Single-cell profiling of prurigo nodularis demonstrates immune-stromal crosstalk driving profibrotic responses and reversal with nemolizumab.

BACKGROUND: Prurigo nodularis (PN) is a chronic neuroimmune skin disease characterized by bilaterally distributed pruritic hyperkeratotic nodules on extremities and trunk. Neuroimmune dysregulation and chronic scratching are believed to both induce and maintain the characteristic lesions. OBJECTIVES: This study sought to provide a comprehensive view of the molecular pathogenesis of PN at the single-cell level to identify and outline key pathologic processes and the cell types involved. Features that distinguish PN skin from the skin of patients with atopic dermatitis were of particular interest. We further aimed to determine the impact of the IL31RA antagonist, nemolizumab, and its specificity at the single-cell level. METHODS: Single-cell RNA-sequencing of skin from 15 healthy donors and nonlesional and lesional skin from 6 patients each with PN and atopic dermatitis, combined with spatial-sequencing using the 10x Visium platform. Integration with bulk RNA-sequencing data from patients treated with nemolizumab. RESULTS: This study demonstrates that PN is an inflammatory skin disease characterized by both keratinocyte proliferation and activation of profibrotic responses. This study also demonstrates that the COL11A1+ fibroblast subset is a major contributor to fibrosis and is predominantly found in the papillary dermis of PN skin. Activation of fibrotic responses is the main distinguishing feature between PN and atopic dermatitis skin. This study further shows the broad effect of nemolizumab on PN cell types, with a prominent effect driving COL11A1+ fibroblast and keratinocyte responses toward normal. CONCLUSIONS: This study provides a high-resolution characterization of the cell types and cellular processes activated in PN skin, establishing PN as a chronic fibrotic inflammatory skin disease. It further demonstrates the broad effect of nemolizumab on pathological processes in PN skin.

Large-scale functional inference for skin-expressing lncRNAs using expression and sequence information.

Long noncoding RNAs (lncRNAs) regulate the expression of protein-coding genes and have been shown to play important roles in inflammatory skin diseases. However, we still have limited understanding of the functional impact of lncRNAs in skin, partly due to their tissue specificity and lower expression levels compared with protein-coding genes. We compiled a comprehensive list of 18,517 lncRNAs from different sources and studied their expression profiles in 834 RNA-Seq samples from multiple inflammatory skin conditions and cytokine-stimulated keratinocytes. Applying a balanced random forest to predict involvement in biological functions, we achieved a median AUROC of 0.79 in 10-fold cross-validation, identifying significant DNA binding domains (DBDs) for 39 lncRNAs. G18244, a skin-expressing lncRNA predicted for IL-4/IL-13 signaling in keratinocytes, was highly correlated in expression with F13A1, a protein-coding gene involved in macrophage regulation, and we further identified a significant DBD in F13A1 for G18244. Reflecting clinical implications, AC090198.1 (predicted for IL-17 pathway) and AC005332.6 (predicted for IFN-γ pathway) had significant negative correlation with the SCORAD metric for atopic dermatitis. We also utilized single-cell RNA and spatial sequencing data to validate cell type specificity. Our research demonstrates lncRNAs have important immunological roles and can help prioritize their impact on inflammatory skin diseases.

GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targets.

Psoriasis is a common, debilitating immune-mediated skin disease. Genetic studies have identified biological mechanisms of psoriasis risk, including those targeted by effective therapies. However, the genetic liability to psoriasis is not fully explained by variation at robustly identified risk loci. To move towards a saturation map of psoriasis susceptibility we meta-analysed 18 GWAS comprising 36,466 cases and 458,078 controls and identified 109 distinct psoriasis susceptibility loci, including 45 that have not been previously reported. These include susceptibility variants at loci in which the therapeutic targets IL17RA and AHR are encoded, and deleterious coding variants supporting potential new drug targets (including in STAP2, CPVL and POU2F3). We conducted a transcriptome-wide association study to identify regulatory effects of psoriasis susceptibility variants and cross-referenced these against single cell expression profiles in psoriasis-affected skin, highlighting roles for the transcriptional regulation of haematopoietic cell development and epigenetic modulation of interferon signalling in psoriasis pathobiology.

Retrospective pharmacogenetic study of psoriasis highlights the role of KLK7 in tumour necrosis factor signalling.

BACKGROUND: Multiple treatment options are available for the management of psoriasis, but clinical response varies among individual patients and no biomarkers are available to facilitate treatment selection for improved patient outcomes. OBJECTIVES: To utilize retrospective data to conduct a pharmacogenetic study to explore the potential genetic pathways associated with drug response in the treatment of psoriasis. METHODS: We conducted a retrospective pharmacogenetic study using self-evaluated treatment response from 1942 genotyped patients with psoriasis. We examined 6 502 658 genetic markers to model their associations with response to six treatment options using linear regression, adjusting for cohort variables and demographic features. We further utilized an integrative approach incorporating epigenomics, transcriptomics and a longitudinal clinical cohort to provide biological implications for the topmost signals associated with drug response. RESULTS: Two novel markers were revealed to be associated with treatment response: rs1991820 (P = 1.30 × 10-6) for anti-tumour necrosis factor (TNF) biologics; and rs62264137 (P = 2.94 × 10-6) for methotrexate, which was also associated with cutaneous mRNA expression levels of two known psoriasis-related genes KLK7 (P = 1.0 × 10-12) and CD200 (P = 5.4 × 10-6). We demonstrated that KLK7 expression was increased in the psoriatic epidermis, as shown by immunohistochemistry, as well as single-cell RNA sequencing, and its responsiveness to anti-TNF treatment was highlighted. By inhibiting the expression of KLK7, we further illustrated that keratinocytes have decreased proinflammatory responses to TNF. CONCLUSIONS: Our study implicates the genetic regulation of cytokine responses in predicting clinical drug response and supports the association between pharmacogenetic loci and anti-TNF response, as shown here for KLK7.

Key patient demographics shape innate immune topography in noncritical hypoxic COVID-19 pneumonia.

Risk of severe disease and death due to COVID-19 is increased in certain patient demographic groups, including those of advanced age, male sex, and obese body mass index. Investigations of the biological variations that contribute to this risk have been hampered by heterogeneous severity, with immunologic features of critical disease potentially obscuring differences between risk groups. To examine immune heterogeneity related to demographic risk factors, we enrolled 38 patients hospitalized with clinically homogeneous COVID-19 pneumonia - defined as oxygen saturation less than 94% on room air without respiratory failure, septic shock, or multiple organ dysfunction - and performed single-cell RNA-Seq of leukocytes collected at admission. Examination of individual risk factors identified strong shifts within neutrophil and monocyte/dendritic cell (Mo/DC) compartments, revealing altered immune cell type-specific responses in higher risk COVID-19 patient subgroups. Specifically, we found transcriptional evidence of altered neutrophil maturation in aged versus young patients and enhanced cytokine responses in Mo/DCs of male versus female patients. Such innate immune cell alterations may contribute to outcome differences linked to these risk factors. They also highlight the importance of diverse patient cohorts in studies of therapies targeting the immune response in COVID-19.

MTMR3 risk alleles enhance Toll Like Receptor 9-induced IgA immunity in IgA nephropathy.

Multiple genome-wide association studies (GWASs) have reproducibly identified the MTMR3/HORMAD2/LIF/OSM locus to be associated with IgA nephropathy (IgAN). However, the causal variant(s), implicated gene(s), and altered mechanisms remain poorly understood. Here, we performed fine-mapping analyses based on GWAS datasets encompassing 2762 IgAN cases and 5803 control individuals, and identified rs4823074 as the candidate causal variant that intersects the MTMR3 promoter in B-lymphoblastoid cells. Mendelian randomization studies suggested the risk allele may modulate disease susceptibility by affecting serum IgA levels through increased MTMR3 expression. Consistently, elevated MTMR3 expression in peripheral blood mononuclear cells was observed in patients with IgAN. Further mechanistic studies in vitro demonstrated that MTMR3 increased IgA production dependent upon its phosphatidylinositol 3-phosphate binding domain. Moreover, our study provided the in vivo functional evidence that Mtmr3-/- mice exhibited defective Toll Like Receptor 9-induced IgA production, glomerular IgA deposition, as well as mesangial cell proliferation. RNA-seq and pathway analyses showed that MTMR3 deficiency resulted in an impaired intestinal immune network for IgA production. Thus, our results support the role of MTMR3 in IgAN pathogenesis by enhancing Toll Like Receptor 9-induced IgA immunity.

Shared Genetic Risk Factors for Multiple Sclerosis/Psoriasis Suggest Involvement of Interleukin-17 and Janus Kinase-Signal Transducers and Activators of Transcription Signaling.

OBJECTIVE: Psoriasis and multiple sclerosis (MS) are complex immune diseases that are mediated by T cells and share multiple comorbidities. Previous studies have suggested psoriatic patients are at higher risk of MS; however, causal relationships between the two conditions remain unclear. Through epidemiology and genetics, we provide a comprehensive understanding of the relationship, and share molecular factors between psoriasis and MS. METHODS: We used logistic regression, trans-disease meta-analysis and Mendelian randomization. Medical claims data were included from 30 million patients, including 141,544 with MS and 742,919 with psoriasis. We used genome-wide association study summary statistics from 11,024 psoriatic, 14,802 MS cases, and 43,039 controls for trans-disease meta-analysis, with additional summary statistics from 5 million individuals for Mendelian randomization. RESULTS: Psoriatic patients have a significantly higher risk of MS (4,637 patients with both diseases; odds ratio [OR] 1.07, p = 1.2 × 10-5 ) after controlling for potential confounders. Using inverse variance and equally weighted trans-disease meta-analysis, we revealed >20 shared and opposing (direction of effect) genetic loci outside the major histocompatibility complex that showed significant genetic colocalization (in COLOC and COLOC-SuSiE v5.1.0). Co-expression analysis of genes from these loci further identified distinct clusters that were enriched among pathways for interleukin-17/tumor necrosis factor-α (OR >39, p < 1.6 × 10-3 ) and Janus kinase-signal transducers and activators of transcription (OR 35, p = 1.1 × 10-5 ), including genes, such as TNFAIP3, TYK2, and TNFRSF1A. Mendelian randomization found psoriasis as an exposure has a significant causal effect on MS (OR 1.04, p = 5.8 × 10-3 ), independent of type 1 diabetes (OR 1.05, p = 4.3 × 10-7 ), type 2 diabetes (OR 1.08, p = 2.3 × 10-3 ), inflammatory bowel disease (OR 1.11, p = 1.6 × 10-11 ), and vitamin D level (OR 0.75, p = 9.4 × 10-3 ). INTERPRETATION: By investigating the shared genetics of psoriasis and MS, along with their modifiable risk factors, our findings will advance innovations in treatment for patients suffering from comorbidities. ANN NEUROL 2023;94:384-397.

Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus.

Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.

Genome-Wide Association Study in Acute Tubulointerstitial Nephritis.

SIGNIFICANCE STATEMENT: Polymorphisms of HLA genes may confer susceptibility to acute tubulointerstitial nephritis (ATIN), but small sample sizes and candidate gene design have hindered their investigation. The first genome-wide association study of ATIN identified two significant loci, risk haplotype DRB1*14-DQA1*0101-DQB1*0503 (DR14 serotype) and protective haplotype DRB1*1501-DQA1*0102-DQB1*0602 (DR15 serotype), with amino acid position 60 in the peptide-binding groove P10 of HLA-DR ß 1 key. Risk alleles were shared among different causes of ATIN and HLA genotypes associated with kidney injury and immune therapy response. HLA alleles showed the strongest association. The findings suggest that a genetically conferred risk of immune dysregulation is part of the pathogenesis of ATIN. BACKGROUND: Acute tubulointerstitial nephritis (ATIN) is a rare immune-related disease, accounting for approximately 10% of patients with unexplained AKI. Previous elucidation of the relationship between genetic factors that contribute to its pathogenesis was hampered because of small sample sizes and candidate gene design. METHODS: We undertook the first two-stage genome-wide association study and meta-analysis involving 544 kidney biopsy-defined patients with ATIN and 2346 controls of Chinese ancestry. We conducted statistical fine-mapping analysis, provided functional annotations of significant variants, estimated single nucleotide polymorphism (SNP)-based heritability, and checked genotype and subphenotype correlations. RESULTS: Two genome-wide significant loci, rs35087390 of HLA-DQA1 ( P =3.01×10 -39 ) on 6p21.32 and rs2417771 of PLEKHA5 on 12p12.3 ( P =2.14×10 -8 ), emerged from the analysis. HLA imputation using two reference panels suggested that HLA-DRB1*14 mainly drives the HLA risk association . HLA-DRB1 residue 60 belonging to pocket P10 was the key amino acid position. The SNP-based heritability estimates with and without the HLA locus were 20.43% and 10.35%, respectively. Different clinical subphenotypes (drug-related or tubulointerstitial nephritis and uveitis syndrome) seemed to share the same risk alleles. However, the HLA risk genotype was associated with disease severity and response rate to immunosuppressive therapy. CONCLUSIONS: We identified two candidate genome regions associated with susceptibility to ATIN. The findings suggest that a genetically conferred risk of immune dysregulation is involved in the pathogenesis of ATIN.

Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors.

Introduction: The utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored. Methods: This study aims to assess the capability of a comprehensive claims database in identifying comorbidities linked to 14 specific skin and skin-related conditions and examining temporal changes in their association patterns. This study employed a retrospective case-control cohort design utilizing 13 million skin/skin-related patients and 2 million randomly sampled controls from Optum's de-identified Clinformatics® Data Mart Database spanning the period from 2001 to 2018. A broad spectrum of comorbidities encompassing cancer, diabetes, respiratory, mental, immunity, gastrointestinal, and cardiovascular conditions were examined for each of the 14 skin and skin-related disorders in the study. Results: Using the established type-2 diabetes (T2D) and psoriasis comorbidity as example, we demonstrated the association is significant (P-values<1x10-15) and stable across years (OR=1.15-1.31). Analysis of the 2014-2018 data reveals that celiac disease, Crohn's disease, and ulcerative colitis exhibit the strongest associations with the 14 skin/skin-related conditions. Systemic lupus erythematosus (SLE), leprosy, and hidradenitis suppurativa show the strongest associations with 30 different comorbidities. Particularly notable associations include Crohn's disease with leprosy (odds ratio [OR]=6.60, 95% confidence interval [CI]: 3.09-14.08), primary biliary cirrhosis with SLE (OR=6.07, 95% CI: 4.93-7.46), and celiac disease with SLE (OR=6.06, 95% CI: 5.49-6.69). In addition, changes in associations were observed over time. For instance, the association between atopic dermatitis and lung cancer demonstrates a marked decrease over the past decade, with the odds ratio decreasing from 1.75 (95% CI: 1.47-2.07) to 1.02 (95% CI: 0.97-1.07). The identification of skin-associated comorbidities contributes to individualized healthcare and improved clinical management, while also enhancing our understanding of shared pathophysiology. Moreover, tracking these associations over time aids in evaluating the progression of clinical diagnosis and treatment. Discussion: The findings highlight the potential of utilizing comprehensive claims databases in advancing research and improving patient care in dermatology.

Shared genetic risk factors and causal association between psoriasis and coronary artery disease.

Psoriasis and coronary artery disease (CAD) are related comorbidities that are well established, but whether a genetic basis underlies this is not well studied. We apply trans-disease meta-analysis to 11,024 psoriasis and 60,801 CAD cases, along with their associated controls, identifying one opposing and three shared genetic loci, which are confirmed through colocalization analysis. Combining results from Bayesian credible interval analysis with independent information from genomic, epigenomic, and spatial chromatin organization, we prioritize genes (including IFIH1 and IL23A) that have implications for common molecular mechanisms involved in psoriasis and CAD inflammatory signaling. Chronic systemic inflammation has been associated with CAD and myocardial infarction, and Mendelian randomization analysis finds that CAD as an exposure can have a significant causal effect on psoriasis (OR = 1.11; p = 3×10-6) following adjustment for BMI and waist-hip ratio. Together, these findings suggest that systemic inflammation which causes CAD can increase the risk of psoriasis.

Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank.

BACKGROUND: Rare diseases collectively affect up to 10% of the population, but often lack effective treatment, and typically little is known about their pathophysiology. Major challenges include suboptimal phenotype mapping and limited statistical power. Population biobanks, such as the UK Biobank, recruit many individuals who can be affected by rare diseases; however, investigation into their utility for rare disease research remains limited. We hypothesized the UK Biobank can be used as a unique population assay for rare diseases in the general population. METHODS: We constructed a consensus mapping between ICD-10 codes and ORPHA codes for rare diseases, then identified individuals with each rare condition in the UK Biobank, and investigated their age at recruitment, sex bias, and comorbidity distributions. Using exome sequencing data from 167,246 individuals of European ancestry, we performed genetic association controlling for case/control imbalance (SAIGE) to identify potential rare pathogenic variants for each disease. RESULTS: Using our mapping approach, we identified and characterized 420 rare diseases affecting 23,575 individuals in the UK Biobank. Significant genetic associations included JAK2 V617F for immune thrombocytopenic purpura (p = 1.24 × 10-13) and a novel CALR loss of function variant for essential thrombocythemia (p = 1.59 × 10-13). We constructed an interactive resource highlighting demographic information ( http://www-personal.umich.edu/~mattpat/rareDiseases.html ) and demonstrate transferability by applying our mapping to a medical claims database. CONCLUSIONS: Enhanced disease mapping and increased power from population biobanks can elucidate the demographics and genetic associations for rare diseases.

Alcohol consumption and smoking in relation to psoriasis: a Mendelian randomization study.

BACKGROUND: Alcohol consumption and smoking have been reported to be associated with psoriasis risk. However, a conclusion with high-quality evidence of causality could not be easily drawn from regular observational studies. OBJECTIVES: This study aims to assess the causal associations of alcohol consumption and smoking with psoriasis. METHODS: Genome-wide association study (GWAS) summary-level data for alcohol consumption (N = 941 280), smoking initiation (N = 1 232 091), cigarettes per day (N = 337 334) and smoking cessation (N = 547 219) was obtained from the GSCAN consortium (Sequencing Consortium of Alcohol and Nicotine use). The GWAS results for lifetime smoking (N = 462 690) were obtained from the UK Biobank samples. Summary statistics for psoriasis were obtained from a recent GWAS meta-analysis of eight cohorts comprising 19 032 cases and 286 769 controls and the FinnGen consortium, comprising 4510 cases and 212 242 controls. Linkage disequilibrium score regression was applied to compute the genetic correlation. Bidirectional Mendelian randomization (MR) analyses were conducted to determine casual direction using independent genetic variants that reached genome-wide significance (P < 5 × 10-8 ). RESULTS: There were genetic correlations between smoking and psoriasis. MR revealed a causal effect of smoking initiation [odds ratio (OR) 1·46, 95% confidence interval (CI) 1·32-1·60, P = 6·24E-14], cigarettes per day (OR 1·38, 95% CI 1·13-1·67, P = 0·001) and lifetime smoking (OR 1·96, 95% CI 1·41-2·73, P = 7·32E-05) on psoriasis. Additionally, a suggestive causal effect of smoking cessation on psoriasis was observed (OR 1·39, 95% CI 1·07-1·79, P = 0·012). We found no causal relationship between alcohol consumption and psoriasis (P = 0·379). The reverse associations were not statistically significant. CONCLUSIONS: Our findings provide causal evidence for the effects of smoking on psoriasis risk. What is already known about this topic? Alcohol consumption and smoking have been reported to be associated with psoriasis risk. Whether alcohol consumption and smoking have a causal effect on psoriasis risk remains unclear. What does this study add? This Mendelian randomization study shows a causal association between smoking, but not alcohol consumption, and the risk of developing psoriasis. Restricting smoking could be helpful in reducing the burden of psoriasis.

Roles Played by Stress-Induced Pathways in Driving Ethnic Heterogeneity for Inflammatory Skin Diseases.

Immune-mediated skin conditions (IMSCs) are a diverse group of autoimmune diseases associated with significant disease burden. Atopic dermatitis and psoriasis are among the most common IMSCs in the United States and have disproportionate impact on racial and ethnic minorities. African American patients are more likely to develop atopic dermatitis compared to their European American counterparts; and despite lower prevalence of psoriasis among this group, African American patients can suffer from more extensive disease involvement, significant post-inflammatory changes, and a decreased quality of life. While recent studies have been focused on understanding the heterogeneity underlying disease mechanisms and genetic factors at play, little emphasis has been put on the effect of psychosocial or psychological stress on immune pathways, and how these factors contribute to differences in clinical severity, prevalence, and treatment response across ethnic groups. In this review, we explore the heterogeneity of atopic dermatitis and psoriasis between African American and European American patients by summarizing epidemiological studies, addressing potential molecular and environmental factors, with a focus on the intersection between stress and inflammatory pathways.

Skin-Expressing lncRNAs in Inflammatory Responses.

Long non-coding RNAs (lncRNAs) have attracted attention for their potential roles in modulating keratinocyte differentiation and inflammatory response; however, for many identified skin-expressing lncRNAs, there is no comprehensive characterization regarding their biological roles. In addition, the reported expression profiles for lncRNAs can be ambiguous due to their low-expressing nature. The objective of this review is to utilize large scale genomic data to characterize the prominent skin-expressing lncRNAs, aiming to provide additional insights for their potential roles in the pathology of inflammatory skin of psoriasis and atopic dermatitis by integrating in vitro and in vivo data. We highlighted the different skin-expressing lncRNAs, including H19, which is significantly down-regulated in lesional skin of AD/psoriasis and upon cytokine stimulation in keratinocytes; it is also negatively correlated with CYP1A1 (r = -0.75, p = 8 × 10-73), a gene involved in drug metabolism and skin barrier homeostasis, in keratinocytes. In addition, SPRR2C, a potential regulator that modulates IL-22 stimulation, was upregulated in both atopic dermatitis and psoriasis lesional skin and was also downstream of the IL-17A and IL-17 + TNF signaling in keratinocytes. Using scRNAseq, we further revealed the cell type specificity of lncRNAs, including basal-expressing nature of H19 in the epidermis. Interestingly, instead of having cell type specific expression profile, we found few lncRNAs that are express across different cell types in skin, including MALAT1, NEAT1, and GAS5. While lncRNAs in general have lower expression, our results combining in vitro and in vivo experimental data demonstrate how some of these lncRNAs can play mediator roles in the cytokine-stimulated pathway.

Direct cellular reprogramming enables development of viral T antigen-driven Merkel cell carcinoma in mice.

Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer that frequently carries an integrated Merkel cell polyomavirus (MCPyV) genome and expresses viral transforming antigens (TAgs). MCC tumor cells also express signature genes detected in skin-resident, postmitotic Merkel cells, including atonal bHLH transcription factor 1 (ATOH1), which is required for Merkel cell development from epidermal progenitors. We now report the use of in vivo cellular reprogramming, using ATOH1, to drive MCC development from murine epidermis. We generated mice that conditionally expressed MCPyV TAgs and ATOH1 in epidermal cells, yielding microscopic collections of proliferating MCC-like cells arising from hair follicles. Immunostaining of these nascent tumors revealed p53 accumulation and apoptosis, and targeted deletion of transformation related protein 53 (Trp53) led to development of gross skin tumors with classic MCC histology and marker expression. Global transcriptome analysis confirmed the close similarity of mouse and human MCCs, and hierarchical clustering showed conserved upregulation of signature genes. Our data establish that expression of MCPyV TAgs in ATOH1-reprogrammed epidermal cells and their neuroendocrine progeny initiates hair follicle-derived MCC tumorigenesis in adult mice. Moreover, progression to full-blown MCC in this model requires loss of p53, mimicking the functional inhibition of p53 reported in human MCPyV-positive MCCs.

Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents.

BACKGROUND: A major issue with the current management of psoriasis is our inability to predict treatment response. OBJECTIVE: Our aim was to evaluate the ability to use baseline molecular expression profiling to assess treatment outcome for patients with psoriasis. METHODS: We conducted a longitudinal study of 46 patients with chronic plaque psoriasis treated with anti-TNF agent etanercept, and molecular profiles were assessed in more than 200 RNA-seq samples. RESULTS: We demonstrated correlation between clinical response and molecular changes during the course of the treatment, particularly for genes responding to IL-17A/TNF in keratinocytes. Intriguingly, baseline gene expressions in nonlesional, but not lesional, skin were the best marker of treatment response at week 12. We identified USP18, a known regulator of IFN responses, as positively correlated with Psoriasis Area and Severity Index (PASI) improvement (P = 9.8 × 10-4) and demonstrate its role in regulating IFN/TNF responses in keratinocytes. Consistently, cytokine gene signatures enriched in baseline nonlesional skin expression profiles had strong correlations with PASI improvement. Using this information, we developed a statistical model for predicting PASI75 (ie, 75% of PASI improvement) at week 12, achieving area under the receiver-operating characteristic curve value of 0.75 and up to 80% accurate PASI75 prediction among the top predicted responders. CONCLUSIONS: Our results illustrate feasibility of assessing drug response in psoriasis using nonlesional skin and implicate involvement of IFN regulators in anti-TNF responses.

Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide.

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genomewide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10-14). Transethnic meta-analysis identified two non-MHC psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine-mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C*06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C*06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine-mapping of susceptibility loci.

Transcriptomic characterization of prurigo nodularis and the therapeutic response to nemolizumab.

BACKGROUND: Prurigo nodularis (PN) is a debilitating, difficult-to-treat, intensely pruritic, chronic inflammatory skin disease characterized by hyperkeratotic skin nodules. The pathogenesis of PN is not well understood but is believed to involve cross talk between sensory nerve fibers, immune cells, and the epidermis. It is centered around the neuroimmune cytokine IL-31, driving an intractable itch-scratch cycle. OBJECTIVE: We sought to provide a comprehensive view of the transcriptomic changes in PN skin and characterize the mechanism of action of the anti-IL-31 receptor inhibitor nemolizumab. METHOD: RNA sequencing of biopsy samples obtained from a cohort of patients treated with the anti-IL-31 receptor inhibitor nemolizumab and taken at baseline and week 12. Generation and integration of patient data with RNA-Seq data generated from reconstructed human epidermis stimulated with IL-31 and other proinflammatory cytokines. RESULTS: Our results demonstrate that nemolizumab effectively decreases IL-31 responses in PN skin, leading to effective suppression of downstream inflammatory responses including TH2/IL-13 and TH17/IL-17 responses. This is accompanied by decreased keratinocyte proliferation and normalization of epidermal differentiation and function. Furthermore, our results demonstrate how transcriptomic changes associated with nemolizumab treatment correlate with improvement in lesions, pruritus, stabilization of extracellular matrix remodeling, and processes associated with cutaneous nerve function. CONCLUSION: These data demonstrate a broad response to IL-31 receptor inhibition with nemolizumab and confirm the critical upstream role of IL-31 in PN pathogenesis.

Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.

OBJECTIVE: Drug-drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients). MATERIALS AND METHODS: Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug-gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. RESULTS: Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records. CONCLUSIONS: By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.