Abstract
For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts. By relating the modules to the donor's traits, we uncovered the natural aging- and premature aging disease-associated modules. The STRING functional association networks built with the core module memberships provided a systematic overview of genome-wide transcriptional changes upon aging. We validated the selected candidates via quantitative reverse transcription PCR (RT-qPCR) assay with young and aged human fibroblasts, and uncovered several genes involved in ECM, cell, and nuclear mechanics as a potential aging biomarker. Collectively, our study not only provides a snapshot of functional changes during human fibroblast aging but also presents potential aging markers that are relevant to cell mechanics.
Highlights
For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts
WGCNA analysis of the RNA-seq data (GSE113957), generated with young to old dermal human fibroblasts, summarized into 8 clusters of co-expressed genes. (A) Description of the RNA-seq data (GSE113957)[5]. It was generated with 143 human dermal fibroblasts derived from 133 healthy individuals (1–96 years) and 10 Hutchinson–Gilford progeria syndrome (HGPS) patients. (B) A heatmap of the topological overlap matrix (TOM) with randomly selected 500 genes, showing the pairwise relationships among genes
The gene dendrogram, and module assignment shown as different colour blocks are depicted along the left and top. (C) Identification of modules, clusters of co-expressed genes, via Dynamic Tree Cut algorithm, and subsequently merged if two modules are highly similar, resulting in 8 modules shown as different colour blocks at the bottom: Black, Blue, Green, Greenyellow, Magenta, Pink, Tan, and Turquoise
Summary
For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts. We validated the selected candidates via quantitative reverse transcription PCR (RT-qPCR) assay with young and aged human fibroblasts, and uncovered several genes involved in ECM, cell, and nuclear mechanics as a potential aging biomarker. Our recent study demonstrated that the fibroblasts via nuclear reprogramming and re-differentiation by mechanical constraints showed higher contractility and enhanced ECM remodeling, compared to the control fibroblasts, and these characteristics were reminiscent of cellular r ejuvenation[4] This underscores the importance of changes in the mechanical state of the cells with aging. A comprehensive set of transcriptome data from various age groups was generated in a recent s tudy[5] This dataset is unique because, firstly, it covers all ages ranging from 1- to 96-year, which addresses a major weakness of previous studies where limited age groups were included. Several genes involved in ECM, cell, and nuclear mechanics were identified, and subsequently validated via RT-qPCR assay with young and aged human dermal fibroblasts
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
