Determination of the Neuroprotective Index for Neuroprotective Treatments Based on a Mouse Model of Retinitis Pigmentosa

1
Determination of the Neuroprotective Index for
Neuroprotective Treatments Based on a Mouse
Model of Retinitis Pigmentosa
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
Schematic Overview
Purpose
Clustering of regulated genes

• 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.

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 microarrays probe set and the agreement of
the biological duplicates were analyzed by an
automated protocol measuring each genes 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
dont 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
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,
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r W, Reddy RK, Legrand A, Wicker N, Poch O.
manuscript in preparation
Quality Index Unbiased 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