Novel Epigenetic Clock Biomarkers of Age-Related Macular Degeneration

Advances in Ophthalmic Epigenetics and Implications for Epigenetic Therapies: A Review

The epigenome represents a vast molecular apparatus that writes, reads, and erases chemical modifications to the DNA and histone code without changing the DNA base-pair sequence itself. Recent advances in molecular sequencing technology have revealed that epigenetic chromatin marks directly mediate critical events in retinal development, aging, and degeneration. Epigenetic signaling regulates retinal progenitor (RPC) cell cycle exit during retinal laminar development, giving rise to retinal ganglion cells (RGCs), amacrine cells, horizontal cells, bipolar cells, photoreceptors, and Müller glia. Age-related epigenetic changes such as DNA methylation in the retina and optic nerve are accelerated in pathogenic conditions such as glaucoma and macular degeneration, but reversing these epigenetic marks may represent a novel therapeutic target. Epigenetic writers also integrate environmental signals such as hypoxia, inflammation, and hyperglycemia in complex retinal conditions such as diabetic retinopathy (DR) and choroidal neovascularization (CNV). Histone deacetylase (HDAC) inhibitors protect against apoptosis and photoreceptor degeneration in animal models of retinitis pigmentosa (RP). The epigenome represents an intriguing therapeutic target for age-, genetic-, and neovascular-related retinal diseases, though more work is needed before advancement to clinical trials.

Multifactorial feature extraction and site prognosis model for protein methylation data

Integrated studies (multi-omics studies) comprising genetic, proteomic and epigenetic data analyses have become an emerging topic in biomedical research. Protein methylation is a posttranslational modification that plays an essential role in various cellular activities. The prediction of methylation sites (arginine and lysine) is vital to understand the molecular processes of protein methylation. However, current experimental techniques used for methylation site predictions are tedious and expensive. Hence, computational techniques for predicting methylation sites in proteins are necessary. For predicting methylation sites, various computational methods have been proposed in recent years. Most existing methods require structural and evolutionary information for retrieving features, acquiring this information is not always convenient. Thus, we proposed a novel method, called multi-factorial feature extraction and site prognosis model (MufeSPM), for the prediction of protein methylation sites based on information theory features (Renyi, Shannon, Havrda-Charvat and Arimoto entropy), amino acid composition and physicochemical properties acquired from protein methylation data. A random forest algorithm was used to predict methylation sites in protein sequences. This paper also studied the impact of different features and classifiers on arginine and lysine methylation data sets. For the R methylation data set, MufeSPM yielded 82.45%($\pm $ 3.47) accuracy, and for the K methylation data set, it provided an average accuracy of 71.94%($\pm $ 2.12). Additionally, the area under the receiver operating characteristic curve for different classifiers in predicting methylation site was provided. The experimental results signify that MufeSPM performs better than the state-of-the-art predictors.

Potential Therapeutic Agents on Alzheimer's Disease through Molecular Docking and Molecular Dynamics Simulation Study of Plant-Based Compounds

Globally Alzheimer's disease (AD) is a highly complex, heterogeneous, and multifactorial neurological disease. AD is categorized clinically through a steady loss in memory and progressive decline of cognitive function. So far, there is no effective cure is available for the treatment of AD. Here, we identified Plant-based compounds (PBCs) from seven therapeutic plants through pharmacophore and pharmacokinetics approaches. Subsequently, we retrieved 65 AD associated proteins by Text Mining approach .We observed the interactions between 39 PBCs with 65 AD-associated targets by using molecular docking. Further, we carried out Molecular dynamics simulation analysis to predict the steady binding of top drug-target complexes. The entire MD simulation results analysis was evidence that seven drug-target complexes consistently interacted during the in silico experiment. The top complexes were the target CHLE interacted with 2 PBCs (Pseudojujubogenin and Anahygrine), target VDAC1 interacted with Withanolide R, target THOP1 interacted with Withaolide R, target AOFB interacted with 2 PBCs (Nardostachysin and Viscosalactone B), and target ACHE interacted with the drug (12-Deoxywithastramonolide). These PBCs have stably and flexibly interacted at the protein's active site region. Our results suggest that these PBCs and targets are potential therapeutic candidates for molecular development in AD.