Integrative multi-omics and computational modeling reveal RPL22-driven skin aging mechanisms under PM2.5 exposure and identify high-stability natural inhibitors.

Abstract

The causal mechanisms linking PM2.5 exposure to skin aging remain unclear, and traditional observational studies are susceptible to confounding factors. Although epidemiological evidence suggests that PM2.5 accelerates skin aging, its molecular targets and regulatory pathways are yet to be elucidated. This study integrates genomics and computational biology methods: (1) based on a genome-wide association study (GWAS) of European populations, we employed two-sample Mendelian randomization (MR, using genetic variants as instrumental variables to infer causal relationships) and meta-analysis to assess the causal effects of PM2.5; (2) we analyzed the expression characteristics of target genes through single-cell RNA sequencing (GSE130973 dataset); and (3) for key genes, we evaluated the binding stability of their inhibitors using virtual screening (AutoDock Vina) and molecular dynamics simulations (100 ns, GROMACS). MR analysis shows that PM2.5 significantly increases the risk of skin aging in the European population (p = 0.04), identifying 87 co-localized genes. Single-cell analysis reveals that RPL22 is highly expressed in six cell types of elderly patients (such as keratinocytes, p < 0.05) and is enriched in the MYC pathway (FDR < 0.05). Virtual screening identified two natural products (Ophiopogonin D and Prosapogenin A) with binding energies of - 9.41 and - 9.28 kcal/mol, respectively. Molecular dynamics indicate that Prosapogenin A has a more stable binding (ΔG_total =  - 33.17 kcal/mol). This study reveals the mechanism by which PM2.5 accelerates skin aging through the upregulation of RPL22, providing new targets for anti-aging therapy. The screened small molecules lay the theoretical foundation for the development of inhibitors targeting RPL22.