Introduction to Physiological Genomics Defining the Discipline and

Introduction to Physiological Genomics: Defining the Discipline and its Methods 2005 IUPS Congress Timothy P. O’Connor, Ph. D. Department of Genetic Medicine Weill Cornell Medical College tio 2002@med. cornell. edu

Topics to be Covered • Terminology & jargon • Potential applications of genomics • Tools and methods – Microarrays – Online resources – (SNP chips) • Examples of studies • Thoughts on incorporating genomics into a curriculum

What is Physiological Genomics? • Physiological genomics is the study of the functioning of gene products in the context of the whole organism and its environment.

Multi-dimensional Integrative Physiology See Figure 2 from Liang et al. Journal of Physiology 554: 22 -30, 2003 http: //jp. physoc. org/cgi/content/full/554 /1/22 Free access

Large Scale Approaches • Genomics • Functional genomics • Proteomics: The identification, characterization and quantification of all proteins involved in a particular pathway that can be studied in concert to provide accurate and comprehensive data about that system. • Metabolomics: characterization of the physiological state of a sample by determining the concentration of all the small molecules that contribute to metabolism • Genotyping (SNP) chips

Large Scale Approaches: Genomics and Functional Genomics • Genomics: – Determining the sequences of the genome of an organism and ordering these sequences into individual genes, gene families, and chromosomes – Identification of coding sequences as well as regulatory elements – Determining the patterns of gene expression (gene expression “profiles” or “signatures”) • Functional genomics: – – Understanding the biological role of each gene Mechanism underlying the regulation of gene expression Regulatory interactions among genes Identifying the “functional transcriptome”

Challenge of Functional Genomics • Capacity for collecting data has surpassed the data analysis techniques, and it is only getting worse • Converting data (information) into knowledge is a bottleneck • Currently requires expertise and a laborintensive “hands-on” approach • Ultimate goal is to provide more automation to the process of knowledge discovery

Lag Between “Functional” and “Genomics” Source: UC Davis Genomics Initiative, Technical Report, 2001

Bioinformatics • Bioinformatics helps bridge the gap between functional and genomics • Field at the interface of computer science, statistics, and biology • Goal of the field is to refine and organize biological information into biological knowledge using computers

Gene Expression Patterns • Genes are expressed when they are copied into m. RNA or RNA (transcription) • Differential gene expression: which genes are expressed in which cells or tissues at a given point in time or in the life of the organism. – Total RNA can be isolated from cells or tissues under different experimental conditions and the relative amounts of transcribed RNA can be measured – The change in expression pattern in response to an experimental condition, environmental change, drug treatment, etc. sheds light into the dynamic functioning of a cell

What is a microarray? • A tool for analyzing gene expression that consists of a small membrane or glass slide containing samples of thousands of genes arranged in a regular pattern.

The Boom of Microarray Technology: Number of Publications with Affymetrix Chips Number of publications 1200 1000 800 600 400 200 0 1991 1992 1993 1994 1995 1996 1997 Year 1998 1999 2000 2001 2002 2003

What’s the Point? • Large scale (genome-wide) screening • Eliminate bias of pre-selecting candidate genes • Test multiple hypotheses simultaneously • Generate new hypotheses by identifying novel genes associated with experiment • Identify novel relationships/patterns among genes

Applications of Microarray Technology • Gene expression profiling – – In different cells/tissues During the course of development Under different environmental or chemical stimuli In disease state versus healthy • Molecular diagnosis: – Molecular classification of diseases • Drug development – Identification of new targets • Pharmacogenomics – Individualized medicine

Types of Microarrays • Spotted DNA arrays (“c. DNA arrays”) – Developed by Pat Brown (Stanford) – PCR products (or long oligos) from known genes (~100 nt) spotted on glass, plastic, or nylon support – Customizable and off the shelf • Oligonucleotide arrays: Affymetrix Gene Chips – Large number of 20 -25 mers/gene – Enabled by photolithography from the computer industry – Off the shelf • Ink-jet microarrays (Agilent) – 25 -60 mers “printed” directly on glass – Four cartridges: A, C, G, and T

Challenges in Microarray Studies • What are the difficulties? – Many potential sources of random and systematic measurement error in the microarray process Experimental design Array quantification (from digital image) Statistical analysis and quality control Data mining Examples Experimental design • Hypothesis testing vs. exploratory “fishing expedition” Statistical Analysis • Small number of samples compared to large number of variables (genes) leads to problems with false positives Data mining • Annotated lists • What is the function of the differentially expressed genes? • Extensive use of online resources

How to Add “Functional” to Genomics? • Some automated annotations – Net. Affx: www. affymetrix. com – Batch query with list of gene IDs • Lots of hands-on annotating, one gene at a time, using online databases – Entrez: www. ncbi. nlm. nih. gov – Gene. Cards: http: //bip. weizmann. ac. il/g. html • Can try this out with public databases – GEO: gene expression omnibus via NCBI

GEO: Public Database Example

Clinical Relevance

Gene Expression “Signature” as a Predictor of Survival See figures from van de Vijver et al. New England Journal of Medicine 347: 1999 -2009, 2002 http: //content. nejm. org/content/vol 347 /issue 25/index. shtml Subscription access

Summary: Present and Future Use of Physiological Genomics • • Molecular diagnosis Redefining disease Discovery of new targets for therapeutic intervention Pharmacogenomics – Variable drug effects depending on individual profiles • Multi-dimensional integrative physiology for applications in any subdiscipline – Comparative physiology – Ecological physiology – Evolutionary physiology

Multi-dimensional Integrative Physiology See Figure 2 from Liang et al. Journal of Physiology 554: 22 -30, 2003 http: //jp. physoc. org/cgi/content/full/554 /1/22 Free access

Thoughts on incorporation of genomics into curriculum • Advantages – availability of public databases containing real data – basic analyses can be done with Excel – outstanding online databases for annotating gene lists • Challenges – not effective if boiled down to a lab exercise or 2 – how to effectively convey the integrative potential when you might be working at only 1 level

Useful References • King, H. C. and A. A. Sinha, 2001. “Gene expression profile analysis by DNA microarrays. JAMA, 286: 2280 -2288 • Duggan, D. J. et al. , 1999. Expression profiling using c. DNA microarrays. Nature Genetics Supp. 21: 10 -14. • Lipshutz, R. J. et al. 1999. High density synthetic oligonucleotide arrays. Nature Genetics Supp. 21: 20 -24. • Hackett, N. R. et al. , 2003. Variability of antioxidant-related gene expression in the airway epithelium of cigarette smokers. Am. J. Respir. Cell Molec. Biol. 29: 331 -343. • Cowley Jr. , A. W. 2003 Physiological genomics: tools and concepts. J. Physiol. 554: 3. • Liang et al. 2003. High throughput gene expression profiling: a molecular approach to integrative physiology. J Physiol. 554: 22 -30.

Useful Websites • www. ncbi. nih. gov/About/primer/ – NCBI’s primer on arrays, SNPs, molecular genetics, pharmacogenetics, etc. • www. affymetrix. com – Useful information about new microarrays and publications using Affy chips – Net. Affx tools for automated annotations