Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.08.16.504111

Transcriptomic and epigenetic assessment of ageing female skin and fibroblasts identifies age related reduced oxidative phosphorylation is exacerbated by smoking.

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 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. Stratification of female fibroblast and skin samples combined with female specific normalisation of transcriptomic and methylation data sets increased functional enrichment and consistency across studies. The results identify the importance of considering environmental factors known to increase the rate of ageing (smoking status of donors, and UV-exposure status of skin and fibroblast samples) both independently and in combination for the identification of key ageing signatures. The results identified that in old (> 65) female skin decreases in the expression of transmembrane ion transporters coincide with increased methylation of oxidoreductases, and consequently reductions in respiration. This was further evidenced in old fibroblasts from smokers which identified reductions ion homeostasis, and the transcription of mitochondrial tRNAs, that were accompanied by reduced mitochondrial fission, reduced lipid catabolism and reduced immune signalling. These changes occurred in combination with reductions in cell proliferation, adhesion, ECM organisation, cell movement, cytoskeleton organisation and circulatory system development. Middle and old aged skin without environmental stratifications identified decreased expression of transmembrane ion transporters occurred alongside reductions in keratinisation, reduced mitochondrial fission, and this was associated with reduced metabolism (specifically carbohydrates), and consequently a reduction in the production of lipids (phospholipids for membranes and others) occured, exacerbating ion homeostasis issues at a keratinocyte level. Interestingly in skin the combined impacts of UV-exposure, smoking and ageing yielded different results, increased expression of calcium homeostasis genes, cell adhesion molecules (integrins), structural membrane constituents (loricrin, mucins, keratins and collagens), increased cornification, as well as structural cytoskeletal molecules (KRTAPs). This occurred alongside increased expression of genes involved in skin peeling (kalikriens), proliferation and differentiation, glycosylation, oxidative stress, autophagy, lactose metabolism, and lipid catabolism. Aged UV-exposed skin from smokers is on the whole more fibrous, with cells showing significant cell membrane and cytoskeletal structural changes, similar to those seen in skin cancers. Interestingly in non-UV-exposed skin from smokers most of these processes were reduced, and in within age group comparisons of smokers they were also reduced, suggesting that smoking reduced skin development and regeneration. Female specific analysis of smokers from different age groups enrichment results identified additional factors relating to tissue development, cell adhesion, vasculature development, peptide cross-linking, calcium homeostasis, cancer and senescence, 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 human cytomegalovirus and herpes simplex virus are identified by the age only analysis, suggesting that viral infections and ageing have similar molecular consequences for cells.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pease, L., Wordsworth, J., Shanley, D.. 2022-08-18. Transcriptomic and epigenetic assessment of ageing female skin and fibroblasts identifies age related reduced oxidative phosphorylation is exacerbated by smoking.. https://doi.org/10.1101/2022.08.16.504111

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

KEEP EXPLORING

Related preprints

Comparative functional genomics reveals distributed SERPINE1 like characteristics among Drosophila serpins

Serpins (serine protease inhibitors) regulate proteolytic activity through a suicide-substrate inhibitory mechanism, whereby the serpin covalently traps and inactivates their target protease. Serpins are broadly conserved across eukaryotes, but the evolutionary and functional relationships between human SERPINE1, and Drosophila Serpins in incompletely defined. To identify candidate Drosophila functional analogs of SERPINE1/PAI-1, a multi-omics framework was applied to thoroughly compare the serpins across species. The multi-level analysis integrated orthology prediction, protein sequence and structural characterization, phylogenetic reconstruction, bulk tissue transcriptomics, single-cell transcriptomics, and aging-associated expression analysis. Candidate serpins (Spn42Da, Spn42Db, Spn42Dc, Spn42Dd, Spn28B, and Spn28F) were evaluated using DIOPT, AlphaFold/ChimeraX structural comparisons, reactive center loop analyses, phylogenetic distances, GTEx, Human Protein Atlas, FlyAtlas2, Fly Cell Atlas, and Aging Fly Cell Atlas datasets. Comparative analyses demonstrated full-length sequence and reactive center loop (RCL) divergence despite preservation of the canonical serpin fold. Tissue, cell-type, and age-associated expression analysis revealed distinct candidate-specific patterns. Spn42Dd comparisons uncovered similarities to human SERPINE1 in sequence/structural and broad expression but did not represent the strongest overall cross-species expression correlations. Spn42Da and Spn42Db received the highest cross species integrated support. Quality check of the ranking confirmed that Spn42Da and Spn42Db were stable top candidates across orthology, sequence alignment, and expression domains. The multi-domain analysis was unable to identify a single definitive Drosophila serpin as the closest counterpart to human SERPINE1, indicating that cross-species similarity is distributed across multiple candidate serpins. This study therefore provides a quantitative method for prioritizing potential evolutionary and functional analogs across highly divergent species.

bioinformatics↗

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗

DiffTernary: a Diffusion Model for Molecular Glue Ternary Complex Docking

Molecular glues (MGs) modulate protein function by inducing or stabilizing protein interactions. However, accurately modeling MG ternary complexes remains challenging. Here, we present DiffTernary, a diffusion-based docking method for the joint prediction of relative protein arrangement and MG binding pose. We introduce Ternary-PDB, a dataset of 76,702 experimentally determined ternary complexes curated from the Protein Data Bank using structural filters and a release cutoff aligned with AlphaFold 3 (AF3). When trained on this dataset, DiffTernary achieved acceptable protein-protein interaction (PPI) predictions (DockQ > 0.23) for 50.0% of MGBench complexes with a single prediction, compared with 51.1% for AF3. In comparisons with AF3, OpenFold3, ESMFold2, Protenix-v1 and Protenix-v2, DiffTernary ranked second with a best-of-25 PPI prediction success rate of 78.4%, behind ESMFold2 (90.9%) and ahead of AF3 (72.7%). A separate DiffTernary model trained on TernaryDB achieved a mean DockQ of 0.306 on the 94 complexes in the TernaryDB MGD test set with a single prediction, compared with 0.228 for DeepTernary. Confidence-based selection among 40 samples yielded acceptable PPI predictions for 41.5% of complexes, compared with 30.9% for DeepTernary. An E2Former variant increased training and sampling throughput 6.90-fold and 4.37-fold, respectively, under the evaluated configurations. Together, DiffTernary and Ternary-PDB provide an efficient docking approach for MG complex prediction and a valuable structural resource for MG discovery and design.

bioinformatics↗