Epigenomic signatures in age-related macular degeneration: Focus on their role as disease modifiers and therapeutic targets

Caputo, Valerio; Strafella, Claudia; Termine, Andrea; Fabrizio, Carlo; Ruffo, Paola; Cusumano, Andrea; Giardina, Emiliano; Ricci, Federico; Cascella, Raffaella

doi:10.1177/11206721211028054

Cited by 4 publications

(4 citation statements)

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“…It is well-known that a complex interplay among genetic and non-genetic factors (smoking habits, lifestyle, aging, diet, family history) contributes to the onset of AMD [ 8 , 9 , 10 , 11 ]. The impact of these risk factors has been extensively explored among worldwide populations, highlighting prominent differences [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

WARE: Wet AMD Risk-Evaluation Tool as a Clinical Decision-Support System Integrating Genetic and Non-Genetic Factors

Fabrizio

Termine

Caputo

et al. 2022

JPM

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Given the multifactorial features characterizing age-related macular degeneration (AMD), the availability of a tool able to provide the individual risk profile is extremely helpful for personalizing the follow-up and treatment protocols of patients. To this purpose, we developed an open-source computational tool named WARE (Wet AMD Risk Evaluation), able to assess the individual risk profile for wet AMD based on genetic and non-genetic factors. In particular, the tool uses genetic risk measures normalized for their relative frequencies in the general population and disease prevalence. WARE is characterized by a user-friendly web page interface that is intended to assist clinicians in reporting risk assessment upon patient evaluation. When using the tool, plots of population risk distribution highlight a “low-risk zone” and a “high-risk zone” into which subjects can fall depending on their risk-assessment result. WARE represents a reliable population-specific computational system for wet AMD risk evaluation that can be exploited to promote preventive actions and personalized medicine approach for affected patients or at-risk individuals. This tool can be suitable to compute the disease risk adjusted to different populations considering their specific genetic factors and related frequencies, non-genetic factors, and the disease prevalence.

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Section: Introductionmentioning

confidence: 99%

WARE: Wet AMD Risk-Evaluation Tool as a Clinical Decision-Support System Integrating Genetic and Non-Genetic Factors

Fabrizio

Termine

Caputo

et al. 2022

JPM

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“…In particular, epigenetic elements are able to shape and modulate gene expression in response to external stimuli (age, environment/lifestyle, diet, and disease condition). To date, at least three epigenetic mechanisms are known to impact gene expression, namely DNA methylation, histone modification, and non-coding-RNAs [ 64 ].…”

Section: Genetic and Epigenetic Factors Involved In Nutrients Metabol...mentioning

confidence: 99%

“…In addition, miRNAs can be detected in several body fluids, such as blood, saliva, urine, and cerebrospinal fluid. In the last decades, several research efforts have been made to disclose the role of miRNAs in neurogenesis and neurodegeneration [ 64 ]. Actually, miRNAs are known to be involved in the production and degradation of toxic proteins accumulating in the brain as well as in neuronal death.…”

Section: Genetic and Epigenetic Factors Involved In Nutrients Metabol...mentioning

confidence: 99%

“…In addition, they can be expressed as a result of cellular or tissue damage, aging, and dysfunction of pro-survival proteins. Currently, miRNA-29a, miRNA-29b, miRNA-34a, miRNA-103, miRNA-107, miRNA-125a, miRNA-146a, miR-155, miR-196a, miR-499a, and many others have been involved in neuroinflammation, mitochondrial dysfunction, synaptic transmission, endosomal–lysosomal dysfunction, apoptosis, oxidative stress, and membrane and intracellular trafficking [ 64 ].…”

Section: Genetic and Epigenetic Factors Involved In Nutrients Metabol...mentioning

confidence: 99%

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Relationship between Nutrition, Lifestyle, and Neurodegenerative Disease: Lessons from ADH1B, CYP1A2 and MTHFR

Barati

Fabrizio

Strafella

et al. 2022

Genes

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In the present review, the main features involved in the susceptibility and progression of neurodegenerative disorders (NDDs) have been discussed, with the purpose of highlighting their potential application for promoting the management and treatment of patients with NDDs. In particular, the impact of genetic and epigenetic factors, nutrients, and lifestyle will be presented, with particular emphasis on Alzheimer’s disease (AD) and Parkinson’s disease (PD). Metabolism, dietary habits, physical exercise and microbiota are part of a complex network that is crucial for brain function and preservation. This complex equilibrium can be disrupted by genetic, epigenetic, and environmental factors causing perturbations in central nervous system homeostasis, contributing thereby to neuroinflammation and neurodegeneration. Diet and physical activity can directly act on epigenetic modifications, which, in turn, alter the expression of specific genes involved in NDDs onset and progression. On this subject, the introduction of nutrigenomics shed light on the main molecular players involved in the modulation of health and disease status. In particular, the review presents data concerning the impact of ADH1B, CYP1A2, and MTHFR on the susceptibility and progression of NDDs (especially AD and PD) and how they may be exploited for developing precision medicine strategies for the disease treatment and management.

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Identification of differentially expressed and methylated genes and construction of a co-expression network in age-related macular degeneration

Liang¹,

Ma²,

Luo³

et al. 2022

Ann Transl Med

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Background: Age-related macular degeneration (AMD) is the leading cause of blindness for people over 50 years old worldwide. The purpose of this study was to identify differentially expressed and methylated genes (DEMGs) and construct a co-expression network for AMD.Methods: Microarray expression (GSE29801 dataset) and DNA methylation (GSE102952 dataset) profiles were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were analyzed between AMD retina tissues and normal retina tissues. A protein-protein interaction (PPI) network was constructed and hub genes were screened, followed by functional enrichment analysis. Then, weighted gene co-expression network analysis (WGCNA) was conducted.The ARPE-19 cells were maintained in a hypoxic state to construct an AMD cellular model. Enzyme-linked immunosorbent assay (ELISA) and the real-time qPCR (RT-qPCR) were performed for validation.Results: After overlapping, 16 hypermethylated and down-regulated genes and 15 hypomethylated and upregulated genes were identified for extramacular AMD. A total of 4 hub genes (LMNB2, EMD, HLA-A, and HLA-B) were screened for AMD in the extramacular retina. Furthermore, 13 hypermethylated and downregulated genes and 31 hypomethylated and up-regulated genes were identified for macular AMD. Among them, 11 hub genes (HLA-A,

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Epigenomic signatures in age-related macular degeneration: Focus on their role as disease modifiers and therapeutic targets

Cited by 4 publications

References 84 publications

WARE: Wet AMD Risk-Evaluation Tool as a Clinical Decision-Support System Integrating Genetic and Non-Genetic Factors

WARE: Wet AMD Risk-Evaluation Tool as a Clinical Decision-Support System Integrating Genetic and Non-Genetic Factors

Relationship between Nutrition, Lifestyle, and Neurodegenerative Disease: Lessons from ADH1B, CYP1A2 and MTHFR

Identification of differentially expressed and methylated genes and construction of a co-expression network in age-related macular degeneration

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