Download Protein mRNA

Global Expression Analysis: mRNA
Anders Thelin
Molecular Biology
AstraZeneca R&D Mölndal
Department: Molecular Biology
Author: A. Thelin
•A cell is defined by its protein content/activities
•Heredity and environment affect protein content
(gene expression)
cell-cell interaction
nutrition
Department: Molecular Biology
Author: A. Thelin
hormones
disease
Traditional Drug Discovery Process
Medical need
Idea
Model
Chemicals
Lead
CD
Toxicology
Pharmacokinetics Human testing
Department: Molecular Biology
Author: A. Thelin
Black Box Approach
Drug
Unknown
target protein
Model
Experimental animal
Tissue
Cells
Cell preparation
Department: Molecular Biology
Author: A. Thelin
Effect
No effect
Hit
This approach has, historically, been very successful but,
the success of the “black box” approach is dependent on
a relevant model. Potential problems……...
- hard to find relevant disease model
- selected animal model may appear to have the same
disease process as humans but does in fact not.
- identified lead substances have only effect in animal model
may be caused by model protein target is
different compared to human
- effects of lead substance is unspecific, giving problems
toxicology
Department: Molecular Biology
Author: A. Thelin
Molecular Approach
Effect
Drug
No effect
Known drug
target
Department: Molecular Biology
Author: A. Thelin
Hit
A molecular understanding of the disease mechanism
would allow…..
- selection of optimal target (protein)
- design of drugs for specific effect
- development of HTS models
- development of transgenic model animals
Department: Molecular Biology
Author: A. Thelin
How do we find molecular targets?
Department: Molecular Biology
Author: A. Thelin
The proteins in a cell
determine all processes in the cell
protein
N
P
I
R
mRNA
DNA
protein
O
Author: A. Thelin
protein
T
protein
protein
protein
Department: Molecular Biology
E
Disease and disease treatment affect proteins
protein
N
P
I
R
Disease
mRNA
DNA
protein
O
Author: A. Thelin
protein
T
protein
protein
protein
Department: Molecular Biology
E
How can gene expression be analyzed?
•Functional assay
•Proteomics
•Genetic profiling
mRNA levels
Department: Molecular Biology
Author: A. Thelin
Global measurement of protein activity
Can’t be done
But….
Department: Molecular Biology
Author: A. Thelin
Protein amount
or
mRNA amount
can!
Correlation between amount and activity?
Correlation between change in amount and activity?
Department: Molecular Biology
Author: A. Thelin
Assumptions
Understanding gene expression
is important for understanding how
cells function at the molecular level
Diseases and disease treatment
affect gene expression
Department: Molecular Biology
Author: A. Thelin
•Protein content or change in protein content
•mRNA content or change in mRNA content
Protein
mRNA
sensitivity
post-translational
modifications
molecular biology
follow-up
closer to protein activity
Complementing methods
Department: Molecular Biology
Author: A. Thelin
Smallest genome of free living organism codes for <500 genes
(Mycoplasma genitalium)
Saccharomyces cerevisiae genome codes for 6500 genes
Mammalian genomes codes for 40.000 genes
Department: Molecular Biology
Author: A. Thelin
How many genes are expressed in a mammalian cell?
10.000-20.000
A liver cell contains 106 mRNA molecules
About 100 species are abundant with 5.000-50.000 copies/cell
Several hundred species have 100-1.000 copies/cell
Several thousand species have 0.1-1 copies/cell
Department: Molecular Biology
Author: A. Thelin
What affects gene expression?
Development/differentiation
Hormones/growth factors
Cell-to-cell contact
Environment
nutrition
heat shock
toxic substances
pathogens
injuries/inflammation
Department: Molecular Biology
Author: A. Thelin
Different cells express different genes
Expression of genes are regulated
Regulation can occur at several levels
1. Transcription
2. mRNA stability
3. Translation
4. Protein stability
Department: Molecular Biology
Author: A. Thelin
•Single gene expression
-Northern blot
-Ribonuclease protection assay (RPA)
-Reverse transcriptase polymerase chain reaction (RT-PCR)
•Global (multiple) gene expression
-Differential display
-Representational difference analysis (RDA)
-Serial analysis of gene expression (SAGE)
-DNA microchip arrays
Department: Molecular Biology
Author: A. Thelin
RNA
Northern blot
Probe (radiolabeled antisense cDNA or RNA)
Agarose gel
Nylon membrane
Hybridization
Autoradiography
Department: Molecular Biology
Author: A. Thelin
Ribonuclease protection assay
RNA sample
Add radiolabeled RNA probe Add RNase A and T1
Separate fragments on gel
autoradiography
Department: Molecular Biology
Author: A. Thelin
Reverse transcriptase polymerase chain reaction
Reverse transcription
RNA sample
cDNA
Reverse transcriptase, primer
PCR amplification
cDNA
Taq polymerase, specific primer
Department: Molecular Biology
Author: A. Thelin
Agarose gel
PCR product
Real-time reverse transcriptase PCR
-Very sensitive
-Robust
-”Fast”
Department: Molecular Biology
Author: A. Thelin
F Q
Department: Molecular Biology
Author: A. Thelin
3´
5´
3´
5´
Differential Display
(A)
Total RNA
(>2 µg)
(B)
cDNA
Anchored primers
PCR
Random primers
1a 2a 3a 4a
1b 2b 3b 4b
1a 1b 2a 2b 3a 3b 4a 4b
Department: Molecular Biology
Author: A. Thelin
Representational Difference Analysis (RDA)
in collaboration with RIT
1
2
totalRNA
>50ng
cDNA
Linker
PCR
cDNA(1)PCR
Amplifies cDNA which
are specific for tissue 1
Department: Molecular Biology
Author: A. Thelin
Representational Difference Analysis
Tissue 2
Tissue 1
mRNA
mRNA
Restriction digest
cDNA
Restriction digest cDNA
Add linker
In excess
mix, melt, anneal
Fill in the ends
exponential
Department: Molecular Biology
Author: A. Thelin
linear
PCR amplify
cDNA specific for tissue 1 enriched
Serial Analysis of Gene Expression
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
Biotinylated anchored primers
cDNA synthesis
AAAAAAAA
TTTTTTTTT
GATC
GATC
GATC
A
A
A
GGATGCATG
CCTACGTAC
GGATGCATG
CCTACGTAC
GGATGCATG
CCTACGTAC
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
Imobilisation streptavidin beads
restriction enzyme digestion
Divide in two pools
add linker A and B
Cut with tagging enzyme
Ligate blunt ends
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
A
GGATGCATG XXXXXXXXX
CCTACGTAC XXXXXXXXX
OOOOOOOOO GGATGCATG
OOOOOOOOO CCTACGTAC
B
B
B
GGATGCATG
CCTACGTAC
GGATGCATG
CCTACGTAC
GGATGCATG
CCTACGTAC
B
XXXXXXXXX OOOOOOOOO CATG XXXXXXXXX OOOOOOOOO CATG XXXXXXXXX OOOOOOOOO CATG
XXXXXXXXX OOOOOOOOO GTAC XXXXXXXXX OOOOOOOOO GTAC XXXXXXXXX OOOOOOOOO GTAC
Department
Author
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
AAAAAAAA
TTTTTTTTT
Ditag
Cut with CATG
restriction enzyme
Ligate to multi ditag
DNA microarray
UGAACUGAUAGAUGACGUAG
mRNA
Poly A
Complementary probe (cDNA, oligonucleotide
ACTTGACTAT C T ACTGCATC
Put probe on chip
Label –
Surface (nylon, glass)
Label –
Hybridize labeled total mRNA (radioactivity, fluoresence)
Detect label
Department: Molecular Biology
Author: A. Thelin
24µm
24µm
1.28 cm
107-108 identical
probes/feature
1.28 cm
270,000 features/chip
Department: Molecular Biology
Author: A. Thelin
Photolithography (Affymetrix)
Light
(deprotection)
Mask
OOOOO
HO HO O O O
T–
TTOOO
Substrate
Light
(deprotection)
C AT A T
Mask
AGCTG
TTCCO
TTOOO
Substrate
Department: Molecular Biology
Author: A. Thelin
C–
T TCCG
REPEAT
Affymetrix DNA chip
3’ mRNA
5’
PM 10-20 pairs
MM
PM -25 bases with perfect
match to probe sequence
MM -25 bases with one base
mismatch to probe sequence
Department: Molecular Biology
Author: A. Thelin
Commercial Affymetrix Arrays
Human 30.000 genes
Mouse 30.000 genes
Rat 21.000 genes
Yeast 6.100 genes
Custom made arrays
Department: Molecular Biology
Author: A. Thelin
Total RNA >15µg
T7-poly-T primer
Reverse transcriptase
T7-cDNA
Biotin labeled
UTP, CTP
T7 RNA polymerase
cRNA-*
Department: Molecular Biology
Author: A. Thelin
Hybridize cRNA-* on chip
Wash away unbound cRNA
Add streptavidin conjugated phycoerythrin
Wash again
Detect fluoresence using a
confocal microscope
Department: Molecular Biology
Author: A. Thelin
Probe Arrays
(chips)
Fluidics Station
Scanner
Software
Department: Molecular Biology
Author: A. Thelin
a
b, c, d
e
f
Department: Molecular Biology
Author: A. Thelin
•Generate huge amounts of complex data
-Data storage
•Expression of many genes will be changed
-Individual variation
-Experimental variation
-Direct, indirect effect
•Reduce complexity
-Experimental design
-Mathematical/Statistical analysis
-Bioinformatic analysis
Department: Molecular Biology
Author: A. Thelin
Experimental design
•Individual variation
•Experimental variation
•Paired samples vs. Multiple samples
Department: Molecular Biology
Author: A. Thelin
DNA micro array data can be used
in two types of analysis
•Find specific genes
•Find gene or samples with similar gene expression patterns
Department: Molecular Biology
Author: A. Thelin
SpotFire
Datavisualisation
•Remove genes
with non-significant
signals
•Remove genes
with fold-change<2
•Remove genes
with interindividual
variation
Department: Molecular Biology
Author: A. Thelin
Mathematical/Statistical analysis
Expression level
18 genes
Time
Department: Molecular Biology
Author: A. Thelin
Clustering
Expression level
Time
Department: Molecular Biology
Author: A. Thelin
Cluster analysis
•Find groups of genes with similar expression patterns
or
•Find groups of samples with similar gene expression patterns
Genes with similar
expression patterns
after leptin treatment.
One cluster contained
several genes regulated by SREBP-1.
Suggest that leptin
partly may act via
SREBP-1.
Subgrouping of different types of breast cancer
Soukas et al. In Genes & Development, 14:963-980, (2000)
Samples with no histological difference could
be grouped into subgroups using expression patterns.
These subgroups had different clinical prognosis.
Sorli et al. PNAS 98: 10869-10874 (2001)
Department: Molecular Biology
Author: A. Thelin
Model validation using cluster analysis
100
90
80
70
60
50
40
30
20
10
0
67
60
53
46
39
29
22
1
15
Control
Low gainers
High gainers
Normal gainers
Restricted diet
Days
HighG1
HighG4
HighG5
HighG2
LowG2
LowG5
LowG3
LowG4
HighG3
LowG1
Analyze gene expression in
hypothalamus from five HighG
and five LowG.
Cluster individuals with
similar gene expression
patterns using hierchical
clustering
Fig. 1
Study 24665
% weight increase
Obesity model: high-low gainer.
Eating behavour is controlled by
hypothalamus. Is differences in
eating behavour reflected by
differences in hypothalamic
gene expression?
Geneexpression in hypothalamus reflect eating behaviour.
One sample/animal is an outlier.
Department: Molecular Biology
Author: A. Thelin
Bioinformatics
An array experiment produce lists of genes
which you are mostly unfamiliar with
Biology information databases
Litterature
Experts
Department: Molecular Biology
Author: A. Thelin
Department: Molecular Biology
Author: A. Thelin
Gene Profiling Follow-up Experiments
•Expression profiling findings needs to be verified
-New tissues
-Tissue distribution
-New similar conditions
-Better resolution
•Establish relation
-Cause-effect
-Temporal-spatial
Department: Molecular Biology
Author: A. Thelin
Future and current development
Smaller samples
•Microdissection
•Sample amplification
Department: Molecular Biology
Author: A. Thelin
Insulin Resistance Syndrome
•Metabolic disease
•Initially increasing levels of insulin and glucose
•Later collapse of insulin production with elevated glucose
•Multifactoral disease
•Obesity important factor
•Untreated IRS leads to an increased risk for
cardiovascular disease
•IRS is increasing in the western world
Department: Molecular Biology
Author: A. Thelin
Insulin
Glucose
Triglycerides
Department: Molecular Biology
Author: A. Thelin
Insulin
THIAZOLIDINEDIONE
(TZD)
8
7
6
5
4
3
2
1
0
INSULIN
Insulin
Glucose
0
2
4
35
30
25
20
15
10
5
0
6
8
Glucose
Triglycerides
0
5
GLUCOSE
10
8
PPARg
6
4
Triglycerides
TRIGLYCERIDES
2
0
0
5
10
ADIPOCYTE-FABP
KERATINOCYTE LBP
PEPCK
PPAR-RE
Department: Molecular Biology
Author: A. Thelin
ob/ob Mice were treated with TZD for one week
Tissues were isolated:
muscles, fats and liver
Department: Molecular Biology
Author: A. Thelin
•12 INDIVIDUAL LIVERS
•78000 DATAPOINTS
•6400 GENES
•TRANSFER DATA TO EXCEL
•CALCULATE AVERAGE
•COMPARE CHANGE IN AVERAGE
OVER TIME
•REMOVE GENES WHICH SHOW
LESS THAN 5-FOLD CHANGE
OVER TIME
APPROXIMATELY 1000 GENES SHOWED
GREATER THAN 5-FOLD CHANGE
Department: Molecular Biology
Author: A. Thelin
SUBSTANTIAL INDIVIDUAL
VARIATION, EVEN IN INBREAD MICE
1200
1000
800
600
400
200
0
0
2
4
6
8
350
300
250
200
150
100
50
0
0
2
4
6
0
2
4
6
8
800
600
400
200
0
Department: Molecular Biology
Author: A. Thelin
8
SORT OUT GENES WHERE
INDIVIDUAL VARIATION IS
SUBSTANTIAL
0
2
4
6
8
0
2
4
6
8
REMOVE ALL GENES WHERE LESS
THAN TWO TIME POINTS CONTAIN
SIGNIFICANT DATA
326 GENES WERE >5-FOLD REGULATED
AND HAD AT LEAST TWO TIME-POINTS
WITH SIGNIFICANT DATA
Department: Molecular Biology
Author: A. Thelin
SORT GENES FOR DIFFERENT
TIMEPATTERNS
11
22
64
73
67
89
Department: Molecular Biology
Author: A. Thelin
Expected
Unexpected
Msa.450.0_at
1500
600
400
Msa.450.0_at
100
1
2
3
3
4
Msa.1808.0_at
4
300
200
Msa.376.0_at
100
0
2
3
1
4
2
Msa.23298.0_at
3
Msa.2082.0_at
200
3
2
3
600
-200 1
500
400
300
200
Msa.172.0_at
100
0
-100 1
2
3
4
Department: Molecular Biology
Author: A. Thelin
2
3
Msa.34777.0_at
4
1200
1000
800
600
400
200
0
-200 1
Msa.2082.0_at
0
Msa.172.0_at
Msa.2803.0_at
4
200
PPAR-g
0
0
400
4
4
1
1
1
2
3
Msa.2803.0_at
-200
0
1
2
200
400
Msa.2433.0_at
2
3
4
Msa.35441.0_
at
Msa.18652.0_a
t
400
Msa.23298.0_at
600
4
100
600
800
3
200
800
1000
2
3
4
Msa.18652.0_at
1000
Msa.2433.0_at
E-FABP
-50 1
2
4
1200
1600
1400
1200
1000
800
600
400
200
0
0
400
350
300
250
200
Msa.1808.0_at
150
100
50
0
-50 1
400
A-FABP
Msa.35441.0_at
50
1
500
Msa.376.0_at
10000
8000
6000
4000
2000
0
-2000 1
2
Msa.19360.0_
at
Msa.5142.0_at
100
20
0
0
1
150
80
60
40
200
0
200
Msa.35500.0_at
300
500
250
120
100
500
1000
300
160
140
700
Adipsin
Msa.19360.0_at
Msa.5142.0_at
Msa.35500.0_at
2
Msa.34777.0_at
3
4
2
3
4
Summary
•DNA arrays generate large amounts of data
•Experimental design important
•Confirmation
•Bioinformatics
•Follow-up experiments
Department: Molecular Biology
Author: A. Thelin