Study of Gene Expression:Statistics, Biology, and

MicroarraysKer-Chau Li

Statistics Department

UCLA

kcli@stat.ucla.edu

PART I. Cellular Biology

Macromolecules: DNA, mRNA, protein

Why Biology?

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

• 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

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

Anticipated Benefits

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

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

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

What is a gene ?

SNP and Genetic Disease

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.

PART II. Microarray

Genome-wide expression profiling

Differential Gene expression:tissues, organs

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

Microarray

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

MicroArray Technique:

Synthesize GeneSpecific DNA Oligos

Attach oligo toSolid Support

Tissue or Cell

extract mRNA

Amplificationand Labeling

Hybridize

Scan and Quantitate

Reverse-transcriptionColor : cy3, cy5 green, red

Exploring the Metabolic and Genetic Control ofGene Expression on a Genomic Scale

Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown*

A B C D E …..

A -- 2.1 0.8 1.3 0.5

B 0.2 -- -0.5 2.3 0.22

… -1.2 -- 0.3 -1.1

Expression level

Time0

1

Change of Condition

Or:

Time Course:

PART III. Statistics

Low-level analysis

Comparative expression

Feature extraction

Classification,clustering

Pearson correlation

Liquid association

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,

Genome-wide expression profileA basic structure

cond1 cond2 …….. condp

Gene1 x11 x12 …….. x1p

Gene2 x21 x22 …….. x2p

… … ...

… … ...

Genen xn1 xn2 …….. xnp

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 sampleso that cellular signals can be amplified- Yeast Cell Cycle data : ideally all cells are engaged in the same activities- synchronization

Example 1

Comparative expression

Normal versus cancer cells

ALL versus AML

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

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

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