used gene array technology to identify unique compounds that have antiaging and/or anti-inflammatory effects in multiple skin cell types. In one study we assessed the ability of a novel nitrone spin-trap compound to modulate the expression of aging-related genes in older human dermal fibroblasts. The fibroblasts were grown in the presence (experimental) or absence (control) of the spin-trap nitrone compound, CX-412, for 48 hours at which time the mRNA from each cell culture group was isolated, converted to a complementary DNA (cDNA), labeled with radioactive nucleotides, and hybridized to IntegriDerm DermArray gene filters. These microarray filters contain over 4400 unique cDNAs specifically chosen due to their expression in skin cells and relevance to dermatologic research. Genes that are commonly expressed in skin are spotted in duplicate at different sites on the gene filters to provide an estimate of reproducibility of the hybridization reaction. The hybridization images are imported into a computer program that normalizes the data set and provides a color-coded picture of which expressed genes in the CX-412 treated fibroblasts were upregulated (coded in green) or downregulated (coded in red) relative to the untreated fibroblast cultures (Figure 31.2C). Figures 31.2A and B show the actual filter images of the hybridized radioactive cDNAs which were upregulated or downregulated by the spin-trap, CX-412.

Fig. 31.2 IntegriDerm gene array results in fibroblasts treated with the spin-trap nitrone compound, CX-412. cDNA from cells treated with control (A) or CX-412 (B) was hybridized to gene array nylon membranes. Following hybridization, the blots were developed using a Packard Cyclone phosphorimager. (C) The computer-generated comparison image showed that CX-412 reduced the expression of monocyte chemotactic protein (MCP-1) and collagenase-1 (MMP-1) as evidenced by the red hybridization spots, but induced the expression of tissue inhibitor of metalloproteinase-1 (TIMP-1) as evidenced by the green hybridization spots

After quantifying the level of all expressed genes in control and CX-412 treated cells, we found that aged fibroblasts treated with CX-412 shifted their expression patterns from matrix destruction to matrix production (Box 31.1). For example, inhibition of the matrix metalloproteinases collagenase 1 and 92 kDa gelatinase was noted, while the naturally occurring inhibitors of MMPs, TIMP-1 and TIMP-2, were upregulated. Furthermore, the expression of collagen types I, II, and III were enhanced in fibroblasts. In addition to age-related genes we also found that certain inflammation-associated genes, including uPA, tPA, PAI-1, IL-1α, and IL-6 were markedly inhibited by the spin-trap compound tested.

Once the data from gene arrays are analyzed, simpler tests, including ELISAs and newly developed protein antibody arrays, can be used to confirm the gene array results for any given compound and to provide the necessary dose–response data needed to decide on the optimum concentration of the compound to use in topical formulations.

SUMMARY

The use of gene array technology allows one to obtain, from a small number of experiments, a wealth of information on specific genes that are both stimulated and repressed by any compound being considered for cosmeceutical development. The technique can be used to identify new drug candidates for treating such dermatologic diseases as psoriasis, atopic dermatitis, seborrheic dermatitis, and even actinic keratoses, and as shown above is useful for identifying compounds that can reduce, delay, or even reverse the skin-aging process. Finally, gene arrays provide an opportunity to quickly analyze cosmeceutical ‘actives’ and botanical extracts currently in skin care products to determine what positive and negative effects these ‘actives’ may actually have on skin function.