Shigeo Yoshida

Age-related macular degeneration: a high-resolution genome scan for susceptibility loci in a population enriched for late-stage disease.

Age-related macular degeneration (AMD) is a complex multifactorial disease that affects the central region of the retina. AMD is clinically heterogeneous, leading to geographic atrophy (GA) and/or choroidal neovascularization (CNV) at advanced stages. Considerable data exists in support of a genetic predisposition for AMD. Recent linkage studies have provided evidence in favor of several AMD susceptibility loci. We have performed a high-resolution (5-cM) genome scan of 412 affected relative pairs that were enriched for late-stage disease (GA and/or CNV). Nonparametric linkage analysis was performed using two different diagnostic criteria and also by dividing the affected individuals according to GA or CNV phenotype. Our results demonstrate evidence of linkage in regions that were suggested in at least one previous study at chromosomes 1q (236–240 cM in the Marshfield genetic map), 5p (40–50 cM), and 9q (111 cM). Multipoint analysis of affected relatives with CNV provided evidence of additional susceptibility loci on chromosomes 2p (10 cM) and 22q (25 cM). A recently identified Gln5345Arg change in HEMICENTIN-1 on chromosome 1q25 was not detected in 274 affected members in the restricted group with AMD, 346 additional patients with AMD, and 237 unaffected controls. Our results consolidate the chromosomal locations of several AMD susceptibility loci and, together with previous reports, should facilitate the search for disease-associated sequence variants.

Mechanisms of aging in senescence-accelerated mice

BackgroundProgressive neurological dysfunction is a key aspect of human aging. Because of underlying differences in the aging of mice and humans, useful mouse models have been difficult to obtain and study. We have used gene-expression analysis and polymorphism screening to study molecular senescence of the retina and hippocampus in two rare inbred mouse models of accelerated neurological senescence (SAMP8 and SAMP10) that closely mimic human neurological aging, and in a related normal strain (SAMR1) and an unrelated normal strain (C57BL/6J).ResultsThe majority of age-related gene expression changes were strain-specific, with only a few common pathways found for normal and accelerated neurological aging. Polymorphism screening led to the identification of mutations that could have a direct impact on important disease processes, including a mutation in a fibroblast growth factor gene, Fgf1, and a mutation in and ectopic expression of the gene for the chemokine CCL19, which is involved in the inflammatory response.ConclusionWe show that combining the study of inbred mouse strains with interesting traits and gene-expression profiling can lead to the discovery of genes important for complex phenotypes. Furthermore, full-genome polymorphism detection, sequencing and gene-expression profiling of inbred mouse strains with interesting phenotypic differences may provide unique insights into the molecular genetics of late-manifesting complex diseases.

Mouse eye gene microarrays for investigating ocular development and disease

Microarray technology can facilitate simultaneous expression analysis of thousands of genes and assist in delineating cellular pathways involved in development or disease pathogenesis. Since public databases and commercial cDNA microarrays have an under-representation of eye-expressed genes, we generated over 3000 expressed sequence tags from three unamplified mouse eye/retina cDNA libraries. These eye-expressed genes were used to produce cDNA microarrays. Methodology for printing of slides, hybridization, scanning and data analysis has been optimized. The I-gene microarrays will be useful for establishing expression profiles of the mouse eye/retina and provide a resource for defining molecular pathways involved in development, aging and disease.

Clustering gene expression signals from retinal microarray data

We introduce a robust method for detecting evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene clustering via multi-objective optimization to reveal genes with interesting and statistically significant temporal patterns. We illustrate this gene filtering methodology in the context of exploring the time trajectories of mouse retinal genes acquired at different points over the lifetimes of a population of mice. For 6 time points sampled over 24 mouse subjects, our method can reliably reveal genes whose expression level increases or decreases monotonically, hits a peak or valley at birth, or exhibits other trends.