Multi-omics analysis to decipher the molecular link between chronic exposure to pollution and human skin dysfunction

Environmental pollution is composed of several factors, namely particulate matter (PM(2.5), PM(10)), ozone and Ultra Violet (UV) rays among others and first and the most exposed tissue to these substances is the skin epidermis. It has been established that several skin disorders such as eczema, acne, lentigines and wrinkles are aggravated by exposure to atmospheric pollution. While pollutants can interact with skin surface, contamination of deep skin by ultrafine particles or Polycyclic aromatic hydrocarbons (PAH) might be explained by their presence in blood and hair cortex. Molecular mechanisms leading to skin dysfunction due to pollution exposure have been poorly explored in humans. In addition to various host skin components, cutaneous microbiome is another target of these environment aggressors and can actively contribute to visible clinical manifestation such as wrinkles and aging. The present study aimed to investigate the association between pollution exposure, skin microbiota, metabolites and skin clinical signs in women from two cities with different pollution levels. Untargeted metabolomics and targeted proteins were analyzed from D-Squame samples from healthy women (n = 67 per city), aged 25–45 years and living for at least 15 years in the Chinese cities of Baoding (used as a model of polluted area) and Dalian (control area with lower level of pollution). Additional samples by swabs were collected from the cheeks from the same population and microbiome was analysed using bacterial 16S rRNA as well as fungal ITS1 amplicon sequencing and metagenomics analysis. The level of exposure to pollution was assessed individually by the analysis of polycyclic aromatic hydrocarbons (PAH) and their metabolites in hair samples collected from each participant. All the participants of the study were assessed for the skin clinical parameters (acne, wrinkles, pigmented spots etc.). Women from the two cities (polluted and less polluted) showed distinct metabolic profiles and alterations in skin microbiome. Profiling data from 350 identified metabolites, 143 microbes and 39 PAH served to characterize biochemical events that correlate with pollution exposure. Finally, using multiblock data analysis methods, we obtained a potential molecular map consisting of multi-omics signatures that correlated with the presence of skin pigmentation dysfunction in individuals living in a polluted environment. Overall, these signatures point towards macromolecular alterations by pollution that could manifest as clinical sign of early skin pigmentation and/or other imperfections.