A computational framework for detecting inter-tissue gene-expression coordination changes with aging
Abstract Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explor...
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| Format: | Article |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94043-9 |
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| author | Shaked Briller Gil Ben David Yam Amir Gil Atzmon Judith Somekh |
| author_facet | Shaked Briller Gil Ben David Yam Amir Gil Atzmon Judith Somekh |
| author_sort | Shaked Briller |
| collection | DOAJ |
| description | Abstract Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging. |
| format | Article |
| id | doaj-art-aec08e46caf148f5b1d64d74ec75f2d7 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aec08e46caf148f5b1d64d74ec75f2d72025-08-20T02:08:09ZengNature PortfolioScientific Reports2045-23222025-03-0115111610.1038/s41598-025-94043-9A computational framework for detecting inter-tissue gene-expression coordination changes with agingShaked Briller0Gil Ben David1Yam Amir2Gil Atzmon3Judith Somekh4Department of Information Systems, University of HaifaDepartment of Human Biology, University of HaifaDepartment of Human Biology, University of HaifaDepartment of Human Biology, University of HaifaDepartment of Information Systems, University of HaifaAbstract Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.https://doi.org/10.1038/s41598-025-94043-9 |
| spellingShingle | Shaked Briller Gil Ben David Yam Amir Gil Atzmon Judith Somekh A computational framework for detecting inter-tissue gene-expression coordination changes with aging Scientific Reports |
| title | A computational framework for detecting inter-tissue gene-expression coordination changes with aging |
| title_full | A computational framework for detecting inter-tissue gene-expression coordination changes with aging |
| title_fullStr | A computational framework for detecting inter-tissue gene-expression coordination changes with aging |
| title_full_unstemmed | A computational framework for detecting inter-tissue gene-expression coordination changes with aging |
| title_short | A computational framework for detecting inter-tissue gene-expression coordination changes with aging |
| title_sort | computational framework for detecting inter tissue gene expression coordination changes with aging |
| url | https://doi.org/10.1038/s41598-025-94043-9 |
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