Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.08.16.504102

Transcriptomic and epigenetic assessment of ageing male skin identifies disruption of Ca2+ homeostasis; exacerbated by smoking and UV exposure

Abstract

Skin ageing has been widely associated with the formation and presence of increasing quantities of senescent cells, the presence of which are thought to reduce cell renewal. This study aimed to identify key factors influencing fibroblast and skin aging in European males using RNA-seq data. Key differences in study designs included known sources of biological differences (sex, age, ethnicity), experimental differences, and environmental factors known to accelerate skin ageing (smoking, UV exposure) as well as study specific batch effects which complicated the analysis. To overcome these complications samples were stratified by these factors and differential expression assessed using Salmon and CuffDiff. Functional enrichment and consistency across studies, stratifications and tools identified age related alterations in the transcriptomes of fibroblasts and skin. Functional enrichment of results identified alterations in protein targeting to membranes and the ER, and altered calcium homeostasis in aged fibroblasts. Extension to skin controlled for differences in fibroblast culturing methods confirming transient age related alterations in intracellular calcium homeostasis. In middle aged males (40-65) increased keratinisation, skin, epithelial and epidermal development was seen in conjunction with alterations to ER Ca2+ uptake, leading to the identification of related processes including; an unfolded protein response, altered metabolism, increased MMP expression, and altered Ca2+ handling, which were further exacerbated by UV-exposure. Interestingly the genes and processes subsequently decreased in old males (> 65), which exhibited signs of increased senescence. Extension to Illumina 450k array data from ageing skin uncovered evidence of epigenetic regulation; genes and isoforms with overlapping differentially methylated CpGs were differentially expressed. Smoking led to additional enrichment of genes relating to tissue development, cell adhesion, vasculature development, peptide cross-linking, calcium homeostasis, cancer and senescence. The results consistently identified alterations in ER and golgi Ca2+ uptake, which disrupt intracellular and extracellular calcium gradients that regulate metabolic and differentiation signalling in skin and fibroblasts, leading to age related declines skin structure and function. Interestingly many diseases and infections with overlapping molecular consequences, (ER Ca2+ stress, reduced protein targeting to membranes) including COVID-19 are identified by the analysis, suggesting that COVID-19 infection compounds pre-existing cellular stresses in aged males, which could help explain higher COVID-19 mortality rates in aged males, as well as highlighting potential ways to reduce them.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pease, L., Wordsworth, J., Shanley, D.. 2022-08-16. Transcriptomic and epigenetic assessment of ageing male skin identifies disruption of Ca2+ homeostasis; exacerbated by smoking and UV exposure. https://doi.org/10.1101/2022.08.16.504102

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗