CD(8+ )T lymphocytes in lung tissue from patients with idiopathic pulmonary fibrosis

BACKGROUND: Several studies have implicated a role of inflammation in the pathogenesis of lung damage in idiopathic pulmonary fibrosis (IPF). Parenchymal lung damage leads to defects in mechanics and gas exchange and clinically manifests with exertional dyspnea. Investigations of inflammatory cells in IPF have shown that eosinophils, neutrophils and CD(8+ )TLs may be associated with worse prognosis. We wished to investigate by quantitative immunohistochemistry infiltrating macrophages, neutrophils and T lymphocytes (TLs) subpopulations (CD(3+), CD(4+ )and CD(8+)) in lung tissue of patients with IPF and their correlation with lung function indices and grade of dyspnoea. METHODS: Surgical biopsies of 12 patients with IPF were immunohistochemically stained with mouse monoclonal antibodies (anti-CD(68 )for macrophages, anti-elastase for neutrophils, and anti-CD(3), anti-CD(4), anti-CD(8 )for CD(3+)TLs, CD(4+)TLs, and CD(8+)TLs respectively). The number of positively stained cells was determined by observer-interactive computerized image analysis (SAMBA microscopic image processor). Cell numbers were expressed in percentage of immunopositive nuclear surface in relation to the total nuclear surface of infiltrative cells within the tissue (labeling Index). Correlations were performed between cell numbers and physiological indices [FEV(1), FVC, TLC, DLCO, PaO(2), PaCO(2 )and P(A-a)O(2))] as well as dyspnoea scores assessed by the Medical Research Council (MRC) scale. RESULTS: Elastase positive cells accounted for the 7.04% ± 1.1 of total cells, CD(68+ )cells for the 16.6% ± 2, CD(3+ )TLs for the 28.8% ± 7, CD(4+ )TLs for the 14.5 ± 4 and CD(8+ )TLs for the 13.8 ± 4. CD(8+)TLs correlated inversely with FVC % predicted (r(s )= -0.67, p = 0.01), TLC % predicted (r(s )= -0.68, p = 0.01), DLCO % predicted (r(s )= -0.61, p = 0.04), and PaO(2 )(r(s )= -0.60, p = 0.04). Positive correlations were found between CD(8+)TLs and P(A-a)O(2 )(r(s )= 0.65, p = 0.02) and CD(8+)TLs and MRC score (r(s )= 0.63, p = 0.02). Additionally, CD(68+ )cells presented negative correlations with both FVC % predicted (r(s )= -0.80, p = 0.002) and FEV(1 )% predicted (r(s )= -0.68, p = 0.01). CONCLUSION: In UIP/IPF tissue infiltrating mononuclear cells and especially CD(8+ )TLs are associated with the grade of dyspnoea and functional parameters of disease severity implicating that they might play a role in its pathogenesis

Blood-based epigenome-wide analyses on the prevalence and incidence of nineteen common disease states

This is the author accepted manuscriptAvailability of Data and Material: According to the terms of consent for Generation Scotland participants, access to data must be reviewed by the Generation Scotland Access Committee. Applications should be made to [email protected]. All code is available with open access at the following Gitlab repository: https://github.com/marioni-group MethylPipeR (version 1.0.0) is available at: https://github.com/marioni-group/MethylPipeR MethylPipeR-UI is available at: https://github.com/marioni-group/MethylPipeR-UI. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA Project Application Self Service Tool (https://epi.helmholtz-muenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board.Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction and intervention. While standard risk factors have shown good predictive performance, we show that the use of blood-based DNA methylation information leads to a significant improvement in the prediction of 10-year T2D incidence risk. Previous studies have been largely constrained by linear assumptions, the use of CpGs one-at43 a-time, and binary outcomes. We present a flexible approach (via an R package, MethylPipeR) based on a range of linear and tree-ensemble models that incorporate time-to-event data for prediction. Using the Generation Scotland cohort (training set ncases=374, ncontrols=9,461; test set ncases=252, ncontrols=4,526) our best-performing model (Area Under the Curve (AUC)=0.872, Precision Recall AUC (PRAUC)=0.302) showed notable improvement in 10-year onset prediction beyond standard risk factors (AUC=0.839, PRAUC=0.227). Replication was observed in the German-based KORA study (n=1,451, ncases = 142, p=1.6x10-5 49 ).Wellcome TrustChief Scientist Office of the Scottish Government Health DirectoratesScottish Funding Counci