molecular basis of disease - · “molecular basis of disease” microarrays and other methods of...

“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’


5’ AAAAAAA 3’5’ AAAAAAA 3’


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’




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’


TTTTTTTT----------- 5'3’


TTTTTTTT----------- 5'3’

ds cDNA transcription template

TTTTTTTT 53 TTTTTTTT 53 TTTTTTTT 53 TTTTTTTT 53

5’ AAAAAAA----------- 3’TTTTTTTT 5'3’


TTTTTTTT 5'3’


TTTTTTTT 5'3’


TTTTTTTT 5'3’


TTTTTTTT----------- 5'3’

Ambion columnsStep 4. Clean up of double stranded cDNA

TTTTTTTT----------- 5'3’


TTTTTTTT----------- 5'3’ TTTTTTTT----------- 5'3’


Ambion columnsAmbion columnsAmbion columns



TTTTTTTT----------- 5'3’


TTTTTTTT----------- 5'3’


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



TTTTTTTT----------- 5'3’


TTTTTTTT----------- 5'3’


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


SAPE

G t ti t t idi


Goat anti-streptavidin

Bi ti l t d t I G


Bi ti l t d t I G


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

molecular basis of disease - · “molecular basis of disease” microarrays and other methods of...

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