1. 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 tio2002@med.cornell.edu

2. 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

3. 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.

4. 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

5. 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

6. 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”

7. 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 labor- intensive “hands-on” approach Ultimate goal is to provide more automation to the process of knowledge discovery

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

9. 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

10. Gene Expression Patterns Genes are expressed when they are copied into mRNA 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

11. 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.

12. The Boom of Microarray Technology: Number of Publications with Affymetrix Chips 0 200 400 600 800 1000 1200 Year 199119921993199419951996199719981999200020012002 2003 Number of publications

13. 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

14. 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

15. Types of Microarrays Spotted DNA arrays (“cDNA 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 20-25mers/gene –Enabled by photolithography from the computer industry –Off the shelf Ink-jet microarrays (Agilent) –25-60mers “printed” directly on glass –Four cartridges: A, C, G, and T

16. Challenges in Microarray Studies What are the difficulties? –Many potential sources of random and systematic measurement error in the microarray process 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 Statistical analysis and quality control Data mining Experimental design Array quantification (from digital image)

17. How to Add “Functional” to Genomics? Some automated annotations –NetAffx: 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 –GeneCards: http://bip.weizmann.ac.il/g.html Can try this out with public databases –GEO: gene expression omnibus via NCBI

18. GEO: Public Database Example

31. Clinical Relevance

32. 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/vol347 /issue25/index.shtml Subscription access

33. 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

34. 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

35. 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

36. 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 cDNA 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.

37. 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 –NetAffx tools for automated annotations