Gene's hubs in retinal diseases: A retinal disease network

Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene)

INTRODUCTION

Inherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service.

METHODS AND ANALYSIS

The data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC.

ETHICS AND DISSEMINATION

This research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) 'Eye2Gene: accelerating the diagnosis of IRDs' Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format.

Analysis of the MCTP Amino Acid Sequence Reveals the Conservation of Putative Calcium- and Lipid-Binding Pockets Within the C2 Domains In Silico

MCTPs (Multiple C2 Domains and Transmembrane region Proteins) are evolutionarily and structurally related to other C2 proteins, which are central to exocytosis and membrane trafficking; however, their specific function has been little studied. MCTPs are associated with endosomes and the endoplasmic reticulum and possess three C2 domains (C2A-C2C) and two transmembrane regions (TMRs) well conserved in different species. Here, we generated structural models of the MCTP C2 domains of C. elegans and analyzed their putative function by docking, which revealed that these domains possess Ca²⁺- and lipid-binding pockets, suggesting that MCTPs play a significant, calcium-dependent role in membrane physiology.

Investigation of genotype-phenotype relationship in Turkish patients with inherited retinal disease by next generation sequencing

BACKGROUND

Inherited retinal dystrophies (IRDs) are a group of retinal diseases genetically and clinically highly heterogeneous and associated with more than 300 genes. This study aims to investigate the genetic basis of Turkish patients with IRDs.

MATERIALS AND METHODS

In the study, genes related to retinal diseases in 86 IRDs patients were analyzed using the Next Generations Sequencing method (NGS).

RESULTS

The mean age of 86 patients was 35 and the mean age at diagnosis was 18. There was consanguinity between the parents of 62% of these patients. Fifty-six retinal disease-associated genes of 46 patients and 230 retinal disease-associated genes of 40 patients were examined. Genetic analysis provides a molecular diagnosis in a total of 53 (61.6%) patients. The genes responsible for the IRDs phenotype were frequently identified as ABCA4 (25%), EYS (11%), and RDH12 (9%). There was no significant difference between those with and without a molecular diagnosis in terms of demographic characteristics and family history.

CONCLUSIONS

Determination of genetic cause by NGS method in IRDs subgroups that are difficult to define by ophthalmic examination ensures that patients receive accurate diagnosis, treatment and counseling. This study contributed to the understanding of the genotype-phenotype relationship of Turkish patients with IRDs.

RPGeNet v2.0: expanding the universe of retinal disease gene interactions network

RPGeNet offers researchers a user-friendly queriable tool to visualize the interactome network of visual disorder genes, thus enabling the identification of new potential causative genes and the assignment of novel candidates to specific retinal or cellular pathways. This can be highly relevant for clinical applications as retinal dystrophies affect 1:3000 people worldwide, and the causative genes are still unknown for 30% of the patients. RPGeNet is a refined interaction network interface that limits its skeleton network to the shortest paths between each and every known causative gene of inherited syndromic and non-syndromic retinal dystrophies. RPGeNet integrates interaction information from STRING, BioGRID and PPaxe, along with retina-specific expression data and associated genetic variants, over a Cytoscape.js web interface. For the new version, RPGeNet v2.0, the database engine was migrated to Neo4j graph database manager, which speeds up the initial queries and can handle whole interactome data for new ways to query the network. Further, user facilities have been introduced as the capability of saving and restoring a researcher customized network layout or as novel features to facilitate navigation and data projection on the network explorer interface. Responsiveness has been further improved by transferring some functionality to the client side.