Atopic dermatitis (AD) affects up to 20% of children and adults worldwide. To gain a deeper understanding of the pathophysiology of AD, we conducted a large-scale transcriptomic study of AD with deeply sequenced RNA-sequencing samples using long (126-bp) paired-end reads. In addition to the comparisons against previous transcriptomic studies, we conducted in-depth analysis to obtain a high-resolution view of the global architecture of the AD transcriptome and contrasted it with that of psoriasis from the same cohort. By using 147 RNA samples in total, we found striking correlation between dysregulated genes in lesional psoriasis and lesional AD skin with 81% of AD dysregulated genes being shared with psoriasis. However, we described disease-specific molecular and cellular features, with AD skin showing dominance of IL-13 pathways, but with near undetectable IL-4 expression. We also demonstrated greater disease heterogeneity and larger proportion of dysregulated long noncoding RNAs in AD, and illustrated the translational impact, including skin-type classification and drug-target prediction. This study is by far the largest study comparing the AD and psoriasis transcriptomes using RNA sequencing and demonstrating the shared inflammatory components, as well as specific discordant cytokine signatures of these two skin diseases.

Collectively, our data identify IFN-κ as a critical IFN in CLE pathology via promotion of enhanced IFN responses and photosensitivity. IFN-κ is a potential novel target for UVB prophylaxis and CLE-directed therapy.

Hidradenitis suppurativa (HS) is a debilitating chronic inflammatory skin disease characterized by chronic abscess formation and development of multiple draining sinus tracts in the groin, axillae, and perineum. Using proteomic and transcriptomic approaches, we characterized the inflammatory responses in HS in depth, revealing immune responses centered on IFN-γ, IL-36, and TNF, with lesser contribution from IL-17A. We further identified B cells and plasma cells, with associated increases in immunoglobulin production and complement activation, as pivotal players in HS pathogenesis, with Bruton’s tyrosine kinase (BTK) and spleen tyrosine kinase (SYK) pathway activation as a central signal transduction network in HS. These data provide preclinical evidence to accelerate the path toward clinical trials targeting BTK and SYK signaling in moderate-to-severe HS.

The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R 2 for transcriptomic data and the TWAS power over PrediXcan when R1% cis-SNPs co-regulate gene expression and gene expression heritability %0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool ''TIGAR'' (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8+ T cell–mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.

Background: Although multiple studies have assessed molecular changes in chronic atopic dermatitis (AD) lesions, little is known about the transition from acute to chronic disease stages, and the factors and mechanisms that shape chronic inflammatory activity. Objectives: We sought to assess the global transcriptome changes that characterize the progression from acute to chronic stages of AD. Methods: We analyzed transcriptome changes in paired nonlesional skin, acute and chronic AD lesions from 11 patients and 38 healthy controls by RNA-sequencing, and conducted in vivo and histological assays to evaluate findings. Results: Our data demonstrate that approximately 74% of the genes dysregulated in acute lesions remain or are further dysregulated in chronic lesions, whereas only 34% of the genes dysregulated in chronic lesions are altered already in the acute stage. Nonlesional AD skin exhibited enrichment of TNF, T H 1, T H 2, and T H 17 response genes. Acute lesions showed marked dendritic-cell signatures and a prominent enrichment of T H 1, T H 2, and T H 17 responses, along with increased IL-36 and thymic stromal lymphopoietin expression, which were further heightened in chronic lesions. In addition, genes involved in skin barrier repair, keratinocyte proliferation, wound healing, and negative regulation of T-cell activation showed a significant dysregulation in the chronic versus acute comparison. Furthermore, our data show progressive changes in vasculature and maturation of dendritic-cell subsets with chronicity, with FOXK1 acting as immune regulator. Conclusions: Our results show that the changes accompanying the transition from nonlesional to acute to chronic inflammation in AD are quantitative rather than qualitative, with chronic AD having heightened T H 2, T H 1, T H 17, and IL36 responses and skin barrier repair mechanisms. These findings provide novel insights and highlight underappreciated pathways in AD pathogenesis that may be amenable to therapeutic targeting. (J

Psoriatic arthritis (PsA) is a complex chronic musculoskeletal condition that occurs in ~30% of psoriasis patients. Currently, no systematic strategy is available that utilizes the differences in genetic architecture between PsA and cutaneous-only psoriasis (PsC) to assess PsA risk before symptoms appear. Here, we introduce a computational pipeline for predicting PsA among psoriasis patients using data from six cohorts with >7000 genotyped PsA and PsC patients. We identify 9 new loci for psoriasis or its subtypes and achieve 0.82 area under the receiver operator curve in distinguishing PsA vs. PsC when using 200 genetic markers. Among the top 5% of our PsA prediction we achieve >90% precision with 100% specificity and 16% recall for predicting PsA among psoriatic patients, using conditional inference forest or shrinkage discriminant analysis. Combining statistical and machine-learning techniques, we show that the underlying genetic differences between psoriasis subtypes can be used for individualized subtype risk assessment.

27The transcriptome-wide association studies (TWAS) that test for association between the study 28 trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-29 eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. 30By using the gene expression imputation models fitted from reference datasets that have both 31 genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while 32 accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and 33 FUSION use parametric imputation models that have limitations for modeling the complex genetic 34 architecture of transcriptomic data. Therefore, to improve on this, we propose to use a Bayesian 35 method that assumes a data-driven nonparametric prior to impute gene expression. The 36nonparametric Bayesian method is flexible and general because it includes both of the parametric 37 imputation models used by PrediXcan and FUSION as special cases. Our simulation studies 38 2 showed that the nonparametric Bayesian model improved both imputation " for transcriptomic 39 data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian 40 method fitted transcriptomic imputation models for 57.6% more genes over PrediXcan, thus 41 improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred 42 for modeling the complex genetic architecture of transcriptomes and is expected to enhance 43 transcriptome-integrated genetic association studies. We implement our Bayesian approach in a 44 convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), 45 which imputes transcriptomic data and performs subsequent TWAS using individual-level or 46 summary-level GWAS data. 47 48 Introduction 49Genome-wide association studies (GWAS) have successfully identified thousands of 50 genetic risk loci for complex traits. However, the majority of these loci are located within noncoding 51 regions whose molecular mechanisms remain unknown 1-3 . Recent studies have shown that these 52 associated regions were enriched for regulatory elements such as enhancers (H3K27ac marks) 4; 53 5 and expression of quantitative trait loci (eQTL) 6; 7 , suggesting that the genetically regulated gene 54 expression might play a key role in the biological mechanisms of complex traits. Multiple studies 55 have recently generated rich transcriptomic datasets for diverse tissues of the human body, e.g., 56the Genotype-Tissue Expression (GTEx) project for 44 human tissues 6 , Genetic European 57Variation in Health and Disease (GEUVADIS) for lymphoblastoid cell lines 8 , Depression Genes 58 and Networks (DGN) for whole-blood samples 9 , and the North American Brain Expression 59 Consortium (NABEC) for cortex tissues 10 . Previous studies [11][12][13][14][15][16] have also shown that integrating 60 transcriptomic data in GWAS can help identify novel functional loci. 61The majority of GWAS projects do not possess tra...