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Genome-wide association studies (GWASs) for atopic dermatitis (AD) have uncovered 81 risk loci in European participants, however translating these findings into functional and therapeutic insights remains challenging. We conducted a transcriptome-wide association study (TWAS) in AD leveraging cis-eQTL data from sun exposed (n=517), non-sun exposed skin (n=602) and whole blood (n=670) tissues and the latest GWAS of AD in Europeans (n=864982). We implemented the OTTERS pipeline that combines polygenic risk score (PRS) techniques accommodating diverse assumptions in the architecture of gene regulation. We also used differential expression meta-analysis and co-expression networks (n=186) to characterize the transcriptomic landscape of AD. We identified 176 gene-tissue associations covering 126 unique genes (53 previously unreported). Most TWAS risk genes were identified by adaptive PRS frameworks, with non-significant differences compared to clumping and thresholding approaches. TWAS risk genes were enriched in allergic reactions (e.g., AQP7, AFF4), skin barrier integrity (e.g., ACER3) and inflammatory pathways (e.g., TAPBPL). By integrating co-expression networks of lesional AD skin, we identified 16 hub genes previously identified as TWAS risk genes (6 previously unreported) that orchestrate inflammatory responses (e.g., HSPA4) and keratinization (e.g., LCE3E, LCE3D), serving as potential drug targets through drug-gene interactions. Consistent associations between all analyses were reported for FOSL1 and RORC. Collectively, our findings provide additional risk genes for AD with potential implications in therapeutic approaches., (Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.)

With the advent of next-generation sequencing (NGS), experimental techniques that capture the biological significance of DNA loci or RNA molecules have emerged as fundamental tools for studying the epigenome and transcriptional regulation on a genome-wide scale. The volume of the generated data and the underlying complexity regarding their analysis highlight the need for robust and easy-to-use computational analytic methods that can streamline the process and provide valuable biological insights. Our solution, aPEAch, is an automated pipeline that facilitates the end-to-end analysis of both DNA- and RNA-sequencing assays, including small RNA sequencing, from assessing the quality of the input sample files to answering meaningful biological questions by exploiting the rich information embedded in biological data. Our method is implemented in Python, based on a modular approach that enables users to choose the path and extent of the analysis and the representations of the results. The pipeline can process samples with single or multiple replicates in batches, allowing the ease of use and reproducibility of the analysis across all samples. aPEAch provides a variety of sample metrics such as quality control reports, fragment size distribution plots, and all intermediate output files, enabling the pipeline to be re-executed with different parameters or algorithms, along with the publication-ready visualization of the results. Furthermore, aPEAch seamlessly incorporates advanced unsupervised learning analyses by automating clustering optimization and visualization, thus providing invaluable insight into the underlying biological mechanisms.

The clinical heterogeneity regarding the response profile of the antitumor necrosis factor (anti-TNF) in patients with Crohn's disease (CD) and psoriasis (PsO) is attributed, amongst others, to genetic factors that influence the regulatory mechanisms which orchestrate the inflammatory response. Here, we investigated the possible associations between the MIR146A rs2910164 and MIR155 rs767649 variants and the response to anti-TNF therapy in a Greek cohort of 103 CD and 100 PsO patients. We genotyped 103 CD patients and 100 PsO patients via the PCR-RFLP method, utilizing the de novo formation of a restriction site for the SacI enzyme considering the MIR146A rs2910164, while Tsp45I was employed for the MIR155 rs767649 variant. Additionally, we investigated the potential functional role of the rs767649 variant, exploring in silico the alteration of transcription factor binding sites (TFBSs) mapped on its genomic location. Our single-SNP analysis displayed a significant association between the rare rs767649 A allele and response to therapy (Bonferroni-corrected p value = 0.012) in patients with PsO, a result further enhanced by the alteration in the IRF2 TFBS caused by the above allele. Our results highlight the protective role of the rare rs767649 A allele in the clinical remission of PsO, implying its utilization as a pharmacogenetic biomarker.

Despite the increasing research and clinical interest in the predisposition of psoriasis, a chronic inflammatory skin disease, the multitude of genetic and environmental factors involved in its pathogenesis remain unclear. This complexity is further exacerbated by the several cell types that are implicated in Psoriasis's progression, including keratinocytes, melanocytes and various immune cell types. The observed interactions between the genetic substrate and the environment lead to epigenetic alterations that directly or indirectly affect gene expression. Changes in DNA methylation and histone modifications that alter DNA-binding site accessibility, as well as non-coding RNAs implicated in the post-transcriptional regulation, are mechanisms of gene transcriptional activity modification and therefore affect the pathways involved in the pathogenesis of Psoriasis. In this review, we summarize the research conducted on the environmental factors contributing to the disease onset, epigenetic modifications and non-coding RNAs exhibiting deregulation in Psoriasis, and we further categorize them based on the under-study cell types. We also assess the recent literature considering therapeutic applications targeting molecules that compromise the epigenome, as a way to suppress the inflammatory cutaneous cascade.