Exome sequencing analysis identifies novel homozygous mutation in ABCA4 in a Chinese family with Stargardt disease

AIM: To identify the disease-associated mutations in a Chinese Stargardt disease(STGD) family, extend the existing spectrum of disease-causing mutations and further define the genotype-phenotype correlations.METHODS: A Chinese STGD family and 200 normal controls were collected. Whole exome sequencing(WES) and bioinformatics analysis were performed to find the pathogenic gene mutation. Physico-chemical parameters of mutant and wildtype proteins were computed by Prot Param tool. Domains analysis was performed by SMART online software. HOPE online software was used to analyze the structural effects of mutation. Immunofluorescence, quantitative real-time polymerase chain reaction and Western blotting were used for expression analysis.RESULTS: Using WES, a novel homozygous mutation(NM_000350: c.G3190 C, p.G1064 R) in ABCA4 gene was identified. This mutation showed co-segregation with phenotype in this family. It was not found in the 200 unrelated health controls and absent from any databases. It was considered "Deleterious" as predicted by five function prediction softwares, and was highly conserved during evolution. ABCA4 was expressed highly in the human eye and mouse retina. The p.G1064 R was located in AAA domain, may force the local backbone into an incorrect conformation, disturb the local structure, and reduce the activity of ATPase resulting in the disease pathology. CONCLUSION: We define a novel pathogenic mutation(c.G3190 C of ABCA4) of STGD. This extends the existing spectrum of disease-causing mutations and further defines the genotype-phenotype correlations.

Artificial intelligence for assessment of Stargardt macular atrophy

Stargardt disease(also known as juvenile macular degeneration or Stargardt macular degeneration)is an inherited disorder of the retina,which can occur in the eyes of children and young adults.It is the most prevalent form of juvenile-onset macular dystrophy,causing progressive(and often severe)vision loss.Images with Stargardt disease are characterized by the appearance of flecks in early and intermediate stages,and the appearance of atrophy,due to cells wasting away and dying,in the advanced stage.The primary measure of late-stage Stargardt disease is the appearance of atrophy.Fundus autofluorescence is a widely available two-dimensional imaging technique,which can aid in the diagnosis of the disease.Spectral-domain optical coherence tomography,in contrast,provides three-dimensional visualization of the retinal microstructure,thereby allowing the status of the individual retinal layers.Stargardt disease may cause various levels of disruption to the photoreceptor segments as well as other outer retinal layers.In recent years,there has been an exponential growth in the number of applications utilizing artificial intelligence for help with processing such diseases,heavily fueled by the amazing successes in image recognition using deep learning.This review regarding artificial intelligence deep learning approaches for the Stargardt atrophy screening and segmentation on fundus autofluorescence images is first provided,followed by a review of the automated retinal layer segmentation with atrophic-appearing lesions and fleck features using artificial intelligence deep learning construct.The paper concludes with a perspective about using artificial intelligence to potentially find early risk factors or biomarkers that can aid in the prediction of Stargardt disease progression.

Targeting molecular pathways for the treatment of inherited retinal degeneration

Inherited retinal degeneration is a major cause of incurable blindness characterized by loss of retinal photoreceptor cells.Inherited retinal degeneration is characterized by high genetic and phenotypic heterogeneity with several genes mutated in patients affected by these genetic diseases.The high genetic heterogeneity of these diseases hampers the development of effective therapeutic interventions for the cure of a large cohort of patients.Common cell demise mechanisms can be envisioned as targets to treat patients regardless the specific mutation.One of these targets is the increase of intracellular calcium ions,that has been detected in several murine models of inherited retinal degeneration.Recently,neurotrophic factors that favor the efflux of calcium ions to concentrations below toxic levels have been identified as promising molecules that should be evaluated as new treatments for retinal degeneration.Here,we discuss therapeutic options for inherited retinal degeneration and we will focus on neuroprotective approaches,such as the neuroprotective activity of the Pigment epithelium-derived factor.The characterization of specific targets for neuroprotection opens new perspectives together with many questions that require deep analyses to take advantage of this knowledge and develop new therapeutic approaches.We believe that minimizing cell demise by neuroprotection may represent a promising treatment strategy for retinal degeneration.

For your eyes only:Harnessing human embryonic stem cell-derived retinal pigment epithelial cells to improve impaired vision

Vision loss or impairment resulting from the degeneration of the retinal pigment epithelium and photoreceptor death affects millions worldwide.Recent exciting results from clinical studies of small numbers of patients treated with human embryonic stem cell-derived retinal pigment epithelial cells may provide hope for affected individuals.