Genetic variation at the glycosaminoglycan metabolism pathway contributes to the risk of psoriatic arthritis but not psoriasis

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Psoriasis and Psoriatic Arthritis-Associated Genes, Cytokines, and Human Leukocyte Antigens

In recent years, research has intensified in exploring the genetic basis of psoriasis (PsO) and psoriatic arthritis (PsA). Genome-wide association studies (GWASs), including tools like ImmunoChip, have significantly deepened our understanding of disease mechanisms by pinpointing risk-associated genetic loci. These efforts have elucidated biological pathways involved in PsO pathogenesis, particularly those related to the innate immune system, antigen presentation, and adaptive immune responses. Specific genetic loci, such as TRAF3IP2, REL, and FBXL19, have been identified as having a significant impact on disease development. Interestingly, different genetic variants at the same locus can predispose individuals to either PsO or PsA (e.g., IL23R and deletion of LCE3B and LCE3C), with some variants being uniquely linked to PsA (like HLA B27 on chromosome 6). This article aims to summarize known and new data on the genetics of PsO and PsA, their associated genes, and the involvement of the HLA system and cytokines.

Genetic mapping across autoimmune diseases reveals shared associations and mechanisms

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

A novel method for multiple phenotype association studies based on genotype and phenotype network

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

BACKGROUND

In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA.

RESULTS

In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool.

CONCLUSIONS

Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.

Gene selection by incorporating genetic networks into case-control association studies

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based regression approach has been developed to incorporate a biological genetic network and to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in a gene to capture gene-level signals. We employ three linear combination approaches: optimally weighted sum (OWS), beta-based weighted sum (BWS), and LD-adjusted polygenic risk score (LD-PRS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods.

Investigating causal associations among gut microbiota, metabolites, and psoriatic arthritis: a Mendelian randomization study

Background

Currently, there has been observed a significant alteration in the composition of the gut microbiome (GM) and serum metabolites in patients with psoriatic arthritis (PsA) compared to healthy individuals. However, previous observational studies have shown inconsistent results regarding the alteration of gut microbiota/metabolites. In order to shed light on this matter, we utilized Mendelian randomization to determine the causal effect of GM/metabolites on PsA.

Methods

We retrieved summary-level data of GM taxa/metabolites and PsA from publicly available GWAS statistics. Causal relationships between GM/metabolites and PsA were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the robustness of our findings, we conducted sensitivity analyses, multivariable MR analysis (MVMR), and additional analysis including replication verification analysis, LDSC regression, and Steiger test analysis. Furthermore, we investigated reverse causality through a reverse MR analysis. Finally, we conducted an analysis of expression quantitative trait loci (eQTLs) involved in the metabolic pathway to explore potential molecular mechanisms of metabolism.

Results

Our findings reveal that eight GM taxa and twenty-three serum metabolites are causally related to PsA (P < 0.05). Notably, a higher relative abundance of Family Rikenellaceae (ORIVW: 0.622, 95% CI: 0.438-0.883, FDR = 0.045) and elevated serum levels of X-11538 (ORIVW: 0.442, 95% CI: 0.250-0.781, FDR = 0.046) maintain significant causal associations with a reduced risk of PsA, even after adjusting for multiple testing correction and conducting MVMR analysis. These findings suggest that Family Rikenellaceae and X-11538 may have protective effects against PsA. Our sensitivity analysis and additional analysis revealed no significant horizontal pleiotropy, reverse causality, or heterogeneity. The functional enrichment analysis revealed that the eQTLs examined were primarily associated with glycerolipid metabolism and the expression of key metabolic factors influenced by bacterial infections (Vibrio cholerae and Helicobacter pylori) as well as the mTOR signaling pathway.

Conclusion

In conclusion, our study demonstrates that Family Rikenellaceae and X-11538 exhibit a strong and negative causal relationship with PsA. These particular GM taxa and metabolites have the potential to serve as innovative biomarkers, offering valuable insights into the treatment and prevention of PsA. Moreover, bacterial infections and mTOR-mediated activation of metabolic factors may play an important role in this process.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Insights into suicidal behavior among psoriatic arthritis patients: A systematic review and a genetic linkage disequilibrium analysis

OBJECTIVES

To systematically assess the magnitude of suicidal behavior among PsA patients and identify associated risk factors. Also identify common genes or coinherited single nucleotide polymorphisms (SNPs) implicated in suicidal behavior and PsA.

METHODS

Based on the PRISMA guidelines, we conducted a systematic literature review of the online databases PubMed/Medline, Web of Science, and EMBASE from inception to May 2022. Full-text original articles that describe suicidal behavior in PsA patients were eligible. All registered genome-wide association study (GWAS) data in the GWAS catalog database for PsA and psychiatric traits, such as suicidal behavior, and depression, were downloaded for further analysis.

RESULTS

A total of 48 articles were identified, and 6 were relevant to the study question .Among the 122,160 PsA patients, 700 had suicidal behavior (0,57%). The range of age in one study was between 30 and 49 years, and 64% of PsA patients with suicidal behavior were female. Among 13,899 PsA patients 74 had suicidal ideation (0.53%) and 125 suicide attempts occurred (0.9%). In two studies, among 17,383 patients, 13 complete suicides occurred (0.07%). A genetic haplotype on chromosomal region 6p21.1, spanning from 29,597,596 to 32,251,264 Mb, contains predisposing SNPs for PsA and depression. 6p21.1-6p21.3 is the chromosomal region containing the HLA genes of classes I, II and III.

CONCLUSION

Suicide behavior in PsA patients was associated with depression and other psychiatric comorbidities. Further evidence supports a genetic origin, at least partly. Awareness of these findings can help clinicians to recognize suicide behavior and prevent suicide attempts.

Genetic Studies Investigating Susceptibility to Psoriatic Arthritis: A Narrative Review

PURPOSE

Approximately 30% of patients with psoriasis will develop psoriatic arthritis (PsA), leading to a decreased quality of life for the patient caused by increasing disability and additional health complications. The identification of risk factors for the development of PsA would facilitate the development of risk prediction models in which patients with psoriasis at high risk of developing PsA could be targeted in a stratified medicine approach, enabling early intervention and treatment. PsA is known to have a genetic contribution to susceptibility, and the identification of genetic risk factors that differentiate PsA from cutaneous-only psoriasis is a key area of research. This narrative review summarizes the discovery of genetic risk factors and, with the aid of a primer on risk prediction models, discusses their potential role for the classification of PsA risk and diagnosis.

METHODS

All relevant research articles were identified through searches of the PubMed database for literature published up until December 2022. Search terms included psoriatic arthritis, genetic susceptibility, genetic association, genome-wide association study, GWAS, prediction, and polygenic risk score.

FINDINGS

The current literature reveals considerable overlap between the genetic susceptibility loci for PsA and psoriasis. Several PsA-specific genetic risk factors have been reported, and most notably these implicate the HLA-B and IL23R genes. Efforts to include genetic risk factors in prediction models for the development of PsA have reported good discrimination.

IMPLICATIONS

Key messages emerging from this narrative are as follows: the limited number of PsA-specific susceptibility loci reported to date suggest larger studies are required, facilitated by international collaboration, to achieve the power to detect further genetic factors; the early promising results for genetic-based risk prediction require further validation in independent datasets; and risk prediction models combining clinical and genetic risk factors have yet to be explored.

Genetic underpinnings of the psoriatic spectrum

The psoriatic field includes both rare and common subtypes. Common complex forms include psoriasis vulgaris and psoriatic arthritis. In these subtypes, certain HLA alleles remain the most relevant genetic factors, although genome-wide association studies lead to the detection of more than 80 susceptibility loci. They mainly affect innate and adaptive immunity and explain over 28 % of the heritability. Pustular psoriasis comprises a group of rarer subtypes. Using exome sequencing, several disease genes were identified for mainly generalized pustular psoriasis, and an oligogenic inheritance is likely. Treatment studies based on the affected IL-36 pathway indicate a high response rate in this subtype further supporting the pathophysiological relevance of the affected gene products.

Characterising the axial phenotype of psoriatic arthritis: a study comparing axial psoriatic arthritis and ankylosing spondylitis with psoriasis from the REGISPONSER registry

AIMS

To explore the clinical and radiographical characteristics of axial psoriatic arthritis (PsA) and to compare it with ankylosing spondylitis (AS) with psoriasis.

METHODS

Cross-sectional study from the national multicentre registry REGISPONSER where participants fulfilled the European Spondyloarthropathy Study Group spondyloarthritis criteria at entry. Clinical, laboratory and radiographical characteristics between patients classified as axial PsA and AS with psoriasis by their rheumatologist are compared according to HLA-B27 status.

RESULTS

Of 2367 patients on REGISPONSER, n=405 had PsA, of whom 27% (n=109) had axial involvement as per the treating rheumatologist. 30% (n=26/86) of axial PsA were HLA-B27 positive. In the AS group, 9% (127/1422) had a history of psoriasis and were more frequently male, with longer diagnostic delay and more anterior uveitis than those with axial PsA who had more peripheral involvement and nail disease. Patients with HLA-B27-negative axial PsA reported less inflammatory pain and structural damage compared with AS with psoriasis. By contrast, HLA-B27-positive axial PsA shared clinical characteristics similar to AS and psoriasis although with a lower BASRI score. In the multivariable analysis, patients with AS and psoriasis were independently associated with HLA-B27 positivity (OR 3.34, 95% CI 1.42 to 7.85) and lumbar structural damage scored by BASRI (OR 2.14, 95% CI 1.4 to 3.19).

CONCLUSION

The more prevalent axial PsA phenotype is predominantly HLA-B27 negative and presents different clinical and radiological manifestations when compared with AS with psoriasis. There is great heterogeneity in what rheumatologists consider axial PsA from a clinical and imaging perspective, highlighting the need for research into possible genetic drivers and a consensus definition.

Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy.

Methods

Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients.

Findings

Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi.

Interpretation

Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems.

Funding

The Instituto de Salud Carlos III.

Comparative genetic analysis of psoriatic arthritis and psoriasis for the discovery of genetic risk factors and risk prediction modelling

Objectives

Psoriatic arthritis (PsA) has a strong genetic component, and the identification of genetic risk factors could help identify the ~30% of psoriasis patients at high risk of developing PsA. Our objectives were to identify genetic risk factors and pathways that differentiate PsA from cutaneous‐only psoriasis (PsC) and to evaluate the performance of PsA risk prediction models.

Methods

Genome‐wide meta‐analyses were conducted separately for 5,065 patients with PsA and 21,286 healthy controls and separately for 4,340 patients with PsA and 6,431 patients with PsC. The heritability of PsA was calculated as a single‐nucleotide polymorphism (SNP)–based heritability estimate (h²_SNP) and biologic pathways that differentiate PsA from PsC were identified using Priority Index software. The generalizability of previously published PsA risk prediction pipelines was explored, and a risk prediction model was developed with external validation.

Results

We identified a novel genome‐wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 × 10⁻⁹), and key pathways that differentiate PsA from PsC, including NF‐κB signaling (adjusted P = 1.4 × 10⁻⁴⁵) and Wnt signaling (adjusted P = 9.5 × 10⁻⁵⁸). The heritability of PsA in this cohort was found to be moderate (h²_SNP = 0.63), which was similar to the heritability of PsC (h²_SNP = 0.61). We observed modest performance of published classification pipelines (maximum area under the curve 0.61), with similar performance of a risk model derived using the current data.

Conclusion

Key biologic pathways associated with the development of PsA were identified, but the investigation of risk classification revealed modest utility in the available data sets, possibly because many of the PsC patients included in the present study were receiving treatments that are also effective in PsA. Future predictive models of PsA should be tested in PsC patients recruited from primary care.

Advances of Genomic Medicine in Psoriatic Arthritis

Psoriatic arthritis (PsA) is a common type of inflammatory arthritis found in up to 40% of patients with psoriasis. Although early diagnosis is important for reducing the risk of irreversible structural damage, there are no adequate screening tools for this purpose, and there are no clear markers of predisposition to the disease. Much evidence indicates that PsA disorder is complex and heterogeneous, where genetic and environmental factors converge to trigger inflammatory events and the development of the disease. Nevertheless, the etiologic events that underlie PsA are complex and not completely understood. In this review, we describe the existing data in PsA in order to highlight the need for further research in this disease to progress in the knowledge of its pathobiology and to obtain early diagnosis tools for these patients.

Cognitive Process of Psoriasis and Its Comorbidities: From Epidemiology to Genetics

Psoriasis (PsO) is a chronic inflammatory skin disease that affects approximately 2% of the population all over the world. Comorbidities of PsO have increasingly garnered more interest in the past decades. Compared with the normal population, the incidences of comorbidities are higher among patients with PsO. In the last 20 years, researchers have focused on studying the genetic components of PsO, and genetic associations between PsO and its comorbidities were elucidated. This review provides an in-depth understanding and summarization of the connection between PsO and its comorbidities from the perspectives of epidemiology and genetics. Further understanding of PsO and its comorbidities will promote research on the pathogenesis, drug development, novel therapy methods, and personalized and precision treatment of PsO and its comorbidities.

Genetic markers for psoriatic arthritis in patients with psoriasis. Part I: non-HLA genes

Psoriatic arthritis often develops in patients with psoriasis and can lead to joint deformity, stiffness, dysfunction, and disability. Psoriatic arthritis is a polygenic disease. and the issue of personalizing the prognosis of its development can only be resolved taking into account the variability of plenty genomic loci associated with the development of the disease. The personification of the prognosis of the disease can be solved taking into account the variability of the set of genomic loci with which its development is associated. The review examines genomic polymorphisms associated with the development of psoriatic arthritis not psoriasis, except of HLA polymorphisms. Genome regions containing polymorphisms, allelic variants of which are associated both with the development of psoriatic arthritis and reducing the likelihood of its occurrence, are described. It has been reported that the predisposition to the development of psoriatic arthritis in patients with psoriasis is determined by genes encoding proteins involved in inflammation and bone metabolism.

Genetic effects of B3GNT2 on ankylosing spondylitis susceptibility and clinical manifestations in Taiwanese

BACKGROUND/PURPOSE

The intergenic SNP rs10865331 at 2p15 was identified as a major risk factor for ankylosing spondylitis (AS) susceptibility in genome-wide association studies (GWAS). B3GNT2 gene regulates polylactosamine synthesis is potentially functionally relevant to AS disease development. We investigated whether SNP rs10865331 and two B3GNT2 SNPs (rs11900673 and rs1136151) are associated with AS susceptibility and disease severity in Taiwanese.

METHODS

Distributions of genotypes, alleles, and haplotypes of three SNPs were compared between 1,472 AS patients and 2,117 healthy blood donors and among AS patients stratified by clinical characteristics.

RESULTS

The intergenic SNP rs10865331 was significantly associated with AS (P_FDR = 1.02E-05) in Taiwanese. In AS patients stratified by positivity of HLA-B27 and syndesmophyte formation, all three B3GNT2 locus SNPs (rs11900673, rs1136151, and rs10865331) were significantly associated with syndesmophyte formation among HLA-B27 positive AS patients. Haplotype analyses revealed that the "CTA" (rs11900673C/rs1136151T/rs10865331A) haplotype was significantly associated with AS susceptibility (Padj = 0.0177) and syndesmophyte formation (Padj = 0.016) in HLA-B27 positive patients. In contrast, "TCG" (rs11900673T/rs1136151C/rs10865331G) haplotype showed protection against AS development (Padj = 0.0005 for HLA-B27 positive and Padj = 0.004 for HLA-B27 negative, respectively) and syndesmophyte formation (Padj = 0.0017) in HLA-B27 positive patients. Furthermore, B3GNT2 mRNA expressions were negatively associated with erythrocyte sedimentation rate (ESR, P = 0.0103), C-reactive protein (CRP, P = 0.0353), Bath ankylosing spondylitis functional index (BASFI, P = 0.0171), and syndesmophyte formation (P = 0.0148).

CONCLUSION

Our data suggest that B3GNT2 gene may contribute to AS development and affect AS severity by interacting with HLA-B27 in Taiwanese.

Unraveling the complexities of psoriatic arthritis by the use of -Omics and their relevance for clinical care

-Omic technologies represent a novel approach to unravel ill-defined aspects of psoriatic arthritis (PsA). Large-scale information can be acquired from analysis of affected tissues in PsA via high-throughput studies in the domains of genomics, transcriptomics, epigenetics, proteomics and metabolomics. This is a critical overview of the current knowledge of -omics in PsA, with emphasis on the pathophysiological insights of diagnostic and therapeutic relevance, the advent of novel biomarkers and their potential use for precision medicine in PsA.

Genetics of PlGF plasma levels highlights a role of its receptors and supports the link between angiogenesis and immunity

Placental growth factor (PlGF) is a member of the vascular endothelial growth factor family and is involved in bone marrow-derived cell activation, endothelial stimulation and pathological angiogenesis. High levels of PlGF have been observed in several pathological conditions especially in cancer, cardiovascular, autoimmune and inflammatory diseases. Little is known about the genetics of circulating PlGF levels. Indeed, although the heritability of circulating PlGF levels is around 40%, no studies have assessed the relation between PlGF plasma levels and genetic variants at a genome-wide level. In the current study, PlGF plasma levels were measured in a population-based sample of 2085 adult individuals from three isolated populations of South Italy. A GWAS was performed in a discovery cohort (N = 1600), followed by a de novo replication (N = 468) from the same populations. The meta-analysis of the discovery and replication samples revealed one signal significantly associated with PlGF circulating levels. This signal was mapped to the PlGF co-receptor coding gene NRP1, indicating its important role in modulating the PlGF plasma levels. Two additional signals, at the PlGF receptor coding gene FLT1 and RAPGEF5 gene, were identified at a suggestive level. Pathway and TWAS analyses highlighted genes known to be involved in angiogenesis and immune response, supporting the link between these processes and PlGF regulation. Overall, these data improve our understanding of the genetic variation underlying circulating PlGF levels. This in turn could lead to new preventive and therapeutic strategies for a wide variety of PlGF-related pathologies.

Clinical, laboratory, and genetic markers for the development or presence of psoriatic arthritis in psoriasis patients: a systematic review

Twenty to thirty percent of psoriasis (Pso) patients will develop psoriatic arthritis (PsA). Detection of Pso patients that are (at risk for) developing PsA is essential to prevent structural damage. We conducted a systematic search of five bibliographic databases, up to May 2020. We searched for studies assessing markers (clinical, laboratory, genetic) associated with the development or presence of PsA in Pso patients. Study selection and quality assessment of the included studies was performed, followed by a qualitative best evidence synthesis to determine the level of evidence for a marker and its association with concomitant/developing PsA in Pso. Overall, 259 possible markers were identified in 119 studies that met the inclusion criteria. Laboratory markers related to inflammation and bone metabolism reached a strong level of evidence for the association (not prediction) of PsA in Pso. Only CXCL10 showed strong evidence for a positive predictive value for PsA in Pso. The importance of timely detecting PsA in a Pso population, and finding more (bio)markers contributing to early detection, remains high.

Investigating Pleiotropy Between Depression and Autoimmune Diseases Using the UK Biobank

Background

Epidemiological studies report increased comorbidity between depression and autoimmune diseases. The role of shared genetic influences in the observed comorbidity is unclear. We investigated the evidence for pleiotropy between these traits in the UK Biobank (UKB).

Methods

We defined autoimmune and depression cases using hospital episode statistics, self-reported conditions and medications, and mental health questionnaires. Pairwise comparisons of depression prevalence between autoimmune cases and controls, and vice versa, were performed. Cross-trait polygenic risk score (PRS) analyses tested for pleiotropy, i.e., whether PRSs for depression could predict autoimmune disease status, and vice versa.

Results

We identified 28,479 cases of autoimmune diseases (pooling across 14 traits) and 324,074 autoimmune controls, and 65,075 cases of depression and 232,552 depression controls. The prevalence of depression was significantly higher in autoimmune cases than in controls, and similarly, the prevalence of autoimmune disease was higher in depression cases than in controls. PRSs for myasthenia gravis and psoriasis were significantly higher in depression cases than in controls (p < 5.2 × 10^-5, R ² ≤ 0.04%). PRSs for depression were significantly higher in inflammatory bowel disease, psoriasis, psoriatic arthritis, rheumatoid arthritis, and type 1 diabetes cases than in controls (p < 5.8 × 10^-5, R ² range = 0.06%-0.27%), and lower in celiac disease cases than in controls (p < 5.4 × 10^-7, R ² range = 0.11%-0.15%).

Conclusions

Consistent with the literature, depression was more common in individuals with autoimmune diseases than in controls, and vice versa. PRSs showed some evidence for involvement of shared genetic factors, but the modest R ² values suggest that shared genetic architecture accounts for a small proportion of the increased risk across traits.

Clinical and molecular significance of genetic loci associated with psoriatic arthritis

Psoriatic arthritis (PsA) is caused by a combination of environmental and multiple genetic factors, with clear evidence for a strong genetic basis. The remarkable accumulation of knowledge gained from genetic, pharmacogenetic, and therapeutic response of biologic agents in PsA has fundamentally changed and advanced our understanding of disease pathogenesis and has identified key signalling pathways. However, only one-quarter of the genetic contribution of PsA has been accounted for; and dissecting the genetic contributors of the cutaneous disease from those that would identify joint disease has been challenging. More importantly, the clinical utility of multiple proposed loci is unclear. In this review, we summarize the potential clinical relevance from established genetic associations and provide insight on the proposed molecular pathways that arise from these associations.

The causal effect of interleukin-17 on the risk of psoriatic arthritis: a Mendelian randomization study

OBJECTIVE

To determine causal associations between genetically predicted TNF-α, IL-12p70 and IL-17 levels and risk of PsA.

METHODS

The publicly available summary-level findings from genome-wide association studies (GWAS) was used to identify loci influencing normal physiological concentrations of TNF-α, IL-12p70 and IL-17 (n = 8293) among healthy individuals as exposure and a GWAS for PsA from the UK Biobank (PsA = 900, control = 462 033) as the outcome. A two-sample Mendelian randomization (MR) analysis was performed using the inverse-variance weighted (IVW), weighted median and MR-Egger regression methods. Sensitivity analysis and MR-Egger regression analysis were performed to evaluate the heterogeneity and pleiotropic effects of each variant.

RESULTS

Single-nucleotide polymorphisms (SNPs) at genome-wide significance from GWASs on TNF-α, IL-12p70 and IL-17 were identified as the instrumental variables. The IVW method indicated a causal association between increased IL-17 level and risk of PsA (β = -0.00186 per allele, s.e. = 0.00043, P = 0.002). Results were consistent in the weighted median method (β = -0.00145 per allele, s.e. = 0.00059, P = 0.014) although the MR-Egger method suggested a non-significant association (β = -0.00133 per allele, s.e. = 0.00087; P = 0.087). Single SNP MR results revealed that the C allele of rs117556572 was robustly associated with risk of PsA (β = 0.00210, s.e. = 0.00069, P = 0.002). However, no evidence for a causal effect was observed between TNF-α, IL-12p70, decreased IL-17 levels and risk of PsA.

CONCLUSION

Our findings provide preliminary evidence that genetic variants predisposing to higher physiological IL-17 level are associated with decreased risk of PsA.

Insights into the pathogenesis of psoriatic arthritis from genetic studies

Psoriatic arthritis (PsA) is a relatively common inflammatory arthritis, a spondyloarthritis (SpA), that occurs most often in patients with psoriasis, a common immune-mediated inflammatory skin disease. Both psoriasis and PsA are highly heritable. Genetic and recent genomic studies have identified variants associated with psoriasis and PsA, but variants differentiating psoriasis from PsA are few. In this review, we describe recent developments in understanding the genetic burden of PsA, linkage, association and epigenetic studies. Using pathway analysis, we provide further insights into the similarities and differences between PsA and psoriasis, as well as between PsA and other immune-mediated inflammatory diseases, particularly ankylosing spondylitis, another SpA. Environmental factors that may trigger PsA in patients with psoriasis are also reviewed. To further understand the pathogenetic differences between PsA and psoriasis as well as other SpA, larger cohort studies of well-phenotyped subjects with integrated analysis of genomic, epigenomic, transcriptomic, proteomic and metabolomic data using interomic system biology approaches are required.

Overview of the molecular determinants contributing to the expression of Psoriasis and Psoriatic Arthritis phenotypes

Psoriasis and psoriatic arthritis are multifactorial chronic disorders whose etiopathogenesis essentially derives from the alteration of several signalling pathways and the co-occurrence of genetic, epigenetic and non-genetic susceptibility factors that altogether affect the functional and structural property of the skin. Although shared and differential susceptibility genes and molecular pathways are known to contribute to the onset of pathological phenotypes, further research is needed to dissect the molecular causes of psoriatic disease and its progression towards Psoriatic Arthritis. This review will therefore be addressed to explore differences and similarities in the etiopathogenesis and progression of both disorders, with a particular focus on genes involved in the maintenance of the skin structure and integrity (keratins and collagens), modulation of patterns of recognition (through Toll-like receptors and dectin-1) and immuno-inflammatory response (by NLRP3-dependent inflammasome) to microbial pathogens. In addition, special emphasis will be given to the contribution of epigenetic elements (methylation pattern, non-coding RNAs, chromatin modifiers and 3D genome organization) to the etiopathogenesis and progression of psoriasis and psoriatic arthritis. The evidence discussed in this review highlights how the knowledge of patients' clinical and (epi)genomic make-up could be helpful for improving the available therapeutic strategies for psoriasis and psoriatic arthritis treatment.

Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

BACKGROUND

Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging.

METHODS

Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension "basis" that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging.

RESULTS

We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations.

CONCLUSIONS

Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

Pathogenesis of psoriasis in the "omic" era. Part IV. Epidemiology, genetics, immunopathogenesis, clinical manifestation and treatment of psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic, progressive, inflammatory arthropathy associated with psoriasis as well as a complex pathogenesis. Genetic and environmental factors trigger the development of the immune-mediated auto-inflammatory response in different sites: skin, bone marrow, entheses and synovial tissues. Studies of the last two decades have changed the view of PsA from a mild, non-progressive arthritis to an inflammatory systemic disease with serious health consequences, not only associated with joint dysfunction, but also with an increased risk of cardiovascular disease and socioeconomic consequences with significantly reduced quality of life. The joint damage starts early in the course of the disease, thus early recognition and treatment with modern biological treatments, which may modify the natural history and slow down progression of this debilitating disease, is essential for the patient long-term outcome.

Two Variants in the NOTCH4 and HLA-C Genes Contribute to Familial Clustering of Psoriasis

Psoriasis is a multifactorial immune-mediated skin disease with a strong genetic background. Previous studies reported that psoriasis with a family history (PFH) and sporadic psoriasis (SP) have a distinct manifestation and genetic predisposition. However, the genetic heterogeneity of PFH and SP in the major histocompatibility complex (MHC) region has not been fully elucidated. To explore genetic variants in the MHC region that drive family aggregation of psoriasis, we included a total of 8,127 psoriasis cases and 9,906 healthy controls from Han Chinese and divided psoriasis into two subtypes, PFH (n = 1,538) and SP (n = 5,262). Then, we calculated the heritability of PFH and SP and performed a large-scale stratified association analysis. We confirmed that variants in the MHC region collectively explained a higher heritability of PFH (16.8%) than SP (13.3%). Further stratified association analysis illustrated that HLA-C∗06:02 and NOTCH4:G511S contribute to the family aggregation of psoriasis, and BTNL2:R281K specifically confers risk for SP. HLA-C∗06:02 and NOTCH4:G511S could partially explain why patients with PFH have a stronger genetic predisposition, more complex phenotypes, and more frequent other autoimmune diseases. The identification of the SP-specific variant BTNL2:R281K revealed that the genetic architecture of SP is not just a subset of PFH.

Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics

Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.

Strategies to Improve Outcomes in Psoriatic Arthritis

PURPOSE OF REVIEW

The therapeutic response to biologic agents in psoriasis is significantly higher than observed in psoriatic arthritis (PsA). In this review, specific actions to improve treatment outcomes in PsA are discussed.

RECENT FINDINGS

Increased understanding of disease pathogenesis derived from improved preclinical models and advances in cell-based and molecular technologies provide new tools to identify therapeutic targets. In addition to the important contributions of metabolic comorbidities, chronic pain and the lack of a diagnostic biomarker signal the need for new strategies to improve outcomes. Potential strategies include the following: (1) discover a novel pathway or cellular subset, (2) apply stratification biomarkers to individualize therapy, (3) preclinical intervention, (4) combination therapy, (5) lifestyle modification, (6) address chronic pain and fatigue, and (7) multidisciplinary care. The future holds great promise for enhanced treatment responses in PsA based on improved understanding of individual variation in disease pathophysiology coupled with comprehensive and integrated treatment programs.

A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis

Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA.

Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses.

Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (P = 0.0015) and infliximab (P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (P = 0.041).

Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.