UNIVERSITY OF UNIVERSITY OF OSLOOSLO

Cytokine expression profiling of the myocardium reveals a role for CX3CL1 (fractalkine) in heart failure

Cathrine Husberg (1,8), Ståle Nygård (1,2,8), Alexandra Vanessa Finsen (1,8), Jan Kristian Damås (4), Arnoldo Frigessi(3), Erik Øye(6,7,8), Lars Gullestad(4,8), Pål Aukrust(4,5), Arne Yndestad(4,8), Geir Christensen(1,8)

1.Institute for Experimental Medical Research, Ullevål University Hospital2.Department of Mathematics, University of Oslo3.Department of Biostatistics, University of Oslo4. Research Institute for Internal medicine, Rikshospitalet-Radiumhospitalet Medical Center5. Section of Clinical Immunology and Infectious Diseases, Rikshospitalet-Radiumhospitalet Medical Center6. Department of Cardiology, Rikshospitalet-Radiumhospitalet Medical Center7. Institute for Surgical Research, Rikshospitalet-Radiumhospitalet Medical Center8. Center for Heart Failure Research, University of Oslo

AimAim

Identify cytokines imortant for the Identify cytokines imortant for the development of heart failure (HF)development of heart failure (HF)

- and that are not previously associated - and that are not previously associated with HFwith HF

StrategyStrategy

Gene modified Gene modified micemice

Cell culturesCell cultures

Microarray studyMicroarray study

Time

MI 3d 7d 14d

5d

At each time point tissues from 5 mice with myocardial infarction (MI) and 5 SHAM operated mice were used for cDNA microarray screening.

Microarray preprosessing by Microarray preprosessing by MAANOVAMAANOVA

Following the MicroArray ANOVA (MAANOVA) model of Kerr et al (2000), log-transformed intensity for a gene g on array i with dye j (Cy5 or Cy3) and treatment k (MI or SHAM) was modelled by

ijkgkgiggkjiijkg VGAGGVDAy εμ +++++++=

We are mainly interested in the quantity VG1g-VG2g , as it represents the gene-specific effect of MI.

Adding the other (nuisance) parameters results in a model based normalisation of the expression measurements.

R package: maanovaR package: maanova

1. Make data and design file (see maanova manual for detailed instructions)

2. Start R

3. Make script file with R commands something like (NB! Only an extract of the full code, won’t work...)

> library(maanova)> l<-read.madata("lowint.txt", header=FALSE, designfile="des.txt”......> h<-read.madata("highint.txt", header=FALSE, designfile="des.txt”......> source("M:/Rlibs/sat_corr.txt")> r<-s.c(l,h) #correct for saturated spots according to Lyng et al (2004)> d<-createData(r)> n<-transform.madata(d,method="rlowess",draw="off") #”pre-normalisation” using

lowess> m<-makeModel(n,formula=~Dye+Type+Array) #Type=MI or SHAM> a<-fitmaanova(n,m)> t<-matest(n,m,term="Type",MME.method="noest",n.perm=100)> res<-cbind(d$gene.name,a$Type[,1]- a$Type[,2], t$Fs$Pvalperm) #make result table with important quantities (gene symbols, ratio estimate, p-values)> write.table(res,file=”result-file.txt”,sep=”\t”)

4. Read result file using Excel.

Significance assessmentSignificance assessment

Two criteria:

1. At least 30% up- or downregulation (a rough estimate of what can be of functional importance)

2. P-value<0.05. That is, we ar not performing multiple testing because we will

- only consider the cytokines (i.e. a subset of all genes)

- post-verify the significant genes by qRT-PCR (higher accuracy)

UpregulatedChemokine (C-X3-C motiv) ligand 1 (Cx3cl1, Fractalkine)GranulinChemokine (C-C motiv) ligand 8 (Ccl8, MCP-2)Chemokine (C-X-C motiv) ligand 10 (Cxcl10)Chemokine-like MARVEL transmembrane domain factor containing 6 (Cmtm6)Ccl4Cmtm3Ccl6Cmtm7Cxcl4

DownregulatedInterleukin 15Cmtm8D-dopachrome tautomerase

Significantly regulated cytokines not Significantly regulated cytokines not previously associated with heart previously associated with heart

failure. failure.

Verification by qRT-PCR and in humans.Verification by qRT-PCR and in humans.

Ratio

Fractalkine

6,0

4,0

2,0

0,0

Array

qRT-PCR Human

serum

Murine tissiue

Human tissue 50

37

75HF C HF C HF C HF C HF C HF C HF

Ammount of circulating fractalkine related to extent of disease.

3x upregulated in human 3x upregulated in human tissuetissue

Exploring fractalkine’s molecular Exploring fractalkine’s molecular functionfunction

Stimulate cells with fractalkine, and screen for differentially expressed genes.

Use CXCR knock-out mice, and screen for differentially expressed genes

Identify signalling pathways significantly affected by fractalkine stimulation/ knock-out using the software Ingenuity Pathway Analysis (enrichment analysis).

Ingenuity Pathway AnalysisIngenuity Pathway Analysis

Commercial software for understanding complex biological systems.

Uses a knowledge base containing (millions of) biological and chemical relationships (manually) extracted from the scientific literature.

Key components: * Signaling and Metabolic Pathways Analysis * Cellular and Disease Process Analysis * Molecular Network Analysis

Pathway visualisation

IPA generated network

Signaling pathways activated by Signaling pathways activated by fractalkinefractalkine

Signalling pathways in adult cardiomyocytes stimulated by fractalkine (identified by Ingenuity Pathways Analysis

software)

Pathway Number of regulated genes

P-value

GM-CSF signaling 8 0.008

cAMP mediated singaling

16 0.009

VEGF signaling 8 0.026

Leucocyte extravation signaling

11 0.036

Ephrin signaling receptor

11 0.037