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“Molecular basis of disease”Microarrays and other methods ofMicroarrays and other methods of
studying gene expression: Experimental and clinical applications
Marie TowardMarie Toward
26th January 2009
OutlineOutline
R i d f ti f l i id• Reminder of properties of nucleic acids• Why measure gene expression?• Methods of measuring gene expressionMethods of measuring gene expression.• Microarrays
– What are microarrays?– How do they work?– Outline of a microarray experiment– Validation of microarray experiments– Validation of microarray experiments
• Applications of microarrays– Research– Clinical
• Summary
The central dogma of biologyThe central dogma of biology
DNA DNAReplication
R DNA DNARNA PolymeraseTranscription
DNA PolymeraseReverse
Transcription
by
RNAReverse
Transcriptase
tRNARibosomesTranslation
ProteinFoldinggPost‐translational modification
Biological Function
5’ endO‐
P O
O
O‐PHOSPHATESugar
O
5’CH2 base5 CH2 OSugar
SUGAR
3’Sugar
SUGAR
P O
O‐
O‐
OSugar
3’ end
T5’ end
3’ end
T
ASugar
SugarCytosine Guanine
CG
P
ThymineAdenineSugar
P
Sugar
Phosphate –deoxyribosebackbone
ThymineAdenine
SugarSugar
backbone
CytosineGuanineSugar
P
SugarP
SugarThymine Adenine
3’ end
P
5’ end
T5’ end
3’ end
T
ASugar
Sugar
CG
P
Sugar
P
SugarCovalent bonds
SugarSugar
bonds
Sugar
P
SugarP
Sugar
3’ end
P
5’ endH‐bonds
C GT A G T5’ 3’
CA CG T A 5’3’
CTAGGT5’ 3’
ACCTAG5’ 3’CC G
5’ endO‐
5’ endO‐
PHOSPHATEP OO‐
O
P OO‐
O
PHOSPHATE
O
base5’CH2 O
O
base5’CH2 Obase
DNA
base
RNASUGAR SUGAR
O‐
3’
O‐
3’
OH
P O
O
O‐ P O
O
O‐
O O
3’ end 3’ end
Why Study Gene Expression?Searching for Biomarkers
S h f di t ti b t DNA• Search for direct connections between DNA mutations or mRNA levels and disease susceptibilitysusceptibility
• mRNA levels can be modulated by extracellular or intracellular signalsintracellular signals
• mRNA levels may be causal or associated with a disease statedisease state
• If either relationship is established the mRNA levels can be considered a biomarker
• A good drug target has extraordinary value for developing pharmaceuticals.p g p
Analysing DNA and RNAAnalysing DNA and RNA
• Genomic DNA: Mutations, deletions, duplications etc. p
M RNA ( RNA) G i• Messenger RNA (mRNA): Gene expression levels, alternative splicing etc.
Studying DNA/RNASouthern/northern blotting
D tSample DNA (Southern) XXX
Denature
H b idi ith
X
Hybridize with32P DNA probeRestriction
enzyme digestion
Expose to X‐ray film
X
digestion
Agarose gel Electrophoresis
p y
‐
Electrophoresis+ alkali denaturation
A t di h
XBlotting ontonitrocellulose
Autoradiograph showing band(s)+
membrane
Modern, Low‐Throughput Gene AnalysisPolymerase chain reaction
mRNADenaturation
mRNA
cDNA
Reverse transcription (37°C)1.
Denaturation (94‐96°C)2.
Primer annealing
Primer annealing (50‐65°C)3
Primer annealing
Primer annealing (50‐65 C)
Fwd
3.
Rev
and so on through many cyclesExtension (72°C)
Taq4 ........and so on through many cyclesTaqPol.
4.
Tissue 2 shows greater expression of the target gene than Tissue 1 and Tissue 3than Tissue 1 and Tissue 3 shows no expression of this gene.
The house keeping gene shows the same expression in all samples indicating thatTarget gene all samples indicating that RNA levels are the same
Housekeeping gene
g g
Quantitative Real time RT PCRQuantitative Real time RT‐PCR
• Uses fluorescent signals to quantify the amount of DNA present after each PCR cyclep y
T l• Two examples– Double stranded DNA dyes (SYBR GREEN)
– Fluorescent reporter molecule (TaqMan)
Quantitative Real time RT‐PCRwith Double‐Stranded DNA Dyes
SYBR G d
F d
SYBR Green dye
TaqFwd
Rev
SYBR green fluoresces on binding dsDNA
The fluorescent signal is detected by a computer after each cycle.
As PCR progresses through more cycles the signal will increase in proportion to theAs PCR progresses through more cycles the signal will increase in proportion to the amount of PCR product.
Quantitative RT‐PCRwith Fluorescent Reporter Probesp
TaqManR = Reporter dye (FAM)FRET
F d
R Q
p y ( )Q = Non‐fluorescent quencher
AmpliTaq Q3’
R
AmpliTaqFwd
Rev
3
RQ3’
AmpliTaqR Q
3’
The fluorescent signal is detected by a computer after each cycle.
As PCR progresses through more cycles the signal will increase in proportion to the amount of PCR product.
Sample A
Plateau (saturation)
Sample ASample A
Plateau (saturation)
Sample B
nce
Sample B
nce
Sample B
nce
CT
Exponential phase
uore
scen
CT
Exponential phase
uore
scen
CT
Exponential phase
uore
scen
Baseline ThresholdFlu
Baseline ThresholdFlu
Baseline ThresholdThresholdFlu
CT A CT BCT A CT BCT A CT B
PCR cycle number
CT A CT B
PCR cycle number
CT A CT B
PCR cycle number
CT A CT B
High Throughput Analysis of Gene Expression
• Completed Human, Rat and mouse genomes.
• Now the “Transcriptome” is ready for analysis.
• Which genes are differentially expressed?Which genes are differentially expressed?– Under certain conditions ( i t l/d l t l)(environmental/developmental)
– Disease vs. normal
Higher Throughput Gene Analysisg g p ySubtractive hybridisation
Driver mRNA Tester mRNA
Biotin label Reverse transcription
cDNA
p
Mix, denature, re‐anneal
Sequences present in driver are removed with
Streptavidin Clone the unique testerare removed with streptavidin
MicroarraysMicroarrays
O d d t f DNA fi d t lid f• Ordered sets of DNA fixed to solid surfaces• Basic research
Can be used to identify genetic differences between– Can be used to identify genetic differences between individuals allowing better understanding of how biology of living systems works
• Pharmaceutical– Can be used in a similar manner but primarily to identify drug targetsdrug targets
• Clinical– Can be used to classify tumours, diagnose diseases and y , gpotentially used to tailor‐make treatment regimes for individuals
High Density MicroarraysHigh Density Microarrays
• High density microarrays are manufactured in a similar manner to computer chipsp p
Thi ll illi f b b• This allows millions of probes to be synthesised directly on to the substrate
“G Chi ” i t d k f th• “GeneChip” is a trademark of the company that produces these chips, Affymetrix.
AffymetrixAffymetrix
• The company was formed in 1991
• First chips available in 1996
• Affymetrix uses semiconductor manufacturingAffymetrix uses semiconductor manufacturing techniques alongside combinatorial chemistry t b ild t f bi l i l d tto build enormous amounts of biological data on to tiny glass chips
www.affymetrix.com
PhotolithographyPhotolithographyBlocking group (photolabile)
Top down view
Glass slide
Linker
Substrate
X X
Laser
X XX X
X XX X XMask
G G GNucleotide with blocking group
LLaser
New mask
Top down view
X XX
G G G XX X
X X X XX X
XXX
XX
X
G G GT
T
T
T Next nucleotide with blocking group is attached
G G GT T
Probe setsProbe sets
CellWhole Chip~20‐30,00020 30,000Probe Sets
Probe Pair(PM + MM)(PM + MM)
Probe Set (11‐20 pairs)
Microarray ExperimentMicroarray Experiment
• Before you begin:
– Design experiment to meet the needs of your questionquestion
– Extract RNA from your samples
– Check the quality of the RNA
RNA ExtractionRNA ExtractionHomogeniser Chloroform EthanolCentrifugeo oge se C o o o
Aqueous phase(RNA)
Ce t uge
(RNA)
Sample RNARNA
Phenol andG idi S l i h k t i I t h /O i hGuanidine thiocynatesolution
Sample is shaken to mix Interphase/Organic phase(protein and DNA)
RNA Quality ControlRNA Quality Control
28S rRNA
18S rRNA
Bioanalyser analysis: Good quality RNA should have a ratio for the area underBioanalyser analysis: Good quality RNA should have a ratio for the area under the peaks of 28S:18S rRNA = 1.5‐2.0Nanodrop analysis: A260/280 = ~2.0; A260/230 = >1.5
Total RNA5’ AAAAAAA 3’
T7 promoter = ----------Total RNA5’ AAAAAAA 3’Total RNA5’ AAAAAAA 3’5’ AAAAAAA 3’
T7 promoter = ----------
5’ AAAAAAA 3’
TTTTTTTT---------- 5'Step 1. Primer hybridisationoligo d(T) primers bearing
5’ AAAAAAA 3’
TTTTTTTT---------- 5'Step 1. Primer hybridisationoligo d(T) primers bearing
5’ AAAAAAA 3’5’ AAAAAAA 3’
TTTTTTTT---------- 5'Step 1. Primer hybridisationoligo d(T) primers bearing
5’ AAAAAAA 3’TTTTTTTT 5'
g ( ) p gT7pomotor sequence
5’ AAAAAAA 3’TTTTTTTT 5'
g ( ) p gT7pomotor sequence
5’ AAAAAAA 3’TTTTTTTT 5'
5’ AAAAAAA 3’TTTTTTTT 5'
g ( ) p gT7pomotor sequence
TTTTTTTT----------- 5'Step 2. First strand cDNA synthesis RT: ArrayScript™
TTTTTTTT----------- 5'Step 2. First strand cDNA synthesis RT: ArrayScript™
TTTTTTTT----------- 5'TTTTTTTT----------- 5'Step 2. First strand cDNA synthesis RT: ArrayScript™
5’ AAAAAAA 3’TTTTTTTT----------- 5'3’
5’ AAAAAAA 3’TTTTTTTT----------- 5'3’
5’ AAAAAAA 3’TTTTTTTT----------- 5'3’
5’ AAAAAAA 3’TTTTTTTT----------- 5'3’
Step 3. Second strand cDNA synthesis DNA polymeraseStep 3. Second strand cDNA synthesis DNA polymeraseStep 3. Second strand cDNA synthesis DNA polymerase
5’ AAAAAAA----------- 3’TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template
TTTTTTTT 53 TTTTTTTT 53 TTTTTTTT 53 TTTTTTTT 53
5’ AAAAAAA----------- 3’TTTTTTTT 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT 5'3’
ds cDNA transcription template
TTTTTTTT----------- 5'3’
Ambion columnsStep 4. Clean up of double stranded cDNA
TTTTTTTT----------- 5'3’
Ambion columnsStep 4. Clean up of double stranded cDNA
TTTTTTTT----------- 5'3’ TTTTTTTT----------- 5'3’
Ambion columnsStep 4. Clean up of double stranded cDNA
Ambion columnsAmbion columnsAmbion columns
5’ AAAAAAA----------- 3’TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template
TTTTTTTT----------- 53
Step 5. Amplification, in vitro transcription and biotin labelling of antisense cRNA C
CC U
TTTTTTTT----------- 53 TTTTTTTT----------- 53
Step 5. Amplification, in vitro transcription and biotin labelling of antisense cRNA CC
CCCC UUand biotin labelling of antisense cRNA C
U CU C Biotinylated
ribonucleotides
UT7 RNA polymerase
and biotin labelling of antisense cRNA CC
UU CCUU CC Biotinylated
ribonucleotides
UUT7 RNA polymerase
5’ AAAAAAA----------- 3’TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template5’ AAAAAAA----------- 3’
TTTTTTTT----------- 5'3’
ds cDNA transcription template
Step 5. Amplification, in vitro transcription and biotin labelling of antisense cRNA C
U
CC UStep 5. Amplification, in vitro transcription
and biotin labelling of antisense cRNA CCUU
CCCC UUg
U CU C Biotinylated
ribonucleotides
UT7 RNA polymerase
g
UU CCUU CC Biotinylated
ribonucleotides
UUT7 RNA polymerase
UUUUUUUU 5'3’ UUUUUUUU 5'3’ UUUUUUUU 5'3’
UUUUUUUU 5'3’ UUUUUUUU 53’Biotin labelled cRNA
UUUUUUUU 5'3’ UUUUUUUU 5'3’ UUUUUUUU 53’ UUUUUUUU 53’Biotin labelled cRNA
Step 6. Clean up of biotin labelled cRNA
Qiagen columns
Step 6. Clean up of biotin labelled cRNA
Qiagen columns
UUUUUUUU 5'3’
UUUUUUUU ' UUUUUUUU 5'3’
UUUUUUUU 5'3’ UUUUUUUU 5'3’
UUUUUUUU 'UUUUUUUU ' UUUUUUUU 5'3’ UUUUUUUU 5'3’UUUUUUUU 5'3’ UUUUUUUU 53
Step 7. Fragmentation Metal ion induced cRNA hydrolysis
Biotin labelled cRNAUUUUUUUU 5'3’ UUUUUUUU 5'3’ UUUUUUUU 53 UUUUUUUU 53
Step 7. Fragmentation Metal ion induced cRNA hydrolysis
Biotin labelled cRNA
Step 7. Fragmentation Metal ion induced cRNA hydrolysisStep 7. Fragmentation Metal ion induced cRNA hydrolysis
UUUUUUUU 5'
UUUUUUUU 5'
UUUUUUUU 5'UUUUUUUU 5'UUUUUUUU 5'
UUUUUUUU 5'UUUUUUUU 5'
UUUUUUUU 5'UUUUUUUU 5'
UUUUUUUU 5'UUUUUUUU 5'UUUUUUUU 5'UUUUUUUU 5'
Hybridisation cocktailHybridisation cocktailHybridisation cocktail
Step 8. Hybridisation of fragmented cRNA to the GeneChip
Labelled probe Un-labelled probesStep 8. Hybridisation of fragmented cRNA to the GeneChip
Labelled probe Un-labelled probesStep 8. Hybridisation of fragmented cRNA to the GeneChip
Labelled probe Un-labelled probes
Biotin
Hybridisation oven
Biotin
Hybridisation oven
Biotin
Hybridisation oven
BiotinBiotinBiotin
Step 9. GeneChip is washed with automated fluidics station
WashGeneChip
Step 9. GeneChip is washed with automated fluidics station
WashStep 9. GeneChip is washed with automated fluidics station
WashGeneChip
Step 10 Staining and signal amplification
GeneChip
Step 10 Staining and signal amplificationStep 10 Staining and signal amplification
GeneChip
SAPE
G t ti t t idi
Step 10. Staining and signal amplification
SAPE
G t ti t t idi
Step 10. Staining and signal amplification
SAPE
G t ti t t idi
Step 10. Staining and signal amplification
Goat anti-streptavidin
Bi ti l t d t I G
Goat anti-streptavidin
Bi ti l t d t I G
Goat anti-streptavidin
Bi ti l t d t I GBiotinylated goat IgGBiotinylated goat IgGBiotinylated goat IgG
~ λ 570 nm~ λ 570 nm~ λ 570 nm
Step 11 Scan GeneChipStep 11 Scan GeneChipStep 11 Scan GeneChipStep 11. Scan GeneChipStep 11. Scan GeneChipStep 11. Scan GeneChip
SAPE = streptavidin‐phycoerythrin
Statistical analysisStatistical analysis
N li ti• Normalisation– Per ChipPer Gene– Per Gene
• Hypothesis testing: large number of genes on a• Hypothesis‐testing: large number of genes on a single array means that the experimenter must take into account a multiple testing problemtake into account a multiple testing problem– even if each gene is extremely unlikely to randomly yield a result of interest, the combination of all the
i lik l h l fgenes is likely to show at least one or a few occurrences of this result which are “false positives”
What does Microarray analysis offer?What does Microarray analysis offer?
l i f h d f• Analyse expression of thousands of genes simultaneously
• Identify drug targetsIdentify drug targets
f f l l l• Identify candidates for clinical trial
• Tailored medical care
ReplicatesReplicates
• Technical vs. Biological
– Biological: Independent samples of mRNA extracted from different animals/tissues/cellextracted from different animals/tissues/cell samples etc.
– Technical: Increase confidence in the reproducibility of the technique
Validating Microarray DataValidating Microarray Data
• Check expression differences using either northern blotting or PCRg
Ti• Tissue source– Same tissue used for microarray experiment
– New tissue yielding new RNA from a separate sample groupsample group
Microarrays: Basic ResearchMicroarrays: Basic Research
• Microarray studies: hypothesis generating studies that aim to identify new candidates for yfurther research.
• Often these can be described as “Fishing” exercises because you are never sure what information you might generateinformation you might generate
My ResearchMy Research
• Hypertension: brainstem control of blood pressurep
• Hypertensive modelsS t l H t i R t (SHR)– Spontaneously Hypertensive Rat (SHR)
– Wistar Kyoto rat (WKY; normotensive)
• Brainstem microvasculature
• 3 weeks old (prehypertensive)• 3 weeks old (prehypertensive)
BRAIN
Increased sympathetic
i i
Baroreceptors
BRAIN is implicated in development and maintenance
of
nerve activity
Altered
HYPERTENSION baroreceptorreflex
sensitivityNA
NTS
RVLMDMN
NA
CVLM
NTS: Nucleus Tractus SolitariusDMN: Dorsal Motor Nucleus of the vagusNA: Nucleus AmbiguusCVLM: Caudal Ventrolateral MedullaRVLM: Rotral Ventrolateral Medulla
Endothelial genes altered in hypertension:
Sympathetic outflowi i
BLOOD VESSELS
eNOS, JAM‐1
Parasympathetic slows the HEART
•Vasoconstriction•Increases heart Rate/ contractility
VESSELSWaki et al (2006) Hypertension 48: 644‐650
Waki et al (2007) Hypertension 49: 1321‐1327
Rat brainstem blood vessels stained with an anti‐RECA‐1
y p
(3 week old rats were used)4 brainstems were pooled per sample
Density Centrifugation
mRNA
Enriched fraction of
cDNA synthesis and chip hybridisation In vivo gene
microvessels transfer
RT‐PCRRT‐PCR
5 Chips per strain Analysis Validation Proof of Concept
ResultsResults
30 000 b t
109 UP in SHR
~30,000 probe sets
117 DOWN in
SHR
Total 226 transcripts Rat 230 2.0 pchanging >1.5 fold in
SHR vs. WKY
(Affymetrix)
2 27
1
113 1 1
1Angiotensin II signalling
Cell adhesion
Cell-cell signalling
17
Cell cell signalling
Cytokine signalling
DNA repair
Electron transport
9
Embryo development
Extracellular matrix
Fatty acid metabolism
Hypoxia
108
Hypoxia
Immune response
Inflammation
Intracellular signalling
30Ion transport
Metabolism
muscle contraction
M li
3
Myelin
Protein synthesis and folding
Protein transport
ROS generation/response
18
g p
Transcription
Transport
Wnt signalling
3252
1
43
1
Unknown
InflammationImmune Response
Cellular Metabolism
Intracellular signalling
ValidationqRT‐PCR
*
Pre‐hypertensive SHR vs. WKYmicroarray
226 transcripts identified as altered >1.5 fold in SHR vs. WKY
Inflammation Cellular metabolism Intracellular signallingInflammation Cellular metabolism Intracellular signalling
Complement components
Glycolysis enzymesPFKL
Pla2g12aEphx2
Enriched fraction of microvessels
C3C2SERPING1C1S
ADP‐GK Bmpr1a
microvesselsC1S
Genes identified by the microarray suggest a pattern of
Red = Up in SHRmicroarray suggest a pattern of vascular inflammation and
altered cellular metabolism in the pre hypertensive SHR
pBlue = Down in SHR
Affymetrix GeneChipRat 230 2.0
the pre‐hypertensive SHR.
Microarrays clinicalMicroarrays ‐ clinical
• Oncology: Tumour type classification
• Oncology: Response to therapeuticsOncology: Response to therapeutics
• Oncology: Prognosis
• DiabetesDiabetes
• Pre‐eclampsia
Melanoma and MicroarraysMelanoma and Microarrays
• Identified potential predictors of malignancy– Activator of S‐phase kinase (ASK)
– Tumour potentiation region (Tpr)
• Both significantly ↑ in 1°melanomas, s.c. metastases & melanoma cell lines.
• 86% metastases over expressed86% metastases over expressed ASK and Tpr
Nambiar, S. et al. Arch Dermatol (2005) 141(2): 165‐73
Breast cancer and MicroarraysBreast cancer and Microarrays
• Non‐BRCA1/2 familial breast cancers are very heterogeneous
• Assessment of intermediate grade breast cancer is more g ade b eas ca ce s o eeffective with “genomic signature” gradingg g g
Leukaemia and MicroarraysLeukaemia and Microarrays
• Increased molecular taxonomy of Leukaemias and lymphomas
• Working towards the use of microarrays as a reliable prognostic tool
• Gene expression profile can Ge e e p ess o p o e caindicate aggressiveness and identify possible new y ptherapeutic targets
Clinical Trials and MicroarraysClinical Trials and Microarrays
• Arrays used to monitor responses to treatment
M h l di i di id l• May help to predict individual responses to treatment based on genetic profiling
P ti t ti b k d d t f• Patient genetic background and outcome of treatment
MicroarraysyStill a way to go
I ith P b G l ti hi• Issues with Probe‐Gene relationship– Many gene functions still unknown– Probe ID’s not always reliable as based on EST information onlyy y
• Standardisation– Procedures for tissue sampling/storage– Controls and careful experimental design– Standardisation of data presentationStandardisation of data presentation
• Microarrays should not be used as a single tool– Validation is still very important
SummarySummary
• Microarrays provide high throughput expression profiling of 1000’s genesp p g g
• Many uses in basic research and the clinic
Id if di k d h i• Identify disease markers and therapeutic targets
• Diagnosis and prognosis indication in the clinic
E ll t t ti i t f di bi k• Excellent starting point for disease biomarker identification
Further ReadingFurther Reading
Vlacich, G., C. Roe, et al. (2007). "Technology Insight: microarrays,research and clinical applications." Nat Clin Pract End Met 3(8): 594‐605.
Gabriele, L. et al. (2006) “The use of Microarray technologies in clinical oncology”. J Translational Med 4(8).
www.Affymetrix.com
Founds, S et al. (2008). “Microarray technology applied to the complex disorder of preeclampsia”. JOGNN 37: 146‐157.
Repsilber, D. et al. (2005) “ Tutorial on Microarray gene expression: An i t d ti ” M th d I f M d 44 392 399introduction”. Methods Inf Med 44: 392‐399.
Group discussionGroup discussion
Wh t th h ti b i dd d ith• What was the research question being addressed with microarrays?
• What tissue samples were used? (Age, sex, tissue type)p ( g , , yp )• How many genes were investigated?• What level of expression change was deemed biologically
i ifi ?significant?• How many genes were changing?• What type of validation if any did they use?• What type of validation if any did they use?• How would you improve the studies?• How would you follow up the studies?How would you follow up the studies?• Which study provided the most information on their
results?
Post – Post‐ GenomicThe Era of Proteomics?
h l i / lid i• Further analysis/validation:– Western blots (protein levels)– Alternative splicing? Is there a change in post transciptional processing?
h f l h /• Other ways of analysing changes in gene/protein expression:– 2D Difference in gel electrophoresis (DIGE)– Lipid arrays– Carbohydrate arrays– Proteins arrays
In vivo Telemetry Recordings from Angiotensin II Infused rats
ANGII
Osmotic minipump
ANGII
10 Days10 Days
Cardiovascular parameters measured using telemetry
Mean Arterial pressureMean Arterial pressure
180
Saline50ng·kg-1·min-1 n = 12 800ng·kg-1·min-1 n = 12
50 ng∙kg‐1∙min‐1 n = 12
800 ng∙kg‐1∙min‐1 n = 12
e (m
mH
g)
140
160
**
**
* * * * Saline n = 9
al P
ress
ure
100
120*
#
$
Mea
n A
rter
i
80
$
-3 -2 -1 1 2 3 4 5 6 7 8 9 10
M
40
60
ANGII
Day of Infusion
-3 -2 -1 1 2 3 4 5 6 7 8 9 10
Proteomic Analysis of Brainstem Microvessels from ANGII infused rats
Enriched fraction of microvessels
1201
1543
1806
2462
1201
1543
1806
1201
1543
1806
2462
1201
1543
1806
2231 2462 2463
2660 2702
2719
2889
3104
3656
3656
2462 2463 2231
3104
2719
28892660
2702
2231 2462 2463
2660 2702
2719
2889
3104
3656
3656
2462 2463 2231
3104
2719
28892660
2702
Proteins identified by the 2D‐gels 3769
4049
4231 4246
3769
4231 42464049
3769
4049
4231 4246
3769
4231 42464049
suggest a pattern altered cellular metabolism in the ANGII infused
rats.
5558
5602 5606
5780
6015
6027
6061 6077
5780
5606 5602
6077 6061 6015
60275558 5558
5602 5606
5780
6015
6027
6061 6077
5780
5606 5602
6077 6061 6015
60275558
Inflammation Cellular metabolism
RT class 1 Glycolysis enzymesRT class 1
ROS generationGpx1
Glycolysis enzymesAldoCGAPDHEno1
Red = Up in ANGII induced
Gpx1 Eno1Oxidative PhosphorylationCox5a
hypertensionBlue = Down in ANGII induced hypertension
Cox5bATP5h
SummarySummary
• mRNA does not always equal protein level
• The function of a protein can be changed byThe function of a protein can be changed by post‐translational modifications
O h h i f i i d• Other techniques focusing on proteomics and post translational modifications– 2D DIGE as an example
• Lower cost than microarrays but not the same highLower cost than microarrays but not the same high resolution
• May be useful to complement microarray screeningy p y g
FutureFuture
di d b• Few common diseases are caused by mutations in a single gene
• Combo of mutations and altered expressionCombo of mutations and altered expression patterns in several genes confer susceptibility to a given diseaseto a given disease.
• Combinations of techniques will provide the most comprehensive information in future