Download No Slide Title

Gene Expression
Biology 224
Instructor: Tom Peavy
October 4 & 6, 2010
<Images from Bioinformatics and Functional Genomics
by Jonathan Pevsner>
Lecture Outline
• cDNAs, ESTs and UniGene
• Digital Differential Display
• SAGE
• Microarrays
Gene expression is regulated
in several basic ways
• by region (e.g. brain versus kidney)
• in development (e.g. fetal versus adult tissue)
• in dynamic response to environmental signals
(e.g. immediate-early response genes)
• in disease states
• by gene activity
DNA
RNA
protein
DNA
cDNA
RNA
cDNA
UniGene
SAGE
Microarray
protein
Analysis of gene expression in cDNA libraries
A fundamental approach to studying gene expression
is through cDNA libraries.
• Isolate RNA (always from a specific
organism, region, and time point)
insert
• Convert RNA to complementary DNA
• Subclone into a vector
• Sequence the cDNA inserts.
These are expressed sequence tags
(ESTs)
vector
Types of cDNA libraries
• standard cDNA libraries in a vector that can be propagated
• PCR-based cDNA libraries using PCR adaptors
• normalized libraries (mRNA hybridized to cDNA-beads)
•Subtraction libraries (mRNA from target is hybridized to
cDNA-beads from other tissue)
UniGene: unique genes via ESTs
• www.ncbi.nlm.nih.gov/UniGene
• UniGene clusters contain many ESTs
• UniGene data come from many cDNA libraries.
Thus, when you look up a gene in UniGene
you get information on its abundance
and its regional distribution.
Cluster sizes in UniGene
This is a gene with
1 EST associated;
the cluster size is 1
Cluster sizes in UniGene
This is a gene with
10 ESTs associated;
the cluster size is 10
Cluster sizes in UniGene
1) Gene links are found for ESTs.
2) The set of mRNA sequences is
compared with itself.
3) Sequence pairs that are
sufficiently similar are linked
together to form initial clusters.
**Thus, this is a single cluster
with a size of 10 (number of
ESTs linked to site)
This is a gene with
10 ESTs associated;
the cluster size is 10
Cluster sizes in UniGene
Cluster size
Number of clusters
1
34,000
2
14,000
3-4
15,000
5-8
10,000
9-16
6,000
17-32
4,000
500-1000
500
2000-4000
50
8000-16,000
3
>16,000
1
Digital Differential Display (DDD) in UniGene
• http:/www.ncbi.nlm.nih.gov/UniGene/ddd.cgi
• Given that UniGene data come from many
cDNA libraries and cluster contain many ESTs
• Libraries can be compared electronically to
look for expression differences
UniGene brain
libraries
UniGene lung
libraries
CamKII
up-regulated
in brain
n-sec1 up-regulated
in brain
surfactant upregulated in lung
Pitfalls in interpreting cDNA library data
• bias in library construction
• variable depth of sequencing
• library normalization
• error rate in sequencing
• contamination (chimeric sequences)
Serial analysis of gene expression (SAGE)
• 9 to 11 base “tags” correspond to genes
• measure of gene expression in different
biological samples
• SAGE tags can be compared electronically
• Longer SAGE tags can be produced and
have greater specificity
e.g. I-Sage™ Long from Invitrogen
SAGE Library Construction
& Analysis
Microarrays: tools for gene expression
A microarray is a solid support (such as a membrane
or glass microscope slide) on which DNA of known
sequence is deposited in a grid-like array.
RNA is isolated from matched samples of interest.
The RNA is typically converted to cDNA, labeled with
fluorescence (or radioactivity), then hybridized to
microarrays in order to measure the expression levels
of thousands of genes.
Advantages of microarray experiments
Fast
Data on 20-50,000 genes in days
Comprehensive
Entire genome represented on 1-2 chip(s)
Flexible
• Countless organisms available
• Custom arrays can be made
to represent genes of interest
Easy
You can submit RNA samples
to a core facility for analysis
Cheap?
Chip set representing 47,000 genes for $350
Robotic spotter/scanner cost $100,000
In-house much cheaper, time consuming
Observation
Microarrays - Global Gene Expression
Hypothesis Generation
Generate hypotheses about the mechanisms
underlying observed phenotypes (disease)
Ability to uncover unanticipated connections
What can you do with information about the expression of
thousands of genes?
Examples?
•Breast cancer samples that have same tissue
appearance but why different survival of patients?
•Genes involved in biological processes
•Genes involved in disease pathogenesis
•Pathways for drug targets; Pathways targeted by drugs!
Disadvantages of microarray experiments
Many researchers can’t afford to do
appropriate controls, replicates
Cost
RNA
Do mRNA levels reflect Protein expression?
significance
Quality
control*
Cross hybridization
Imperfections on arrays leading to error
Difficulty of data analysis: statistics to evaluate
In-house; repeatability by others?
*this is less of an issue as the technology matures
and becomes more common place: use of commercial arrays
A microarray is a tool to rapidly evaluate gene expression
(mRNA level) for tens of thousands of genes in a sample
GeneChip is
a brand
microarray
made by
Affymetrix
1.3cm x 1.3cm
Stage 1: Experimental design
[1] Biological samples: technical vs biological replicates
(technical- repetition of same samples;
biological- use multiple biological sources)
[2] RNA extraction, conversion, labeling, hybridization
[3] Microarray platform (dual color or single color)
X
Pooling of samples and mRNA
Single color (one
sample on one
microarray)
Dual color (two
samples on one
microarray)
Sample
acquisition
RNA: purify, label
Data
acquisition
Microarray:
hybridize,
wash, image
Data
analysis
Data
confirmation
(validation)
Biological insight
Stage 2: RNA and sample preparation
For Affymetrix chips, need total RNA (about 2-10 ug)
Confirm purity by running agarose gel
Measure A260/A280 to confirm purity & quantity
or use a Bioanalyzer (capillary electrophoresis) even
better yet (can also determine quality)
“Garbage in = Garbage out” RNA quality is key!
Stage 3: hybridization to DNA arrays
The array consists of cDNA or oligonucleotides
Oligonucleotides can be deposited by photolithography
The sample is converted to cRNA or cDNA
------------------Hybridization for hours or overnight… sample bind to
complimentary sequences on microarray
Stage 4: Image analysis
mRNA expression levels are quantitated
Fluorescence intensity is measured with a scanner,
or radioactivity with a phosphorimager
Control Sample #1
Test Sample #1
Stage 5: Microarray data analysis
Hypothesis testing
• How can arrays be compared?
• Which RNA transcripts (genes) are regulated?
• Are differences authentic?
• What are the criteria for statistical significance?
Clustering
• Are there meaningful patterns in the data (e.g. groups)?
Classification
• Do RNA transcripts predict predefined groups, such as
disease subtypes?
Page 180
Microarray data analysis
preprocessing
global normalization
local normalization
scatter plots
inferential
statistics
t-tests
ANOVA
Ratio
exploratory
statistics
clustering
Rattus norvegicus Ceruloplasmin (ferroxidase) (Cp), mRNA.
ANOVA analysis, P = 0.00000566
RATIO ANALYSIS, fold change 4.3 upregulated in Diabetic Group
2000
1800
1600
Average Expression Intensity
(n=5, biological replicates)
1400
1200
1000
800
600
400
200
0
Control
Diabetic
Quantified Gene Expression
Differentially Expressed Genes
(Based on p-value and fold change)
Biological Interpretation
Gene
Ontology
Literature
Mining
(Pubmatrix)
Pathways
(KEGG)
BLAST
ESTs
Clustering
grouping
Identifying Genes Selectively Expressed in a group
Two-dimensional hierarchical clustering using complete link and
Pearson correlation using only those genes with comparison
p-value  0.01 between at least two groups.
Stage 6: Confirmation and Validation
The differential up- or down-regulation of specific
genes can be measured using independent assays
such as
-- Northern blots (not done much anymore)
-- Polymerase chain reaction (qRT-PCR)
-- In situ hybridization
--Western blot
--Immunohistochemistry
Stage 7: Microarray databases
There are two main repositories:
Gene expression omnibus (GEO) at NCBI
ArrayExpress at the European Bioinformatics
Institute (EBI)
Array Express at the European Bioinformatics Institute
http://www.ebi.ac.uk/arrayexpress/