Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning
BackgroundIn conjunction with age, aqueous humor (AH) proteomics can affect the occurrence and development of age-related eye diseases, which are poorly understood.ObjectiveWe characterized the proteomic changes in AH throughout the aging process to better understand the aging mechanisms of the intr...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Cell and Developmental Biology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2025.1583330/full |
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| author | Xiaosheng Huang Tiansheng Chou Tiansheng Chou Xinhua Liu Kun Zeng Liangnan Sun Zonghui Yan Shaoyi Mei Wenqun Xi Zongyi Zhan Yi Liu Songguo Dong Siqi Liu Jun Zhao |
| author_facet | Xiaosheng Huang Tiansheng Chou Tiansheng Chou Xinhua Liu Kun Zeng Liangnan Sun Zonghui Yan Shaoyi Mei Wenqun Xi Zongyi Zhan Yi Liu Songguo Dong Siqi Liu Jun Zhao |
| author_sort | Xiaosheng Huang |
| collection | DOAJ |
| description | BackgroundIn conjunction with age, aqueous humor (AH) proteomics can affect the occurrence and development of age-related eye diseases, which are poorly understood.ObjectiveWe characterized the proteomic changes in AH throughout the aging process to better understand the aging mechanisms of the intraocular environment.MethodsWe 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.ResultsAH 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.ConclusionThese 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. |
| format | Article |
| id | doaj-art-a4b5f977751f4e588d9aac92ea307326 |
| institution | DOAJ |
| issn | 2296-634X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj-art-a4b5f977751f4e588d9aac92ea3073262025-08-20T03:13:08ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2025-07-011310.3389/fcell.2025.15833301583330Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learningXiaosheng Huang0Tiansheng Chou1Tiansheng Chou2Xinhua Liu3Kun Zeng4Liangnan Sun5Zonghui Yan6Shaoyi Mei7Wenqun Xi8Zongyi Zhan9Yi Liu10Songguo Dong11Siqi Liu12Jun Zhao13Shenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaDepartment of Proteomics, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, ChinaNational Medical Metabolomics International Collaborative Research Center, Xiangya Hospital, Central South University, Changsha, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaShenzhen Eye Medical Center, Shenzhen Eye Hospital, Southern Medical University, Shenzhen, ChinaDepartment of Proteomics, Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, ChinaDepartment of Ophthalmology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, ChinaBackgroundIn conjunction with age, aqueous humor (AH) proteomics can affect the occurrence and development of age-related eye diseases, which are poorly understood.ObjectiveWe characterized the proteomic changes in AH throughout the aging process to better understand the aging mechanisms of the intraocular environment.MethodsWe 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.ResultsAH 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.ConclusionThese 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.https://www.frontiersin.org/articles/10.3389/fcell.2025.1583330/fullproteomesaqueous humoraging proteinsenescence modulatormachine learning |
| spellingShingle | Xiaosheng Huang Tiansheng Chou Tiansheng Chou Xinhua Liu Kun Zeng Liangnan Sun Zonghui Yan Shaoyi Mei Wenqun Xi Zongyi Zhan Yi Liu Songguo Dong Siqi Liu Jun Zhao Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning Frontiers in Cell and Developmental Biology proteomes aqueous humor aging protein senescence modulator machine learning |
| title | Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| title_full | Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| title_fullStr | Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| title_full_unstemmed | Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| title_short | Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| title_sort | revealing age related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning |
| topic | proteomes aqueous humor aging protein senescence modulator machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2025.1583330/full |
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