Explore the vast range of titles available.

Psoriatic arthritis (PsA) is a chronic autoimmune disease that causes a variety of musculoskeletal abnormalities. Musculoskeletal ultrasound in PsA is becoming increasingly popular, both in clinical practice and research. This narrative reviews recent literature on the utility of ultrasound in PsA. A search of PubMed was used to identify publications written in English, with titles containing the term psoriatic arthritis and either ultrasound, ultrasonography, or sonographic. A total of 178 publications were identified; those that were not relevant (n = 59), were not original research (n = 45), or that had small (<30) sample sizes (n = 34) were excluded, leaving 40 studies for review of the use of ultrasound in various aspects of PsA. Publications with similar findings were grouped into seven domains: (1) the use of ultrasound findings compared to clinical assessment; (2) the use of ultrasound in the assessment of enthesitis; (3) the use of ultrasound in the assessment of nails; (4) the use of ultrasound as a screening tool in patients with psoriasis at risk for PsA; (5) the use of ultrasound in differentiating PsA from other similar conditions; (6) the use of ultrasound as a measure of disease activity; and (7) the use of ultrasound compared to MRI. In recent studies, ultrasound measures of inflammation tended to agree with objective clinical findings of disease, such as swollen joint counts, while being less influenced by subjective measures, such as pain. Ultrasound has utility in the assessment of enthesitis and psoriatic nail disease in PsA, and as an overall measure of disease activity. Ultrasound-based outcomes measures have been used in observational studies and in clinical trials involving PsA, and may have utility as a measure of treatment response. The findings from recent studies suggest that ultrasound may have utility in improving the accuracy and precision of screening programs designed to identify subclinical PsA in cohorts of patients with psoriasis; however, cost-efficacy remains to be determined. Beyond screening, ultrasound may have utility in the diagnosis of PsA in patients with suspected inflammatory arthritis, and ultrasound measures of inflammation agree with MRI measures of inflammation, meaning that incorporating ultrasound into clinical practice might help to overcome the barriers associated with MRI. As ultrasound technology continues to advance, and associated costs decrease, it is likely that ultrasound will become more integrated into the clinical journeys of patients with PsA.

Background Genome-wide association studies (GWAS) have uncovered the genetic basis behind many diseases and conditions. However, most of these genetic loci affect regulatory regions, making the interpretation challenging. Chromatin conformation has a fundamental role in gene regulation and is frequently used to associate potential target genes to regulatory regions. However, previous studies mostly used small sample sizes and immortalized cell lines instead of primary cells. Results Here we present the most extensive dataset of chromatin conformation with matching gene expression and chromatin accessibility from primary CD4 + and CD8 + T cells to date, isolated from psoriatic arthritis patients and healthy controls. We generated 108 Hi-C libraries (49 billion reads), 128 RNA-seq libraries and 126 ATAC-seq libraries. These data enhance our understanding of the mechanisms by which GWAS variants impact gene regulation, revealing how genetic variation alters chromatin accessibility and structure in primary cells at an unprecedented scale. We refine the mapping of GWAS loci to implicated regulatory elements, such as CTCF binding sites and other enhancer elements, aiding gene assignment. We uncover BCL2L11 as the probable causal gene within the rheumatoid arthritis (RA) locus rs13396472, despite the GWAS variants’ intronic positioning relative to ACOXL , and we identify mechanisms involving SESN3 dysregulation in the RA locus rs4409785. Conclusions Given these genes’ significant role in T cell development and maturation, our work deepens our comprehension of autoimmune disease pathogenesis, suggesting potential treatment targets. In addition, our dataset provides a valuable resource for the investigation of immune-mediated diseases and gene regulatory mechanisms.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Objectives To investigate a shared genetic aetiology for skin involvement in psoriasis and psoriatic arthritis (PsA) by genotyping single-nucleotide polymorphisms (SNPs), reported to be associated in genome-wide association studies of psoriasis, in patients with PsA. Methods SNPs with reported evidence for association with psoriasis were genotyped in a PsA case and control collection from the UK and Ireland. Genotype and allele frequencies were compared between PsA cases and controls using the Armitage test for trend. Results Seven SNPs mapping to the IL1RN, TNIP1, TNFAIP3, TSC1, IL23A, SMARCA4 and RNF114 genes were successfully genotyped. The IL23A and TNIP1 genes showed convincing evidence for association (rs2066808, p = 9.1×10−7; rs17728338, p = 3.5×10−5, respectively) whilst SNPs mapping to the TNFAIP3, TSC1 and RNF114 genes showed nominal evidence for association (rs610604, p = 0.03; rs1076160, p = 0.03; rs495337, p = 0.0025). No evidence for association with IL1RN or SMARCA4 was found but the power to detect association was low. Conclusions SNPs mapping to previously reported psoriasis loci show evidence for association to PSA, thus supporting the hypothesis that the genetic aetiology of skin involvement is the same in both PsA and psoriasis.

Background: Human leucocyte antigen (HLA) genes predict disease severity in psoriasis (HLA-Cw6) and rheumatoid arthritis (shared epitope (SE)), but the situation is unclear for psoriatic arthritis (PsA). Aim: To determine the association of the HLA-Cw6 and HLA-DRB1 gene with disease severity in a large UK cohort with PsA. Methods: Genotyping of the HLA-Cw and HLA-DRB1 loci was undertaken in DNA samples from patients with PsA (n = 480). Stratification and regression analysis were used within the PsA cases to determine whether HLA-Cw6, HLA-DRB1 or the presence of the SE alleles predicted disease severity as measured by the Health Assessment Questionnaire score, the total number of damaged or involved joints adjusted for disease duration and disease-modifying antirheumatic treatments. Results:HLA-Cw6 was found to be in linkage disequilibrium with HLA-DRB1*07 (r2 = 0.46). Patients with PsA who carried both HLA-Cw6 and HLA-DRB1*07 had fewer damaged or involved joints (41% fewer damaged (95% CI 23% to 55%, p = 0.02) and 31% fewer involved joints (95% CI 16% to 44%, p<0.001)) compared with those who carried neither HLA-Cw6 nor HLA-DRB1*07 alleles. Those who carried either HLA-Cw6 or HLA-DRB1*07 alleles alone had no evidence of a reduction in joint involvement. The SE, HLA-DRB1*03 and HLA-DRB1*04 alleles did not predict severity using these outcome measures. Conclusion: Patients with PsA carrying both HLA-Cw6 and HLA-DRB1*07 alleles have a less severe course of arthritis. This suggests that a protective locus lies on a haplotype marked by these alleles. No association was detected with disease severity and SE status.

Objective A number of rheumatoid arthritis (RA) susceptibility genes have been identified in recent years. Given the overlap in phenotypic expression of synovial joint inflammation between RA and psoriatic arthritis (PsA), the authors explored whether RA susceptibility genes are also associated with PsA. Methods 56 single nucleotide polymorphisms (SNPs) mapping to 41 genes previously reported as RA susceptibility loci were selected for investigation. PsA was defined as an inflammatory arthritis associated with psoriasis and subjects were recruited from the UK and Ireland. Genotyping was performed using the Sequenom MassArray platform and frequencies compared with data derived from large UK control collections. Results Significant evidence for association with susceptibility to PsA was found toa SNP mapping to the REL (rs13017599, ptrend=5.2×104) gene, while nominal evidence for association (ptrend<0.05) was found to seven other loci including PLCL2 (rs4535211, p=1.7×10−3); STAT4 (rs10181656, p=3.0×10−3) and the AFF3, CD28, CCL21, IL2 and KIF5A loci. Interestingly, three SNPs demonstrated opposite effects to those reported for RA. Conclusions The REL gene, a key modulator of the NFκB pathway, is associated with PsA but the allele conferring risk to RA is protective in PsA suggesting that there are fundamental differences in the aetiological mechanisms underlying these two types of inflammatory arthritis.

Mucinoses are a heterogeneous group of diseases that are characterised by rapid proliferation of fibroblasts, extensive dermal mucin deposition, and fibrosis.2 Scleromyxoedema is a rare generalised subtype that can present with multiple extracutaneous signs, most commonly a monoclonal paraproteinaemia (in more than 80% of cases).2 Other extracutaneous associations include muscle weakness, central nervous system involvement, and oesophageal dysmotility. Therapies include intravenous immunoglobulins, high dose systemic corticosteroids, extracorporeal photopheresis, autologous stem-cell transplantation, and agents directed against associated paraproteinaemia (for example, thalidomide, melphalan).3 Our patient showed striking, rapid improvement within 48 h of receiving intravenous immunoglobulins (2 g/kg), probably because we started treatment before onset of extensive fibrosis.

Skin microbiota are likely to be important in the development of conditions such as psoriatic arthritis. Profiling the bacterial community in the psosriatic plaques will contribute to our understanding of the role of the skin microbiome in these conditions. The aim of this work was to determine the optimum study design for work on the skin microbiome with use of the MiSeq platform. The objectives were to compare data generated from two platforms for two primer pairs in a low density mock bacterial community. DNA was obtained from two low density mock communities of 11 diverse bacterial strains (with and without human DNA supplementation) and from swabs taken from the skin of four healthy volunteers. The DNA was amplified with primer pairs covering hypervariable regions of the 16S rRNA gene: primers 63F and 519R (V1-V3), and 347F and 803R (V3-V4). The resultant libraries were indexed for the MiSeq and Roche454 platforms and sequenced. Both datasets were de-noised, cleaned of chimeras, and analysed by use of QIIME software (version 1.8.0). No significant difference in the diversity indices at the phylum and the genus level between the platforms was seen. Comparison of the diversity indices for the mock community data for the two primer pairs demonstrated that the V3-V4 hypervariable region had significantly better capture of bacterial diversity than did the V1-V3 region. Amplification with the same primer pairs showed strong concordance within each platform (98·9–99·8%), with negligible effect of spiked human DNA contamination. Comparison at the family level classification between samples processed on the MiSeq and Roche454 platforms using the V3-V4 hypervariable region also showed a high level of concordance (87%), although less so for the V1-V3 primers (10%). The pilot data from healthy volunteers were similar. Results obtained from the V3-V4 16S rRNA hypervariable region, sequencing on the MiSeq and Roche454 platforms, were concordant between replicates, and between each other. These findings suggest that the MiSeq platform, and these primers, is a comparable method for determining skin microbiota to the widely used Roche454 methodology. NIHR Manchester Musculoskeletal Biomedical Research Unit.

Background The composition of the skin microbiome is predicted to play a role in the development of conditions such as atopic eczema and psoriasis. 16S rRNA gene sequencing allows the investigation of bacterial microbiota. A significant challenge in this field is development of cost effective high throughput methodologies for the robust interrogation of the skin microbiota, where biomass is low. Here we describe validation of methodologies for 16S rRNA (ribosomal ribonucleic acid) gene sequencing from the skin microbiome, using the Illumina MiSeq platform, the selection of primer to amplify regions for sequencing and we compare results with the current standard protocols.. Methods DNA was obtained from two low density mock communities of 11 diverse bacterial strains (with and without human DNA supplementation) and from swabs taken from the skin of healthy volunteers. This was amplified using primer pairs covering hypervariable regions of the 16S rRNA gene: primers 63F and 519R (V1-V3); and 347F and 803R (V3-V4). The resultant libraries were indexed for the MiSeq and Roche454 and sequenced. Both data sets were denoised, cleaned of chimeras and analysed using QIIME. Results There was no significant difference in the diversity indices at the phylum and the genus level observed between the platforms. The capture of diversity using the low density mock community samples demonstrated that the primer pair spanning the V3-V4 hypervariable region had better capture when compared to the primer pair for the V1-V3 region and was robust to spiking with human DNA. The pilot data generated using the V3-V4 region from the skin of healthy volunteers was consistent with these results, even at the genus level (Staphylococcus, Propionibacterium, Corynebacterium, Paracoccus, Micrococcus, Enhydrobacter and Deinococcus identified at similar abundances on both platforms). Conclusions The results suggest that the bacterial community diversity captured using the V3-V4 16S rRNA hypervariable region from sequencing using the MiSeq platform is comparable to the Roche454 GS Junior platform. These findings provide evidence that the optimised method can be used in human clinical samples of low bacterial biomass such as the investigation of the skin microbiota.

Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n  = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients. Biomarker analysis of the phase 4 R4RA trial identifies pretreatment synovial biopsy features selectively associated with response to rituximab or tocilizumab, and leads to the development of models that might predict treatment benefit in patients with rheumatoid arthritis