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.

Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model.

BACKGROUND: Sufficient exposure to natural environments, in particular soil and its microbes, has been suggested to be protective against allergies. OBJECTIVE: We aim at gaining more direct evidence of the environment-microbiota-health axis by studying the colonization of gut microbiota in mice after exposure to soil and by examining immune status in both a steady-state situation and during allergic inflammation. METHODS: The gastrointestinal microbiota of mice housed on clean bedding or in contact with soil was analyzed by using 16S rRNA gene sequencing, and the data were combined with immune parameters measured in the gut mucosa, lung tissue, and serum samples. RESULTS: We observed marked differences in the small intestinal and fecal microbiota composition between mice housed on clean bedding or in contact with soil, with a higher proportion of Bacteroidetes relative to Firmicutes in the soil group. The housing environment also influenced mouse intestinal gene expression, as shown by upregulated expression of the immunoregulatory markers IL-10, forkhead box P3, and cytotoxic T lymphocyte-associated protein 4 in the soil group. Importantly, using the murine asthma model, we found that exposure to soil polarizes the immune system toward TH1 and a higher level of anti-inflammatory signaling, alleviating TH2-type allergic responses. The inflammatory status of the mice had a marked influence on the composition of the gut microbiota, suggesting bidirectional communication along the gut-lung axis. CONCLUSION: Our results provide evidence of the role of environmentally acquired microbes in alleviating against TH2-driven inflammation, which relates to allergic diseases.

Acinetobacter species in the skin microbiota protect against allergic sensitization and inflammation.

BACKGROUND: The human commensal microbiota interacts in a complex manner with the immune system, and the outcome of these interactions might depend on the immune status of the subject. OBJECTIVE: Previous studies have suggested a strong allergy-protective effect for Gammaproteobacteria. Here we analyze the skin microbiota, allergic sensitization (atopy), and immune function in a cohort of adolescents, as well as the influence of Acinetobacter species on immune responses in vitro and in vivo. METHODS: The skin microbiota of the study subjects was identified by using 16S rRNA sequencing. PBMCs were analyzed for baseline and allergen-stimulated mRNA expression. In in vitro assays human monocyte-derived dendritic cells and primary keratinocytes were incubated with Acinetobacter lwoffii. Finally, in in vivo experiments mice were injected intradermally with A lwoffii during the sensitization phase of the asthma protocol, followed by readout of inflammatory parameters. RESULTS: In healthy subjects, but not in atopic ones, the relative abundance of Acinetobacter species was associated with the expression of anti-inflammatory molecules by PBMCs. Moreover, healthy subjects exhibited a robust balance between anti-inflammatory and TH1/TH2 gene expression, which was related to the composition of the skin microbiota. In cell assays and in a mouse model, Acinetobacter species induced strong TH1 and anti-inflammatory responses by immune cells and skin cells and protected against allergic sensitization and lung inflammation through the skin. CONCLUSION: These results support the hypothesis that skin commensals play an important role in tuning the balance of TH1, TH2, and anti-inflammatory responses to environmental allergens.