Genomic wide analysis of nucleic acids. Lecture 9 Genomic wide analysis of nucleic acids.

A Genome Revolution in Biology and Medicine We are in the midst of a "Golden Era" of biology The Human Genome Project has produced a huge storehouse of data that will be used to change every aspect of biological research and medicine The revolution is about treating biology as an information science, not about specific biochemical technologies. .

Historical Milestones Year. Milestone 1866 Historical Milestones Year Milestone 1866 Mendel’s discovery of genes 1871 Discovery of nucleic acids 1951 First protein sequence (insulin) 1953 Double helix structure of DNA 1960s Elucidation of the genetic code 1977 Advent of DNA sequencing 1975-79 First cloning of human genes 1986 Fully automated DNA sequencing 1995 First whole genome (Haemophilus Influenza) 1999 First human chromosome(Chr #22) 2000 Drosophila / Arabidopsis genomes 2001 Human and mouse genomes Much more genomes since them!!

Genomic data Whole genome data sets. According to http://www.ebi.ac.uk/genomes/ as at 30-sept-04 Archea – 19 Bacteria – 167 Eukaryota - 36 Organelles – 569 Phages – 137 Plasmids – 204 Viroids – 36 Viruses – 911 TOTAL:2079

The ….omics

Genomics The experimental study of complete genomes. The application of high-throughput automated technologies to molecular biology. The experimental study of complete genomes.

Genomics Technologies Automated DNA sequencing Automated annotation of sequences DNA microarrays gene expression (measure RNA levels) single nucleotide polymorphisms (SNPs) Protein chips (SELDI, etc.) Protein-protein interactions

New Types of Biological Data Microarrays - gene expression Multi-level maps: genetic, physical, sequence splicing, expression, function Networks of protein-protein interactions Cross-species relationships homologous genes chromosome organization (synteny) common regulatory sequences

Biological Information Protein 2-D gel mRNA Expression Protein 3-D Structure Mass Spec. Genome sequence The Cell

What is gene expression? The amount of RNA produced from a gene. Level of RNA produced from a gene is controlled by: Transcription Degradation Transcriptome - Expressed transcripts in a cell under defined experimental conditions. mRNA(5-10% of total RNA). rRNA, tRNA - make up most of total RNA

Analysis of gene expression at the single gene level. Northern Blots Measure RNA levels by hybridization of a labeled probe to total RNA. Reporter Genes Use of an enzyme to measure the amount of transcription from a promoter. Quantitative RT-PCR.

Assaying the regulation of 1000s of genes in a single experiment DNA microarrays DNA molecules printed at high density used to determine the level of RNA or DNA in a sample. Can be thought of a “reverse Northern blots” Other technologies - SAGE - Microbeads SAGE = Systematic Analysis of Gene Expression Microbeads = new technology. You can check it at http://www.lynxgen.com/ani_img/megaclone.swf

DNA Microarrays Spotted DNA arrays (glass slides) Competitive binding of samples Fluorescent detection - Cy3 and Cy5 Small sample sizes (10-30µl). PCR or cDNA arrays Long oligonucleotide arrays Short oligonucleotide arrays ex. Affymetrix DNA spotted onto nylon membranes (macroarrays)

Applications of DNA microarrays Expression profiling Determining the relative levels of RNA in two or more samples. DNA/DNA hybridizations Investigate gene content between different strains Determine gene dosage 16S arrays - microbial communities (being developed). Identification of protein binding sites ChIP-Chip. Immunoprecipitation of protein/DNA complexes. Assaying those interactions with microarrays.

cDNA spotted microarrays

Labeling RNA or DNA with Cy3 or Cy5. Cy3 and Cy5 - most often used fluorescent molecules used to label samples for microarray analysis. Absorb light at one wavelength and emit at another. Emission and Excitation spectra do not overlap significantly. In arrays Cy3 and Cy5 are usually false colored green (Cy3) and red (Cy5) for ease of visualization.

Affymetrix Gene Chips

Microarray Experiment - labeling, hybridizing, scanning

Each gene on an Affy chip is represented by a probe set Affymetrix = Oligonucleotide Microarray Each gene on an Affy chip is represented by a probe set affymetrix arrays uses 25 base-pairs Oligonucleotides to probe genes. There are two types of probes, the perfect match and the mismatch probes. 11-20 of these probe pairs of PP and MM represent a genes.

Rationality of Affy analysis MM probes are used to measure background signals due to non-specific sources and scanner offset. Using a MM probe as an estimate of background seems them great in theory. - The expression value for a gene is a combination of the (PM-MM) signals for each of the probes (i.e. the average) The MM probes are designed to measure non-specific binding and optical noise components in PM. Many expression measures are based on PM-MM, with the intention of correcting for non-specific binding and background noise. •Problems: –MMsare PMsfor some genes, –removing the middle base does not make a difference for some probes . –Subtracting MM adds variance. Especially at low end.

Microarray Data Analysis Data mining and visualization Controls and normalization of results Statistical validation Linkage between gene expression data and gene sequence/function/metabolic pathways databases Clustering and pattern detection Discovery of common sequences in co-regulated genes

Regulons and Stimulons Operon - group of genes co-expressed on a single transcript. One location of the genome Regulon - genes that are regulated by a single transcription factor. Genes and operons throughout the genome Stimulon - collection of genes that are regulated in response to environmental changes. Can be multiple regulons affected at once. Regulatory network - alternative term for regulon.

Identifying genes whose expression changes at specific stages of the cell cycle

RBK1 PHO87 BUD5 MATa2 MATa1 TSM1 YCR043C HO 12346 hr Microarray analysis of 150 damage-regulated mRNAs after a single unrepaired HO-induced DSB 4 kb/hr 1 2 3 4 6 hr YCR033W YCR034W FEN3 YCR035C RRP43 YCR036W RBK1 YCR037C PHO87 YCR038C BUD5 YCR039C MATa 2 MATa 1 YCR040W YCR041W YCR042C TSM1 YCR043C YCR044C YCR045C Audrey Gasch Moreshwar Vaze

Circadian Rhythms Genes whose expression changes during the day day night day night day night Circadian Rhythms Genes whose expression changes during the day in fruit flies

Cancer can be qualified from the transcriptome

Bioinformatics Genomics produces high-throughput, high-quality data, and bioinformatics provides the analysis and interpretation of these massive data sets. It is impossible to separate genomics laboratory technologies from the computational tools required for data analysis.

What type of data we can use to build a transcriptional network? Protein-Protein interaction data Expression data ChIP data

CHIP ON CHIP

Comparative Genomics The Assumption that underlies comparitive genomics is that the two genomes had a common ancestor and that each organism is a combination of the ancestor and the action of evolution. Evolution can be broadly thought of as the combination of two processes: mutational forces that generate random mutations in the genome sequence, and selection pressures that 1. Eliminate random mutations (negative selection), 2. Have no effect on mutations (neutral selection) or, 2. Increase the frequency of mutant alleles in the population as a result of a gain in fitness (positive selection). The combined action of mutation and selection is represented generally by a RATE MATRIX of base-pair changes between the two observed genomes.

Comparative Genomics Human Mouse Rat C.Elegans Evolutionary relationship between metazoans that are sequenced, or due for sequencing. Evolutionary distances are in millions of years. C.Elegans

Comparative Genomics Comparative genomics may be defined as the derivation of genomic information following comparison of the information content of 2 or more species genome sequences

The similarity is such that human chromosomes can be cut (schematically at least) into about 150 pieces (only about 100 are large enough to appear here), then reassembled into a reasonable approximation of the mouse genome. http://www.ornl.gov/TechResources/Human_Genome/graphics/slides/ttmousehuman.html

Harnessing the genome to answer real problems How do we control infectious disease? How do we slow or stop the effects of cancer? How can we detect and treat genetic disorders? Only 2% of human diseases are due to single gene defects the rest involve networks of gene expression. Most pharmaceutical drugs act on individual proteins or sets of proteins.

The study of the ‘proteome’ Proteomics The study of the ‘proteome’ While an organism has only one genome, it has many transcriptomes, proteomes and metabolomes

mRNA level  expressed protein level nor does it indicate the nature of the functional protein product Functional Protein Product Genomic Sequence Protein Product mRNA Translational Control Transcriptional Control Post-Translational Control

Temporal Changes in mRNA and protein Gene Expression t When you measure expression affects what you find

Does mRNA level correlate with protein level? 20 liver proteins and corresponding mRNAs Glutathione-S-transferase in 60 human cell lines x Lung Ovarian CNS Leukemia Renal Melanoma Breast 0.1 1.0 10 100 1000 R = 0.43 Protein (Affinity-HPLC) mRNA (Northern) 0.1 1 10 100 1000 R=0.48 Protein (2D gels) mRNA (EST clones) Anderson & Anderson Electrophoresis 1998 19:1853-1861 From Tew et al 1996 Anderson & Seilhamer Electrophoresis 1997 18:533-537

Genomics, proteomics era. Lots of data (lots of real data and lots of noise!). Needs validation! Dangers : + Become too descriptive and reductionist + Forget about the biological problem

Year by year we are becoming better equipped to accomplish the things we are striving for. But what are we actually striving for? - Bertrand de Jouvenel, 1903-1987 Success is the ability to go from failure to failure without losing your enthusiasm. - Winston Churchill, 1874-1965