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Gene Expression

Chapter 9

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What is Gene Expression?

• The process of transcribing and translating a gene to yield a protein product

• Why are we interested in gene expression?

– Tells us which genes are involved in which functions

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Gene Expression

• Cells are different because of differential gene expression - proteome

• About 40% of human genes are expressed at one time.

• Gene is expressed by transcribing DNA into single-stranded mRNA - transcriptome

• mRNA is later translated into a protein

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Molecular Biology Overview Cell Nucleus

Chromosome

Protein Gene (DNA)Gene (mRNA), single strand

cDNA

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Gene Expression• Genes control cell behavior by

controlling which proteins are made by a cell

• House keeping genes vs. cell/tissue specific genes

• Regulation:

• Transcriptional (promoters and enhancers)

• Post Transcriptional (RNA splicing, stability, localization -small non coding RNAs)

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Gene Expression

Regulation:

• Translational (3’UTR repressors, poly A tail)

• Post Transcriptional (RNA splicing, stability, localization -small non coding RNAs)

• Post Translational (Protein modification: carbohydrates, lipids, phosphorylation, hydroxylation, methlylation, precursor protein)

cDNA

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How do you measure Gene Expression?

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Traditional Methods

• Northern Blotting– Single RNA isolated– Probed with labeled cDNA

• Western Blotting– Multiple proteins– Probed with antibodies to a specific protein

• RT-PCR– Primers amplify specific cDNA transcripts

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How do Microarrays work?

• Microarray:– New Technology (first paper: 1995)

• Allows study of thousands of genes at same time

– Glass slide of DNA molecules • Molecule: string of bases (25 bp – 500 bp) • uniquely identifies gene or unit to be studied

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Gene Expression Microarrays

The main types of gene expression microarrays:

• Short oligonucleotide arrays (Affymetrix)• cDNA or spotted arrays (Brown/Botstein).• Long oligonucleotide arrays (Agilent Inkjet);• Fiber-optic arrays• ...

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Fabrications of Microarrays

• Size of a microscope slide

Images: http://www.affymetrix.com/

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Differing Conditions

• Ultimate Goal:– Understand expression level of genes under

different conditions

• Helps to:– Determine genes involved in a disease– Pathways to a disease– Used as a screening tool

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Gene Conditions

• Cell types (brain vs. liver)• Developmental (fetal vs. adult)• Response to stimulus• Gene activity (wild vs. mutant)• Disease states (healthy vs. diseased)

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Expressed Genes

• Genes under a given condition– mRNA extracted from cells– mRNA labeled– Labeled mRNA is mRNA present in a given

condition– Labeled mRNA will hybridize (base pair) with

corresponding sequence on slide

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Two Different Types of Microarrays

• Custom spotted arrays (up to 20,000 sequences)– cDNA– Oligonucleotide

• High-density (up to 100,000 sequences) synthetic oligonucleotide arrays– Affymetrix (25 bases)

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Microarray Technology

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Microarray Image Analysis

• Microarrays detect gene interactions: 4 colors: – Green: high control– Red: High sample– Yellow: Equal– Black: None

• Problem is to quantify image signals

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Microarray Animations

• Davidson University:• http://www.bio.davidson.edu/courses/genomics/chip/chip.html

• Imagecyte:• http://www.imagecyte.com/array2.html

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Microarray analysis

Operation Principle:

Samples are tagged with flourescentmaterial to show pattern of sample-probe interaction (hybridization)

Microarray may have 60K probe

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Microarray Processing sequence

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Gene Expression Data

Gene expression data on p genes for n samples

Genes

mRNA samples

Gene expression level of gene i in mRNA sample j

=Log (Red intensity / Green intensity)

Log(Avg. PM - Avg. MM)

sample1 sample2 sample3 sample4 sample5 …

1 0.46 0.30 0.80 1.51 0.90 ...2 -0.10 0.49 0.24 0.06 0.46 ...3 0.15 0.74 0.04 0.10 0.20 ...4 -0.45 -1.03 -0.79 -0.56 -0.32 ...5 -0.06 1.06 1.35 1.09 -1.09 ...

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Some possible applications?

• Sample from specific organ to show which genes are expressed

• Compare samples from healthy and sick host to find gene-disease connection

• Probes are sets of human pathogens for disease detection

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Huge amount of data from single microarray

• If just two color, then amount of data on array with N probes is 2N

• Cannot analyze pixel by pixel

• Analyze by pattern – cluster analysis

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Major Data Mining Techniques

• Link Analysis– Associations Discovery– Sequential Pattern Discovery– Similar Time Series Discovery

• Predictive Modeling– Classification– Clustering

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Some clustering methods and software

• Partitioning ： K-Means, K-Medoids, PAM, CLARA …

• Hierarchical ： Cluster, HAC 、 BIRCH 、 CURE 、 ROCK

• Density-based ： CAST, DBSCAN 、 OPTICS 、 CLIQUE…

• Grid-based ：STING 、 CLIQUE 、 WaveCluster…

• Model-based ： SOM (self-organized map) 、COBWEB 、 CLASSIT 、 AutoClass…

• Two-way Clustering• Block clustering

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Eisen et al.Proc. Natl. Acad. Sci. USA 95 (1998)

data clustered randomized row column both

time

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A dendrogram (tree) for clustered genes

Let p = number of genes.1. Calculate within class

correlation.2. Perform hierarchical

clustering which will produce (2p-1) clusters of genes.

3. Average within clusters of genes.

4 Perform testing on averages of clusters of genes as if they were single genes.

1 2 3 4 5

Cluster 6=(1,2)

Cluster 7=(1,2,3)Cluster 8=(4,5)

Cluster 9= (1,2,3,4,5)

E.g. p=5

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A real case

Nature Feb, 2000Paper by Allzadeh. A et al

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

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Discovering sub-groups

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Time Course Data

Gene Expression is Time-Dependent

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Sample of time course of clustered genes

time time time

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Limitations

• Cluster analyses:– Usually outside the normal framework of

statistical inference– Less appropriate when only a few genes are

likely to change– Needs lots of experiments

• Single gene tests:– May be too noisy in general to show much– May not reveal coordinated effects of

positively correlated genes.– Hard to relate to pathways

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But a few Links• Affymetrix www.affymetrix.com

• Stanford MicroArray Database http://smd.stanford.edu/resources/restech.shtml

• Yale Microarray Database http://www.med.yale.edu/microarray/

• NCBI Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/

• University of North Carolina Database https://genome.unc.edu/