Application of Genomics Tools for

Personal Care Product Development

Anna Langerveld, PhD

IMSCC Scientific Meeting

August, 2010

www.genemarkersllc.com

Phone: 269-365-9006

Overview of Presentation

Genemarkers Capabilities

Genomics

◦ History & brief overview of molecular biology

◦ Current applications in personal care

Commonly Used Genomics Methods in Personal Care

◦ Microarrays

◦ PCR

Good Study Design & Interpreting Data

Genemarkers LLC

Founded in 2007; privately funded

◦ Biotech spin-off from Western Michigan University

Currently lease lab space from MI hospital research facility

Personnel are highly skilled with many years experience with

genomics technologies

Established working collaborations with local biotech businesses to

expand capabilities

◦ Hospital run research lab provides histology services

◦ Michigan High Throughput Screening Center (MHTSC) – cell

culture facility, cell culture assays

◦ Statistical/bioinformatics experts

Genemarkers LLC

Provide contract research services

◦ Wide range of projects – study sponsors include:

Large, mid and small-sized personal care/skin care companies

Large pharma

Medical device

Biotech and molecular diagnostic development companies

Academics

In-house project to develop a molecular diagnostic test to differentiate rare forms of Parkinson’s disease (multiple system atrophy)

◦ Collaboration with researchers at Western Michigan University and Vanderbilt University Medical School

Genemarkers Personal Care Services

Gene expression, genotyping (SNP analysis), microRNA expression

◦ Affymetrix

◦ Applied Biosystems Taqman methods

Qualified testing lab to perform MatTek Skin Irritancy Test (SIT assay)

◦ MTT assay

Cell culture capabilities

Process many types of cells and tissues; human skin punch biopsies, hair

Genomics assays for assessing sun damage

◦ Measure induction of mitochondrial common deletion

◦ p53 gene expression

◦ p53 SNP analysis

Study Design and Planning

Sample Processing

Data Analysis and

Interpretation

GenomicsHistory and Applications in Personal Care

Genomics: History and Current Applications

Field was born from the human genome project

◦ identified and sequenced ~20,000 - 30,000 human genes

◦ 2000 first draft of human genome was published

Produced high throughput technologies that measure/analyze:

◦ Gene expression (which genes are turned on or off in a given condition)

◦ Gene sequence (determines if the DNA code is altered)

SNP’s – single nucleotide polymorphisms

Technologies are being used in all areas of human health (personalized

medicine), animal health, environmental and agricultural industries

Rapidly emerging in the personal care industry

Increasing Use of Genomics Language

in Marketing to Consumers

Novel Ingredient – “skingenecell”

Increase in Gene Expression Data Presented to

Formulators by Ingredient Suppliers

The Biological Basis of Skin Characteristics: How

Do Genes Relate to the Skin’s Appearance?

Physical Characteristics

Fine lines, wrinkles, age spots, dullness

Changes in Biological/Physiological Processes

Inflammation, cell cycle/regeneration,

oxidant formation/anti-oxidant production, aging molecules,

extracellular matrix integrity

Changes in gene expression and protein expression

(sirtuins, collagens, keratins, growth factors,

metalloproteinases, interleukins)

= Biomarkers

Bio

mar

kers

Current Applications of Genomics-Based

Methods in Personal Care

Efficacy & Claims Support

Product Development

Safety Assessment

Personalized Skin Care

The Benefits of Genomics Methods for

Efficacy and Claims Support

Determine how specific ingredients or finished products regulate gene

expression

◦ For example, product may benefit skin by increasing expression of

COL1A1 and decrease expression of MMP1 = “anti-aging”

Characterize molecular mechanisms of action

◦ Inflammation (redness) produced through multiple biological

pathways; i.e. TNF- , IL-1

◦ Define how your product works more specifically

The Benefits of Genomics Methods for

Product Development

Identify gene expression signatures/profiles of specific conditions and/or

diseases (old vs young skin)

◦ Specific genes become “targets” for product development

◦ Scientifically sound approach to achieving product’s goals

Identify dose response relationships to conserve the use of costly

ingredients

◦ For example, anti-oxidants typically more effective at lower doses

◦ Organic ingredients can be expensive

Understand how combinations of ingredients work together

◦ For example, if you add 5 actives, do you get a 5X response?

GENOMICS

GENOTYPING & GENE

EXPRESSION

Genotyping: Analysis of DNA Code

• All cells in the human body have 23 pairs of chromosomes

• Chromosomes comprised of DNA/genes

• DNA is comprised of a “code” that contains combinations of 4 bases

(Adenine, Thymine, Guanine, Cytosine)

• Analysis of “code” = gene sequencing/genotyping

• Approximately 1% difference in DNA sequence accounts for individual

differences in people

Gene Expression is Based on the Central Dogma

of Biology

All cells in a person’s body have the same DNA or “genes”

When the gene is activated it is turned into RNA

Different cell types are produced by activation of unique sets of genes

Specific RNAs assemble specific proteins such as collagen and keratin in skin cells

Aging, disease and other conditions will influence the regulation of specific genes

Gene expression technologies measure the amount of RNA in a given cell or tissue

Different Patterns of Gene Expression

Produce Unique Cell Types

NeuronRed Blood Cells

•Each of these cell types has the same DNA

•Different genes are “turned on or off” to produce the different cell types

•Similarly, patterns of gene expression change within a cell type as cells age or

are exposed to different conditions (i.e., disease, treatments, etc.)

Gene expression = RNA “amount”

DNA RNA

ProteinsNucleus

Cell membrane

DNA RNA

ProteinsNucleus

Cell membrane

Untreated Treated

Gene expression technologies measure the amount of specific

RNAs within a cell or tissue

2 PRIMARY TECHNOLOGIES FOR

MEASURING GENE EXPRESSION

1. Microarrays (large screen)

2. Quantitative PCR (qPCR; smaller numbers of genes)

Genomics Experimental Workflow

Treat cells, tissues, subjects with test article

Isolate RNA

cDNA synthesis

qPCR

Analysis

Data Analysis

Array sample labeling

Array Processing

Data Analysis

qPCR Microarray

2 Basic Microarray Platforms

Slide based systems

Multiple companies

Up to several thousand genes on a

slide

Affymetrix GeneChip

Up to 45,000 transcripts

•Use different chemistry systems and have different levels of sensitivity and detection

•Significant differences in cost

•Similar principle – gene specific “probes” (DNA) are placed on matrix, sample hybridized to the

probe

Affymetrix Microarrays (GeneChips)

Measure up to 45,000 transcripts at a time

Chips available for over 30 organisms

Ideal for discovery-based studies

◦ What does my product/ingredient do?

◦ Identifying novel mechanisms of action

◦ Building database of gene expression signatures of different conditions

Used as a screening tool; results are often confirmed with qPCR

ProbeArrays(chips)

GeneChip®

System

Scanner

Scanner

SoftwareData Analysis

FluidicsStation

Affymetrix Sample Processing

Overview

Hybridization of sample (purple) to probes (green) on chip

Complementary (matched)

sequences will form pair

Unmatched sequences will

not bind to chip

Affymetrix GeneChip® Data

Bioinformatics

Multiple software programs to analyze the data

◦ Affymetrix Gene Command Console (AGCC), GeneSpring

Statistical data analysis to identify genes that are upregulated and

downregulated

◦ Fold change value that quantifies the change relative to a control

sample

Datasets are further analyzed to sort genes into biological functions

and pathways

Tools for Visualizing/Analyzing Microarray Data

Heatmaps/Cluster

Analyses

Venn Diagrams

Compare Treatment

Group DatasetsBiological Pathway Analysis

PCRPolymerase Chain Reaction

PCR – Polymerase Chain Reaction

Method for amplifying specific regions of DNA/genes

Used to identify

◦ changes in DNA sequence

◦ changes in gene expression

PCR methods have evolved dramatically over the past 15-20 years

◦ More sensitive

◦ Automated and highly reproducible

◦ More quantitative (semi-quantitative to quantitative real time PCR)

Similar to microarrays, there are different instrumentation platforms and different chemistries

◦ SYBR Green

◦ Fluorescent probes

◦ Taqman probes

Taqman Real Time PCR (Polymerase Chain

Reaction)

Taqman Low Density Array (TLDA)

Less expensive than microarrays; very sensitive method – highly quantifiable

Can measure up to 384 genes at a time

Arrays are custom-designed with your genes of interest

More focused experimental approach

Taqman Real Time PCR

Each target gene (i.e., COL1A1) is amplified using a set of primers and a fluorescent labeled probe that contain complementary sequences (DNA code) to the gene of interest

The probe contains a fluorescent reporter dye (R) on one end and a quencher dye (Q) that inhibits the fluorescent signal

As the reaction DNA synthesis reaction proceeds, the quencher is displaced from the probe, causing the an increase in fluorescent signal

The instrument reads the level of fluorescence in each well every 7 seconds and records this data in real time

Samples with greater amounts of starting material will produce more copies of DNA and will emit greater levels of fluorescence

Taqman PCR Data Analysis

The PCR reaction takes place over 40 cycles and fluorescent signal is recorded with regard to cycle number

Rn refers to the level of fluorescence

A threshold line is drawn during the “amplification phase” of the PCR reaction (red line)

◦ Amplification phase refers to the point during which the amplification takes place exponentially

◦ Other phases refer to baseline or plateau and are not the best phases to compare samples

The cycle at which the sample crosses the threshold line is called the Ct value

Ct values are compared for each sample

Samples that have lower Ct values amplify at a faster rate and thus, have greater amounts of starting material (RNA) for the target gene

◦ Sample 1 > Sample 2

Sample 2

Sample 1

qPCR Data Analysis Results

Results are expressed in “relative quantitative” values

◦ Value (Ct) of target gene is first normalized to an endogenous

control gene (reference)

Delta Ct

◦ Relative = “treated” compared to a “control”

Log10RQ

Statistics (t-tests) used to identify statistically significant changes in

gene regulation

Provide p-value, direction, fold change value (log scale – log10RQ)

Presentation of qPCR Results

*

*

*

qPCR results are often presented using bar graphs that show increased or

decreased gene expression

Graphs may show data in LOG10RQ or linear format

Figure captions should describe study and statistical results

* Statistically significant at p<0.01, N=4

Evaluating Study Results

Replicates; large enough sample size for statistical data analysis

◦ Figures, data presentation will show p values, special symbol for results that are statistically significant

Appropriate controls included in the study

◦ For example, if dissolving ingredient in a solvent, the solvent alone should be included in the analysis

◦ Some studes include a positive control

With qRT-PCR analyses, the results are typically normalized to an endogenous control gene; a gene that shows stable expression in all of the samples (regardless of treatment)

◦ ****Very important to screen endogenous control genes to identify a stable gene

◦ Not always the same in all studies

◦ Most studies will select 18S, GAPDH or -actin based on “old school” dogma that these genes are stable

◦ More current data shows these 3 are not always the best choices

◦ Results can be very different based on endogenous control gene choice

Studies with good design and robust methods will show optimization parameters in report, while others will merely omit these details

Example of Endogenous Control Gene

Amplification

18S

GUSBGAPDH

HPRT

In this study, 18S showed the least variability in amplification between

samples and was the best endogenous control gene

Example of Endogenous Control Gene

Amplification

In this study, the treatment

influenced expression of

GAPDH and -actin

◦ The top figure shows 2 clearly

different patterns of

amplification

Our screening process

identified GUSB and PPIA as

the best endogenous

control genes for the study

◦ The bottom figure shows similar

amplification profiles for the

samples in the study

GAPDH -actin

GUSB PPIA

Benefits of Genomics-Based Studies to the

Personal Care Industry

Use genomics information to guide product formulation

◦ identify specific molecular targets for achieving desired effects

Cost-savings

◦ Identify dose-response relationships

◦ May find that a small amount of expensive ingredient produces same effect as higher concentration

Validate efficacy

Ensure product safety

◦ Identification of validated safety biomarkers

◦ Negative response on established toxicity markers

Ultimately, improve marketability and consumer confidence

Contact information Phone: 269-365-9006

anna@genemarkersllc.com

www.genemarkersllc.com