Abstract 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.
Recently, epigenetics has become an important field of research, with several diseases related to alteration in methylation patterns. Whether these epimutations are early events due to a local perturbation causing the initial epigenetic event and then leading to pathogenesis, or a later consequence of the pathology itself remains open for discussion. The objective of this study is to assess the methylation patterns of CpG islands in naïve and memory T-cells and monocytes from drug-naïve, early Rheumatoid arthritis (RA) patients.
Materials and methods
The methylation patterns of 480,000 CpGs were measured in 6 healthy controls and 10 RA patients using an Illumina methylation genome-wide array (EWAS). T-tests were used to calculate the significance of the methylation differences at each CpG site. A p-value of 1 × 10–4 was used to elucidate highly specific differences. Methylation patterns of individual promoters were then reconstructed using the Human genome database for the position and length of CpG islands. The genes with 3 or more differentially methylated CpGs clustered over a short distance and localised in potentially regulatory regions of the gene were deemed relevant.
Results
Analysis of the EWAS data showed 4123 CpG sites differently methylated in naïve T-cells, 2672 in memory T-cells and 904 in monocytes. Of the relevant genes, TNFR1 appeared particularly interesting. We therefore focused our next analysis on the TNF-α signalling pathway as this has proven pivotal in early disease. Results highlight 9 genes implicated in the 3 TNF-signalling cascades: TNFR1, LTBR, TRAF1, BCL2, UACA, RNF36, MIR21, DAXX and MAP3K14-AS1. These 9 loci exhibited different methylation patterns in naïve T-cells. Each had between 3 and 10 CpGs with differential methylation over short DNA regions (<250 bp) suggesting a potential effect the 3-dimentional structure of the DNA possibly affecting gene expression. These patterns were highly specific to naïve T-cells and were not observed in memory T-cells or monocytes in RA.
Conclusion
Naïve T-cells have been implicated in RA pathogenesis through activation/differentiation by cytokine rather than antigen.1 This data confirms the selective role of naïve T-cells in early RA primarily linked to differences in up- and down-regulation of elements involved in the TNF-signalling pathway.
In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.
Alteration in epigenetic patterns have been related to several diseases including RA. Early detection of RA will enable more effective treatment. The aim of our project is to identify the early changes in DNA methylation patterns of naïve and memory CD4+T-cells, and monocytes in Early RA patient to help understanding early disease pathology and to help finding the potential for biomarker development.
Methods
The methylation patterns of 480,000 CpGs were measured in 3 cell types (memory T-cell, naïve T-cell and monocytes) in 6 healthy control and 10 RA patients using an Illumina methylation genome-wide array (EWAS). Standard t-test were performed to associate p-value to CpG. Hierarchical clustering of CpG (p<0.001) were performed and Heat maps were generated using R. Venn diagram were used to compare common CpG between cell types. Gene annotation and function analysis was performed using Panther.
Results
The analysis of the EWAS data showed 20,578 CpG sites differently methylated in RA for naïve T-cells, 15 794 for memory T-cells and 7180 for monocytes. Heat maps showed 42.97% over and 57.03% under methylated in RA for naïve T-cells, 89.95% over and 10.05% under methylated for memory T-cells, and 53.59% over and 46.41% under methylated for monocytes. The top 10 over and under methylated genes were identified with transcription factors, immunity-related protein/function, signalling protein, musculoskeletal protein, enzymes and activity related to nucleotides. Overlapping of 4 genes (ANKRD1, FAM20C, EFCAB1, HRAT92) were shown between 3 cell subsets. Venn diagrams showed 249 CpG common to all 3 subsets, 2662 common to the T-cell subsets, and 473 or 271 between monocytes and naïve or memory T-cells, respectively. A repeated Panther analysis of gene on the common 249 CpG showed genes to be related to HLA (1%), binding between protein DNA (17%), binding between proteins (19%), enzymatic activity (32%), receptors (9% of which 3% was associated with TNF signalling), and signalling (5%).
Conclusion
This analysis clearly demonstrate over and under methylation in several CpG islands in RA in all 3 subsets. The genes differentially methylated in all 3 subsets may offer potential for the development of biomarker for the early diagnosis of RA.
Alterations in DNA methylation patterns have been related to several diseases, including Rheumatoid Arthritis (RA).
Objectives
To identify changes in DNA methylation pattern of naïve and memory CD4 +T cells and monocytes in early, drug naïve RA patients to help understand early event in disease pathology.
Methods
The methylation patterns of 480,000 CpGs were analysed in the 3 cell types from 6 healthy control (HC) and 10 RA patients using an Illumina genome-wide array. Standard t-tests were performed to associate p-value to individual CpG-probe. A scoring system was developed to select and prioritise differentially methylated CpGs with potential cumulative effect due to proximity with other significant CpGs. Rules for scoring were designed with respect to the significance of each CpGs and the distance (in bp) between them. Further filtering was applied to initially select the CpGs which highest significant (p-value≤0.0001) which have at least 2 proximal significant CpGs (p-value≤0.01). Rules were coded in R for systematic analysis. Lists of selected CpGs for all three cells for both hypo- and hyper-methylation were generated. Commonality was analysed using Venn diagram.
Results
Different changes in methylation patterns were observed between HC and RA in the 3 cell types and thresholds of significance were set at p-value 0.01, 0.001 and 0.0001. The total number of differentially methylated CpGs was enriched in naïve T-cells (18,020) compared to memory (14,197) and monocytes (6,490) (p-value≤0.01). Using our designed rules, we were able to prioritise cluster of differentially methylated CpGs which were then associated to 420, 7, and 48 hypomethylated genes, and 420, 719 and 21 hypermethylated genes respectively in naïve/memory T-cells and monocytes. Venn diagrams of hypermethylated gene showed only 1 genes (ABAT) in common to all 3 subsets. Hypomethylated gene showed no commonalities between the 3 cell subsets, and only 1 gene common between T-cell subsets (SLC43A2). Of note, the TNF gene was second on the priority list for naïve T-cell hypomethylation while no difference was observed in memory/monocytes. The IL-17 gene was de-methylated only in memory T-cells, hypermethylated in RA, but fully methylated in naïve/monocytes. Hypomethylation of socs3 was specific to monocytes.
Conclusions
These data suggest quite different types of changes in patterns of DNA methylation affecting the 3 subsets early in the RA disease process. Our scoring system to prioritise clusters of differential methylation highlighted genes known to be related to early RA pathogenesis. Further work remains to explore the relationship between these genes and the biological effect at the transcriptional/translational level resulting from these alterations in DNA methylation.
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