Integrative transcriptome analysis deciphers mechanisms of nickel contact dermatitis.

BACKGROUND: Nickel-induced allergic contact dermatitis (nACD) remains a major occupational skin disorder, significantly impacting the quality of life of suffering patients. Complex cellular compositional changes and associated immunological pathways are partly resolved in humans; thus, the impact of nACD on human skin needs to be further elucidated. METHODS: To decipher involved immunological players and pathways, human skin biopsies were taken at 0, 2, 48, and 96 hours after nickel patch test in six nickel-allergic patients. Gene expression profiles were analyzed via microarray. RESULTS: Leukocyte deconvolution of nACD-affected skin identified major leukocyte compositional changes at 48 and 96 hours, including natural killer (NK) cells, macrophage polarization, and T-cell immunity. Gene set enrichment analysis mirrored cellular-linked functional pathways enriched over time. NK cell infiltration and cytotoxic pathways were uniquely found in nACD-affected skin compared to sodium lauryl sulfate-induced irritant skin reactions. CONCLUSION: These results highlight key immunological leukocyte subsets as well as associated pathways in nACD, providing insights into pathophysiology with the potential to unravel novel therapeutic targets.

Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47, BATF, FASLG, RGS16, SYNPO, SELE, PTPN7, WARS, PRC1, EXO1, RRM2, PBK, RAD54L, KIFC1, SPC25, PKMYT, HISTH1A, TPX2, DLGAP5, TPX2, CH25H, and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies.