A study of environmental characterization of conventional and advanced aluminum alloys for selection and design. Phase 2: The breaking load test method

A technique is demonstrated for accelerated stress corrosion testing of high strength aluminum alloys. The method offers better precision and shorter exposure times than traditional pass fail procedures. The approach uses data from tension tests performed on replicate groups of smooth specimens after various lengths of exposure to static stress. The breaking strength measures degradation in the test specimen load carrying ability due to the environmental attack. Analysis of breaking load data by extreme value statistics enables the calculation of survival probabilities and a statistically defined threshold stress applicable to the specific test conditions. A fracture mechanics model is given which quantifies depth of attack in the stress corroded specimen by an effective flaw size calculated from the breaking stress and the material strength and fracture toughness properties. Comparisons are made with experimental results from three tempers of 7075 alloy plate tested by the breaking load method and by traditional tests of statistically loaded smooth tension bars and conventional precracked specimens

T-cell subset abnormalities predict progression along the Inflammatory Arthritis disease continuum: implications for management

The presence of a disease continuum in inflammatory arthritis (IA) is a recognised concept, with distinct stages from at-risk stage (presence of anti citrullinated-peptide autoantibody) to diagnosis of rheumatoid arthritis (RA), including therapy-induced remission. Despite T-cell dysregulation being a key feature of RA, there are few reports of T-cell phenotyping along the IA-continuum. We investigated the disturbances of naïve, regulatory and inflammation related cell (IRC) CD4+ T-cell subsets in 705 individuals across the IA-continuum, developing a simple risk-score (summing presence/absence of a risk-associated with a subset) to predict progression from one stage to the next. In 158 at-risk individuals, the 3 subsets had individual association with progression to IA and the risk-score was highly predictive (p < 0.0001). In evolving IA patients, 219/294 developed RA; the risk-score included naïve and/or Treg and predicted progression (p < 0.0001). In 120 untreated RA patients, the risk-score for predicting treatment-induced remission using naïve T-cells had an odds ratio of 15.4 (p < 0.0001). In RA patients in treatment-induced remission, a score using naïve T-cells predicted disease flare (p < 0.0001). Evaluating the risk of progression using naïve CD4+ T-cells was predictive of progression along the whole IA-continuum. This should allow identification of individuals at high-risk of progression, permitting targeted therapy for improved outcomes

DNA Hypomethylation in the TNF-Alpha Gene Predicts Rheumatoid Arthritis Classification in Patients with Early Inflammatory Symptoms

Biomarkers for the classification of rheumatoid arthritis (RA), and particularly for anti-citrullinated peptide antibody (ACPA)-negative patients, remain an important hurdle for the early initiation of treatment. Taking advantage of DNA-methylation patterns specific to early RA, quantitative methylation-specific qPCR (qMSP) offers a robust technology for the development of biomarkers. We developed assays and established their value as RA classification biomarkers. Methods: DNA-methylation data were screened to select candidate CpGs to design qMSP assays. Eight assays were developed and tested on two early inflammatory arthritis cohorts. Logistic regression and bootstrapping were used to demonstrate the added value of the qMSP assays. Result: Differentially methylated CpG data were screened for candidate CpG, thereby meeting the qMSP assay requirements. The top CpG candidate was in the TNF gene, for which we successfully developed a qMSP assay. Significantly lower DNA-methylation levels were observed in RA (p < 4 × 10−9), with a high predictive value (OR < 0.54/AUC < 0.198) in both cohorts (n = 127/n = 157). Regression using both datasets showed improved accuracy = 87.7% and AUC = 0.944 over the model using only clinical variables (accuracy = 85.2%, AUC = 0.917). Similar data were obtained in ACPA-negative patients (n = 167, accuracy = 82.6%, AUC = 0.930) compared to the clinical variable model (accuracy = 79.5%, AUC = 0.892). Bootstrapping using 2000 datasets confirmed that the AUCs for the clinical+TNF-qMSP model had significant added value in both analyses. Conclusion: The qMSP technology is robust and can successfully be developed with a high specificity of the TNF qMSP assay for RA in patients with early inflammatory arthritis. It should assist classification in ACPA-negative patients, providing a means of reducing time to diagnosis and treatment

Interleukin-7 deficiency in rheumatoid arthritis: consequences for therapy-induced lymphopenia

We previously demonstrated prolonged, profound CD4+ T-lymphopenia in rheumatoid arthritis (RA) patients following lymphocyte-depleting therapy. Poor reconstitution could result either from reduced de novo T-cell production through the thymus or from poor peripheral expansion of residual T-cells. Interleukin-7 (IL-7) is known to stimulate the thymus to produce new T-cells and to allow circulating mature T-cells to expand, thereby playing a critical role in T-cell homeostasis. In the present study we demonstrated reduced levels of circulating IL-7 in a cross-section of RA patients. IL-7 production by bone marrow stromal cell cultures was also compromised in RA. To investigate whether such an IL-7 deficiency could account for the prolonged lymphopenia observed in RA following therapeutic lymphodepletion, we compared RA patients and patients with solid cancers treated with high-dose chemotherapy and autologous progenitor cell rescue. Chemotherapy rendered all patients similarly lymphopenic, but this was sustained in RA patients at 12 months, as compared with the reconstitution that occurred in cancer patients by 3–4 months. Both cohorts produced naïve T-cells containing T-cell receptor excision circles. The main distinguishing feature between the groups was a failure to expand peripheral T-cells in RA, particularly memory cells during the first 3 months after treatment. Most importantly, there was no increase in serum IL-7 levels in RA, as compared with a fourfold rise in non-RA control individuals at the time of lymphopenia. Our data therefore suggest that RA patients are relatively IL-7 deficient and that this deficiency is likely to be an important contributing factor to poor early T-cell reconstitution in RA following therapeutic lymphodepletion. Furthermore, in RA patients with stable, well controlled disease, IL-7 levels were positively correlated with the T-cell receptor excision circle content of CD4+ T-cells, demonstrating a direct effect of IL-7 on thymic activity in this cohort

Differential CpG DNA methylation in peripheral naïve CD4+ T-cells in early rheumatoid arthritis patients

Background: The genetic risk associated with rheumatoid arthritis (RA) includes genes regulating DNA methylation, one of the hallmarks of epigenetic re-programing, as well as many T-cell genes, with a strong MHC association, pointing to immunogenetic mechanisms as disease triggers leading to chronicity. The aim of our study was to explore DNA methylation in early, drug-naïve RA patients, towards a better understanding of early events in pathogenesis. Result: Monocytes, naïve and memory CD4+ T-cells were sorted from 6 healthy controls and 10 RA patients. DNA methylation was assessed using a genome-wide Illumina 450K CpG promoter array. Differential methylation was confirmed using bisulfite sequencing for a specific gene promoter, ELISA for several cytokines and flow cytometry for cell surface markers. Differentially methylated (DM) CpGs were observed in 1047 genes in naïve CD4+ T-cells, 913 in memory cells and was minimal in monocytes with only 177 genes. Naive CD4+ T-cells were further investigated as presenting differential methylation in the promoter of > 500 genes associated with several disease-relevant pathways, including many cytokines and their receptors. We confirmed hypomethylation of a region of the TNF-alpha gene in early RA and differential expression of 3 cytokines (IL21, IL34 and RANKL). Using a bioinformatics package (DMRcate) and an in-house analysis based on differences in β values, we established lists of DM genes between health and RA. Publicly available gene expression data were interrogated to confirm differential expression of over 70 DM genes. The lists of DM genes were further investigated based on a functional relationship database analysis, which pointed to an IL6/JAK1/STAT3 node, related to TNF-signalling and engagement in Th17 cell differentiation amongst many pathways. Five DM genes for cell surface markers (CD4, IL6R, IL2RA/CD25, CD62L, CXCR4) were investigated towards identifying subpopulations of CD4+ T-cells undergoing these modifications and pointed to a subset of naïve T-cells, with high levels of CD4, IL2R, and CXCR4, but reduction and loss of IL6R and CD62L, respectively. Conclusion: Our data provided novel conceptual advances in the understanding of early RA pathogenesis, with implications for early treatment and prevention

T cell subsets: An immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals

Objectives Anticitrullinated protein antibody (ACPA)+ individuals with non-specific musculoskeletal symptoms are at risk of inflammatory arthritis (IA). This study aims to demonstrate the predictive value of T cell subset quantification for progression towards IA and compare it with previously identified clinical predictors of progression. Methods 103 ACPA+ individuals without clinical synovitis were observed 3-monthly for 12 months and then as clinically indicated. The end point was the development of IA. Naïve, regulatory T cells (Treg) and inflammation related cells (IRCs) were quantified by flow cytometry. Areas under the ROC curve (AUC) were calculated. Adjusted logistic regressions and Cox proportional hazards models for time to progression to IA were constructed. Results Compared with healthy controls (age adjusted where appropriate), ACPA+ individuals demonstrated reduced naïve (22.1% of subjects) and Treg (35.8%) frequencies and elevated IRC (29.5%). Of the 103 subjects, 48(46.6%) progressed. Individually, T cell subsets were weakly predictive (AUC between 0.63 and 0.66), although the presence of 2 T cell abnormalities had high specificity. Three models were compared: model-1 used T cell subsets only, model-2 used previously published clinical parameters, model-3 combined clinical data and T cell data. Model-3 performed the best (AUC 0.79 (95% CI 0.70 to 0.89)) compared with model-1 (0.75 (0.65 to 0.86)) and particularly with model-2 (0.62 (0.54 to 0.76)) demonstrating the added value of T cell subsets. Time to progression differed significantly between high-risk, moderate-risk and low-risk groups from model-3 (p=0.001, median 15.4 months, 25.8 months and 63.4 months, respectively). Conclusions T cell subset dysregulation in ACPA+ individuals predates the onset of IA, predicts the risk and faster progression to IA, with added value over previously published clinical predictors of progression