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

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UNIVERSITY OF OSLO

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University of oslo

UNIVERSITY OF

OSLO

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 Hospital

2.Department of Mathematics, University of Oslo

3.Department of Biostatistics, University of Oslo

4. Research Institute for Internal medicine, Rikshospitalet-Radiumhospitalet Medical Center

5. Section of Clinical Immunology and Infectious Diseases, Rikshospitalet-Radiumhospitalet Medical Center

6. Department of Cardiology, Rikshospitalet-Radiumhospitalet Medical Center

7. Institute for Surgical Research, Rikshospitalet-Radiumhospitalet Medical Center

8. Center for Heart Failure Research, University of Oslo


University of oslo

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

- and that are not previously associated with HF

Aim


Strategy

Strategy

Cell cultures

Gene modified mice


Microarray study

MI

3d

5d

7d

14d

Time

Microarray study

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 maanova

Microarray preprosessing by MAANOVA

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

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 maanova

R package: maanova

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

  • Start R

  • 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 assessment

Significance assessment

Two criteria:

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

  • 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)


Significantly regulated cytokines not previously associated with heart failure

Significantly regulated cytokines not previously associated with heart failure.


University of oslo

Human tissue

HF C HF C HF C HF C HF C HF C HF

75

Fractalkine

50

37

Verification by qRT-PCR and in humans.

Array

qRT-PCR

Human serum

6,0

Ratio

4,0

  • Ammount of circulating fractalkine related to extent of disease.

2,0

0,0

  • 3x upregulated in human tissue

Murine tissiue


Exploring fractalkine s molecular function

Exploring fractalkine’s molecular function

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 analysis

Ingenuity 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

Pathway visualisation


Ipa generated network

IPA generated network


Signaling pathways activated by fractalkine

Signaling pathways activated by fractalkine

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


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