2. Study of Gene Expression: Statistics, Biology, and Microarrays Ker-Chau Li Statistics Department UCLA kcli@stat.ucla.edu

3. PART I. Cellular Biology Macromolecules: DNA, mRNA, protein

4. Why Biology?

5. Human Genome Project Begun in 1990, the U.S. Human Genome Project is a 13-year effort coordinated by the U.S. Department of Energy and the National Institutes of Health. The project originally was planned to last 15 years, but effective resource and technological advances have accelerated the expected completion date to 2003. Project goals are to ■ identify all the approximate 30,000 genes in human DNA, ■ determine the sequences of the 3 billion chemical base pairs that make up human DNA, ■ store this information in databases, ■ improve tools for data analysis, ■ transfer related technologies to the private sector, and ■ address the ethical, legal, and social issues (ELSI) that may arise from the project. Recent Milestones: ■ June 2000 completion of a working draft of the entire human genome ■ February 2001 analyses of the working draft are published Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001

6. Gene number, exact locations, and functions Gene regulation DNA sequence organization Chromosomal structure and organization Noncoding DNA types, amount, distribution, information content, and functions Coordination of gene expression, protein synthesis, and post-translational events Interaction of proteins in complex molecular machines Predicted vs experimentally determined gene function Evolutionary conservation among organisms Protein conservation (structure and function) Proteomes (total protein content and function) in organisms Correlation of SNPs (single-base DNA variations among individuals) with health and disease Disease-susceptibility prediction based on gene sequence variation Genes involved in complex traits and multigene diseases Complex systems biology including microbial consortia useful for environmental restoration Developmental genetics, genomics Future Challenges: What We Still Don’t Know Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001

7. Medicine and the New Genomics Gene Testing Gene Therapy Pharmacogenomics Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001 improved diagnosis of disease earlier detection of genetic predispositions to disease rational drug design gene therapy and control systems for drugs personalized, custom drugs Anticipated Benefits

8. Molecular Medicine improved diagnosis of disease earlier detection of genetic predispositions to disease rational drug design gene therapy and control systems for drugs pharmacogenomics "custom drugs" Microbial Genomics rapid detection and treatment of pathogens (disease-causing microbes) in medicine new energy sources (biofuels) environmental monitoring to detect pollutants protection from biological and chemical warfare safe, efficient toxic waste cleanup Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001

9. Agriculture, Livestock Breeding, and Bioprocessing disease-, insect-, and drought-resistant crops healthier, more productive, disease-resistant farm animals more nutritious produce biopesticides edible vaccines incorporated into food products new environmental cleanup uses for plants like tobacco Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001 Anticipated Benefits

11. Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001

12. What is a gene ?

14. SNP and Genetic Disease

18. Mitochondrial ATP Synthase E. coli ATP Synthase These images depicting models of ATP Synthase subunit structure were provided by John Walker. Some equivalent subunits from different organisms have different names.

20. PART II. Microarray Genome-wide expression profiling

21. Differential Gene expression: tissues, organs

23. Next Step in Genomics Transcriptomics involves large ‑ scale analysis of messenger RNAs (molecules that are transcribed from active genes) to follow when, where, and under what conditions genes are expressed. Proteomics—the study of protein expression and function—can bring researchers closer than gene expression studies to what’s actually happening in the cell. Structural genomics initiatives are being launched worldwide to generate the 3 ‑ D structures of one or more proteins from each protein family, thus offering clues to function and biological targets for drug design. Knockout studies are one experimental method for understanding the function of DNA sequences and the proteins they encode. Researchers inactivate genes in living organisms and monitor any changes that could reveal the function of specific genes. Comparative genomics—analyzing DNA sequence patterns of humans and well ‑ studied model organisms side ‑ by ‑ side—has become one of the most powerful strategies for identifying human genes and interpreting their function. Human Genome Program, U.S. Department of Energy, Genomics and Its Impact on Medicine and Society: A 2001 Primer, 2001

24. Microarray

25. MicroArray Allows measuring the mRNA level of thousands of genes in one experiment -- system level response The data generation can be fully automated by robots Common experimental themes: –Time Course –Mutation/Knockout Response

26. Reverse-transcription Color : cy3, cy5 green, red

27. Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown*

29. PART III. Statistics Low-level analysis Comparative expression Feature extraction Classification,clustering Pearson correlation Liquid association

30. Image analysis Convert an image into a number representing the ratio of the levels of expression between red and green channels Color bias Spatial, tip, spot effects Background noises cDNA, oligonucleotide arrays,

31. Genome-wide expression profile A basic structure cond1 cond2 …….. condp Gene1 x11 x12 …….. x1p Gene2 x21 x22 …….. x2p … …... Genen xn1 xn2 …….. xnp

32. Cond1, cond2, …, condp denote various environmental conditions, time points, cell types, etc. under which mRNA samples are taken Note : numerous cells are involved Data quality issues : 1. chip (manufacturer) 2. mRNA sample (user) It is important to have a homogeneous sample so that cellular signals can be amplified - Yeast Cell Cycle data : ideally all cells are engaged in the same activities- synchronization

33. Example 1 Comparative expression Normal versus cancer cells ALL versus AML

34. E.Lander’s group at MIT Cancer classification (leukemia) ALL; AML (arising from lymphoid or myeloid precursors) Require different treatments Traditional methods ; nuclear morphology; Enzyme-based histochemical analysis(1960) Antibodies (1970) Genome wide expression comparision

35. ALL (acute lymphoblastic leukemia) AML(acute myeloid leukemia)

36. Gene selection For each gene (row) compute a score defined by sample mean of X - sample mean of Y divided by standard deviation of X + standard deviation of Y X=ALL, Y=AML Genes (rows) with highest scores are selected. Works ???? 34 new leukemia samples 29 are predicated with 100% accuracy; 5 weak predication cases