A Model of Pupillometric Signals for Studying Inherited Retinal Diseases in Childhood Population

Disruption of retinal pigment epithelial (RPE) barrier integrity is involved in the pathology of several blinding retinal diseases including age-related macular degeneration (AMD) and diabetic retinopathy (DR), but the underlying causes and pathophysiology are not completely well-defined. Mitochondria dysfunction has often been considered as a potential candidate implicated in such a process. In this study, we aimed to dissect the role of different mitochondrial components; specifically, those of oxidative phosphorylation (OxPhos), in maintaining the barrier functionality of RPE. Electric cell-substrate impedance sensing (ECIS) technology was used to collect multi-frequency electrical impedance data to assess in real-time the barrier formation of the RPE cells. For this purpose, the human retinal pigment epithelial cell line—ARPE-19—was used and treated with varying concentrations of specific mitochondrial inhibitors that target different steps in OxPhos: Rotenone for complex I (the largest protein complex in the electron transport chain (ETC)); oligomycin for ATP synthase; and carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) for uncoupling ATP synthesis from the accompanying ETC. Furthermore, data were modeled using the ECIS-Zθ software to investigate in depth the effects of these inhibitors on three separate barrier parameters: cell–cell interactions (Rb), cell–matrix interactions (α), and the cell membrane capacitance (Cm). The viability of ARPE-19 cells was determined by lactate dehydrogenase (LDH) Cytotoxicity Assay. The ECIS program’s modeling demonstrated that FCCP and thus OxPhos uncoupling disrupt the barrier function in the ARPE-19 cells across all three components of the total resistance (Rb, α, and Cm) in a dose-dependent manner. On the other hand, oligomycin and thus ATP synthase inhibition mostly affects the ARPE-19 cells' attachment to their substrate evident by a significant decrease in α resistance in a dose-dependent manner, both at the end and throughout the duration of the experiment. On the contrary, rotenone and complex I inhibition mostly affect the ARPE-19 paracellular resistance Rb in a dose-dependent manner compared to basolateral resistance α or Cm. Our results clearly demonstrate differential roles for different mitochondrial components in maintaining RPE cell functionality in which uncoupling of OxPhos is a major contributing factor to the disruption barrier function. Such differences can be used in investigating gene expression as well as for screening of selective agents that improve the OxPhos coupling efficiency to be used in the therapeutic approach for treating RPE-related retinal diseases.

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The Landscape of Non-Viral Gene Augmentation Strategies for Inherited Retinal Diseases

International Journal of Molecular Sciences ◽

10.3390/ijms22052318 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2318

Author(s):

Lyes Toualbi ◽

Maria Toms ◽

Mariya Moosajee

Keyword(s):

Viral Vectors ◽

Matrix Attachment Region ◽

Great Promise ◽

Viral Gene ◽

Retinal Diseases ◽

Gene Copies ◽

Augmentation Strategies ◽

Viral Gene Therapy ◽

Effective Option ◽

Attachment Region

Inherited retinal diseases (IRDs) are a heterogeneous group of disorders causing progressive loss of vision, affecting approximately one in 1000 people worldwide. Gene augmentation therapy, which typically involves using adeno-associated viral vectors for delivery of healthy gene copies to affected tissues, has shown great promise as a strategy for the treatment of IRDs. However, the use of viruses is associated with several limitations, including harmful immune responses, genome integration, and limited gene carrying capacity. Here, we review the advances in non-viral gene augmentation strategies, such as the use of plasmids with minimal bacterial backbones and scaffold/matrix attachment region (S/MAR) sequences, that have the capability to overcome these weaknesses by accommodating genes of any size and maintaining episomal transgene expression with a lower risk of eliciting an immune response. Low retinal transfection rates remain a limitation, but various strategies, including coupling the DNA with different types of chemical vehicles (nanoparticles) and the use of electrical methods such as iontophoresis and electrotransfection to aid cell entry, have shown promise in preclinical studies. Non-viral gene therapy may offer a safer and effective option for future treatment of IRDs.

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An optometrist’s guide to the top candidate inherited retinal diseases for gene therapy

Clinical and Experimental Optometry ◽

10.1080/08164622.2021.1878851 ◽

2021 ◽

pp. 1-13

Author(s):

Fleur O’Hare ◽

Thomas L Edwards ◽

Monica L Hu ◽

Doron G Hickey ◽

Alexis C Zhang ◽

...

Keyword(s):

Gene Therapy ◽

Retinal Diseases

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Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

Journal of Personalized Medicine ◽

10.3390/jpm11050321 ◽

2021 ◽

Vol 11 (5) ◽

pp. 321

Author(s):

Kyoung Min Kim ◽

Tae-Young Heo ◽

Aesul Kim ◽

Joohee Kim ◽

Kyu Jin Han ◽

...

Keyword(s):

Network Models ◽

Retinal Disease ◽

Age Related Macular Degeneration ◽

Diagnostic Tools ◽

Retinal Images ◽

Dense Layer ◽

Retinal Diseases ◽

Fundus Image ◽

Neural Network Models ◽

Age Related

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

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