1 / 14

UNIVERSITY OF OSLO

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

zarifa
Download Presentation

UNIVERSITY OF OSLO

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Identify cytokines imortant for the development of heart failure (HF) - and that are not previously associated with HF Aim

  3. Strategy Cell cultures Gene modified mice

  4. 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.

  5. 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.

  6. 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.

  7. 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)

  8. Significantly regulated cytokines not previously associated with heart failure.

  9. 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

  10. 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).

  11. 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

  12. Pathway visualisation

  13. IPA generated network

  14. Signaling pathways activated by fractalkine Signalling pathways in adult cardiomyocytes stimulated by fractalkine (identified by Ingenuity Pathways Analysis software)

More Related