Masters Thesis Defense

Microarray Analysis of the Effects of Rosiglitazoneon Gene Expression in Neonatal Rat Ventricular

Myocytes

Elliot KleimanSan Diego State University

Masters Thesis Defense in Computational ScienceSeptember 17, 2009

Outline

1 IntroductionIllumina BeadArray technology

2 Materials & MethodsData Analysis

3 ResultsDifferential expressionKEGG pathway analysisGene ontology analysis

4 Discussion

5 Acknowledgements

Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 2 / 41

Diabetes

What is it?How many people are affected?Cardiovascular complications


Rosiglitazone

Prescription drug which lowers blood sugar levelsAvandia®(1999, GlaxoSmithKline), U.S. patent 2012Controversial drug


Previous work

Shah et al. 2002 (M.Sci Biology, SDSU) found that Rosiglitazone:Improves cardiac contractility by enhancing cytosolic calciumremovalIncreases SERCA2 mRNA, protein, and promoter activityIncreases NFκB promoter and IL-6 protein secretion


Current thesis work

Are there other genes affected by rosiglitazone in addition toSERCA2?Can we:

identify these genes?determine their functional relationships?classify these genes as early or late responders over time?

How to implement these objectives?


Gene expression primer

DNA

RNA

PROTEIN

Replication

(RNA synthesis)

(Protein synthesis)

Transcription

Translation

Genes

Phenotype

GeneExpression


Experimental approach

DNA microarrays, useful why?because one can measure the gene expression levels of thousandsof genes simultaneouslybecause measuring the levels of mRNA is easier than measuringlevels of proteinsbecause mRNA is a good surrogate marker for protein (or is it?)because when you don’t have a hypothesis, microarrays can helpyou find one


Illumina BeadArray technology

Source: Illumina.com, Mark Dunning


Bead design

Address Probe

29b 50b

LabelledcRNA

BEAD DESIGN

Gene-speci�c probes are concatenatedwith a short "address sequence."

Source: Illumina.com


Materials & Methods

Drug = rosiglitazoneControl = dimethylsulfoxide (DMSO)Two samples of ≈100 newborn (neonatal) rats

isolated and cultured neonatal rat ventricular myocytes (NRVMs)

48 arrays or 4 Illumina RatRef-12 Expression BeadChips


Study Design

Table: 12×2 Factorial Design

Timea

(hour)

0b ½ 1 2 4 6 8 12 18 24 36 48

Drug DMSO -c +d + + + + + + + + + +Rosiglitazone - + + + + + + + + + + +

DMSO, dimethylsulfoxide.a Exposure time to drug treatment.b Untreated RNA.c No drug administered.d Drug administered.


Array hybridization layout

Sample 1

1677718214

06/21/07

R 0.5hrD 48hr

D 36hrD 24hr

D 18hr

D 8hr

D 4hr

D 1hr

D12 hr

D 6hr

D 2hr

D 0.5hr

1677718210

06/21/07

UU

R 48hrR 36hr

R 24hr

R 12hr

R 6hr

R 2hr

R 18hr

R 8hr

R 4hr

R 1hr

AB

CD

E

G

I

K

F

H

J

L

A

C

E

G

I

KL

J

H

F

D

B

Sample 2

1677718217

07/10/07

R 0.5hrD 48hr

D 36hrD 24hr

D 18hr

D 8hr

D 4hr

D 1hr

D12 hr

D 6hr

D 2hr

D 0.5hr

1677718209

07/10/07

UU

R 48hrR 36hr

R 24hr

R 12hr

R 6hr

R 2hr

R 18hr

R 8hr

R 4hr

R 1hr

AB

CD

E

G

I

K

F

H

J

L

A

C

E

G

I

KL

J

H

F

D

B


Microarray Experiment Steps

1 Biological Question2 Design of Experiment3 Sample Preparation (mRNA extraction)4 Array Processing5 Image Analysis6 Pre-processing of Data (Normalization, Filter)7 Data Analysis8 Statistical Inference

Source: Sonia Jain, Ph.D (Microarray Technologies, 2006)


Data Analysis

Data analysis goal: to find an association between treatmentcondition and gene expressionCommon gene selection strategies:

Fold changeParametric test: two sample t-testNon-parametric tests: rank sum, signed-rank testsANOVAPermutation or bootstrap resampling. . . zillions of others!


Linear models of microarrays (LIMMA)

Linear Model

log(ygi) = µg + βgRxRi + βgDxDi + βgR:DxRi xDi + εgi (1)

Idea: use a linear model to parameterize the effects of drug andtime from our factorial designed experiment

Source: Smyth, Limma (2004)


Moderated, bayesian t-test

Moderated t-statistic

s2g =

d0s20 − dgs2

g

d0 + dg

t∗g =βg

sgug

(2)

Std.Err used in test-statistic is a weighted average of s20 + s2

g


Significant contrasts of interest

Table: Numbers of genes regulated during significant exposure times torosiglitazone vs. DMSO in NRVMs

Significant exposure times for rosiglitazone vs. DMSO(hour)

2 4 6 8 12 18 24 36 48

No. genesregulated

-1a 0 0 0 0 0 0 2 8 90b 22516 22513 22514 22513 22506 22506 22498 22491 224911c 1 4 3 4 11 11 17 18 17

a Numbers of genes down-regulated.b Numbers of genes unchanged.c Numbers of genes up-regulated.


Differentially expressed genes

1-10Abca1

Acaa2

Acadv1

Acot7

Adfp

Aldh3a2

Angptl4

Aqp7

Arhgdib

Ccl12

11-20Cidea

Cyp1b1

Dapp1

Decr1

Dpt

Ech1

Entpd2

Etfdh

Grip2

Gusb

21-30Hmgcs2

Impa2

Kel

LOC501283

LOC501396

LOC691522

Lpcat3

Olr472

Psmb9

Ptprr

31-37RGD1309930

RGD1310039

RT1-CE15

Rassf6

Retsat

Tap1

Vipr2

Angptl4 and Adfp most consistently expressed (up-regulated) over time course!


Time course expression profile 4 h

0.0

0.5

1.0

1.5

2.0

2.5

Time ((hour))

Log 2

fold

cha

nge

0 4 8 12 16 20 24 28 32 36 40 44 48

●

●

Gene

Angptl4Cyp1b1Olr472Adfp

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●


Time course expression profile 36 h

−0.

50.

00.

51.

01.

52.

02.

53.

0

Time ((hour))

Log 2

fold

cha

nge

0 4 8 12 16 20 24 28 32 36 40 44 48

●

●

●

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●

Gene

Angptl4Ech1Abca1Hmgcs2Acaa2Lpcat3Impa2Decr1AdfpAcot7EtfdhAcadvlRetsatCideaGrip2Vipr2Aqp7Aldh3a2KelDapp1LOC501396LOC691522PtprrEntpd2GusbDpt

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Hcl heatmap 4 h

0.5

hour

1 ho

ur

2 ho

ur

4 ho

ur

6 ho

ur

8 ho

ur

12 h

our

18 h

our

24 h

our

36 h

our

48 h

our

Cyp1b1

Olr472

Adfp

Angptl4

−2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0

dummy.x

1


Hcl heatmap 36 h

0.5

hour

1 ho

ur

2 ho

ur

4 ho

ur

6 ho

ur

8 ho

ur

12 h

our

18 h

our

24 h

our

36 h

our

48 h

our

Angptl4Entpd2GusbDptLOC691522LOC501396PtprrDapp1KelEch1Acaa2Abca1Hmgcs2Vipr2CideaAqp7Aldh3a2Grip2Impa2AdfpAcot7Decr1Lpcat3RetsatAcadvlLpcat3Etfdh

−2 −1 0 1 2

dummy.x

1


What is a biological pathway?

Biological process: The set of all molecules required to perform abiological function

Biological pathway: The set of all molecular interactions that belong toa biological process


Overrepresented KEGG pathways

PPAR signalingFatty acid metabolismSynthesis and degradation of ketone bodiesValine, leucine, and isoleucine degradationButanoate metabolismBile acid metabolismATP binding cassette transporters, general

biol. pathway theme: fatty acid and lipid metabolism and mitochondrial energy transfer


PPAR signaling 48 h


Fatty acid metabolism 48 h


Gene ontology, what is it?

structured vocabulary for describing genes and gene productsmolecular function (what it does)biological process (how it contributes)cellular component (where it does it)


Hypergeometric testing

test of association between two categories of interest (equivalentto Fisher’s Exact test)used to assess the over-representation of GO termshow many genes in the universe(array) are annotated at a giventerm?how many of those are also in the set of interesting genes?


Hypergeometric testing case example

Universe = 1000 genes, 400 are DE, GO term has 40 annotationsWhat is the Prob that 10 of the 40 genes in GO term are also in the setof DE?

DE DE Total

In GO term 10 30 40On Array 390 570 960Total 400 600 1000

Falcon, GOstats, 2007


Hypergeometric random variable

e.g., sampling balls from an urn model without replacement each trialis dependent on the previous one

Hypergeometric random variable

P(y) =

(ky

)(N−kn−y

)(Nn

)where N = population size k = number of population successes n =sample size y = number of sample successes

from prev slide we would have,

P(10) =

(40010

)(60030

)(100040

) = 0.99


Overrepresented GO terms

Biological processprimary metabolicprocess

lipid metabolic process

cellular lipid metabolicprocess

oxidation reduction

response to drug

Cellular componentlipid particle

mitochondrion

mitochondral membrane

mitochondral innermembrane

nuclearenvelope-endoplasmicreticulum network

Molecular functioncatalytic activity

electron carrier activity

transferase activity

transferring acyl groups

acyltransferase activity

oxidoreductase activity

gene ontology theme: energetic and metabolism activities


Induced GOBP 8 h

1525

1568

1666

1944 6066

6082

6461

6629

6631

6694

6695

6720

6810

6869

6915

6950

7154

7165

7275

7584

8150

8152

8202

8203

8219

8299

8610

9058

9605

9653

9719

9725

9887

9987

99910033 0876

2501 4070

5908

5909

6043

6125

6126

6265

9216

9222

9752

991526070154 1667

2501 2502

2787

30362221

2493

2981

3066

3067

3069

3086 34343933

4237

4238

4249 42556950

6951

8513

8514

8519

85238646

8731

8856

8869

0789

0790 0793 0794

0896

1004

1005

1093

1179

1234

1259

1260

1336

1346 0191

0192

5003

5007

5009

0271

●

●

●

p < 0.01

p >= 0.01

None from gene list


Angptl4 & Adfp

Angptl4, Angiopoietin-like protein 4up-regulated 3 to 7 fold

potent inhibitor of LPL

plays key role in modulating cardiacsubstrate metabolism

decreases TG delivery to heart for FA βoxidation

Adfp, Adipose differentiation proteinup-regulated 1.5 to 1.7 fold

plays a key role in formation of lipiddroplets

lipid droplet associated protein

adipocyte differentiation

responsible for increase in subcutaneoustissue mass observed in rosiglitazone


Lipid & energy metabolism in cardiomyocytes

FFA

Fatty Acyl-CoA

TG synthesis

Lipoprotein secretion

TG

lipolysis

Energy uncoupling

ATP

FATP CD36

LACS

MitochondriaIntermembrane

space

AcylcarnitineCarnitine

Coenzyme A

Acyl-CoA + Acetyl-CoA

Acyl-CoA

TCA cycle

Enoyl-CoA

3-OH-acyl-CoA

3-ketoacyl-CoA

CPT-I

FAD +FADH

NAD +

NAD+H +

H2O

CoA-SH

CO 2

UCP2,UCP3

CPT-II CACT

Outer membrane

Inner membrane

E lec tron re

s pirato

ry c

hain

LCAD

Enoyl-CoAhydratase

HAD

Thiolase

VLDLTG

ChylomicronsTG

AlbuminFFA

LDLTG

Yang & Li 2007Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 35 / 41

Actions of PPARγ in FA trapping

Semple, 2006


Molecular mechanisms of TZDs

PPARg

PPARg PPARg

PPARg

PPARg RXR

Transactivation Transrepression

Coactivator binding site

Ligand binding siteCoactivator fragment

X X X

Ligands Ligands

Coactivator

PPRE PPRE

PPAR target genes

p65 p50 Fos Jun STAT1 STAT3

NF-kB-RE TRE ISGF-RE

noitavitca dnagiLnoitavitca dnagiL

Cofactor recruitment

DZTDZT

Hannele Yki-Jarvinen, 2004Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 37 / 41

Limitations

DrugNo PCR validationNo assay for PPARγ levelstechnician-level variationLimitations of the array technologySample size of 2No db/db disease model


Acknowledgements

SDSUPaul Paolini

Jose Castillo

Peter Salamon

James Otto

Frank Gonzales

Lynelle Garnica

KirubelGebresenbet

Magda Nemeth

David Torres

UC San DiegoGary Hardiman

Roman Sasik

Charles Berry

Jennifer Lapira

NorthwesternUniversityDenise Scholtens

Pan Du

Simon Lin

UC RiversideThomas Girke

EvriSeth Falcon

Linux administrationGreg Chandler

Illumina, Inc.Andrew Carmen

EMD Biosciences

Huda Shubeita


Non-normalized array data

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●

un2−

1un

2−2

R48

−2

R36

−2

R24

−2

R18

−2

R12

−2

R8−

2R

6−2

R4−

2R

2−2

R1−

2un

1−1

un1−

2R

48−

1R

36−

1R

24−

1R

18−

1R

12−

1R

8−1

R6−

1R

4−1

R2−

1R

1−1

R.5

−1

D48

−1

D36

−1

D24

−1

D18

−1

D12

−1

D8−

1D

6−1

D4−

1D

2−1

D1−

1D

.5−

1R

.5−

2D

48−

2D

36−

2D

24−

2D

18−

2D

12−

2D

8−2

D6−

2D

4−2

D2−

2D

1−2

D.5

−2

6

8

10

12

14

log2

inte

nsity

Array (Treatment)Elliot Kleiman (SDSU) Microarray Analysis Sept. 17, 2009 40 / 41

Normalized array data

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un2−

1un

2−2

R48

−2

R36

−2

R24

−2

R18

−2

R12

−2

R8−

2R

6−2

R4−

2R

2−2

R1−

2un

1−1

un1−

2R

48−

1R

36−

1R

24−

1R

18−

1R

12−

1R

8−1

R6−

1R

4−1

R2−

1R

1−1

R.5

−1

D48

−1

D36

−1

D24

−1

D18

−1

D12

−1

D8−

1D

6−1

D4−

1D

2−1

D1−

1D

.5−

1R

.5−

2D

48−

2D

36−

2D

24−

2D

18−

2D

12−

2D

8−2

D6−

2D

4−2

D2−

2D

1−2

D.5

−2

2.8

3.0

3.2

3.4

3.6

3.8

Log 2

inte

nsity

Array (Treatment)

BeadChip

1234


Masters Thesis Defense

Education

Transcript of Masters Thesis Defense