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Norimoto Gotoh, Radu Cojocaru, Linn Gieser, Rafael Villasmil, Arezu Haghighi, Matthew Brooks, Ying Han, David N. Zacks, Tiziana Cogliati, Anand Swaroop; Aging and Retinal Degeneration in Rod Photoreceptors: System Biology to detect Stochastic Functional Changes. Invest. Ophthalmol. Vis. Sci. 2011;52(14):5349.
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© ARVO (1962-2015); The Authors (2016-present)
Early intervention in retinal degeneration and age related macular degeneration (AMD) is one of the major issues in ophthalmology. Aging is the major risk factor for AMD, with rod photoreceptors displaying vulnerability early in the aging process. Furthermore, rod photoreceptors are good therapeutic targets since, irrespective of the genetic mutation, the earliest degeneration changes occur in this cell type. We adopted a system biology approach to identify early biomarkers and pathways driving the molecular and functional changes in these conditions.
Several mouse models were used, including rd1, rdS, rd10, rd15, rd16, and AIPL1 knockout mice. Time points were selected for each individual model based on the progression of degeneration, from postnatal day (P)0 to a maximum of 3 months. Gene expression profiles were also generated for Nrlp-eGFP mice, starting at embryonic day (E)14, to 18 months of age. Rods were purified from Nrlp-eGFP wild type and mutant mice using flow cytometry (Akimoto et al. 2006). Extracted total RNA including microRNA was analyzed using Affymetrix GeneChip mouse Exon 1.0 ST arrays and/or next-generation RNA/microRNA sequencing with Illumina Solexa Genome Analyzer IIx. Quality control and data analysis were performed using several computational approaches and commercial packages.
The microarray dataset generated using samples from retinal degeneration mice yielded 1,878,352 data points, whereas the same technology yielded 2,331,169 data points when samples of aging wild type mice were analyzed. However, when data were classified based on the change of direction of gene expression, as little as 30 patterns covered more than 98% of gene expression in aging, while more variable patterns of expression were observed for the retinal degeneration dataset. Next generation RNA/microRNA sequencing strengthened our analysis.
New genomic methodologies that allow whole transcriptome analyses and the advancement of computer technology make it possible to combine more comprehensive data. Extraction algorithm is key issue, under developing, and evaluated through molecular biological examination. Validation will constitute promising biomarkers for therapeutic intervention.
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