Background: Defining regulatory mechanisms through which noncoding risk variants influence the cell-mediated pathogenesis of immune-mediated disease (IMD) has emerged as a priority in the post-genome-wide association study era. Objectives: With a focus on rheumatoid arthritis, we sought new insight into genetic mechanisms of adaptive immune dysregulation to help prioritize molecular pathways for targeting in this and related immune pathologies.

ObjectiveRheumatoid arthritis (RA) is a genetically complex disease of immune dysregulation. This study sought to gain further insight into the genetic risk mechanisms of RA by conducting an expression quantitative trait locus (eQTL) analysis of confirmed genetic risk loci in CD4+ T cells and B cells from carefully phenotyped patients with early arthritis who were naive to therapeutic immunomodulation.Methods RNA and DNA were isolated from purified B and/or CD4+ T cells obtained from the peripheral blood of 344 patients with early arthritis. Genotyping and global gene expression measurements were carried out using Illumina BeadChip microarrays. Variants in linkage disequilibrium (LD) with non‐HLA RA single‐nucleotide polymorphisms (defined as r2 ≥ 0.8) were analyzed, seeking evidence of cis‐ or trans‐eQTLs according to whether the associated probes were or were not within 4 Mb of these LD blocks.ResultsGenes subject to cis‐eQTL effects that were common to both CD4+ and B lymphocytes at RA risk loci were FADS1,FADS2,BLK,FCRL3,ORMDL3,PPIL3, and GSDMB. In contrast, those acting on METTL21B,JAZF1,IKZF3, and PADI4 were unique to CD4+ lymphocytes, with the latter candidate risk gene being identified for the first time in this cell subset. B lymphocyte–specific eQTLs for SYNGR1 and CD83 were also found. At the 8p23 BLK–FAM167A locus, adjacent genes were subject to eQTLs whose activity differed markedly between cell types; in particular, the FAM167A effect displayed striking B lymphocyte specificity. No trans‐eQTLs approached experiment‐wide significance, and linear modeling did not identify a significant influence of biologic covariates on cis‐eQTL effect sizes.ConclusionThese findings further refine the understanding of candidate causal genes in RA pathogenesis, thus providing an important platform from which downstream functional studies, directed toward particular cell types, may be prioritized.

Bayesian networks can be used to identify possible causal relationships between variables based on their conditional dependencies and independencies, which can be particularly useful in complex biological scenarios with many measured variables. Here we propose two improvements to an existing method for Bayesian network analysis, designed to increase the power to detect potential causal relationships between variables (including potentially a mixture of both discrete and continuous variables). Our first improvement relates to the treatment of missing data. When there is missing data, the standard approach is to remove every individual with any missing data before performing analysis. This can be wasteful and undesirable when there are many individuals with missing data, perhaps with only one or a few variables missing. This motivates the use of imputation. We present a new imputation method that uses a version of nearest neighbour imputation, whereby missing data from one individual is replaced with data from another individual, their nearest neighbour. For each individual with missing data, the subsets of variables to be used to select the nearest neighbour are chosen by sampling without replacement the complete data and estimating a best fit Bayesian network. We show that this approach leads to marked improvements in the recall and precision of directed edges in the final network identified, and we illustrate the approach through application to data from a recent study investigating the causal relationship between methylation and gene expression in early inflammatory arthritis patients. We also describe a second improvement in the form of a pseudo-Bayesian approach for upweighting certain network edges, which can be useful when there is prior evidence concerning their directions.

Background:DNA methylation patterns differ between leukocyte subsets and mediate the impact of environmental exposures on the molecular and functional phenotype of immune cells. Besides differences in mean methylation of CpG positions amongst patients with immune mediated diseases, recent evidence indicates variability of site-specific DNA methylation also contributes to pathogenesis1,2.Objectives:To seek evidence of altered DNA methylation patterns in RA, controlling for systemic inflammation and immunotherapy use.Methods:Patients with confirmed clinical diagnoses were enrolled from the Northeast Early Arthritis Cohort (NEAC). CD4+and CD19+lymphocytes were isolated from fresh blood by positive selection prior to therapeutic immune modulation. Methylation was quantified in cell subset-specific DNA (Infinium MethylationEPIC BeadChip, Illumina)3. Differentially methylated positions and regions (DMPs, DMRs) between RA and non-RA patients were identified (linear modelling, filtering on 5% pairwise difference in mean DNA methylation, and DMRcate package). Next, to identify instances where methylation variance differed between comparator groups, Bartlett’s test was performed using the iEVORA package, which accounts for outlier values4. Findings were controlled for technical confounders and subject to multiple test correction (FDR). A validated hypergeometric test was used to annotate enriched pathways.Results:After sample- and probe-level quality control, CD4+ and B lymphocyte specific data were respectively available for 45 and 49 RA patients, and 64 and 81 disease controls matched for systemic inflammation (CRP, ESR). No DMPs were identified in either cell type at FDR < 0.05 and Δβ ≥0.05. Only following relaxation of multiple test correction was it possible to identify DMRs in either cell type, most notably encapsulating 10 CpGs relatively hypomethylated at the promoter of the endosome protein-encodingRUFY1gene in CD4+ lymphocytes of RA patients (Δβ = 0.076). By contrast, striking evidence for differential variation in DNA methylation was observed at 291 and 601 CpGs of CD4+ and B lymphocytes, respectively (exemplars depicted in Figure 1). Only 15 of these differentially variable positions (DVPs) were common to both cell types. Pathway analysis highlighted potential functional consequences of DVP associations; for example, RA-specific hypervariability implicates prostaglandinsignalling in CD4+ lymphocytes.Conclusion:We highlight a role for altered variability in DNA methylation during the molecular pathogenesis of RA, and emphasise the importance of its study in relevant cell subsets.References:[1]Paul DSet al. Nature Communications 7, 13555 doi: 10.1038/ncomms13555 (2016).[2]Webster AP et al. Genome Medicine 10, 64 (2018)doi:10.1186/s13073-018-0575-9.[3]Clark AD et al. Journal of Allergy and Clinical Immunology 2019; doi: 10.1016/j.jaci.2019.12.910[4]Teschendorff AE et al. Nature Communications 2016; 7:12.Disclosure of Interests:Alexander Clark: None declared, Najib Naamane: None declared, Nisha Nair: None declared, Amy Anderson: None declared, Nishanthi Thalayasingam: None declared, Julie Diboll: None declared, Anne Barton Consultant of: AbbVie, Stephen Eyre: None declared, John D Isaacs Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Gilead, Janssen, Merck, Pfizer, Roche, Louise Reynard: None declared, Arthur Pratt Grant/research support from: Pfizer, GlaxoSmithKlein

The pathogenesis of chronic obstructive pulmonary disease (COPD) is a multifaceted process involving the alteration of pulmonary vasculature. Such vascular remodeling can be associated with inflammation, shear stress, and hypoxia-conditions commonly seen in patients with lung diseases. Particularly, the overproduction of reactive oxygen species (ROS) in the diseased lungs contributes greatly to pulmonary vascular remodeling. ROS play an important role in vascular homeostasis, yet excessive ROS can alter pulmonary vasculature and impair lung function, as implicated in COPD at all stages. Increased inflammatory cell infiltration and endothelial dysfunction both correspond to the severity of COPD. As a byproduct of vascular remodeling, pulmonary hypertension negatively affects the long-term survival rate of COPD patients. While there is currently no cure for COPD, several treatment options have focused on alleviating COPD symptoms. Interventions such as long-term oxygen therapy, endothelium-targeted treatment, and pharmacological therapies show promising results in improving the life span of COPD patients and attenuating the progression of pulmonary hypertension. In this chapter, we aim to discuss the contributing factors of pulmonary vascular remodeling in COPD with an emphasis on the ROS, as well as potential redox treatments for COPD-related vascular remodeling.

Background/Aims  Early diagnosis and intervention improves outcomes of immune mediated rheumatic and musculoskeletal diseases (RMDs) but may be hampered by diagnostic uncertainty. The extent to which rationally selected molecular parameters add value to clinical characteristics for diagnostic prediction in undifferentiated disease states warrants investigation. B lymphocytes play an increasingly recognised role in rheumatoid arthritis (RA) pathogenesis, and cell-specific methylation patterns link environmental exposures to genetic risk. We derived and tested the practical utility of a B lymphocyte-derived DNA methylation signature for predicting RA in an early arthritis clinic cohort. Methods  CD19+ B cell and peripheral blood mononuclear cell (PBMC) whole genome DNA methylation array data were available, respectively, from 109 inflammatory arthritis patients naïve to immunomodulatory drugs (Newcastle, UK; 38% confirmed to have a diagnosis of RA within 1 year) and 50 untreated undifferentiated arthritis (UA) patients (Leiden, The Netherlands; 68% classifiable RA within 1 year by 1987 ACR criteria versus alternate diagnoses). A bespoke machine learning pipeline employed a sequential model-based optimisation (SMBO) procedure for selecting, tuning and applying methods amongst ten feature-selection, six data-sampling and two classification algorithms in the Newcastle “training cohort.” The predictive performance of the resultant optimised molecular classifier was assessed in the independent Leiden “test cohort” alongside a previously described clinical prediction rule, using comparative area under receiver operating characteristic (AUROC) curves. A modification to the clinical prediction rule that incorporated a single parameter to reflect molecular classification was also assessed. The pipeline was implemented using the R machine learning package mlr. Results  Using the SMBO approach, 27 CpGs maximally discriminatory for RA were selected from B lymphocyte DNA methylome training data, and a molecular classifier was derived using the random forest algorithm. Applied to the independent PBMC methylome in UA patients, the classifier and the validated Leiden prediction rule performed similarly in predicting RA (AUROC [95% CI] = 0.8 [0.66-0.94] versus 0.78 [0.64-0.92]). Interestingly, incorporating a molecular risk score based on the 27-CpG signature into the validated Leiden clinical prediction rule significantly improved its performance (AUROC [95% CI] = 0.89 [0.79-0.98] versus 0.78 [0.64-0.92]; p = 0.048). When applied to the sub-cohort of 25 patients in the Leiden cohort who were negative for anti-citrullinated peptide autoantibodies (ACPA), enhanced performance of the modified over the un-modified clinical prediction rule was maintained (AUROC [95% CI] = 0.82 [0.65-1] versus 0.70 [0.45-0.95], respectively), although the difference did not reach statistical significance in this smaller cohort. Conclusion  We provide a proof of principle for the application of a B lymphocyte-derived epigenetic signature to enhance prediction of RA in UA patients using stored PBMCs. Further refinement of our pipeline represents a plausible means to expedite the diagnosis in undifferentiated RMDs and could offer pathophysiological insight. Disclosure  N. Naamane: None. E. Niemantsverdriet: None. N. Thalayasingam: None. N. Nair: None. A.D. Clark: None. K. Murray: None. B. Hargreaves: None. L.N. Reynard: None. S. Eyre: None. A. Barton: None. A.H.M. van der Helm-van Mil: None. A.G. Pratt: None.

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