Genome-wide analysis of disease progression in age-related macular degeneration

Late-onset retinal degeneration pathology due to mutations in CTRP5 is mediated through HTRA1

Late-onset retinal degeneration (L-ORD) is an autosomal dominant macular degeneration characterized by the formation of sub-retinal pigment epithelium (RPE) deposits and neuroretinal atrophy. L-ORD results from mutations in the C1q-tumor necrosis factor-5 protein (CTRP5), encoded by the CTRP5/C1QTNF5 gene. To understand the mechanism underlying L-ORD pathology, we used a human cDNA library yeast two-hybrid screen to identify interacting partners of CTRP5. Additionally, we analyzed the Bruch's membrane/choroid (BM-Ch) from wild-type (Wt), heterozygous S163R Ctrp5 mutation knock-in (Ctrp5S163R/wt ), and homozygous knock-in (Ctrp5S163R/S163R ) mice using mass spectrometry. Both approaches showed an association between CTRP5 and HTRA1 via its C-terminal PDZ-binding motif, stimulation of the HTRA1 protease activity by CTRP5, and CTRP5 serving as an HTRA1 substrate. The S163R-CTRP5 protein also binds to HTRA1 but is resistant to HTRA1-mediated cleavage. Immunohistochemistry and proteomic analysis showed significant accumulation of CTRP5 and HTRA1 in BM-Ch of Ctrp5S163R/S163R and Ctrp5S163R/wt mice compared with Wt. Additional extracellular matrix (ECM) components that are HTRA1 substrates also accumulated in these mice. These results implicate HTRA1 and its interaction with CTRP5 in L-ORD pathology.

Gene-based association analysis for bivariate time-to-event data through functional regression with copula models

Several gene-based association tests for time-to-event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene-based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age-related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age-related Eye Disease Study, and to identify the gene regions that are associated with AMD progression.

Copula-based semiparametric regression method for bivariate data under general interval censoring

This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, age-related macular degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.

The cGAS/STING pathway: a sensor of senescence-associated DNA damage and trigger of inflammation in early age-related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among the elderly. Considering the relatively limited effect of therapy on early AMD, it is important to focus on the pathogenesis of AMD, especially early AMD. Ageing is one of the strongest risk factors for AMD, and analysis of the impact of ageing on AMD development is valuable. Among all the ageing hallmarks, increased DNA damage accumulation is regarded as the beginning of cellular senescence and is related to abnormal expression of inflammatory cytokines, which is called the senescence-associated secretory phenotype (SASP). The exact pathway for DNA damage that triggers senescence-associated hallmarks is poorly understood. Recently, mounting evidence has shown that the cGAS/STING pathway is an important DNA sensor related to proinflammatory factor secretion and is associated with another hallmark of ageing, SASP. Thus, we hypothesized that the cGAS/STING pathway is a vital signalling pathway for early AMD development and that inhibition of STING might be a potential therapeutic strategy for AMD cases.

Copula-based score test for bivariate time-to-event data, with application to a genetic study of AMD progression

Motivated by a genome-wide association study to discover risk variants for the progression of Age-related Macular Degeneration (AMD), we develop a computationally efficient copula-based score test, in which the dependence between bivariate progression times is taken into account. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and observed information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards assumption are considered. Extensive simulation studies are conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method to a large randomized trial data, the Age-related Eye Disease Study, to identify susceptible risk variants for AMD progression. The top variants identified on Chromosome 10 show significantly differential progression profiles for different genetic groups, which are critical in characterizing and predicting the risk of progression-to-late-AMD for patients with mild to moderate AMD.

Effects of astaxanthin on antioxidant parameters in ARPE-19 cells on oxidative stress model

AIM

To observe the protective effect of astaxanthin (AST) against hydroquinone (HQ) mediated cell death in the apoptotic cascade and evaluate intracellular Ca²⁺ release, caspase-3, and -9 activation, reactive oxygen species (ROS) production in ARPE-19 cells.

METHODS

We cultured ARPE-19 cells in special mediums and performed MTT tests to determine protective effect of AST, before exposing the cells to HQ in an incubator. We analyzed intracellular Ca²⁺ release experiments, mitochondrial membrane depolarization, glutathione (GSH), glutathione peroxidase (GSH-Px) and ROS experiments, and apoptosis assay.

RESULTS

ROS production ranges depend on the amount of cell death. We computed the correlation between ROS ranges and cell death by 20,70-dichlorofluorescein fluorescence, and Ca²⁺ levels by Fura-2-AM. HQ-induced cell death found out to rise ranges of caspase-3 and -9, and mitochondrial depolarization. These three steps were delayed by AST management.

CONCLUSION

ARPE-19 cells are avoided from HQ-induced ROS production and caspase-3 and -9 activation by AST. AST may limit the range of caspase synthesis, Ca²⁺ release and excess production of ROS with antiapoptotic effect. This study proposes a new therapeutic approach for the treatment of age-related macular degeneration.

Expression of a CARD Slows the Retinal Degeneration of a Geographic Atrophy Mouse Model

Age-related macular degeneration (AMD) has been linked to oxidative damage and para-inflammation, an activation of inflammasome signaling in the retinal pigment epithelium (RPE) and the underlying choriocapillaris. Herein, we tested the efficacy of a gene-delivered caspase-1 inhibitor in controlling the retinal degeneration observed in two models of RPE-choroid oxidative damage. In an acute model of oxidative stress (NaIO3 injection), eyes pre-treated with the sGFP-TatCARD (trans-activator of transcription; caspase activation and recruitment domain) vector demonstrated a recovery of retinal function and partial protection of RPE structure 1 month after damage, in contrast with control-treated eyes. In a model of chronic oxidative stress (RPE-specific deletion of Sod2), eyes treated with the sGFP-TatCARD vector after the onset of degeneration had a significantly slower decline in retinal function when compared to control-treated eyes. Earlier treatment of this model with the same adeno-associated virus (AAV) vector resulted in a greater protection of RPE function in eyes treated with the TatCARD when compared to control-treated eyes. Our results demonstrate that intravitreal delivery of sGFP-TatCARD reduces inflammation and can protect the retina from both acute and sustained oxidative damage within the RPE and choroid. Therefore, gene therapy with a cell-penetrating inflammasome inhibitor such as CARD may stem the progression of AMD.

The Utah Protocol for Postmortem Eye Phenotyping and Molecular Biochemical Analysis

Purpose

Current understanding of local disease pathophysiology in AMD is limited. Analysis of the human disease-affected tissue is most informative, as gene expression, expressed quantitative trait loci, microenvironmental, and epigenetic changes can be tissue, cell type, and location specific. Development of a novel translational treatment and prevention strategies particularly for earlier forms of AMD are needed, although access to human ocular tissue analysis is challenging. We present a standardized protocol to study rapidly processed postmortem donor eyes for molecular biochemical and genomic studies.

Methods

We partnered with the Utah Lions Eye Bank to obtain donor human eyes, blood, and vitreous, within 6 hours postmortem. Phenotypic analysis was performed using spectral-domain optical coherence tomography (SD-OCT) and color fundus photography. Macular and extramacular tissues were immediately isolated, and the neural retina and retinal pigment epithelium/choroid from each specimen were separated and preserved. Ocular disease phenotype was analyzed using clinically relevant grading criteria by a group of four ophthalmologists incorporating data from SD-OCT retinal images, fundus photographs, and medical records.

Results

The use of multimodal imaging leads to greater resolution of retinal pathology, allowing greater phenotypic rigor for both interobserver phenotype and known clinical diagnoses. Further, our analysis resulted in excellent quality RNA, which demonstrated appropriate tissue segregation.

Conclusions

The Utah protocol is a standardized methodology for analysis of disease mechanisms in AMD. It uniquely allows for simultaneous rigorous phenotypic, molecular biochemical, and genomic analysis of both systemic and local tissues. This better enables the development of disease biomarkers and therapeutic interventions.

Artificial intelligence in retina

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.