1. Gene Expression
Chapter 9

2. 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
Although all the cells in the human body have the same genome, only a fraction of the genes are being expressed for any given function.

3. 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

4. Molecular Biology Overview
Nucleus
Cell
Chromosome
Protein
Gene (DNA)
Gene (mRNA),
single strand
cDNA

5. 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)

6. 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

7. How do you measure Gene Expression?

8. Traditional Methods Northern Blotting Western Blotting RT-PCR
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

10. How do Microarrays work?
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

11. 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
...

12. Fabrications of Microarrays
Size of a microscope slide
Images:

13. Differing Conditions Ultimate Goal: Helps to:
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

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

15. 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

16. 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)

17. Microarray Technology

18. 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

19. Microarray Animations
Davidson University:
Imagecyte:

20. Microarray analysis Operation Principle: Samples are tagged with
flourescent
material to
show pattern of
sample-probe
interaction
(hybridization)
Microarray may
have 60K probe
Microarray analysis

21. Microarray Processing sequence

22. Gene Expression Data Gene expression data on p genes for n samples
mRNA samples
sample1 sample2 sample3 sample4 sample5 …
Genes 3
Gene expression level of gene i in mRNA sample j
Log (Red intensity / Green intensity) =
Log(Avg. PM - Avg. MM)

23. 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

24. 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

25. Major Data Mining Techniques
Link Analysis
Associations Discovery
Sequential Pattern Discovery
Similar Time Series Discovery
Predictive Modeling
Classification
Clustering

26. 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
Actually a number of clustering methods have been proposed. I’ll go through some representative types in the following slides.

27. randomized
row column both
data clustered
Eisen et al.
Proc. Natl. Acad. Sci.
USA 95 (1998)
time

28. A dendrogram (tree) for clustered genes
E.g. p=5
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.
Cluster 6=(1,2)
Cluster 7=(1,2,3)
Cluster 8=(4,5)
Cluster 9=
(1,2,3,4,5) 14 1 2 3 4 5

29. A real case Nature Feb, 2000 Paper by Allzadeh. A et al
Distinct types of
diffuse large
B-cell lymphoma
identified by gene
expression
profiling 6

30. Discovering sub-groups7

31. Gene Expression is Time-Dependent
Time Course Data
basically, this mining task can be achieved by using clustering techniques. As shown in this sample clustering results, Each curve represents the expression of a gene over conducted experiments, and all genes are classify into six clusters. As you can see, the genes in a same group have very similar pattern, while each group differs from another pretty much.

32. Sample of time course of
clustered genes
time
time
time
basically, this mining task can be achieved by using clustering techniques. As shown in this sample clustering results, Each curve represents the expression of a gene over conducted experiments, and all genes are classify into six clusters. As you can see, the genes in a same group have very similar pattern, while each group differs from another pretty much.

33. Limitations Cluster analyses: Single gene tests:
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

34. But a few Links Affymetrix www.affymetrix.com
Stanford MicroArray Database
Yale Microarray Database
NCBI Gene Expression Omnibus
University of North Carolina Database