university of oslo
DESCRIPTION
UNIVERSITY OF OSLO. Cytokine expression profiling of the myocardium reveals a role for CX3CL1 (fractalkine) in heart failure. - PowerPoint PPT PresentationTRANSCRIPT
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