ARVO Poster 3093/B6-46

1. rd1 mice at PN15 +/- Neuroprotective Treatment (48h) GDNF CNTF PN15 PBS Isolation of RNA form retina Preparation of Biological Duplicate Samples, QC Affymetrix Transcription Profiling MG U74-2 arrays (36k transcripts) Quality Index Assessment of transcription profiling data Clustering of co-regulated Genesby Mixture Models & AIC (for determination of # of clusters) Search for Enrichment of Specific Functionsusing Cluspack and RetScope/GOAnno annotation protocol Determination of Neuroprotective Index GDNF induces histological and functional protection of rod photoreceptors in the rd1 mouse GDNF PBS Frasson et al., 1999 ARVO Poster 3093/B6-46 W.Raffelsberger1, R.Reddy1, F.Chalmel1, N.Wicker1, A.Legrand1, N.Chadderton2, P.Humphries2, J.Sahel3, T.Leveillard3 and O.Poch1 1 RetNet Team VI : Laboratoire de Bioinformatique et Génomique Intégrative, Department of Structural Biology, IGBMC, 1 rue Laurent Fries, F-67404 Illkirch Cedex, France; 2 RetNet Team II : Trinity College,Department of Genetics, Smurfit Institute, Dublin, Ireland; 3 RetNet Team V: INSERM-U592, Institut de la Vision, Paris, France Determination of the Neuroprotective Index for Neuroprotective Treatments Based on a Mouse Model of Retinitis Pigmentosa Purpose Schematic Overview Clustering of regulated genes data used in clustering : log Signal Intensity PN17 (untreated), secure Gene Regulation (untreated PN15, PBS, GDNF, CNTF, Diltiazem versus PN17) Diltiazem log Sign Intensity / secure Gene Regulation Methods The neuroprotective agents GDNF, CNTF and Diltiazem as well as mock-controls were injected to rd1 mice at 15 days postnatal and RNA was isolated from mouse retina 48 h later. Duplicate samples were subjected to Affymetrix transcription profiling experiments and that were analyzed using a novel bioinformatics protocol based on the assessment of a quality indexes. The degree of homogeneity for the probes defining each microarray’s probe set and the agreement of the biological duplicates were analyzed by an automated protocol measuring each gene’s apparent quality assessment index. These indexes were then considerated in the subsequent clustering analysis based on the Mixture Models algorithm (combined with AIC for the determination of the number of clusters) to characterize regulated genes representing the molecular targets of RP and measuring the neuroprotective index. Conclusions from Clustering ◦ apr. 30 000 genes don’t change expression levels upon treatment with GDNF, CNTF or Diltiazem ◦ GDNF : many genes with weaker upregulation (148) ◦11 genes are stronger upregulated both by PBS and CNTF ◦ apr. 600 genes show very small changes at their expression levels Retinitis Pigmentosa (RP) is an inheritable degeneration of photoreceptors though two subsequent steps characterized by i) the loss of rods in a cell autonomous manner which is followed by ii) loss of cones in a non cell autonomous manner, leading to complete blindness. The rd1 mouse serves as animal model for RP as both stages can be observed during retinal degenration. We investigated the neuroprotective index of neuroprotective substances in the mouse transcriptome during the first phase of RP in rd1 mice. Conclusions The determination of a neuroprotective index across genes and neuroprotective agents combined with a very low risk of false positives was made achieved through clustering of transcription profiles while considering the apparent quality assessment index. This project is supported though the European Retinal Research Training Network ‘RETNET’, MRTN-CT-2003-504003. Quality Index Assessment for Affymetrix Transcriptomics Data Homogeneity of Redundancy within ProbeSet Agreement of Replicates Biological Duplicates onindependent Affymetrix Arrays References: Frasson M, Sahel JA, Fabre M, Simonutti M, Dreyfus H, Picaud S. Retinitis pigmentosa: rod photo-receptor rescue by acalcium-channel blocker in the rd mouse. Nat Med. 1999 Oct;5(10):1183-7 Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL. Model-based clustering and data transformations for gene expression data. Bioinformatics. 2001 Oct;17(10):977-87 Raffelsberger W, Dembele D, Neubauer MG, Gottardis MM, Gronemeyer H. Quality indicators increase the reliability of microarray data. Genomics. 2002 Oct;80(4):385-94 Wicker N, Dembele D, Raffelsberger W, Poch O. Density of points clustering, application to transcriptomic data analysis.Nucleic Acids Res. 2002 Sep 15;30(18):3992-4000 Leveillard T, Mohand-SaidS, LorentzO,HicksD,FintzAC,ClerinE,SimonuttiM,ForsterV,CavusogluN,ChalmelF,DolleP,PochO,LambrouG,SahelJA. Identification and characterization of rod-derived cone viability factor. Nat Genet. 2004 Jul;36(7):755-9 Chalmel F, Chalmel F, Lardenois A, Thompson JD, Muller J, Sahel JA, Leveillard T, Poch O. GOAnno: GO annotation based on multiple alignment. Bioinformatics. 2005 Jan 12 Dortet-Bernadet JL and Wicker N. Model based clustering on the unit sphere of standardized gene experssion profiles. Biostatistics, submitted Raffelsberger W, Reddy RK, Legrand A, Wicker N, Poch O. manuscript in preparation Quality IndexUnbiased to Signal Intensity Integrated & Automated Model Results Improvement of duplicate agreement as revealed by quality indexduplicate: This procedure allowed to significantly reduce the amount of false positives among the genes reported, which is especially important for low level expressed genes as they are typically subject to less precise measurements. These results were further examined for enrichment of functional ontologies and signalling cascades. Finally, we defined and determined the neuroprotective index considering for each i) treatments and ii) all transcripts. dChips 1.3 MAS 4 improved duplicateagreement |norm Difference|