BACKGROUNDMultiple 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.OBJECTIVESWe aim to utilize retrospective data to conduct a pharmacogenetic study. This design can bypass the obstacle of patient follow-up in prospective study and increase the sample size. Successful implementation of the study can nominate new candidates and their target genes to explore the potential genetic pathways associated with drug response in the treatment of psoriasis.METHODSWe conducted a retrospective pharmacogenetic study using self-evaluated treatment response from 1,942 genotyped psoriatic patients. We examined 6,502,658 genetic markers to model their associations with responses from six treatment options using linear regression, adjusting for cohort variables and demographic features. We further utilized an integrative approach incorporating epigenomic, transcriptomic, and a longitudinal clinical cohort to provide biological implications for the top signals associated with drug response.RESULTSTwo novel markers were revealed to be associated with treatment response: rs1991820 (p = 1.30×10-6) for anti-TNF biologics; and rs62264137 (p = 2.94×10-6) for methotrexate, which 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 demonstrate that KLK7 expression is increased in the psoriatic epidermis as shown by immunohistochemistry as well as single-cell RNA-sequencing, and we highlight its responsiveness to anti-TNF treatment. By inhibiting the expression of KLK7, we further illustrate keratinocytes have decrease in pro-inflammatory responses to TNF.CONCLUSIONSOur 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.

Background: This study aimed to discover genes associated with psoriatic arthritis (PsA) and identify plasma proteins related to these genes that may be useful as biomarkers for predicting the development of PsA in patients with cutaneous-only psoriasis (PsC).Methods: Whole-exome sequencing and Cox proportional hazards regression were performed on a discovery set of 1290 psoriasis patients to identify genes that increase PsA risk. Findings were confirmed in a separate group of 4533 psoriasis patients. Based on the results, protein candidates were selected and tested for association with the transition from PsC to PsA. This was done by comparing plasma concentration of the candidates in 96 converters (PsC patients who transitioned to PsA after enrollment) to that in 101 non-converters (PsC patients who had been followed for at least ten years without converting to PsA).Findings: The gene osteoclast-stimulating factor-1 (OSTF1) and the pathways Osteoclast Differentiation and Cobalamin Binding were associated with an elevated risk of PsA. Six protein candidates were selected from these pathways; three of them were significantly associated with the transition from PsC to PsA: OSTF1, TRAP/ACP5, and TCN1. Adding TRAP or TCN1 to clinical predictors of PsA improved the accuracy of PsA prediction among psoriasis patients, including presymptomatic individuals.Interpretation: This study identified OSTF1 as a PsA susceptibility gene, Osteoclast Differentiation and Cobalamin Binding as susceptibility pathways, and plasma OSTF1, TRAP, and TCN1 as biomarkers of early PsA. These findings may improve the timely diagnosis and treatment of PsA, leading to better patient outcomes.Funding: Pfizer, Regeneron Genetics Center, National Psoriasis Foundation, National Institutes of Health, Babcock Memorial Trust, University of Utah, Ann Arbor VA Hospital.Declaration of Interest: The PERCH software, for which B.-J.F. is the inventor, has been non-exclusively licensed to Ambry Genetics Corporation for their clinical genetic testing services and research. B-J.F. also reports funding and sponsorship to his institution on his behalf from Pfizer Inc., Regeneron Genetics Center LLC., and AstraZeneca. K.C.D. has consulted for Amgen/Celgene, Abbvie, Lilly, Novartis, Leo, Boehringer-Ingelheim, Janssen, UCB, CorEvitas, and Bristol-Myers Squib. L.C.T. has received support from Janssen and Galderma. The remaining authors declare no potential conflicts of interest relevant to this article.Ethical Approval: This study was approved by the Institutional Review Board (IRB) at the University of Utah (IRB_00010681). The recruitment of patients was approved by the IRB at the University of Michigan Medical School (HUM00037994). All participants included in this study have provided their informed written consent to participate.

Psoriasis is an immune-mediated multifactorial genetic disease characterized by cutaneous and joint symptoms ( Boehncke and Schön, 2015 Boehncke W.H. Schön M.P. Psoriasis. Lancet. 2015; 386: 983-994 Abstract Full Text Full Text PDF PubMed Scopus (1690) Google Scholar ). Whereas psoriasis mainly affects the skin and joints, chronic skin inflammation affects metabolic pathways and drives systemic inflammation ( Boehncke, 2018 Boehncke W.H. Systemic inflammation and cardiovascular comorbidity in psoriasis patients: causes and consequences. Front Immunol. 2018; 9: 579 Crossref PubMed Scopus (186) Google Scholar ). Epidemiological studies reported an increased psoriasis risk of several HLA-related diseases ( Boehncke and Schön, 2015 Boehncke W.H. Schön M.P. Psoriasis. Lancet. 2015; 386: 983-994 Abstract Full Text Full Text PDF PubMed Scopus (1690) Google Scholar ). Epidemiological studies reported increased comorbidity of psoriasis with multiple metabolic conditions, such as higher levels of low-density lipoprotein cholesterol and triglycerides ( Botelho et al, 2020 Botelho K.P. Pontes M.A.A. Rodrigues C.E.M. Freitas M.V.C. Prevalence of metabolic syndrome among patients with psoriasis treated with TNF inhibitors and the effects of anti-TNF therapy on their lipid profile: a prospective cohort study. Metab Syndr Relat Disord. 2020; 18: 154-160 Crossref PubMed Scopus (12) Google Scholar ; Ramezani et al, 2019 Ramezani M. Zavattaro E. Sadeghi M. Evaluation of serum lipid, lipoprotein, and apolipoprotein levels in psoriatic patients: a systematic review and meta-analysis of case-control studies. Postepy Dermatol Alergol. 2019; 36: 692-702 Crossref PubMed Scopus (17) Google Scholar ). These findings call for a comprehensive, genome-wide interpretation of the comorbidities between immune-mediated and metabolic traits and psoriasis. With the advent and dissemination of large-scale GWASs, genetic correlation studies have been conducted using GWAS summary statistics ( Kanai et al, 2018 Kanai M. Akiyama M. Takahashi A. Matoba N. Momozawa Y. Ikeda M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018; 50: 390-400 Crossref PubMed Scopus (429) Google Scholar ). An earlier cross-trait genetic correlation analysis that included psoriasis and performed with SumHer ( Speed and Balding, 2019 Speed D. Balding D.J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat Genet. 2019; 51: 277-284 Crossref PubMed Scopus (115) Google Scholar ) revealed a significant association of psoriasis with coronary artery disease and nominal associations with four diseases: type 1 diabetes, type 2 diabetes, ulcerative colitis (UC), and celiac disease. In that study, psoriasis GWAS data from the Wellcome Trust Case Control Consortium 2 were relatively small (7,474 samples). Further genetic correlation analyses between quantitative clinical biomarkers and psoriasis were therefore warranted. None of the findings were replicated with linkage disequilibrium score regression: another popular genetic correlation analysis tool ( Bulik-Sullivan et al., 2015 Bulik-Sullivan B.K. Loh P.R. Finucane H.K. Ripke S. Yang J. Schizophrenia Working Group of the Psychiatric Genomics Consortium et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015; 47: 291-295 Crossref PubMed Scopus (2194) Google Scholar ). Comprehensive genetic correlation analyses between quantitative clinical biomarkers and psoriasis were therefore warranted.

Supplementary Materials, Methods and Figure Legends 1-5 from A Novel BH3 Mimetic Reveals a Mitogen-Activated Protein Kinase–Dependent Mechanism of Melanoma Cell Death Controlled by p53 and Reactive Oxygen Species

T-cells undergo polyclonal expansion in cutaneous psoriasis (PsC) and PsA, with persistence of "driver clones" after effective treatment (JCI 127:4031; JI 172:1935). A recent scRNA-seq study found roughly equal compartments of clonal and non-clonal Th17 and Tc17 cells in PsC (Science 371:364). We and others have shown that Th17 expansion from PBMC requires contact between monocytes and memory T-cells in the context of TCR ligation (PNAS 104:17034; SID 139:1245). We stimulated PBMC (n=153) with anti-CD3/CD28 beads for 0 or 24h followed by flow cytometry (CD3+CD45RO+,CD4/CD8,CLA+/CLA-). Activation-related DEGs featured marked up-regulation of Th17 signature mRNAs (IL17A, IL17F, IL22, and CCL22) along with the Th1 cytokine IFNG, with a corresponding induction of IL-17A and IL-22 proteins by flow cytometry. These findings were confirmed by cluster analysis of scRNA-seq libraries of CD3/CD28-activated PBMC (n=4 subjects). Stratified analysis of skin homing in CD3/CD28-activated cells revealed 2.9 to 12.1-fold upregulation of IL17A, IL17F, IL22, and CCL22 in CLA+ vs CLA-, without a corresponding difference in IFNG. IL17A and IL17F were overexpressed in activated T-cells from psoriatics vs. controls (each 1.9-fold, p=4.7x10-4). As revealed by scRNA-seq of lesional psoriatic skin, IL17A was overexpressed (2.2-fold, p= 0.003) in skin-homing (FUT7+) vs non-skin-homing (FUT7-) T-cells, whereas IFNG was not. As reported in a recent CITE-seq study (Front Immunol 12:636720), our bulk- and sc-RNA-seq experiments revealed dramatic disappearance of monocytes within 24h of CD3/CD28 activation, which was confirmed by imaging flow cytometry and morphologically identified as apoptosis by time-lapse microscopy. Taken together with data showing IL-23 expression by inflammatory monocyte-like cells in dermal clusters in PsC (JID 141:1707), our experiments suggest a contact-dependent interplay between activated T-cells and monocyte-derived cells in dermal clusters, which maintains polyclonal activation of skin-homing Th17 cells in psoriatic lesions

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

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.

We generated 1,090 ATAC-seq and 1,057 T-cell RNA-seq libraries from 153 subjects, derived from 8 flow-sorted T-cell subsets (defined by CD4/CD8, CLA+/ CLA-, and 0/24h CD3/CD28 stimulation). Effects of activation and skin-homing were analyzed by DESeq2. After peak calling, 78,234 consensus peaks were present in ≥ 30 ATAC-seq libraries. A Wald test examining simple main effects identified 9,072, 3,934, and 21,174 consensus peaks as differentially accessible regions (DAR; FDR < 0.05, |log2 FC|≥ 0.585) in CD4 vs CD8, CLA+ vs CLA-, and resting vs. activated T-cells, respectively. For functional annotation, DARs were assigned to the closest genes using ChIPseeker. CD4/CD8 DARs were most significantly enriched for the KEGG pathway "Th17 cell differentiation" (FDR=2.4e-07). CLA+/CLA- DARs were most enriched for "MAPK signaling pathway" (FDR=1.5e-06) and included "Th17 cell differentiation" (FDR=4.1e-04). Activation-responsive (0/24h) DARs were most enriched for "T cell receptor signaling pathway" (FDR = 1.0e-07) and included "Th17 cell differentiation" (FDR=2.3e-06). We used the same criteria to identify differentially expressed genes (DEGs) from the RNA-seq libraries, yielding 2,795, 3,629, and 10,673 genes for CD8/CD4, CLA+/CLA-, and 0/24h, respectively. CD4/CD8 DEGs revealed top KEGG enrichment for "Cytokine-cytokine receptor interaction (CCRI)" (FDR = 8.3e-19) and included "Th17 cell differentiation" (FDR = 7.8e-03), with up-regulation of IL17A (3.2-fold), IL17F (1.8-fold), and IL22 (2.9-fold) in CD4. CLA+/CLA- DEGs also revealed top enrichment for "CCRI" (FDR=2.4e-18), with up-regulation of IL17A (3.3-fold), IL17F (2.2-fold), and IL22 (1.9-fold) in CLA+. 0/24h DEGs were also enriched for "CCRI" (FDR = 2.1e-04), with dramatic up-regulation of IL17A (109-fold), IL17F (1052-fold), and IL22 (146-fold) at 24 h. IL17A, IL17F, and IL22 were among the 479 DAR/DEG pairs identified by both the CD4/CD8 and 0/24h comparisons, and IL23R was 1 of 66 DAR/DEG pairs identified by all 3 comparisons. These results support a link between skin-homing and Th17 polarization.

Psoriasis is an immune-mediated inflammatory and hyperproliferative skin condition affecting ∼2% of the US population, with a total annual cost of around 3 billion dollars. Despite the successes of drug development, there can be significant variation in treatment response, which can correlate with patients' genetic variations and baseline skin genomic profiles. However, no study has integrated multiomic information to enhance drug response assessment, potentially because this data is rarely available from the same cohort, and current modeling techniques are limited in their ability to robustly integrate partially overlapping multi-view data. We seek to address the above limitations on a longitudinal RNA-seq cohort of 44 patients that received anti-TNF treatment with documented changes in PASI score, as well as an independent genetic cohort of 428 psoriatic patients with self-reported 5 level outcomes rating the drug prognosis. We used an advanced Kullback-Leibler divergence(KL) based integrative approach to model the multi-view information, leveraging information from genetics data to improve the drug response assessment from the genomics information. We used variant calling to identify common variations in the RNA-seq samples, and regularized regression (LASSO) to improve the identification of informative genetic and genomic markers for the complex trait sparsity structure. Compared with using genomics data alone, the integrative KL model reduced the 5-fold predictive mean squared error (MSE) by 3.8% from 2.61 to 2.51, improved the model R2 from 0.0193 to 0.459 and further identified >30 informative markers that can be used to enhance drug response prediction. Our method highlights the feasibility of using statistical techniques to analyze independent multi-modal biological data, thus providing a significant opportunity to integrate available information from different sources and improving the prognostic prediction accuracy.

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genome-wide 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 rho = 0.78; p < 2 x 10(-14)). Transethnic meta-analysis identified two non-major histocompatibility complex (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.

Despite the success of GWAS in identifying hundreds of loci associated with different complex inflammatory skin conditions, it is not trivial to decipher their molecular mechanisms due to the cell type specific regulatory features and the presence of linkage disequilibrium. We examined the allele specific chromatin accessibility (ASA) of >8 million common genetic variations in 1,227 ATAC-seq samples from resting and activated CD4 and CD8 T cells, and myeloid dendritic cells (mDC) in ∼150 individuals, revealing 54,746 heterozygous sites with high coverage ( 20 reads). Over 10% of these putative regulatory variations show significant allelic bias (FDR 10%) in the ATAC-seq data, and 20 of them reach genomewide significance for at least one inflammatory skin condition (atopic dermatitis, psoriasis, lupus, vitiligo, scleroderma). Notably, 7 and 4 of the variations are specific to activated and non-activated T-cells respectively, 3 are mDC specific, and 6 of them are shared across both mDC and T-cells; and these variations are enriched with transcription factor binding motifs for BATF and NFkB (p<1x10-10). eQTL analysis further highlights 19 of them are associated with gene expression levels in blood (eQTLGen), and their target genes include IFNLR1 (1p36.11 for psoriasis) TNFSF4 (1q24.3 for vitiligo) and JAK2 (9p24.1 for lupus). We performed Hi-C data and confirmed a physical looping connection between the lupus signal and the transcription start site of JAK2. Skin scRNA-seq demonstrated myeloid/T-cell specific expressions in skin for many of the gene targets of these ASA signals, including JAK2 and TNFSF4. These data highlighted that multiomic information can provide complementary insights to untangle the molecular mechanisms of disease-associated genetic variation. Ongoing study will use statistical genetic to further expand the ASA analysis to other putative causal markers mapping to GWAS signals.

Psoriasis and multiple sclerosis (MS) are physiologically distinct yet share multiple genetic signals and have overlapping pathogenesis. Previous studies suggest psoriasis patients are at greater risk of MS than the general population, and the two diseases also have overlapping comorbidities and traits (for example, they are both more prevalent in northern latitudes). To evaluate whether there is a direct causal relationship, we conducted Mendelian randomization (MR) analysis with six different techniques, using existing GWAS for psoriasis (11,024 cases and 16,336 controls) and MS (14,802 cases and 26,703 controls); we also addressed 10 potential confounding exposures, previously suggested to be associated with both diseases, by including GWAS data from body mass index, coronary artery disease, inflammatory bowel disease (IBD), type 1 diabetes (T1D), type 2 diabetes (T2D), asthma, rheumatoid arthritis, drinks per week, cigarettes per day, and vitamin D levels. Four of the MR techniques indicated a significant effect of psoriasis on MS (FDR<0.05) and two showed nominal significance, whereas the effect of MS on psoriasis was not significant in any of the MR analyses. When including all covariates nominally significant from at least one technique in a multivariable MR analysis, psoriasis still has a significant effect on MS (p=5.8×10-3, OR=1.04), independent of T1D (p=4.3×10-7, OR=1.05), T2D (p=2.3×10-3, OR=1.08), IBD (p=1.6×10-11, OR=1.11) and vitamin D (p=9.4×10-3, OR=0.75) in the multivariable analysis, while BMI and drinks/per week were not significant. By applying multiple different MR techniques to multiple comorbidities and traits, we have been able to reveal the most important modifiable risk factors and determine there is indeed an independent causal relationship between psoriasis and MS.

There are over 80 psoriasis-associated loci identified, with a majority playing regulatory roles; however, their gene targets are yet to be identified. Chromatin loops play an important role in gene regulation, and can be detected by chromosome conformation capture techniques like Hi-C. In this study, we deep sequenced Hi-C libraries (∼1 billion reads/reaction) to generate nine PBMC-derived subsets from 2 individuals (∼50,000 cells each): 4 unstimulated CD3+CD45RO+ memory T cells (CD4+CLA-, CD4+CLA+, CD8+CLA-, CD8+CLA+); the same 4 subsets after 24 hours of CD3/CD28 stimulation of CD1c- cells (primarily T cells); and mDC. Using a 5k-10k contact map resolution, we identified 13,186 ± 2,857 Hi-C loops per library, ranging from 30 to 1,980 kb long, using Mustache. Notably, we found that ∼50% of the identified loops have at least one end overlapping a transcription start site, providing a unique resource for mapping long-range promoter interactions in immunocytes. We identified 27 ± 6 loops per library that linked to promoters and whose loop ends could also be mapped to known psoriasis signals, defined by the 95% credible intervals generated from a recent transethnic GWAS (HGG Adv 2022). Of 47 target genes involved in such loops, we found 8 previously suggested gene targets for psoriasis: ANXA6, ELMO1, ETS1, FASLG, MBD2, IFI44, PTGER4 and STARD6. Among them, ANXA6, ETS1, FASLG and IFI44 were present only in T cells, while the rest presented in both T cells and mDC. We further revealed other psoriasis gene candidates including KAT5, NHLRC3/PROSER1, and SUCO, which were linked to known psoriasis signals through loops in multiple Hi-C libraries for both individuals and in both T cells and mDC. This study provides complementary support for known psoriasis-related genes at the level of 3D genome structure, and nominates other genes of interest for future studies.

Polygenic risk scores (PRS) have recently received much attention for genetics risk prediction. While successful for the Caucasian population, the PRS based on the minority population suffer from small sample sizes, high dimensionality and low signal-to-noise ratios, exacerbating already severe health disparities. Due to population heterogeneity, direct trans-ethnic prediction by utilizing the Caucasian model for the minority population also has limited performance. In addition, due to data privacy, the individual genotype data is not accessible for either the Caucasian population or the minority population. To address these challenges, we propose a Bregman divergence-based estimation procedure to measure and optimally balance the information from different populations. The proposed method only requires the use of encrypted summary statistics and improves the PRS performance for ethnic minority groups by incorporating additional information. We provide the asymptotic consistency and weak oracle property for the proposed method. Simulations and real data analyses also show its advantages in prediction and variable selection.