Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Table 1_Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning.xlsx

Title: Table 1_Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning.xlsx
Authors: Xiaosheng Huang; Tiansheng Chou; Xinhua Liu; Kun Zeng; Liangnan Sun; Zonghui Yan; Shaoyi Mei; Wenqun Xi; Zongyi Zhan; Yi Liu; Songguo Dong; Siqi Liu; Jun Zhao
Publication Year: 2025
Collection: Torrens University Australia: Figshare
Subject Terms: Cell Biology; proteomes; aqueous humor; aging protein; senescence modulator; machine learning
Description: Background In conjunction with age, aqueous humor (AH) proteomics can affect the occurrence and development of age-related eye diseases, which are poorly understood. Objective We characterized the proteomic changes in AH throughout the aging process to better understand the aging mechanisms of the intraocular environment. Methods We analyzed the AH proteomes of 33 older and 19 younger individuals using liquid chromatography–tandem mass spectrometry, from which we clustered similar expression trajectories of AH proteomics using local regression analysis. Aging proteins (APs) and their functional enrichment were evaluated using various statistical and bioinformatics methods, while aging modulators were predicted using multiple machine-learning models. Results AH proteomic expression patterns exhibited various types of linear and nonlinear changes across the age groups. A set of 179 proteins identified as significant APs were enriched in various eye processes, such as detoxification, eye development, negative regulation of hydrolase activity, and humoral immune response. According to AH proteomics, hallmarks of aging include oxidative damage, defective extracellular matrices, and loss of proteostasis. A total of 11 APs were considered senescence signatures for predicting AH age with strong predictive ability. Furthermore, 22 APs were classified as modulators that may affect the aging process in the eye. Conclusion These findings establish a framework for age-related changes in the AH proteome and provide potential senescence biomarkers and therapeutic targets for age-related eye diseases.
Document Type: dataset
Language: unknown
Relation: https://figshare.com/articles/dataset/Table_1_Revealing_age-related_changes_in_the_intraocular_microenvironment_and_senescence_modulators_using_aqueous_humor_proteomics_and_machine_learning_xlsx/29579660
DOI: 10.3389/fcell.2025.1583330.s001
Availability: https://doi.org/10.3389/fcell.2025.1583330.s001
Rights: CC BY 4.0
Accession Number: edsbas.29D7E331
Database: BASE