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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/