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Buchanan, N.

Publications and source records attributed to Buchanan, N..

2 recordsLinked to original sources

A spontaneous mutation in ADIPOR1 causes retinal degeneration in mice

Adiponectin receptor 1 (ADIPOR1) is a transmembrane protein necessary for normal anatomy and physiology in the retina. In a recent study of complement factor H knockout mice (Cfh-/-), our lab discovered a flecked retina phenotype and retinal thinning by fundus imaging and optical coherence tomography (OCT), respectively. The phenotype was observed in a subset (50%) of Cfh-/- mice. The thinning observed in vivo is due to an early degeneration of rod photoreceptors. This phenotype has not been reported in published studies of Cfh-/- mice. AdipoR1 knockout mice (AdipoR1-/-) and mice deficient in Membrane Frizzled Related Protein (MFRP) exhibit this phenotype, suggesting an involvement in the emergence of the retinal degeneration observed in a subset of Cfh-/- mice. Cfh and AdipoR1 are located in close proximity on mouse Chromosome 1 (Chr1) and a complementation cross between Cfh and AdipoR1 mice with retinal degeneration produced 100% progeny with retinal degeneration. Sequencing of the Cfh-/- mice revealed a c.841 C > T mutation in AdipoR1. Furthermore, one Cfh wildtype (of Cfh+/+) and 2 heterozygous (of Cfh+/-) mice exhibited retinal degeneration and were homozygous for the point mutation. The c.841 C > T mutation results in a proline to serine conversion at position 281 (P281S) in ADIPOR1. This residue is critical for ADIPOR1 open and closed conformations in the membrane. In silico modeling of candidate ADIPOR1 ligands, 11-cis-retinaldehyde and docosahexaenoic acid (DHA), that are deficient in AdipoR1-/-, suggests that ADIPOR1 is involved in trafficking retinoids and fatty acids and their combined deficiency in the ADIPOR1 mutant retinas might explain the retinal degeneration phenotype.

neuroscience↗

Use of machine learning for quantification of retinal pigment epithelium tight junctions improves assay sensitivity

The retinal pigment epithelium (RPE) is critical for maintaining outer retinal barrier homeostasis. In age-related macular degeneration (AMD), the RPE can undergo a dedifferentiation process that includes tight junction (TJ) loss and displacement of zonula occludens-1 (ZO-1), which may impair structural and functional integrity of the RPE barrier and contribute to disease pathogenesis. Our objective was to develop an automated and sensitive quantification method for TJ aberrations in an RPE immunofluorescence imaging assay, following treatment with TNF or TGF{beta}2. However, quantifying ZO-1 morphological changes in the RPE using standard image analysis methods did not provide a satisfactory assay window. To address this challenge, we developed an imaging assay to quantify ZO-1 changes using a machine learning approach, enabling enhanced phenotypic characterization of the ZO-1 changes in RPE cells and improved assay sensitivity. We were also able to capture and quantify the reversal of these changes using etanercept, an TNF inhibitor, with this imaging assay. Our findings indicated that this machine learning ZO-1 quantification assay could serve as a potential phenotypic readout for RPE dedifferentiation and enabling large-scale mechanistic studies.

bioinformatics↗