Patent Application
20120034613
The present disclosure relates to apparatus and methods for testing to identify and select genes associated with certain physical attributes of skin and methods of assessing the efficacy of a skin anti-aging agent.
The sequencing of the human genome and the continual development of analytical methods to quickly and inexpensively measure whole-genome gene expression changes has created an overload of information and a need for methods to organize, focus and reduce the data resulting from gene level analyses of biological processes.
Analysis of the biological process of aging may be performed at the genomic level. Skin is the largest organ of the human body. The skin aging process is influenced by many different extrinsic (e.g., environmental) or intrinsic (including genetic and biochemical pathway) factors.
However, examining all the changes at the human genome or genomic product level can be overwhelming, particularly when strategies of anti-aging are examined. In skin, there are thousands of changes in gene expression with chronological aging and photo-aging (Robinson et al., Genomic-driven insights into changes in aging skin, J. Drugs Dermatol., 2009, 8(7 Suppl):s8-11). Not only does genome-wide testing produce massive data sets, the literature reporting on genetic research is also constantly growing, making it difficult to bring together all desired knowledge and test results.
Focusing research resources on a smaller group of genes and/or associated proteins, and the biochemical pathways associated with the groups of genes and/or proteins may be used to reduce confusion and distraction from the many unknown or irrelevant factors and variables. In addition, there is a cost to including genes in an analysis. If fewer genes are analyzed due to a better focus, the costs of research are reduced.
On the other hand, for an organ as complex as skin and a problem such as aging that has multiple dimensions, too narrow a focus also may be problematic, by excluding genetic pathways that play a role in skin aging. An approach including an overall strategy to slow down physical manifestations of the aging process in skin by examining at the gene level several mechanisms of aging simultaneously may be used, instead of in-depth analysis of each individual gene.
The present disclosure has been developed against this backdrop.
In a first aspect, the present disclosure is directed to a method of testing to identify genes associated with one or more physical attributes of skin aging. The methods comprise exposing a first sample of human skin cells or tissue to an agent, determining a first set of expression levels of a plurality of genes in the first sample of human skin, comparing the first set of expression levels to a second set of expression levels, the second set of expression levels corresponding to expression levels of human skin tissue not exposed to the agent, to identify a first subset of genes having a fold change difference in expression level between the exposed and unexposed samples that meets a first, selected biological relevance level, selecting from the first subset of genes a second subset of genes, each gene being associated with a biochemical pathway associated with physical appearance of skin aging, selecting from the second subset of genes, at least one skin attribute subset of genes, each gene in the skin attribute subset being associated with a biochemical pathway relating to the skin attribute that is shown in the comparing step to have been regulated in a more youthful direction for that biochemical pathway and skin attribute, exposing a second sample of human skin tissue to the agent, determining the levels of expression for the at least one skin attribute subset of genes in the second sample of human skin tissue using a method for determining expression levels that is different than that used for the first sample of human skin tissue, and selecting a third subset of genes from the at least one skin attribute subset of genes whose expression levels in the second sample of human skin tissue meet a second, selected biological relevance level and whose direction of regulation conforms to the more youthful direction used in selecting the at least one skin attribute subset of genes.
In some embodiments the biochemical pathway associated with the physical appearance of skin aging comprises at least one of skin structural protein synthesis, skin structural degradation and maintenance, extracellular matrix assembly, cellular differentiation, skin barrier component synthesis, skin barrier integrity, water regulation, or regulation of melanin production and control.
In some embodiments, the skin attribute for the at least one skin attribute subset of genes is skin structure, skin pigmentation, skin hydration or cell turnover.
In another embodiment, the first, selected biological relevance level is about a two fold difference between the exposed and unexposed samples.
In some embodiments, the human skin tissue comprises skin cells comprising at least one of keratinocytes, fibroblasts, adipocytes, melanocytes or combinations thereof.
In another embodiment, the first set of expression levels of a plurality of gene comprises expression levels for essentially the full human genome.
In other embodiments, the method of determining expression levels that is different than that used for the first sample of human tissue is a method using an RNA quantification metric.
In some embodiments, in the step of selecting from the second subset of genes, at least one skin attribute subset of genes, each gene in the skin attribute subset being associated with a biochemical pathway relating to the skin attribute that is shown in the comparing step to have been regulated in a more youthful direction for that biochemical pathway and skin attribute comprising performing this step for a plurality of skin attribute subsets of genes, and the step selecting a third subset of genes from the at least one skin attribute subset of genes whose expression levels in the second sample of human skin tissue meet a second, selected biological relevance level and whose direction of regulation conforms to the more youthful direction used in selecting the at least one skin attribute subset of genes comprising performing this step for a plurality of skin attribute subsets of genes.
In another embodiment, the plurality of skin attribute subsets of genes are two or more skin attribute subset of genes selected from the group consisting of skin structure, skin pigmentation, skin hydration and cell turnover.
In other embodiments, the method further comprises determining the levels of expression for additional genes associated with a biochemical pathway associated with skin aging in the second sample of human skin tissue using a method for determining expression levels that is different than that used for the first sample of human tissue, and selecting for the third subset of genes those genes from the additional genes associated with a biochemical pathway associated with skin aging whose expression levels in the second sample of human skin tissue meet a second, selected biological relevance level and whose direction of regulation conforms to the more youthful direction of regulation of the associated biochemical pathway.
In a second aspect, a computer based system of testing to identify genes associated with one or more physical attributes of skin aging comprises a first instrument for exposing a first sample of human skin tissue to an agent and determining a first set of expression levels of a plurality of genes in the first sample of human skin, a computer module for comparing the first set of expression levels to a second set of expression levels, the second set of expression levels corresponding to expression levels of human skin tissue not exposed to the agent to identify a first subset of genes having a fold change difference in expression level between the exposed and unexposed samples that meet a first, selected biological relevance level, a computer module for accessing a stored data set identifying genes, each gene being associated with a biochemical pathway associated with physical appearance of skin aging and for selecting from the first subset a second subset comprising those genes also in the second subset, a computer module for selecting from the second subset of genes, at least one skin attribute subset of genes, each gene in the skin attribute subset being associated with a biochemical pathway relating to the skin attribute that is shown in the comparing step to have been regulated in a more youthful direction for that biochemical pathway and skin attribute, a second instrument for exposing a second sample of human skin tissue to the agent and for determining the levels of expression for the at least one skin attribute subset of genes in the second sample of human skin tissue using a method for determining expression levels that is different than that used for the first sample of human tissue, a computer module for selecting a third subset of genes from the at least one skin attribute subset of genes whose expression levels in the second sample of human skin tissue meet a second, selected biological relevance level and whose direction of regulation conforms to the more youthful direction used in selecting the at least one skin attribute subset of genes.
In some embodiments in the system, the skin attribute for the at least one skin attribute subset of genes is skin structure, skin pigmentation, skin hydration or cell turnover. In another embodiment in the system, the first, selected biological relevance level is about a two fold difference between the exposed and unexposed samples.
In other embodiments in the system, the human skin tissue comprises skin cells comprising at least one of keratinocytes, fibroblasts, adipocytes, melanocytes or combinations thereof.
In some embodiments in the system the first set of expression levels of a plurality of genes comprises expression levels for essentially the full human genome.
In another embodiment in the system, the second instrument for determining expression levels that is different than that used for the first sample of human tissue is an instrument using an RNA quantification metric.
In a third aspect, methods of assessing the efficacy of a skin anti-aging agent are disclosed. The methods comprise exposing a first sample of human skin tissue to an agent, determining a first set of expression levels of a plurality of genes in the first sample of human skin, comparing the first set of expression levels to a second set of expression levels, the second set of expression levels corresponding to expression levels of human skin tissue not exposed to the agent, to identify a first subset of genes having a fold change difference in expression level between the exposed and unexposed samples that meets a first, selected biological relevance level, selecting from the first subset of genes a second subset of genes, each gene being associated with a biochemical pathway associated with physical appearance of skin aging, selecting from the second subset of genes, at least one skin attribute subset of genes, each gene in the skin attribute subset being associated with a biochemical pathway relating to the skin attribute that is shown in the comparing step to have been regulated in a more youthful direction for that biochemical pathway and skin attribute, exposing a second sample of human skin tissue to the agent, determining the levels of expression for the at least one skin attribute subset of genes in the second sample of human skin tissue using a method for determining expression levels that is different than that used for the first sample of human skin tissue, selecting a third subset of genes from the at least one skin attribute subset of genes whose expression levels in the second sample of human skin tissue meet a second, selected biological relevance level and whose direction of regulation conforms to the more youthful direction used in selecting the at least one skin attribute subset of genes, and comparing the third subset of genes to a previously determined third subset of genes for a second agent, thereby showing the efficacy of the skin anti-aging agent.
In some embodiments of the method, the skin attribute for the at least one skin attribute subset of genes is skin structure, skin pigmentation, skin hydration or cell turnover.
In another embodiment, the method for determining expression levels that is different than that used for the first sample of human tissue is a method using an RNA quantification metric.
Those skilled in the art will understand that the drawings, described herein, are for illustration purposes only. The drawings are not intended to limit the scope of the present disclosure.
The term “functional youth gene assembly” refers to groups of genes encompassing one or more biochemical pathways or mechanisms of aging, addressable for functional restoration or stabilization of a more youthful state in the skin.
The term “skin attributes” refers to characteristics or qualities of human skin.
The term “biochemical pathway associated with skin” refers to a sequence of reactions and interactions among genes/proteins leading to a specific biochemical end product relevant to at least one skin biological processes.
The term “biochemical pathway associated with physical appearance of skin aging” refers to a biochemical pathway that leads to biochemical end products that cause a less youthful state in the skin.
The term “skin anti-aging agent” refers to a substance that causes a biological or chemical change in the skin to reflect a more youthful state in the skin.
Reference is now made in detail to certain embodiments of systems and methods. The disclosed embodiments are not intended to be limiting of the claims. To the contrary, the claims are intended to cover all alternatives, modifications, and equivalents.
The present disclosure provides a system and method in which the genes expressed in human derived skin cells in a human equivalent skin tissue model are tested for linkage to the physical appearance of human facial skin aging. Human derived skin cells include, for example, fibroblasts, keratinocytes, adipocytes and melanocytes.
Gene expression profiling using high-throughput methodologies such as DNA microarrays has proven to be a powerful approach in exploring the complex processes of aging, which involve many genetic pathways. This technique allows researchers to scan essentially the entire genome for age-related changes in gene expression, or changes in gene expression as a result of anti-aging strategies. Such analyses have demonstrated an association between aging and widespread gene expression. (Solene M, Fortunel N O, Pageon H, Asselineau D, Aging alters functionally human dermal papillary fibroblasts but not reticular fibroblasts: A new view of skin morphogenesis and aging. PLoS ONE 3(12):e4066, 2008).
If one first examines genes associated with specific mechanisms of aging, then by grouping genes from several mechanisms (associated with attributes of aging or preserving youthfulness) one can compile a functional youth gene profile expression or a functional youth gene assembly that is associated with a specific tissue, such as the skin, and specific attributes of it.
An agent generally refers to a substance that causes a change in tissue observed. An agent is chosen based on the ability (or expected ability) of the agent to affect signs of aging, presumably by reason of an effect on expression levels of genes or gene products, including epigenetic effects.
Salicin is an agent that has shown effects on multiple signs of skin aging. See Applicant's co-pending patent application Ser. No. 12/058,201, publication number US 2009/0246152 A1, hereby incorporated by reference in its entirety.
In some embodiments, the skin anti-aging agent chosen for the experimental testing is salicin. Salicin (C13H18O7) or 2-(Hydroxymethyl)phenyl β-D-glucospyranoside) is an alcoholic beta-glycoside that contains D-glucose. Salicin is obtained from several species of the white willow bark tree. Salicin is commercially available as a white, crystalline, water soluble powder from, for example, Sigma-Aldrich (St. Louis, Mo.).
Other agents may be chosen based on the ability of the substance to bring about a biological or chemical effect in tissue to reflect a more youthful state of the tissue. Choice of an agent is dependent upon the objective one is trying to obtain. An agent could be chosen for its apparent wide-spectrum anti-aging benefits, including effect on skin hydration, skin structure, skin pigmentation and skin cell turnover, or an agent could be chosen specifically for a single objective, e.g., hydration of the skin, associated with a more youthful state of epidermal tissue. For example, ingredients such as retinoids, niacinamide, N-acetyl glucosamine have a wide spectrum of anti-aging benefits.
In various aspects, the present disclosure relates to the analysis of skin tissue samples by microarray-based technology and transforming the microarray output data into useful subsets of data identifying particular genes of interest.
In some embodiments, the methods of the present disclosure comprise analyzing at least one test sample of a skin anti-aging agent on human or human-derived skin tissue, by using microarray-based technology to obtain information relating to changes in expression levels, if any.
A reference sample is a sample that lacks the presence of a skin anti-aging agent. Test and reference samples may be obtained from a biological source comprising human or human-derived skin cells or human or human-equivalent tissue, by any suitable method of nucleic acid isolation and/or extraction. In various aspects, the test sample and the reference sample are extracted RNA.
Array hybridization experiments allow the analysis of thousands of genes in one experiment. Microarrays are solid supports made of either nylon or silicon which house thousands of transcripts at fixed locations. The DNA is printed, spotted or synthesized on the support. This method is based on hybridization probing which uses fluorescently labeled nucleic acids as probes to identify complementary sequences. Single stranded DNA is made up of 4 different nucleotides, adenine (A), thymine (T), guanine (G), and cytosine (C). Adenine pairs with thymine and guanine pairs with cytosine. Hybridization occurs when a group of nucleotides finds their complementary partners. Microarray experiments measure the level of hybridization of each DNA on the support via fluorescently labeled tags.
There are three different types of probes that are commonly used in hybridization experiments: genomic DNA probes, cDNA probes and oligonucleotide probes. This provides the different terms namely “DNA array”, “cDNA array” or “oligonucleotide array” depending on what type of probe is used. (Kumar, A., Goel, G., Fehrenbach, E., Puniya, K. A., Singh, K. Microarrays: The Technology, Analysis and Application, Engineering in Life Sciences 5(3), 215-222 (2005); Jiang, N. et al. Methods for evaluating gene expression from Affymetrix microarray datasets, BMC Bioinformatics 9, 284 (2008); Auer, H., Newsom, D. L., Kornacker, K. Expression Profiling Using Affymetrix GeneChip Microarrays, Methods Mol Biol. 509, 35-46 (2009)).
In conducting a DNA microarray experiment, total RNA is extracted from the samples to be tested. The purified RNA is then analyzed for quality and quantity (>1 micrograms). Reverse transcriptase is then used to transcribe the mRNA into cDNA. The nucleotides used to synthesize the cDNA are labeled with either a green or red dye, one color for reference conditions or the other color for experimental conditions. The test samples and the reference samples may be differentially labeled with any detectable substance or moieties. The detectable substances or moieties may be selected such that they generate signals that can be readily measured and such that the intensity of the signals is proportional to the amount of labeled nucleic acids present in the sample. The detectable substances or moieties may also be selected such that they generate localized signals, thereby allowing resolution of the signals from each spot on an array.
Methods for labeling nucleic acids are well-known in the art. For exemplary reviews of labeling protocols, label detection techniques and recent developments in the field, (see e.g., Kricka, Ann, Clin. Biochem. (2002), 39: 114-129; van Gijlswijk et al., Expert Rev. Mol. Diagn. (2001), 1:81-91; and Joos et al., J. Biotechnol. (1994), 35: 135-153). Standard nucleic acid labeling methods include: incorporation of radioactive agents, direct attachment of fluorescent dyes or of enzymes, chemical modification of nucleic acids to make them detectable immunochemically or by other affinity reactions, and enzyme-mediated labeling methods including, without limitation, random priming, nick translation, PCR and tailing with terminal transferase. Other suitable labeling methods include psoralen-biotin, photoreactive azido derivatives, and DNA alkylating agents. In various embodiments, test sample and reference sample nucleic acids are labeled by Universal Linkage System, which is based on the reaction of monoreactive cisplatin derivatives with the N7 position of guanine moieties in DNA (see, e.g., Heetebrij et al., Cytogenet. Cell. Genet. (1999), 87: 47-52).
Any of a wide variety of detectable substances or moieties can be used to label test and/or reference samples. Suitable detectable substances or moieties include, but are not limited to: various ligands; radioneuclides such as, for example, 32P, 35S, 3H, 14C, 125I, 131I, and others; fluorescent dyes; chemiluminescent agents such as, for example, acridinium esters, stabilized dioxetanes, and others; microparticles such as, for example, quantum dots, nanocrystals, phosphors and others; enzymes such as, for example, those used in an ELISA, horseradish peroxidase, beta-galactosidase, luciferase, alkaline phosphatase and others; colorimetric labels such as, for example, dyes, colloidal gold and others; magnetic labels such as, for example, Dynabeads™ particles; and biotin, dioxigenin or other haptens and proteins for which antisera or monoclonal antibodies are available.
The microarray or chip used for testing is then incubated overnight with both reference and experimental cDNAs. Certain cDNA will hybridize with the complementary strands from its gene that is covalently bound to a grid spot on the chip. The chips are then washed to remove any unbound cDNAs. Two computerized images are then produced by scanning first to detect the grid spots containing cDNAs labeled with green dye, and second to detect the spots containing the red-labeled cDNAs. The computer also produces a combination of the two images showing a yellow spot for grids spots containing both red and green labeled cDNAs. These yellow spots represent transcripts that are expressed under both sets of conditions.
In addition to producing images, microarray experiments yields quantitative data for each spot on the chip, resulting in large datasets where bioinformatics tools are needed for complete analysis. Parametric t-test with a Benjamini and Hochberg false discovery rate correction is the most common statistical parameter used for microarrays to identify genes with a statistically significant p value equal to or less than 0.05 and with a fold change of 2.0 and greater (or other suitable expression level criterion, expressed as a fold change threshold or otherwise). Genes are then either grouped by biological function or relation to a particular disease, depending on the objectives of the study.
These array-based methods of genetic analyses for skin cell samples allow the research analyst to develop data for essentially the entire human genome as an initial step, but economics and the need to limit focus make it desirable to analyze this data with a goal of limiting the number of genes addressed in later analysis steps. Techniques to permit focusing of resources on particular conditions, mechanisms and interventions can save time and cost.
The array-based data is of sufficient volume that it is desirable (and likely necessary) to carry out the analysis that transforms a genome-wide set of data from microarray equipment into smaller, focused sets of data using a computer-based system.
Also in the database 730 are data sets developed from literature 786 on the biological pathways that have been reported as associated with various genes. One data set 754 identifies genes reported as having biological pathways that are significant for skin. (The data set 754 may be derived from literature by automated keyword and/or metadata analysis of the text of scientific journals, patents or other sources reporting on activity of particular genes, including non-published studies, or may be built by the input of one or more scientific experts. For this purpose, it may be helpful to build a database of sources annotated with metadata 787 that permit ready identification of each gene associated with skin (or other organs) and, for genes associated with skin, as addressing biochemical pathways associated with particular skin attributes.) This data set may be used in an intersection analysis to identify the genes in a larger test result data set, for example the full genome microarray data sets 750a, 750b or the fold criterion result data set 752, that are related to skin and also meet the specified fold criterion that led to the fold criterion result data set 752, forming a new skin pathway intersection data set 755. Thus, the literature data set 754 may be used as a filter for the test data to provide a focus on genes for which data from the literature data set supports a pathway of interest in skin.
The data sets derived from literature can also have a narrower focus within the broader area of skin. For example, a skin attribute data set 756 may be derived that identifies genes reported as having biological pathways that are significant for a particular attribute of skin, such as skin structure or skin pigmentation. Such a data set may be used in an intersection analysis to identify the genes in a larger test result data set, for example the skin pathway intersection data set 755, to identify genes that are related to a specific skin structure and also meet the specified fold criterion that led to the fold criterion data set 752, forming a new skin attribute intersection data set 758. The skin attribute intersection data set 758 is then focused on genes related to one specific skin structure; an attribute data set 756 for another skin attribute, e.g., skin pigmentation, can lead to a different skin attribute intersection data set 758. At this level of focus, assuming the goal of focus is to identify genes associated with more youthful manifestations of a skin attribute, the skin attribute data sets also include up or down regulation coding. That is, if a gene is associated with a particular skin attribute, it may be reported as involved in either the up or down regulation of a pathway that either leads to more or less youthful appearance. To the extent a goal is to identify agents to influence biological pathways that enhance youthfulness, it is significant to identify both the genes that can be up-regulated to cause a more youthful state, as well as the genes that can be down-regulated to reduce action of a pathway that leads to less youthful state. Thus, the skin attribute data sets 756 are coded to identify for each gene, the up or down regulation of a pathway that either leads to more or less youthful appearance as to the particular skin attribute involved.
The results embodied in a skin attribute intersection data set 758 may be considered preliminary and will be considered more reliable if they can in some way be confirmed, or refined. The present system in one embodiment develops and analyzes further data to provide possible confirmation and refining. As seen in
The test result data from the PCR Testing instrument 790 show levels of gene expression for the selected candidate genes when the same agent used to develop data in the upper portion of
The discussion below proceeds at two levels. At one level, with reference to
In the present method and system, expression profiling for thousands of genes, substantially all of the genes of the human genome, is performed to determine a first set of expression levels of a plurality of genes in a first sample of agent-exposed human or human derived skin.
Genome refers to all nucleic acid sequences, coding and non-coding, present in each cell type of a subject. The term also includes all naturally occurring or induced variations of these sequences that may be present in a mutant or disease variant of any cell type, including, for example, tumor cells. Genomic DNA and genomic nucleic acids are thus nucleic acids isolated from a nucleus of one or more cells, and include nucleic acids derived from, isolated from, amplified from, or cloned from genomic DNA, as well as synthetic versions of all or any part of a genome.
For example, the human genome consists of approximately 3.0×109 base pairs of DNA organized into 46 distinct chromosomes. The genome of a normal human diploid somatic cell consists of 22 pairs of autosomes (chromosomes 1 to 22) and either chromosomes X and Y (male) or a pair of chromosome Xs (female) for a total of 46 chromosomes.
In some embodiments, Affymetrix® DNA microarray technology is used for measuring global gene expression in human in vitro skin cultures. Microarrays are ideal for simultaneously measuring the effects of a test compound on the activity of thousands of genes in the human genome. (Microchip Methods in Diagnostics, vol. 509, chapter 3, Expression Profiling Using Affymetrix GeneChip Microarrays, Auer et al. (2009)).
For one embodiment, EpidermFT™ Skin Model (EFT-400) full thickness skin cultures (MatTek Corp, Ashland, Mass.) is used as a skin model. These cultures contain normal, human-derived epidermal keratinocytes from neonatal foreskin tissue and normal human-derived dermal fibroblasts, from mammary tissue. These cells are cultured to form a multilayered, highly differentiated model of the human dermis and epidermis. The model parallels human skin and is useful for in vitro testing, where a microarray is used to develop and collect data. Skin models typically contain human derived skin cells cultured to form a model of skin tissue. Generally, these skin models are referred to as “human equivalent skin tissue” or “human derived skin tissue”. Skin cells include keratinocytes, fibroblasts, adipocytes and melanocytes.
The steps begin with selecting an agent that is a candidate to help skin appearance 102 (or explore effects of skin aging). In some embodiments, the agent tested is salicin at a concentration of 0.5% salicin, available from Symrise Corporation (Teterborro, N.J.). The salicin is dissolved in water. An agent that has some known effects on skin aging may be useful for revealing gene-based effects, but other agents may be selected.
The first samples of the human equivalent skin tissue are exposed to the skin anti-aging agent 104. Untreated cultures, or human skin tissues not exposed to the agent, serve as controls or reference samples. (See
RNA is extracted from each of the human skin cells, or cultures, using an RNeasy® Fibrous kit (Qiagen, Valencia, Calif.) following the manufacturer's protocol. (RNeasy® Fibrous Tissue Handbook, November 2006). cDNA is synthesized from 100 ng of total RNA, and then converted to biotin-labeled amplified RNA (aRNA) using an Affymetrix GeneChip® 3′ IVT Express kit, according to the manufacturer's instructions. (Affymetrix User Manual GeneChip® 3′ IVT Express Kit (2008)).
In some embodiments, the samples are hybridized to Affymetrix GeneChip® HG U133 Plus 2.0 microarrays, washed, stained and scanned according to Affymetrix protocols. The microarray laser scanner measures fluorescence intensities of all of the transcripts on the gene chip; the fluorescence of each transcript is compared among each of the samples. (See
Results from these experiments reveal that the skin anti-aging agent selected for exploration may have influenced the activity of any of more than 15,000 genes in the human genome.
The activity is measured by determining expression levels for genes in the first exposed tissue sample (test sample) 106. This involves reading the array that has a human equivalent tissue model exposed to the agent. This results in a set of data that is stored in database 730 of a data processing system 710 for implementing the method described herein, (See
To give a basis for comparison, the method uses a reference tissue that has not been exposed to the agent. The same skin model is used to provide data on expression levels when the agent is not present. The reference level is measured by determining expression levels for genes in the first unexposed sample (reference sample) 108. Again, this results in a set of data that is stored in database 730 of a data processing system 710 for implementing the method described herein. (See
The agent-exposed and reference data are analyzed to determine the affects of agent exposure. The expression level data for each gene of the test sample are compared with the corresponding data of the reference sample to obtain a ratio of the data 110. (See
A bioinformatics statistical analysis is conducted on data 112 to identify and characterize candidate genes. A Parametric t-test with a Benjamini and Hochberg false discovery rate correction is the most common statistical parameter used for microarrays to identify genes with a statistically significant p value equal to or less than 0.05 and with a selected biological relevance level, e.g., a fold change of 2.0 and greater. To help organize the results, genes are either grouped by biological function or relation to a particular disease, depending on the objectives of the study.
Flowchart 100 in
The raw dataset from the first subset of genes, row 208, is then filtered, using bioinformatics methods, to identify and characterize genes. In particular, the data is filtered to focus on genes with biologically relevant fold change values. In data from a typical microarray device, e.g., data from the Affymetrix testing, the up or down regulation of the gene is also identifiable. The row 210 shows the regulation direction of the hypothetical gene.
In some embodiments at least part of the analysis of data is performed by a computer. Statistical data analysis may be carried out using GeneSpring GX software (version 10). A parametric t-test with a Benjamini and Hochberg false discovery rate correction is performed to identify genes with a statistically significant p value equal to or less than 0.05. (Statistical analysis may be performed by one or more of the process application modules 720. (See
Returning to the simplified example, at step 114, the system selects a first subset of genes (b, c, e, f, g and h) with biologically relevant fold changes in the level of expression, for example, those calculated to be at least a two fold change. (See row 208). As noted with reference to rows 202, 204, 206, the ratio is measured from the first exposed sample (test sample), as compared to the first unexposed sample (reference sample). The method also involves identifying which genes are regulated in a particular direction (up or down regulated). (See row 210). In one embodiment, a ratio of data in row 202 to data in row 204, shows up-regulation if the ratio is greater than 1 and down-regulation is the ratio is less that 1.
As shown in row 206, example genes b, c, e, f, g and h have ratios of 3, 2.5, 4, 0.2, 3.33 and 12, respectively. Thus, with a fold change threshold selected at 2, the table in
In the test data from skin model experiments, fold changes of at least about 2.0, about 2.0, of at least about 3.0, between about 2.0 and about 4.0, between about 2.0 and about 7.0, and even between 2.0 and about 200.0 may be selected as biologically relevant. Fold change significance may vary based on the instrument used for testing, tissue sample and other factors. For Affymetrix DNA microarray data, using the specific statistically scientific parameter, a fold change of 2 or more is biologically significant. (The fold change criterion is a selectable parameter 724 in process application modules 720.)
As shown on row 206 of the table on
Correspondingly, as will be seen below and in Table 1, the actual skin model data test results from the microarray instrument 780 were subjected to the same fold change criterion as in the simplified example; thus genes with fold changes reported below 2.0 were not included in any of the tables for skin model actual experimental results.
Table 1 below shows experimental data from a microarray for 0.05% salicin treated cultures (N=7) compared with untreated control cultures (N=4) corresponding to the simplified hypothetical data in the table of
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Because of the large amount of data associated with the gene expression level changes shown in Table 1, it is desirable to provide a further focus on genes associated with skin and aging. In one embodiment, further data sets extracted from the literature identify genes associated with physical skin aging attributes based on current knowledge of the biochemical pathways in skin to define functional youth gene assemblies. Such an assembly may then provide a gene expression focus for future further work on the same agent used to get the initial full genome data set or for other agents. For this next step, the method first uses a data set that identifies particular genes associated with biochemical pathways in skin 118 (see
Data about the biochemical pathways of genes are available from many sources of scientific literature, including databases of journal articles or from available unpublished data. To make it more useful in the present system, data collected may be supplemented with metadata classifying the conclusions reached in terminology or coding that clearly associates genes with skin or particular skin attributes. (See
However approached, the intersection analysis of this step reduces the data of Table 1, by selecting from the first subset of genes a second subset of genes associated with the selected, identified biochemical pathways associated with the physical appearance of skin aging. With reference to the simplified example of
The genes in the second subset for actual test results are categorized according to biological function or association with a plurality of biochemical pathways associated with the physical appearance of skin aging. For the experimental data for 0.05% salicin treated cultures (N=7) compared with untreated control cultures (N=4), Table 2 (see also,
For each gene listed in Table 2, a reference is provided which discusses the mechanism of action/biochemical pathway of the gene. The references are incorporated herein by reference.
Genes not chosen for the second subset of genes may nonetheless be considered for additional research based on secondary research factors 122. For example, a hypothetical gene may have interesting aging-related pathways, not yet associated with skin, or in the case of hypothetical gene h, a gene may have a high fold value.
Applying to Table 1 the data set specifying the associations with biochemical pathways with the physical appearance of skin aging is a meaningful focusing of the data, which results in the list of Table 2 (See
Returning to the flowchart of
According to the method, genes of the second subset are further processed into a plurality of subsets (potentially overlapping) within the second subset by categorizing or associating each gene by an association with one or more skin attribute(s). (Gene b is in the subset of skin structure and also in the subset of skin pigmentation). For example, the genes from the second subset may be transformed into skin attribute subsets, each associated with a particular physical sign of skin aging and appearance, listed as follows in
Skin structure attribute 126.
Skin pigmentation attribute 128.
Skin hydration attribute 130.
Cell turnover attribute 132.
Turning to the second subset as defined for the experimental data listed in Table 2, the use of skin attribute data sets is further explained. A data set identifying the relationship between a particular skin attribute and particular genes is developed based on the literature or on available unpublished data. The data set identifies biochemical pathways of the physical appearance of skin aging known to be associated with one of the genes of Table 2 and a particular skin attribute; it is preferably collected and stored in a format that promotes an intersection analysis with the data of Table 2. This can be done by building a table of all genes known to be associated with a biochemical pathway of the physical appearance of skin aging and a particular skin attribute of interest and finding its intersection with Table 2. In some embodiments, the genes involved in various biochemical pathways related to the particular attribute “skin structure” are chosen for analysis. Some of the biochemical pathways of interest for this skin attribute include skin structural proteins synthesis, degradation and maintenance and extracellular matrix assembly. However, other skin attributes and associated pathways may also be of interest.
The attributes skin structure, pigmentation, cell turnover and hydration are described below. One can derive from the literature for each attribute its own subset of genes associated with that attribute, which permits development of a data set identifying the relationship between each of these particular skin attributes and particular genes. However, a gene may be associated with more than one skin attribute. A brief discussion of general principles of skin aging is a useful preface to a discussion of skin attributes.
The stratum corneum is the layer of the skin that forms the top surface layer and serves to protect the skin while controlling moisture and the flow of substances in and out of the skin. As this barrier function is broken down, the skin suffers damaging effects, thus further contributing to premature aging. These damaging effects causing premature aging of the skin are a concern for many individuals wishing to maintain healthy, youthful looking and feeling skin.
Aging can occur from biological processes or environmental factors, and in some cases environmental factors that impact biological processes. These factors alone and in combination contribute to aging appearance and are responsible for the decline in skin health and function. Biological aging, which is intrinsic, is the result of changes, often genetically determined, that occur naturally within the body. Environmental aging, which is extrinsic, is the result of free radical damage generated by accumulated exposure to sunlight (photoaging), pollution, or cigarette smoke. Also, lifestyle choices like diet, sleep, and stress can affect how quickly one appears to age.
Whether from biological or environmental sources, the appearance of aging results from several mechanisms of action or biochemical pathways. For example, a loss of skin structure, a slowing skin cell turnover, pigmentation changes or a decrease in skin hydration.
One can group many of the detectable/sensible changes that occur with skin aging into four major skin attributes, skin structure, pigmentation, cell turnover and hydration. By defining these attributes and developing metrics for them, based where possible on instrumentation that makes the metrics more objective, research on interventions can be given focus. For example, with a chosen attribute and one or more genes and one or more biochemical pathways associated with it, the research can focus on particular parts of a biochemical pathway that can be enhanced to encourage the biochemical pathways that produce a more youthful version of that attribute or on inhibiting a biochemical pathway that produces a less youthful version of that attribute. It is believed that the biochemical pathways associated with genes can be regulated by many different factors. The focus on particular genes, particular biochemical pathways associated with the genes and particular skin attributes associated with those pathways may permit identifying an “intervention” where a specific technology can target the gene expression activity for a particular skin attribute to reflect a more youthful gene expression profile, ultimately influencing the physical appearance of the skin as it ages.
Functional youth gene assembly, refers to a group of genes, encompassing one or more mechanisms of aging, addressable for functional restoration or stabilization of a more youthful state in the skin. Each functional youth gene assembly may focus on a particular skin attribute that has youthful and non-youthful states. A functional youth gene assembly could also apply to characteristics in other tissues and organs.
By extension, a “youth gene family” is composed of a related group of functional youth gene assemblies and would address multiple (or all) the significant attributes of aging for skin (or another tissue or organ, such as adipose tissue, heart, brain, skeletal muscle, etc.).
Once it is defined, one can examine a functional youth gene assembly for a specific tissue associated with a specific function. For example, in skin, the youth gene family may comprise functional youth gene assemblies for skin pigmentation, structure, hydration and cell turnover. The skin is an easily accessible organ with easily measured or observed aging attributes. Therefore, one can readily examine manifestations of changed expression levels of a functional youth gene assembly for a specific attribute of skin.
This approach is part of an overall strategy to slow down the physical manifestation of the aging process in skin by developing a composition that addresses several genetic mechanisms of aging simultaneously, i.e., through actions targeted to expression levels of the members of functional youth gene assemblies, instead of in-depth analysis of an individual gene. The following focuses on four skin attributes for which it is useful to define a functional youth gene assembly.
A. Skin Structure
The skin structure group has genes that have biochemical pathways associated with the physical appearance of skin aging that include for example, skin structural proteins synthesis, degradation and maintenance and extracellular matrix assembly. Examples of skin structural protein synthesis include elastin formation, keratinocyte differentiation and collagen production.
Younger skin has the ability to balance damage and repair to collagen, a structural protein in the skin. This balance keeps skin looking smooth and wrinkle free. During the aging process, skin begins to lose this balance. Less and less collagen is created and more enzymes are produced which break down this protein resulting in lines and wrinkles. Increasing the production of structural proteins promotes youthful looking skin, whereas inhibiting the production of enzymes that break down the proteins in the skin is also beneficial.
B. Pigmentation
The pigmentation group has genes that have biochemical pathways associated with the physical appearance of skin aging that include for example, regulation of melanin production and control. All normal human skin contains chromophores that give the skin a characteristic coloration. The color of the skin is mostly due to melanin, eumelanin, hemoglobin and, to some degree, collagen and elastin. The primary function of pigmentation is the absorption of short wavelength light capable of damaging structural components in the deeper layers of the skin and the nuclear and mitochondrial DNA of keratinocytes, melanocytes, fibroblasts, lipocytes, Langerhans cells, other immune system cells and neural cells in the skin.
Aside from a sallow appearance, seen in thinner, lighter skinned individuals with poor circulation, around the globe the overall color of the skin does not reflect aging. However, across cultures the irregular distribution of skin color, sometimes called dispigmentation, is a key attribute that characterizes older skin and presents in the form of ephelides, letingines and hyperpigmented or hypopigmented scars.
The biochemical changes in the skin that drive an irregular pigmentation pattern can be grouped into several categories. Increased growth factor signaling by melanocyte-stimulating hormone and related cytokines, increased tyrosinase, an enzyme converting tyrosine to DOPA and on to melanin, and increased amounts of melanin transferred from the melanocytes to the keratinocytes.
During the aging process, melanocytes can cluster together. These clusters of melanocytes can then become overly active resulting in areas of hyperpigmentation, known as age spots or discoloration.
C. Cell Turnover
The cell turnover group has genes that have biochemical pathways associated with the physical appearance of skin aging that include for example, cellular differentiation. During the aging process, the outer layers of the skin do not slough off as they once did. The adhesion of these skin cells result in rough skin texture and dull, lifeless looking skin. When cell renewal slows with age, dead skin cells build up along the pores of the skin. This build up increases the appearance of pores, making them look larger than in youthful looking skin.
By increasing the cell renewal process, younger, healthier skin cells surface, promoting a smoother skin texture. When skin looks smoother, it reflects light more uniformly. Smoother skin appears more radiant and bright. Increasing cell renewal also stimulates a healthy exfoliation that results in the appearance of tighter pores.
D. Hydration
The skin hydration group has genes that have biochemical pathways associated with the physical appearance of skin aging that include for example, skin barrier component synthesis, skin barrier integrity and water regulation. Skin barrier component synthesis includes, for example, hyaluronic acid synthesis and lipid synthesis.
Moisture-binding glycosaminoglycans (GAG) found within the extracellular skin matrix play a role in the hydration and moisture levels within the skin. Ample moisturization within the extracellular matrix is a factor that helps maintain the strength and integrity of the structural proteins. Many GAGs are too large to enter through the epidermis, but there are ingredients that have been shown to increase GAG production.
Each skin attribute may be negatively impacted by one or more mechanisms that contribute to aging in the skin, or may be positively impacted by a mechanism that preserves youthful appearance. If biochemical processes affecting each attribute and the gene expression profile driving those biochemical pathways or processes can be addressed (up-regulated or down-regulated), a skin state more characteristic of a younger individual is established.
Referring to the simplified example of
Correspondingly, for the microarray data in Table 2, from the genes in Table 2 a data set can be prepared that show association for a chosen skin attribute of interest, which of these genes has a pathway relevant to the skin attribute of interest and from the function of that pathway can be determined a direction of regulation of the gene associated with a more youthful appearance. The association of a gene and its pathway and one or more skin attributes may come from published or private research. Table 3 shows a dataset taken form the genes in Table 2, and identifying those genes with a pathway relevant to the skin attribute “skin structure”. For each gene in the list (of Table 3) there is a column entry in which a function relative to the skin attribute appears. The resulting skin attribute data set may be input into a database 730, by a suitable process application module that stores and accesses such data and uses it for processing as contemplated by steps 124, 126, 128, 130, 132 in the hypothetical example.
The intersection analysis for the microarray data in Table 3 is similar to that shown in steps 124, 126, 128, 130, 132 (which deal with all four skin attributes identified above; for the data in Table 2, there is only one example skin attribute addressed in Table 3). The skin attribute data set of Table 3 may be processed to derive a skin attribute subset for a preliminary functional gene assembly. As the data of Table 2 is processed by intersection with a skin attribute data set designating genes with an association with a skin attributes of interest, it becomes necessary to consider for each gene the literature-reported regulation direction for the more youthful state of the skin attribute of interest. If a gene happens to have an association with more than one attribute, then a regulation direction for each skin attribute is identified in the particular skin attribute data set; up-regulation of a gene might be favorable for one skin attribute and down-regulation of the gene favorable for a different attribute.
Referring to again to
As for the microarray data, Table 3, shows a data set comprising genes from Table 2 for which the literature shows a connection to the skin structure attribute, including a function with respect to skin structure which will determine the more youthful regulation direction. Once the more youthful regulation direction is specified for a gene, the data set of Table 3 can be used for intersection processing to find a skin attribute subset list for the gene assembly for the skin structure attribute.
As a gene associated with a skin aging attribute can be a part of a specific biochemical pathway involved in the physical appearance of skin aging that improves the skin attribute in a youthful direction or one that can be a part of a specific biochemical pathway that increases the appearance of skin aging (i.e., changes skin appearance in a non-youthful direction), the intersection processing requires additional logic to include in the gene assembly for a particular skin attribute only those genes that are regulated in a direction reflective of youthful skin appearance. Thus, the genes of Table 3 (see also,
Returning to the simplified, hypothetical example,
For the skin pigmentation attribute 128 of the simplified example, genes b and c have a fold change greater than 2, and the up regulation of genes b and c from the (hypothetical) gene expression level are consistent with the data from the literature indicating that up regulation of both genes b and c provide more youthful skin structure. Therefore, both genes b and c are kept in the group.
In the skin hydration attribute 130 of the simplified example, genes c and g have a fold change greater than 2, but the up regulation shown by the (hypothetical) gene expression level is not consistent with the data from the literature. According to the (hypothetical) literature, down regulation of genes c and g provide a more youthful skin appearance. Gene e has a fold change greater than 2, and the up regulation of that gene from the (hypothetical) gene expression level is consistent with the data from the (hypothetical) literature indicating that up regulation of that gene provides more youthful skin structure. Therefore, genes c and g are dropped from the group at this time, but gene e is kept in the group.
In the cell turnover attribute 132 of the simplified example, gene e has a fold change greater than 2, and the up regulation of that gene from the (hypothetical) gene expression level is consistent with the data from the (hypothetical) literature indicating that up regulation of that gene provides more youthful skin structure. Gene f has a fold change greater than 2, and the down regulation of that gene from the (hypothetical) gene expression level is consistent with the data from the literature that indicates that down regulation of that gene provides more youthful skin structure. Therefore, both genes e and f are kept in the group.
Table 4 shows the result when the processing logic of the simplified example is applied to the data from actual microarray testing of tissue exposed to salicin and when the skin attribute is “skin structure”, which is the focus of the data set in Table 3. The intersection processing module using the data of Table 3 identifies those genes that not only have an association with skin structure but also have been found in the test data to be up-regulating a pathway that provides more youthful skin structure or genes down-regulating a pathway that provides less youthful skin structure.
Table 4 (see also,
Genes in Table 3 not chosen for Table 4 based on the logic requiring the alignment of the Affymetrix testing-derived data with the literature's position on regulation of the gene in a “youthful” direction may nonetheless be considered for additional research on “skin structure” but that must be based on secondary research factors. Table 4 shows only the genes that have the required alignment of “youthful” direction for “skin structure” in both the literature and the Affymetrix testing-derived data.
Youthful direction based on published literature.
As can be seen, with a data set like Table 3 derived for other skin attributes, a table like Table 4 can be derived for skin attributes other than “skin structure”.
A gene assembly developed to the status of Table 4 may be further confirmed and refined by a different methodology with a different gene analysis tool, in particular, by performing further skin model testing that takes advantage of the narrowing of focus to a list of genes as in Table 4.
Different methodologies include determining RNA types and levels by RNA quantification metrics including, for example, Northern blot technique, Ribonuclease Protection Assay (RPA) and Real Time Polymerase Chain Reaction (RT-PCR).
Northern blot is a well-known process for detecting and quantifying mRNA levels. The northern blotting technique is often used for comparison of gene expression patterns for different tissue types. In terms of skin genomics, it is less used than the modern techniques but can be used as confirmation step in understanding gene expression in the skin.
Northern blots start with the extraction and isolation of mRNA from the sample. RNA samples are then separated by gel electrophoresis. Once separated, the RNA is then transferred to a positively charged membrane, most often made of nylon. Once transferred to the membrane, RNA is then immobilized to the membrane through covalent linkage with the use of UV light or heat. Hybridization probes (fragment of DNA or RNA used to detect the presence of specific sequences) to be used for the experiments are labeled and placed on the membrane for hybridization. The membrane is then washed to ensure probe binding is strong as well to avoid background signals. The signals are then detected by X-ray film and can be quantified by densitometry. (Alberts, B., et al. Molecular Biology of the Cell, 5th ed. pp. 538-539, New York: Taylor & Francis Group (2008)).
Ribonuclease protection assay is a sensitive technique for detection, quantification and characterization of RNA. Isolated RNA is hybridized to a single stranded cDNA of the gene of interest. After annealing, the sample is subject to enzymatic digestion to remove all single stranded nucleic acids, leaving only double-stranded RNA. The double stranded nucleic acid fragments are then separated on high-resolution polyacylamide gels. Quantification is carried out similar to that of Northern Blot. The assay is much more sensitive than Northern blot, and can be used to quantify mRNAs that are expressed at low levels. (Applied Biosystems, Inc., The Basics: What is a Nuclease Protection Assay?©2010, last accessed May 18, 2010, from http://www.ambion.com/techlib/basics/npa/index.html).
Real Time Polymerase Chain Reaction is a laboratory technique used for DNA quantification, which measures the accumulation of DNA product after each round of PCR amplification. This laboratory technique is also known as quantitative real time polymerase chain reaction (RTQ-PCR/Q-PCR/qPCR) or kinetic polymerase chain reaction, which is used to amplify and simultaneously quantify a targeted DNA molecule. The technique enables both detection and quantification (as absolute number of copies or relative amount when normalized to DNA input or additional normalizing genes) of one or more specific sequences in a DNA sample.
The amplified DNA is detected as the reaction progresses in real time, as compared to standard PCR, where the product of the reaction is detected at its end. Two common methods for detection of products in real-time PCR are: (1) non-specific fluorescent dyes that intercalate with any double-stranded DNA, and (2) sequence-specific DNA probes consisting of oligonucleotides that are labeled with a fluorescent reporter which permits detection only after hybridization of the probe with its complementary DNA target.
Reverse Transcription PCR (RT-PCT) is used for amplifying DNA from RNA. Reverse transcriptase reverse transcribes RNA into cDNA, which is then amplified by PCR. RT-PCR allows for a high sensitivity detection technique, where low copy number or less abundant RNA molecules can be detected. RT-PCR is widely used in expression profiling, to determine the expression of a gene or to identify the sequence of an RNA transcript, including transcription start and termination sites.
Real-time PCR may be combined with reverse transcription to quantify messenger RNA and Non-coding RNA in cells or tissues. Real-time reverse-transcription PCR is also known as qRT-PCR, RRT-PCR, or RT-rt PCR.
In some embodiments, Real Time Reverse Transcriptase Polymerase Chain Reaction (RT-rt-PCR) experiments on the second subset of genes are performed to confirm activity of the skin anti-aging agent acting on the gene.
In some embodiments, determining the levels of expression for the second subset of genes is done by using an RNA quantification metric. Selecting a further set of genes from a previous set of genes in a second sample of human skin tissue is based on measured levels of expression, which meet a criterion of biological relevance.
As seen in
In some embodiments, the agent tested is salicin at a concentration of 0.5% salicin, available from Symrise Corporation (Teterborro, N.J.). The salicin is dissolved in water. Affymetrix microarray testing provides results for thousands of genes, whereas RT-rt-PCR testing provides results for a smaller gene group. For RT-rt-PCR testing, about 90 genes are tested at a time for this particular experimental design (other designs may test as many as 390 at a time on the equipment identified below). The experimenter may choose this number based on cost.
To start a process of confirmation using a second gene analysis tool that works with smaller arrays and a different, perhaps more sensitive measurement of regulation by the agent, expose a second sample of human skin tissue to the agent and select a set of candidate genes for confirmation. For example, the set of candidate genes may be the genes of a preliminary functional youth gene assembly of a particular skin attribute, supplemented with a few other genes that are of interest based on secondary research. More that one candidate group may of course be explored by confirmation. For example, a candidate group may be built around the preliminary functional youth gene assembly of each of the skin attributes discussed above: skin structure, skin pigmentation, skin hydration and cell turnover.
The subsets of genes related to a particular skin attribute are conveniently tested on one test card. Referring again to the process schematically shown for a simplified hypothetical gene set in
To implement this step, RT-rt-PCR is conducted for specific genes known to be involved in skin aging. In one embodiment Custom TaqMan® Low Density Arrays (TLDA's) were configured using Applied Biosystems validated gene expression assays. The validated gene expression assays contain a TaqMan® fluorescent probe and primers for each target gene. Genes for the TLDA cards are selected based on either, published literature describing the genes functional role in skin cell biology and aging and/or the previous Affymetrix testing results. (See Tables 1-4). Five endogenous control genes may be included on each card. Thus, when a particular gene assembly is tested with RT-rt-PCR, the data resulting may cover more than just the set of genes as in Table 3.
cDNA is synthesized from an aliquot of total RNA using the High Capacity cDNA reverse transcription kit from Applied Biosystems (Foster City, Calif.) according to the manufacturer's suggested protocols. (High-Capacity cDNA Reverse Transcription Kits for 200 and 1000 Reactions Protocol (October, 2006)). cDNA was mixed with TaqMan® Universal Master Mix without UNG and loaded into the wells of the TLDA cards. The cards are run using an Applied Biosystems 7900HT instrument according to the manufacturer's cycling parameters.
As with the microassay, the analysis is done with a skin model exposed to the agent and a reference that is not exposed to the agent. The skin model not exposed to the agent may be used for calibration. The skin model may be human equivalent skin tissue. The target genes get normalized to a stable endogenous control (genes that are invariants in all cell types such as β3-actin). This normalization is to account for variations that may occur during sample loading. The unexposed information gathered is used for comparison against the tested sample.
The formula for ΔCT (delta cycle threshold) is CT (target) minus CT (endogenous control gene). (ΔCT=CT (target)−CT (endogenous control gene)). The test system, data processing system 710 stores the data then uses process application modules 720 to take the CT values for both the exposed and unexposed and get a ΔΔCT value, which is reported for that specific gene in a log ratio scale. Once this data is collected and stored, such as in database 730 (see
At least part of the comparing of the data is performed by a computer system, such as the data processing system 710 (see
The CT or cycle threshold is defined as the number of cycles required for the fluorescent signal to cross the threshold. CT levels are inversely proportional to the amount of target DNA in the same. Standard real time reactions undergo 40 cycles of amplification. CT<20 indicate strong positive reactions and an abundance of the targeted DNA. CT values of 30-37 are positive reactions indicative of moderate amounts of target DNA. CT values of 38-40 are weak reactions indicative of minimal amounts of target DNA. The CT values are an criterion of biological relevance. The experimenter optionally chooses a criterion based on biological relevance for gene expression in aging skin.
Table 5 (see also,
Results from the RT-rt-PCR experiments may or may not confirm that the candidate genes subject to confirmation testing are regulated in the direction reflective of youthful appearing skin based on the published scientific literature.
The RT-rt-PCR data thus provide an additional basis for refining a gene functional youth gene assembly that is derived from steps 102-134 of
Referring now to the simplified example shown in
In
As noted, some genes dropped at earlier stages of the process outlined in
Referring to
Functional youth gene assembly for skin structure 144: Gene b is confirmed because it meets the ΔCT criterion and matches the direction of expression associated with more youthful skin structure per the literature data set on skin structure.
Functional youth gene assembly for skin pigmentation 146: Gene b is confirmed because it meets the ΔCT criterion and matches the direction of expression associated with more youthful skin pigmentation per the literature data set on skin pigmentation. Gene c is not confirmed because it does not meet the ΔCT criterion.
Functional youth gene assembly for skin hydration 148: Gene e is not confirmed because it does not meet the ΔCT criterion. Gene g is added because it meets the ΔCT criterion and matches the direction of expression associated with more youthful skin hydration per the literature data set on skin hydration. This is contrary to the microarray data.
Functional youth gene assembly for cell turnover 150: Gene e is not confirmed because it does not meet the ΔCT criterion; however, if the criterion had been set at 38, it would have met that level. Gene f is confirmed because it meets the ΔCT criterion and matches the direction of expression associated with more youthful skin cell turnover per the literature data set on skin cell turnover.
Genes not chosen for the third subset of genes (gene h) may be considered for additional research based on secondary research factors 152.
Turning to the testing-derived data example (actual Affymetrix data for salicin exposed tissue and data from RT-rt-PCR testing), Table 6 (see also,
The RT-rt-PCR methodology permits not only a second reading on the activity of genes that have met the criteria for a gene assembly in steps 102-134, it provides an opportunity to test a gene that has not met these criteria, but might meet certain secondary research factors that suggest it may be of interest for a particular gene assembly. Secondary research factors may suggest further testing of genes that have a high fold change without any literature support for their relevance in skin tissue, genes associated with anti-aging mechanisms of action but not thought of as skin-related, or genes that are strongly supported by literature as having an effect on skin aging, but not achieving a significant fold change cutoff in testing as described in steps 102-134 of the simplified example. Genes with significant fold change values that are not identified in the literature as having a favorable impact on a skin attribute may be considered for additional research. In the simplified sample set of genes, gene h not chosen for the third subset of genes is considered for additional research based on secondary research factors 152. See
A favorable impact on a skin attribute is a biologically relevant change that establishes a state of an attribute more similar to the non-aged state of the attribute. For example, a favorable impact is recognized when an agent that is applied to the skin results in a more youthful appearance. The present system and method may be used to extend more efficiently the search for agents that cause a favorable impact. For example if the potential of a possible useful agent needs basic exploration, it can be run through the entire method of
For greater efficiency, once researchers have confidence in one or more functional youth gene assemblies, testing of an agent my be done by omitting full genome microarray studies and using only more limited studies for the genes included in one of more of the functional youth gene assemblies.
One outcome of using the entire method described to screen agents that trigger a relevant change in gene expression is to identify genes for further study, even if they are not yet reported in the literature. These may be genes that are not currently associated with any biochemical pathway associated with skin, but may be in the future, as there are advances in technology and further research studies. These genes may optionally be added to an appropriate functional youth gene assembly.
Genes with non-skin related anti-aging mechanisms of action may be subjected to further testing to determine the gene's effect, if any, on skin aging. For example, scientific literature suggests that the β-klotho gene appears to be involved in the aging process. See, U.S. Pat. No. 7,537,903.
Genes that are supported by literature as having an effect on skin aging, but not achieving the biologically relevant fold change cutoff in micro-array testing may be subjected to another round of micro-array testing with different concentrations of the agent or with different anti-aging agent(s).
In some embodiments, the functional youth gene assemblies, the groups of genes identified for a skin attribute in genome-wide microarray tests, are optionally refined based on the results from the RT-rt-PCR experiments. If the literature discloses that a gene with “up” regulation results in better skin structure, and the RT-rt-PCR data shows “down” regulation for this gene, the gene may be set aside for possible further research at a later date. Alternately, if the literature discloses a gene with “up” regulation results in better skin structure, and the RT-rt-PCR data shows “up” regulation for this gene, then the gene may be added to the functional youth gene assembly.
After application of secondary research factors, more genes are optionally added into one or more functional youth gene assembly 154.
A method that utilizes the results of the groups of genes, the functional youth gene assemblies, may be used to guide further research on aging of the skin.
As discussed above,
The data processing system 710 includes a database 730 that receives and stores the data used in the process described above. The process applications modules 720 execute, including statistics modules 722 and applications using user selected process parameters 724 to perform the flowchart (see
The database stores the various data sets involved including the full genome data sets 750a, 750b developed at the full genome microarray device 780, the calculated ratio data 750c and the fold criterion result data set 752, developed by application of the fold change criterion. The database 730 also stores the pathway criterion data set 754 that identifies the association between a gene and one or more biological pathways and the intersection dataset 755 resulting from the intersection of the fold criterion result data set 752 and the pathway criterion data set 754. The database 730 further stores the skin attribute focus data sets 756 that defines the association between a particular skin attribute that is a under study and genes that are associated with that attribute in the literature. After the intersection analysis of a particular skin attribute focus data set 756 with the skin attribute/regulation direction data and the fold criterion result data 752 (which includes determining alignment of the more youthful regulation direction for the particular biochemical pathways), the developed skin attribute subset 758, representing a preliminary functional youth gene assembly for a particular skin attribute is stored in database 730.
The data processing system's database 730 also receives and holds data relevant to the PCR testing and results of the confirmation analysis for the preliminary functional youth gene assembly. This includes storing the PCR candidate data 760, i.e., the listing of the genes based on the preliminary functional youth gene assembly as supplemented with genes of secondary interest that will be subject to PCR testing under the cycle level criterion or other parameters used in the analysis of the PCR testing data. After the PCR tests have been run, the database 730 receives the PCR cycle data 762 including the associated up/down regulation direction observed from testing. From the PCR cycle data set 762, the processing applications 724 derives the Final Attribute Data 770, 772 for one or more skin attributes.
As noted, stored in memory are the process application modules 720. These are software generally in two categories. A first category is the conventional statistical analysis programs 722, such as GeneSpring GX software (version 10) or other commercial software to perform a parametric t-test with a Benjamini and Hochberg false discovery rate correction. The StatMiner (Version 3) software may be used for analysis of the PCR data. The second category is the flowchart process applications that implement the analysis and steps discussed above and shown in
While a primary use of the present methodology is to develop the functional youth gene assemblies that provide a focus for further gene-level research on skin attributes, the methodology may also be used to screen agents for effectiveness to reduce skin aging. An agent may be chosen for testing to assess the efficacy of the particular agent and to explore the genetic pathway focus of its action. Known anti-aging agents have shown significantly different levels of gene expression in genes associated with a plurality of biochemical pathways of the skin. A screening method of this type could significantly lessen the number of costly and lengthy in vivo testing procedures done on many anti-aging product candidates. For example, many consumer studies on facial anti-aging products run for at least 12 weeks. Provided a reliable functional youth gene assembly is identified, testing the effects of an agent on the biochemical pathways associated with particular genes provides a focused way to develop data on the action of the an anti-aging candidate on a much shorter time frame and provides quantitative data for comparison to other agents.
A screening approach may be used to assess the likelihood of another agent working well in an anti-aging skin care product. A new agent triggering levels of gene expression to a functional youth gene assembly similar to or superior to a known skin anti-aging agent may be considered for further study, while a new agent that does not trigger similar levels of gene expression in those genes in a functional youth gene assembly may not be considered for further research investment.
The screening method may also be used for improving the effective properties of existing anti-aging skin care products, selecting new anti-aging ingredients for products, and selecting blends of anti-aging ingredients for products. From an understanding of which genes and which biochemical pathways have skin anti-aging effects, the properties of an agent as a promoter of a biochemical pathway associated with more youthful appearance or an inhibitor of a biochemical pathway associated with less youthful appearance may be improved. Using the screening method on many possible agent candidates instead of time-consuming clinical testing on fewer agent candidates is both a time-efficient and cost-effective way of performing research and development. The method helps to provide consumers with anti-aging products based on the most recent scientific research.
Other agents and agent blends including, for example, arNOX inhibitory agents derived from plant extracts may be tested. The plant for extract is optionally selected from broccoli, shitake, coleus, rosemary, lotus, artichoke, sea rose tangerine, Oenothera biennis, astaxanthin, red orange, Schisandra chinensis, Lonicera, Fagopyrum, carrot, Narcissus tazetta or olive. The arNOX inhibitory agents optionally include salicylates, for example, salicin, salicylic acid, salicyl hydroxamate, derivatives or combinations thereof.
While one embodiment of the present methodology is to develop the functional youth gene assemblies that provide a focus for further gene-level research on skin attributes, when the methodology is used to screen agents for effectiveness to reduce skin aging, it can assist in the formulation of a composition to reduce skin aging. Once an agent has been identified in testing to have efficacy as a promoter of a biochemical pathway associated with more youthful appearance or an inhibitor of a biochemical pathway associated with less youthful appearance for at least one skin attribute, that agent can be a candidate for an active ingredient in a composition to reduce skin aging. Provided a reliable functional youth gene assembly has been identified and efficacy of an agent on the biochemical pathways associated with particular genes in that assembly has been found, the composition can be targeted specifically to improvement of the skin attribute associated with that functional youth gene assembly. A composition can be formulated that addresses multiple skin attributes, once effective agents for the multiple skin attributes are found by the process and system disclosed herein. A composition can also include a pharmaceutically acceptable carrier. A pharmaceutical acceptable carrier refers to a carrier medium that does not interfere with the effectiveness of the biological activity of the active ingredient, is chemically inert, and is not toxic to the patient to whom it is administered. The type of the carrier may include powders, emollients, lotions, creams, liquids and the like.
Thus, the understanding of which genes and which biochemical pathways have skin anti-aging effects and the properties of an agent as a promoter of a biochemical pathway associated with more youthful appearance or an inhibitor of a biochemical pathway associated with less youthful appearance is improved by the methods discussed herein, this understanding can be translated into compositions that are directed to one or more skin attributes associated with a functional youth gene assembly. It is expected that agents showing an anti-aging efficacy will be derived from broccoli, shitake, coleus, rosemary, lotus, artichoke, sea rose tangerine, Oenothera biennis, astaxanthin, red orange, Schisandra chinensis, Lonicera, Fagopyrum, carrot, Narcissus tazetta or olive. They also may be derived from arNOX inhibitory agents that include salicylates, for example, salicin, salicylic acid, salicyl hydroxamate, derivatives or combinations thereof. These agents and their derivatives may then be deployed in skin anti-aging formulations with a sound basis in research at the genetic level.
For further confirmation of the effects of an agent that is viewed as regulating in a youthful direction the pathways of a functional youth gene assembly for a skin attribute, consumer clinical studies may be conducted with a skin care product including the anti-aging agent tested with in vitro methods. Clinical studies with trained observation and measurement of skin parameters confirm changes in particular skin aging attributes as regulated by a particular functional youth gene assembly.
After a group of genes are selected as a functional youth gene assembly, in vitro studies including the assay methods discussed above, are used to screen agent candidates and limit the amount of in vivo studies used in product development.
A skin care product, ageLOC® Future Serum, including 0.5% salicin is the finished formulation used for evaluation in clinical testing. The ageLOC® Future Serum is commercially available from Nu Skin Enterprises, Inc. (Provo, Utah).
Twenty-nine out of thirty subjects completed the clinical study. Table 7 summarized the demographics of the study participants.
The Fitzpatrick skin classification is based on the skin's unprotected response to the first 30 to 45 minutes of sun exposure after a winter season without sun exposure:
I—Always burns easily; never tans; II—Always burns easily; tans minimally; III—Burns moderately; tans gradually; IV—Burns minimally; always tans well; V—rarely burns; tans profusely; VI—Never burns; deeply pigmented.
Clinical Procedures
At baseline (Visit 1), each prospective subject completed an Eligibility and Health Questionnaire, and read and signed a Confidentially Agreement, a Photography Release Form and an Informed Consent Agreement. Each subject was explained the type of study, the detailed procedures and materials to be tested, along with any known adverse reactions that may result from participation. Subjects arrived at the clinic having refrained from applying any moisturizer to the face at least 3 to 5 days prior to visit and having cleansed the face to remove all makeup at least 30 minutes prior to visit. Subjects were consequently not allowed to use any other topical cosmeceuticals, topical retinoid, or moisturizers during the 12 week duration of the study.
Subjects used the study product on their face twice daily for 12 weeks. Ordinal grading on a 9-point scale (0=none, 1-3=mild, 4-6=moderate, 7-9=severe) of facial fine lines, mottled pigmentation, uneven skin tone, tactile roughness, global firmness appearance, jaw-line contour, radiance and overall appearance was performed by investigator at baseline, week 1, week 4, week 8 and week 12.
Digital high-resolution photography was performed on the front, right and left sides of the face. Each image was taken while the subject's eyes were open. Each subject's baseline photograph was compared to each post-baseline photograph to ensure consistent placement and lighting. Color standards were imaged prior to each study visit.
Corneometry measurements were taken on each subject's left ocular bone (in line with pupil) to measure the moisture content of the stratum corneum.
Ultrasound measurements were taken on the left side of each subject's face to measure density of the facial skin in the crow's feet area. Measurements were taken with the probe oriented perpendicular to the body axis while the subjects were resting supine on a padded patient table.
A single cutometer measurement was taken on the right side of the subject's face, in line with the corner of the eye and the edge of the nostril, to measure the extensibility of the skin.
All clinical and corneometer measurements and evaluations were taken at baseline, week 1 (visit 2), week 4 (visit 3), week 8 (visit 4) and week 12 (visit 5). Ultrasound and cutometer measurements and evaluations were taken at baseline, week 4 (visit 3), week 8 (visit 4) and week 12 (visit 5). Completed patient's diaries were reviewed for compliance at each visit.
Biostatistics
Mean clinical grading and instrumentation scores at each visit were statistically compared to baseline scores using paired t-test. Changes from baseline were considered significant at the p≦0.05 level. Mean percent change from baseline and incidence of positive responders were calculated for all attributes. Comparisons, based on the average from baseline, were made between the test materials using analysis of variance (ANOVA).
Results
Twenty-nine of thirty subjects successfully completed the study with one subject unable to complete due to personal reasons. Compliance assessments indicated that subjects were following test formulation use instructions.
The clinical investigator's facial assessments indicated a statistically significant improvement in facial fine lines, tactile roughness, pore size, radiance and overall appearance at week 1 time point (P≦0.05). All of the benefits continued into weeks 4, 8 and 12. Statistically significant improvement in mottled pigmentation, global firmness, sub-categories of wrinkles and jaw-line contour was recorded at week 4 time point (P≦0.05) and continued through to week 12.
Corneometer measurements indicated statistical improvement in hydration at week 1 time point (P≦0.05). This improvement continued through week 8. Hydration of the stratum corneum was decreased significantly (P≦0.05) at the week 12 time point.
Cutometer measurements indicated statistical improvement in extensibility of the skin at week 12 time point (P≦0.05).
Ultrasound measurements indicated statistical improvement in density of the skin at week 4 time point (P≦0.05). This improvement continued through week 12.
No tolerability issues related to erythema, edema and scaling were observed by the investigator. Three subjects reported a slight stinging at week 8 (P≦0.05) and slight itching at week 12 (P≦0.05).
Indicates a statistically significant (p ≦ 0.05) increase compared to Baseline
Indicates a statistically significant (p ≦ 0.05) decrease compared to Baseline
All references disclosed herein, whether patent or non-patent, are hereby incorporated by reference as if each was included at its citation, in its entirety.
Although the present disclosure has been described with a certain degree of particularity, it is understood the disclosure has been made by way of example, and changes in detail or structure may be made without departing from the spirit of the disclosure as defined in the appended claims.
This application claims priority to U.S. Provisional Application No. 61/370,190 filed Aug. 3, 2010, entitled “Apparatus and Method for Testing Relationships Between Gene Expression and Physical Appearance of Skin,” the entire content of which is hereby incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 61370190 | Aug 2010 | US |
