Project description:Characteization host-microbiome interactions in patients with allergic (model: atopic dermatitis) and autoimmune (model: psoriasis) diseases by integration of microarray transcriptome data with 16S microbial profiling. 6mm punch biopsies were collected from the skin of atopic dermatitis and psoriasis patients alongside healthy volunteers, and subjected to analysis using Affymetrix Human Gene ST 2.1 arrays.
Project description:Atopic dermatitis (AD) is a common pruritic dermatitis with macroscopically nonlesional skin that is often abnormal. Therefore, we used high-density oligonucleotide arrays to identify cutaneous gene transcription changes associated with early AD inflammation as potential disease control targets. Skin biopsy specimens analyzed included normal skin from five healthy nonatopic adults and both minimally lesional skin and nearby or contralateral nonlesional skin from six adult AD patients. Keywords: disease state analysis
Project description:Purpose: To determine the transcriptional differences between lesional skin and nonlesional skin from patients with atopic dermatitis Methods: Skin biopsies of lesional and non-lesional sites on atopic dermatitis patients were obtained and stored in RNA Later. Ribosomal RNA was removed and cDNA was generated with the SMARTer kit (CloneTech) with 10 ng of total RNA per sample. Samples were sequenced to an average depth of 34 million 1x50 reads on a HiSeq3000 (Illumina). Reads were aligned to Ensembl release 76 using STAR, gene counts were determined with Subread:featureCount, and sequence performance was assessed with RSeQC.
Project description:Clinical overlaps between psoriasis and atopic dermatitis are sometimes undiscernible, and there is no consensus whether to treat the overlap phenotype as psoriasis or atopic dermatitis. We enrolled patients diagnosed with either psoriasis or atopic dermatitis, and clinically re-stratified them into classic psoriasis, classic atopic dermatitis, and the overlap phenotype between psoriasis and atopic dermatitis. We compared gene expression profiles of lesional and nonlesional skin biopsy tissues between the three comparison groups. Global mRNA expression and T-cell subset cytokine expression in the skin of the overlap phenotype were consistent with the profiles of psoriasis and different from the profiles of atopic dermatitis. Unsupervised k-means clustering indicated that the best number of distinct clusters for the total population of the three comparison groups was two, and the two clusters of psoriasis and atopic dermatitis were differentiated by gene expression. Our study suggests that clinical overlap phenotype between psoriasis and atopic dermatitis has dominant molecular features of psoriasis, and genomic biomarkers can differentiate psoriasis and atopic dermatitis at molecular levels in patients with a spectrum of psoriasis and atopic dermatitis.
Project description:To gain a deeper understanding of the atopic dermatitis (AD) skin transcriptome and the effects of systemic treatment with dupilumab and cyclosporine, we conducted a gene expression study of AD using mRNA-Seq data generated from lesional and non-lesional skin biopsies collected from patients included in the TREATgermany registry. We are able to provide deep characterisation of AD skin transcriptomic signatures by using an assortment of bioinformatic approaches such as differential expression, co-expression network and pathway enrichment analysis.
Project description:mRNA array analysis was carried out using total RNA of skin biopsies from lesional and non-lesional skin of three atopic dermatitits patients and four healthy individuals.
Project description:Atopic dermatitis (AD) is a heritable inflammatory disease, characterised by skin barrier dysfunction. Genome-wide association studies (GWAS) have identified molecular targets with relevance for drug development, but the strongest genetic association, FLG, has not yet been successfully targeted in atopic disease. An AD-associated locus on chromosome 11q13.5 lies between two genes - EMSY and LRRC32 - but the functional mechanisms leading to AD are unclear. We applied a combination of genomic and molecular analytical techniques followed up in patient biopsies, to investigate pathomechanisms at this GWAS locus. Chromosome conformation capture data in keratinocytes shows interaction of the intergenic region in threedimensional space with EMSY. siRNA-mediated knockdown of EMSY in skin organoid culture leads to enhanced development of barrier function, measured by water evaporation and dye penetration. Global proteomic analysis of skin organoids with EMSY knockdown shows increased expression of structural and functional proteins, confirmed by histological and ultrastructural features. Lipid analysis shows an increase in ceramides known to be reduced in AD. Conversely, over-expression of EMSY in primary human keratinocytes leads to a reduction in biomarkers of barrier formation. Finally, skin biopsy samples from patients with AD show greater EMSY staining in the nucleus, consistent with increased functional effect of this DNAbinding protein. Together our findings demonstrate an important role for EMSY in transcriptional regulation and skin barrier formation, supporting EMSY inhibition as a therapeutic approach for AD.
Project description:Atopic dermatitis (AD) is the most prevalent chronic inflammatory skin disease in children characterized by dermatitis and pruritus. MicroRNAs (miRNAs) have been shown as great potential biomarkers for disease fingerprints to predict prognostics. We aimed to identify miRNA signature from serum and urine for the prognosis of AD patient by genome-wide miRNA profiling analysis. Serum from 8 children with AD and 8 healthy children were collected
Project description:Atopic dermatitis (AD) is the most prevalent chronic inflammatory skin disease in children characterized by dermatitis and pruritus. MicroRNAs (miRNAs) have been shown as great potential biomarkers for disease fingerprints to predict prognostics. We aimed to identify miRNA signature from serum and urine for the prognosis of AD patient by genome-wide miRNA profiling analysis. Urine from 3 children with AD and 3 healthy children were collected