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Novel Epigenetic Clock Biomarkers of Age-Related Macular Degeneration.

ABSTRACT: Age-Related Macular Degeneration (AMD) is a bilateral ocular condition resulting in irreversible vision impairment caused by the progressive loss of photoreceptors in the macula, a region at the center of the retina. The progressive loss of photoreceptor is a key feature of dry AMD but not always wet AMD, though both forms of AMD can lead to loss of vision. Regression-based biological age clocks are one of the most promising biomarkers of aging but have not yet been used in AMD. Here we conducted analyses to identify regression-based biological age clocks for the retina and explored their use in AMD using transcriptomic data consisting of a total of 453 retina samples including 105 Minnesota Grading System (MGS) level 1 samples, 175 MGS level 2, 112 MGS level 3 and 61 MGS level 4 samples, as well as 167 fibroblast samples. The clocks yielded good separation among AMD samples with increasing severity score viz., MGS1-4, regardless of whether clocks were trained in retina tissue, dermal fibroblasts, or in combined datasets. Clock application to cultured fibroblasts, embryonic stem cells, and induced Pluripotent Stem Cells (iPSCs) were consistent with age reprograming in iPSCs. Moreover, clock application to in vitro neuronal differentiation suggests broader applications. Interesting, many of the age clock genes identified include known targets mechanistically linked to AMD and aging, such as GDF11, C16ORF72, and FBN2. This study provides new observations for retina age clocks and suggests new applications for monitoring in vitro neuronal differentiation. These clocks could provide useful markers for AMD monitoring and possible intervention, as well as potential targets for in vitro screens.

SUBMITTER: Mallik S

PROVIDER: S-EPMC9244395 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveAge-related macular degeneration (AMD) is a significant cause of blindness, initially characterized by the accumulation of sub-Retinal pigment epithelium (RPE) deposits, leading to progressive retinal degeneration and, eventually, irreversible vision loss. This study aimed to elucidate the differential expression of transcriptomic information in AMD and normal human RPE choroidal donor eyes and to investigate whether it could be used as a biomarker for AMD.MethodsRPE choroidal tissue samples (46 Normal samples, 38 AMD samples) were obtained from the GEO (GSE29801) database and screened for differentially expressed genes in normal and AMD patients using GEO2R and R to compare the degree of enrichment of differentially expressed genes in the GO, KEGG pathway. Firstly, we used machine learning models (LASSO, SVM algorithm) to screen disease signature genes and compare the differences between these signature genes in GSVA and immune cell infiltration. Secondly, we also performed a cluster analysis to classify AMD patients. We selected the best classification by weighted gene co-expression network analysis (WGCNA) to screen the key modules and modular genes with the strongest association with AMD. Based on the module genes, four machine models, RF, SVM, XGB, and GLM, were constructed to screen the predictive genes and further construct the AMD clinical prediction model. The accuracy of the column line graphs was evaluated using decision and calibration curves.ResultsFirstly, we identified 15 disease signature genes by lasso and SVM algorithms, which were associated with abnormal glucose metabolism and immune cell infiltration. Secondly, we identified 52 modular signature genes by WGCNA analysis. We found that SVM was the optimal machine learning model for AMD and constructed a clinical prediction model for AMD consisting of 5 predictive genes.ConclusionWe constructed a disease signature genome model and an AMD clinical prediction model by LASSO, WGCNA, and four machine models. The disease signature genes are of great reference significance for AMD etiology research. At the same time, the AMD clinical prediction model provides a reference for early clinical detection of AMD and even becomes a future census tool. In conclusion, our discovery of disease signature genes and AMD clinical prediction models may become promising new targets for the targeted treatment of AMD.

Project description:PurposeAge-related macular degeneration (AMD) can result in severe vision loss and blurriness in the older population. The early and intermediate stages of AMD typically start without noticeable symptoms and can only be detected with a comprehensive eye exam. Because of the quiet onset of the disease, it is necessary to identify potential biomarkers to aid in the diagnosis, staging, and association with disease onset. Inflammasome signaling proteins are prominent biomarkers in the central nervous system, and the inflammasome has been shown to play a role in the innate inflammatory response in aging and AMD.MethodsSerum from healthy controls and AMD patients were analyzed for the protein levels of Apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), interleukin (IL)-18 and C-reactive protein (CRP) to determine cutoff points, positive and negative predictive values, and receiver operator characteristic curves, as well as univariate and multivariate linear and logistic regression models.ResultsASC, IL-18, and CRP were elevated in the serum of AMD patients when compared to healthy controls. The area under the curve (AUC) for ASC was 0.98 with a cutoff point of 365.6 pg/mL, whereas IL-18 had an AUC of 0.73 and a cutoff point of 242.4 pg/mL, and the AUC for CRP was 0.67 with a cutoff point of 8,684,152 pg/mL. Levels of IL-18 had a statistically significant linear correlation with that of ASC with an adjusted R 2 of 0.1906, indicating that 19% of IL-18 could be explained by ASC protein levels in serum. Moreover, a logistic regression model for the diagnosis of AMD consists of ASC and having a diagnosis of hypertension, indicating that these two factors (elevated levels of ASC and a diagnosis of hypertension [HTN]) are associated with the diagnosis of AMD.ConclusionsASC, IL-18, and CRP are elevated in patients with AMD, and the protein levels of IL-18 are partially the result of ASC protein expression. Moreover, elevated protein levels of ASC in serum and a diagnosis of HTN increase the odds of patients having a diagnosis of AMD.Translational relevanceBiomarkers of AMD may be used to monitor disease risk, response to treatment and disease progression.

Project description:Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.

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Novel Epigenetic Clock Biomarkers of Age-Related Macular Degeneration.

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