CoMon variants at TRAF3IP2 are aSociated with susceptibility to psoriatic arthritis and psoriasis

Psoriatic arthritis (PsA) is an inflammatory joint disease that is distinct from other chronic arthritides and which is frequently accompanied by psoriasis vulgaris (PsV) and seronegativity for rheumatoid factor. We conducted a genome-wide association study in 609 German individuals with PsA (cases) and 990 controls with replication in 6 European cohorts including a total of 5,488 individuals. We replicated PsA associations at HLA-C and IL12B and identified a new association at TRAF3IP2 (rs13190932, P = 8.56 × 10⁻¹⁷). TRAF3IP2 was also associated with PsV in a German cohort including 2,040 individuals (rs13190932, P = 1.95 × 10⁻³). Sequencing of the exons of TRAF3IP2 identified a coding variant (p.Asp10Asn, rs33980500) as the most significantly associated SNP (P = 1.13 × 10⁻²⁰, odds ratio = 1.95). Functional assays showed reduced binding of this TRAF3IP2 variant to TRAF6, suggesting altered modulation of immunoregulatory signals through altered TRAF interactions as a new and shared pathway for PsA and PsV

Predictors of British Isles Lupus Assessment Group-based outcomes in patients with systemic lupus erythematosus: Analysis from the Systemic Lupus International Collaborating Clinics Inception Cohort

BackgroundWe aimed to identify factors associated with a significant reduction in SLE disease activity over 12 months assessed by the BILAG Index.MethodsIn an international SLE cohort, we studied patients from their 'inception enrolment' visit. We also defined an 'active disease' cohort of patients who had active disease similar to that needed for enrolment into clinical trials. Outcomes at 12 months were; Major Clinical Response (MCR: reduction to classic BILAG C in all domains, steroid dose of ≤7.5 mg and SLEDAI ≤ 4) and 'Improvement' (reduction to ≤1B score in previously active organs; no new BILAG A/B; stable or reduced steroid dose; no increase in SLEDAI). Univariate and multivariate logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) and cross-validation in randomly split samples were used to build prediction models.Results'Inception enrolment' (n = 1492) and 'active disease' (n = 924) patients were studied. Models for MCR performed well (ROC AUC = .777 and .732 in the inception enrolment and active disease cohorts, respectively). Models for Improvement performed poorly (ROC AUC = .574 in the active disease cohort). MCR in both cohorts was associated with anti-malarial use and inversely associated with active disease at baseline (BILAG or SLEDAI) scores, BILAG haematological A/B scores, higher steroid dose and immunosuppressive use.ConclusionBaseline predictors of response in SLE can help identify patients in clinic who are less likely to respond to standard therapy. They are also important as stratification factors when designing clinical trials in order to better standardize overall usual care response rates

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible