Nitro-oleic acid regulates T cell activation through post-translational modification of calcineurin

Nitro-fatty acids (NO2-FAs) are unsaturated fatty acid nitration products that exhibit anti-inflammatory actions in experimental mouse models of autoimmune and allergic diseases. These electrophilic molecules interfere with intracellular signaling pathways by reversible post-translational modification of nucleophilic amino-acid residues. Several regulatory proteins have been identified as targets of NO2-FAs, modifying their activity and promoting gene expression changes that result in anti-inflammatory effects. Herein, we report the effects of nitro-oleic acid (NO2-OA) on pro-inflammatory T cell functions, showing that 9- and 10-NOA, but not their oleic acid precursor, decrease T cell proliferation, expression of activation markers CD25 and CD71 on the plasma membrane, and IL-2, IL-4, and IFN-γ cytokine gene expressions. Moreover, we have found that NO2-OA inhibits the transcriptional activity of nuclear factor of activated T cells (NFAT) and that this inhibition takes place through the regulation of the phosphatase activity of calcineurin (CaN), hindering NFAT dephosphorylation, and nuclear translocation in activated T cells. Finally, using mass spectrometry-based approaches, we have found that NO2-OA nitroalkylates CaNA on four Cys (Cys129, 228, 266, and 372), of which only nitroalkylation on Cys372 was of importance for the regulation of CaN phosphatase activity in cells, disturbing functional CaNA/CaNB heterodimer formation. These results provide evidence for an additional mechanism by which NO2-FAs exert their anti-inflammatory actions, pointing to their potential as therapeutic bioactive lipids for the modulation of harmful T cell-mediated immune response

A Single Nucleotide Polymorphism in the Il17ra Promoter Is Associated with Functional Severity of Ankylosing Spondylitis

The aim of this study was to identify new genetic variants associated with the severity of ankylosing spondylitis (AS). We sequenced the exome of eight patients diagnosed with AS, selected on the basis of the severity of their clinical parameters. We identified 27 variants in exons and regulatory regions. The contribution of candidate variants found to AS severity was validated by genotyping two Spanish cohorts consisting of 180 cases/300 controls and 419 cases/656 controls. Relationships of SNPs and clinical variables with the Bath Ankylosing Spondylitis Disease Activity and Functional Indices BASDAI and BASFI were analyzed. BASFI was standardized by adjusting for the duration of the disease since the appearance of the first symptoms. Refining the analysis of SNPs in the two cohorts, we found that the rs4819554 minor allele G in the promoter of the IL17RA gene was associated with AS (p<0.005). This variant was also associated with the BASFI score. Classifying AS patients by the severity of their functional status with respect to BASFI/disease duration of the 60th, 65th, 70th and 75th percentiles, we found the association increased from p60 to p75 (cohort 1: p<0.05 to p<0.01; cohort 2: p<0.01 to p<0.005). Our findings indicate a genetic role for the IL17/ILRA axis in the development of severe forms of AS

How do patient-reported outcome measures affect treatment intensification and patient satisfaction in the management of psoriatic arthritis? A cross sectional study of 503 patients

Objectives The AsseSSing Impact in pSoriatic Treatment (ASSIST) study investigated prescribing in routine PsA care and whether the patient-reported outcome—PsA Impact of Disease questionnaire (PsAID-12)—impacted treatment. This study also assessed a range of patient and clinician factors and their relationship to PsAID-12 scoring and treatment modification. Methods Patients with PsA were selected across the UK and Europe between July 2021 and March 2022. Patients completed the PsAID questionnaire and the results were shared with their physician. Patient characteristics, disease activity, current treatment methods, treatment strategies, medication changes and patient satisfaction scores were recorded. Results A total of 503 patients were recruited. Some 36.2% had changes made to treatment, and 88.8% of these had treatment escalation. Overall, the mean PsAID-12 score was higher for patients with treatment escalation; increase in PSAID-12 score is associated with increased odds of treatment escalation (odds ratio 1.58; P < 0.0001). However, most clinicians reported that PsAID-12 did not impact their decision to escalate treatment, instead supporting treatment reduction decisions. Physician’s assessment of disease activity had the most statistically significant effect on likelihood of treatment escalation (odds ratio 2.68, per 1-point score increase). Escalation was more likely in patients not treated with biologic therapies. Additional factors associated with treatment escalation included: patient characteristics, physician characteristics, disease activity and disease impact. Conclusion This study highlights multiple factors impacting treatment decision-making for individuals with PsA. PsAID-12 scoring correlates with multiple measures of disease severity and odds of treatment escalation. However, most clinicians reported that the PsAID-12 did not influence treatment escalation decisions. Psoriatic Arthritis Impact of Disease (PsAID) scoring could be used to increase confidence in treatment de-escalation

An international multicentre analysis of current prescribing practices and shared decision-making in psoriatic arthritis

Objectives Shared decision-making (SDM) is advocated to improve patient outcomes in PsA. We analysed current prescribing practices and the extent of SDM in PsA across Europe. Methods The ASSIST study was a cross-sectional observational study of PsA patients ≥18 years of age attending face-to-face appointments between July 2021 and March 2022. Patient demographics, current treatment and treatment decisions were recorded. SDM was measured by the clinician’s effort to collaborate (CollaboRATE questionnaire) and patient communication confidence (PEPPI-5 tool). Results A total of 503 patients were included from 24 centres across the UK, France, Germany, Italy and Spain. Physician- and patient-reported measures of disease activity were highest in the UK. Conventional synthetic DMARDs constituted a higher percentage of current PsA treatment in the UK than continental Europe (66.4% vs 44.9%), which differed from biologic DMARDs (36.4% vs 64.4%). Implementing treatment escalation was most common in the UK. CollaboRATE and PEPPI-5 scores were high across centres. Of 31 patients with low CollaboRATE scores (<4.5), no patients with low PsAID-12 scores (<5) had treatment escalation. However, of 465 patients with CollaboRATE scores ≥4.5, 59 patients with low PsAID-12 scores received treatment escalation. Conclusions Higher rates of treatment escalation seen in the UK may be explained by higher disease activity and a younger cohort. High levels of collaboration in face-to-face PsA consultations suggests effective implementation of the SDM approach. Our data indicate that in patients with mild disease activity, only those with higher perceived collaboration underwent treatment escalation. Prospective studies should examine the impact of SDM on PsA patient outcomes

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

The Impact of Comorbidity on Patient-Reported Outcomes in Psoriatic Arthritis: A Systematic Literature Review

INTRODUCTION: A systematic literature review was conducted with the aim to analyse the impact of comorbidity on patient-reported outcomes (PROs) in patients with psoriatic arthritis (PsA). METHODS: A sensitive search strategy of the Medline, Embase and the Cochrane Library (up to March 2019) was applied to retrieve studies for inclusion in this systematic literature review. Abstracts of the ACR and EULAR scientific meetings were also searched. The selection criteria were: (1) patients with PsA (population) with a comorbidity (intervention) and (2) report of any impact of the comorbidity on PROs. Systematic literature reviews, randomized controlled trials and observational were included in this systematic literature review. Two of the authors selected the articles and collected the data. RESULTS: Eighteen articles were included in this systematic literature review, with most being cross-sectional studies that included more than 9000 patients with PsA. Some studies analysed the impact of an individual comorbidity, such as fibromyalgia (FM), and in others the analysis was according to the number of comorbidities. The most frequently analysed PROs were function, quality of life and fatigue. Analysis of the studies included in the review showed that patients with a higher number of comorbidities and/or more severe comorbidities reported worse impacts of their disease on function, patient's global assessment (PGA), pain, fatigue, work disability and quality of life. Specifically, FM had a negative impact on the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), function, quality of sleep and quality of life; anxiety and depression had a negative impact on function and fatigue; metabolic syndrome had a negative impact on BASDAI, function, PGA and quality of life; obesity had a negative impact on function and pain; smoking (current and ex-smokers) had a negative impact on pain, function, fatigue, quality of life and overall health; alcohol intake had a negative impact on pain, function, fatigue, quality of life and overall health. CONCLUSIONS: The prevalence and impact of medical comorbidity on PROs are very high in patients with PsA

Gamificación en el ámbito universitario

Este libro se estructura en tres partes: una primera parte dedicada a la conceptualización del uso de los juegos, los elementos y dinámicas de los juegos en el ámbito de la educación en la que se definen y caracterizan los principales términos (gamificación, game-based learning, serious games, juegos educativos y juegos de simulación) y aproximaciones al objeto de estudio. Una segunda parte orientada a familiarizar al lector con los principales elementos que componen los juegos y los tipos y géneros de juegos a los que dan lugar. Y una tercera parte que aproxima al lector a la investigación sobre el uso de los juegos en educación.2015/UEM25No data (2016)UE

Raw data of manuscript “Nitro-oleic acid regulates T cell activation through post-translational modification of calcineurin”

Methods used for generation of data are described under the Material and Methods section of the manuscript and the Supplementary Methods of the Supplementary Information Text.RTI2018-100815-B-I00 (MICIU/FEDER) and the accompanying 2021 CSIC Exceptional Grant to M.A.I. and J.M.S. R01GM125944-05 and DK112854-04 to F.J.S., and Universidad de la República CSIC Grupos_2018, EI_2020 to R.R.MAIN FIGURES Figure 1: Figure 1A: Exp1: “Histogram_Exp1” and corresponding FACSfiles. Exp2: “Histogram_Exp2”, “Hig resolution”, “LowResolution_Exp2” and corresponding FACSfiles. Exp3: “Histogram Exp3” and corresponding FACSfiles. Exp4: “Histogram Exp4” and corresponding FACSfiles. “Fig1A_Graph”. “Fig1A_Histogram”. “Graph_Fig1A”. Figure 1B: Exp1: FACS files corresponding to CD69 and CD71. Exp2: FACS files corresponding to CD69 and CD71. Exp3: FACS files corresponding to CD69 and CD71. Fig1B_HistogramsCD71 and CD69: FACS files corresponding to Figure1B. “Fig1B_Graph”. “Graph_Fig1B”. Figure 1C: CTV CD4 CD8: FACS files corresponding to cell proliferation. CTV CD4 CD8II: FACS files corresponding to cell proliferation. Histogram_Fig 1C: FACS files and Histogram Figure 1C. Figure 1D: “Graph_Fig1D”. “Fig1D_Graph”. Figure 1E_F: Exp1: FACS files corresponding to Figure 1E and F. Exp2: FACS files corresponding to Figure 1E and F. Exp3: FACS files corresponding to Figure 1E and F. “Fig1E_Graph” “Fig1F_Graph” “Graph_Fig1E_F” “Layout Th2” “Layout Th1” Figure 2 Figure 2A: “Data RT-PCR IL2IFNGIL4vsGAPDH Fig 2A” “RT-PCR IFNg vs GAPDH 100%” “RT-PCR IL2 vs GAPDH 100%” “RT-PCR IL4 vs GAPDH 100%” Figure 2B: IFNgData: “Data Expts IFNgLUC” and “Raw Data Fig 2B IFNgLuc” IL2Data: “Data Expts IL2LUC” and “Raw Data Fig 2B IL2Luc” IL4Data: “Data Expts IL4LUC” and “Raw Data Fig 2B IL4Luc” Figure 3 Figure 3A: NFATLucPMA+Ion: “Graph_Fig3A_PMAI” and “GraphFig3A_PMAI”. NFATLucRaji+SEB: “Graph_Fig3A_SEB” and “GraphFig3A_SEB”. Figure 3B: “Fig3B_Graph_white” and “Graph_Fig3B” Figure 3C: “Fig3C_Graph” and “Graph_Fig3C” Figure 3D: Exp: b-actin: 2 files WB b-actin. RCAN: 4 files WB inducible and constitutive RCAN. Quantification: 3 quantifications (3 image files, 3 graphs files and 3 excel files). Excel “Densitometric analysis_bActin”. Fig 3D. Figure 4 Figure 4A: Photomicrographs_Fig4A: imaging files. Quantitative imaging: imaging files analysed. “Nuclear_NFAT percentage”. “Quantification” Excel file. Figure 4B: Exp: Cytosol: 2 files WB dynamin II and 2 files WB NFATC2. Nucleus: 2 files WB LaminB1 and 2 files WB NFATC2. “Figure 4B”. Figure 4C: Exp: DynaminII: 3 files WB Dynamin II. NFAT: 4 files WB NFATC2. “Fig4C” Figure 4D: “CaNA_DCAM domains”. Figure 4E: “Graph_Fig4E” and “Fig4E_Graph”. Figure 4F: “Graph_Fig4F” and “Fig4F_Graph”. Figure 4G: “Graph_Fig4G” and “Fig4G_Graph”. Figure 4H: Exp: 5 FACSfiles and “Fig4H”. Figure 5 Figure 5A: Exp: 2 files WB nitroalkylation and 1 file WB Coomassie CaNA. “Fig5A”. Figure 5B: Exp: 2 files WB nitroalkylation and 1 file WB Coomassie DCAM- AI. “Fig 5B”. Figure 5C: “NitroalkylAA_Sequence_CaNA”. Figure 5D: “Graph_Fig5D”. “Dots Fig 5D”. “Fig5D” Figure 5E: ”Graph_Fig5E” and “Fig5E_Graph”. Figure 5F: Exp: 4 files WB Biotin_Nitroalkylation and 1 file Coomassie GST-DCAM. “Fig 5F”. “Quantification”. Figure 6 Figure 6A: Exp: CoIPCnB: 2 files WB CaNB. InputCnA: 4 files WB input CaNA. Input CaNB: 1 file WB input CaNB. IPCnA: 1 file WB IP CaNA. “Fig 6A”. “Quantification co_IP Fig6A”. Figure 6B: Exp: 2 files WB CaNB. 1 file WB input CaNB. 1 file Coomassie GST-DCAM-AI. “Fig 6B”. “quantification pull_down_Fig6B”. Figure 6C: Exp: 2 files WB CaNB. 1 file Input CaNB. 1 file Coomassie GST-DCAM-AI. “Fig 6C”. “Quantification Fig6C”. Figure 6D: Exp: CoIP_CaNB: 3 files WB CaNB. Input_CaNB: 1 file WB input CaNB. Input_DCAM_GFP: 2 files WB GFP-DCAM-AI. IP_DCAM_GFP: 2 files WB GFP-DCAM-AI. “Fig6D”. “Quantification Co_IPFig6D”. SUPORTING INFORMATION FIGURES SI_Figure 1 Exp1: “Histogram Exp1” and corresponding FACSfiles. Exp2: “Histogram Exp2” and corresponding FACSfiles. Exp3: “Histogram Exp3” and corresponding FACSfiles. “Graph_SI_Fig1B”. “Histogram_SI_Fig1A”. “Kinetics_CD25_Donor1”. “Kinetics_CD25_Donor2”. “Kinetics_CD25_Donor3”. SI Figure 2 T lymphoblasts Annexin 7AAD: Dot plot analysis and corresponding FACSfiles. “SI_Fig2”. SI Figure 3 SI_Fig 3A: NFkB PMA+Ion: “Graph_FigS3_PMAI” and “GraphFigS3PMAI”. NFkBLuc Raji+SEB: “Graph_FigS3A_SEB” and “FigS3A_SEB_Graph”. SI_Fig 3B: WB Figure 2 SI: 2 files WB IKB and p-IKB. “Fig_S3B”. SI_Figure 4: “Mass Spectra”. SI Figure 5 SI_Fig5A: “DCAM_Mutants” SI_Fig5B: b_actin_129_228_266_372_GFP: 4 files WB Actin. b_actin_153_178_184_256_GFP: 3 files WB Actin. DCAM_129_228_266_372_GFP: 4 files WB DCAM mutants. DCAM_153_178_184_256_GFP: 3 files WB DCAM mutants. “Expression of mutants I_153_178_184_256_GFP”. “Expression of mutants II_129_228_266_372_GFP”. SI Figure 6 SI_Fig6A: “Mass Spectrometry DCAM-GFP”. SI_Fig6BanC: “SI_Fig6BandC”. SI Figure 7 SI_Fig 7A: Exp: CaNB: 2 files WB CaNB. Input CaNB: 2 files WB input CaNB. “Densitometric analysis”. “Dose_binding Graph”. “SI_Fig7A”. SI_Fig 7B: Exp: CaNA: 2 files Coomassie GST-DCAM and 3 files corresponding quantification. CaNB: 5 files WB bound CaNB and 6 files Quantification. “Quantification_BindingCaNB_to_CaNA_kinetics”. “SI-Fig_7B”Peer reviewe

Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.

<p>Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.</p