global expression analysis: mrna

Department: Molecular BiologyAuthor: A. Thelin

Global Expression Analysis: mRNA

Anders ThelinMolecular BiologyAstraZeneca R&D Mölndal


•A cell is defined by its protein content/activities

•Heredity and environment affect protein content(gene expression)

hormonescell-cell interaction

nutrition disease


Traditional Drug Discovery Process

Medical need

Idea

Model

Lead

CD Toxicology

Human testingPharmacokinetics

Chemicals


Drug

Effect

No effect

Hit

ModelExperimental animal

TissueCells

Cell preparation

Black Box Approach

Unknowntarget protein


This approach has, historically, been very successful but,the success of the “black box” approach is dependent ona 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 isdifferent compared to human

- effects of lead substance is unspecific, giving problemstoxicology


Molecular Approach

Drug

Effect

No effect

Hit

Known drugtarget


A molecular understanding of the disease mechanismwould allow…..- selection of optimal target (protein)- design of drugs for specific effect- development of HTS models- development of transgenic model animals


How do we find molecular targets?


PR

OT

EI

N

DNA mRNA

pro-tein

pro-tein

pro-tein

pro-tein

pro-tein

pro-tein

The proteins in a celldetermine all processes in the cell


Disease and disease treatment affect proteins

DiseaseP

RO

TE

IN

DNA mRNA

pro-tein

pro-tein

pro-tein

pro-tein

pro-tein

pro-tein


How can gene expression be analyzed?

•Functional assay•Proteomics•Genetic profilingmRNA levels


Global measurement of protein activity

Can’t be doneBut….


Protein amountor

mRNA amountcan!

Correlation between amount and activity?

Correlation between change in amount and activity?


Understanding gene expressionis important for understanding howcells function at the molecular level

Diseases and disease treatmentaffect gene expression

Assumptions


•Protein content or change in protein content

•mRNA content or change in mRNA content

Protein mRNAsensitivity

post-translationalmodifications

molecular biologyfollow-up

closer to protein activity

Complementing methods


Smallest genome of free living organism codes for <500 genes(Mycoplasma genitalium)

Mammalian genomes codes for 40.000 genes

Saccharomyces cerevisiae genome codes for 6500 genes


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/cellSeveral hundred species have 100-1.000 copies/cellSeveral thousand species have 0.1-1 copies/cell


What affects gene expression?

Development/differentiationHormones/growth factorsCell-to-cell contact

Environmentnutritionheat shocktoxic substancespathogensinjuries/inflammation


Different cells express different genes

Expression of genes are regulated

Regulation can occur at several levels1. Transcription2. mRNA stability3. Translation4. Protein stability


•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


Northern blotRNA

Agarose gel

Nylon membrane

Hybridization

Autoradiography

Probe (radiolabeled antisense cDNA or RNA)


Ribonuclease protection assay

RNA sample Add radiolabeled RNA probe Add RNase A and T1

Separate fragments on gelautoradiography


Reverse transcriptase polymerase chain reaction

RNA sample cDNAReverse transcription

Reverse transcriptase, primer

cDNA PCR productPCR amplification

Taq polymerase, specific primer

Agarose gel


Real-time reverse transcriptase PCR-Very sensitive-Robust-”Fast”

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

1 5 9 13 17 21 25 29 33 37 41 45 49 53

C t

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

1 5 9 13 17 21 25 29 33 37 41 45 49 53

C t


5´3´

5´3´

F Q


Differential Display

Total RNA(>2 µg)

cDNAAnchored primers

PCRRandom primers

1a 1b 2a 2b 3a 3b 4a 4b

(A) (B)

1a 2a 3a 4a 1b 2b 3b 4b


Representational Difference Analysis (RDA)in collaboration with RIT

totalRNA>50ng

cDNA

1 2

Linker

PCR

Amplifies cDNA whichare specific for tissue 1

cDNA(1)PCR


Tissue 2Tissue 1mRNA

cDNA

mRNA

cDNA

mix, melt, anneal

Fill in the ends

PCR amplify

cDNA specific for tissue 1 enriched

Restriction digestAdd linker

In excess

exponential linear

Restriction digest

Representational Difference Analysis

DepartmentAuthor

Serial Analysis of Gene Expression

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

A GGATGCATGCCTACGTAC



AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

B GGATGCATGCCTACGTAC




XXXXXXXXXXXXXXXXXX BGGATGCATG

CCTACGTACOOOOOOOOOOOOOOOOOO

CATGGTAC

XXXXXXXXXXXXXXXXXX

CATGGTAC

OOOOOOOOOOOOOOOOOO

XXXXXXXXXXXXXXXXXX

OOOOOOOOOOOOOOOOOO

XXXXXXXXXXXXXXXXXX

CATGGTAC

OOOOOOOOOOOOOOOOOO

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

AAAAAAAATTTTTTTTT

GATC

GATC

GATC

Biotinylated anchored primerscDNA synthesis

Imobilisation streptavidin beadsrestriction enzyme digestion

Divide in two poolsadd linker A and BCut with tagging enzymeLigate blunt ends

Ditag

Cut with CATGrestriction enzymeLigate to multi ditag


UGAACUGAUAGAUGACGUAGmRNA

Complementary probe (cDNA, oligonucleotide)

ACTTGACTAT C T AC TG CAT C

Poly A

Put probe on chip

Hybridize labeled total mRNA (radioactivity, fluoresence)

Detect label

Label –

Label –

Surface (nylon, glass)

DNA microarray


1.28 cm

1.28 cm

24µm

24µm

107-108 identical probes/feature

270,000 features/chip


O O O O O

Light(deprotection)

HO HO O O O T T O O O

T T C C O

Light(deprotection)

T T O O O

C A T A TA G C T G

T T C C G

MaskMask

SubstrateSubstrate

MaskMask

SubstrateSubstrate

T –T –

C –C –REPEATREPEAT

Photolithography (Affymetrix)


Affymetrix DNA chip

PMMM

5’ 3’

PM -25 bases with perfectmatch to probe sequenceMM -25 bases with one basemismatch to probe sequence

mRNA

10-20 pairs


Commercial Affymetrix Arrays

Human 30.000 genesMouse 30.000 genesRat 21.000 genesYeast 6.100 genes

Custom made arrays


Total RNA >15µg

T7-cDNA

cRNA-*

T7-poly-T primerReverse transcriptase

T7 RNA polymerase

Biotin labeledUTP, CTP


Hybridize cRNA-* on chip

Wash away unbound cRNA

Add streptavidin conjugated phycoerythrin

Wash again

Detect fluoresence using aconfocal microscope


Probe Arrays Probe Arrays

(chips)(chips)

Fluidics StationFluidics StationScannerScanner

SoftwareSoftware


a

b, c, d

e

f


•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


Experimental design

•Individual variation

•Experimental variation

•Paired samples vs. Multiple samples


DNA micro array data can be usedin two types of analysis

•Find specific genes•Find gene or samples with similar gene expression patterns


SpotFireDatavisualisation

•Remove genes with non-significant signals•Remove genes with fold-change<2•Remove genes with interindividual variation


Mathematical/Statistical analysis

Expression level

Time

18 genes


Expression level

Time

Clustering


•Find groups of genes with similar expression patternsor

•Find groups of samples with similar gene expression patterns

Cluster analysis

Soukas et al. In Genes & Development, 14:963-980, (2000)

Sorli et al. PNAS 98: 10869-10874 (2001)

Genes with similarexpression patternsafter leptin treatment.One cluster containedseveral genes regu-lated by SREBP-1.Suggest that leptinpartly may act via SREBP-1.

Subgrouping of different types of breast cancer

Samples with no histological difference couldbe grouped into subgroups using expression patterns.These subgroups had different clinical prognosis.


Model validation using cluster analysis

Fig. 1Study 24665

0102030405060708090

100

1 15 22 29 39 46 53 60 67

Days

% w

eig

ht

incr

ease

Control

Low gainers

High gainers

Normal gainers

Restricted diet

Obesity model: high-low gainer.Eating behavour is controlled byhypothalamus. Is differences ineating behavour reflected bydifferences in hypothalamicgene expression?

HighG

3

HighG

2

HighG

5H

ighG4

HighG

1

LowG

2Low

G5

LowG

3Low

G4

LowG

1

Geneexpression in hypothalamus reflect eating behaviour.One sample/animal is an outlier.

Analyze gene expression inhypothalamus from five HighGand five LowG.Cluster individuals withsimilar gene expressionpatterns using hierchicalclustering


Bioinformatics

An array experiment produce lists of geneswhich you are mostly unfamiliar with

Biology information databasesLitteratureExperts


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


Future and current development

Smaller samples

•Microdissection

•Sample amplification


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


Insulin

Glucose

Triglycerides


PPAR

ADIPOCYTE-FABP

KERATINOCYTE LBP

PEPCK

PPAR-RE

THIAZOLIDINEDIONE(TZD)

INSULIN

GLUCOSE

TRIGLYCERIDES


ob/ob Mice were treated with TZD for one weekTissues were isolated:muscles, fats and liver


•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 SHOWEDGREATER THAN 5-FOLD CHANGE


SUBSTANTIAL INDIVIDUALVARIATION, EVEN IN INBREAD MICE

0

200

400

600

800

1000

1200

0 2 4 6 8

0

200

400

600

800

0 2 4 6 8

050

100150200

250300350

0 2 4 6 8


SORT OUT GENES WHEREINDIVIDUAL VARIATION ISSUBSTANTIAL

0 2 4 6 80 2 4 6 8

REMOVE ALL GENES WHERE LESSTHAN TWO TIME POINTS CONTAINSIGNIFICANT DATA

326 GENES WERE >5-FOLD REGULATEDAND HAD AT LEAST TWO TIME-POINTSWITH SIGNIFICANT DATA


SORT GENES FOR DIFFERENTTIMEPATTERNS

11 22

64 73

67 89


Expected

Adipsin

A-FABP

E-FABP

PPAR-

Unexpected


Summary

•DNA arrays generate large amounts of data•Experimental design important•Confirmation•Bioinformatics•Follow-up experiments

global expression analysis: mrna

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