AbstractBackgroundAtopic dermatitis (AD) arises from a complex interaction between an impaired epidermal barrier, environmental exposures, and the infiltration of Th1/Th2/Th17/Th22 T cells. Transcriptomic analysis has advanced understanding of gene expression in cells and tissues. However, molecular quantitation of cytokine transcripts does not predict the importance of a specific pathway in AD or cellular responses to different inflammatory stimuli.ObjectiveTo understand changes in keratinocyte transcriptomic programmes in human cutaneous disease during development of inflammation and in response to treatment.MethodsWe performed in silico deconvolution of the whole-skin transcriptome. Using co-expression clustering and machine learning tools, we resolved the gene expression of bulk skin (n=7 datasets, n=406 samples), firstly, into unsupervised keratinocyte immune response phenotypes and, secondly, into 19 cutaneous cell signatures of purified populations from publicly available datasets.ResultsWe identify three unique transcriptomic programmes in keratinocytes, KC1, KC2, KC17, characteristic to immune signalling from disease-associated helper T cells. We cross-validate those signatures across different skin inflammatory conditions and disease stages and demonstrate that the keratinocyte response during treatment is therapy dependent. Broad spectrum treatment with ciclosporin ameliorated the KC17 response in AD lesions to a non-lesional immunophenotype, without altering KC2. Conversely, the specific anti-Th2 therapy, dupilumab, reversed the KC2 immunophenotype.ConclusionOur analysis of transcriptomic signatures in cutaneous disease biopsies reveals the complexity of keratinocyte programming in skin inflammation and suggests that the perturbation of a single axis of immune signal alone may be insufficient to resolve keratinocyte immunophenotype abnormalities.
Prenatal exposure to air pollutants and childhood atopic dermatitis and allergic rhinitis by adoptions of machine learning methods
