DNA methylation at IL32 in juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) is the most common autoimmune rheumatic disease of childhood. We recently showed that DNA methylation at the gene encoding the pro-inflammatory cytokine interleukin-32 (IL32) is reduced in JIA CD4+ T cells. To extend this finding, we measured IL32 methylation in CD4+ T-cells from an additional sample of JIA cases and age- and sex-matched controls, and found a reduction in methylation associated with JIA consistent with the prior data (combined case-control dataset: 25.0% vs 37.7%, p = 0.0045). Further, JIA was associated with reduced IL32 methylation in CD8+ T cells (15.2% vs 25.5%, p = 0.034), suggesting disease-associated changes to a T cell precursor. Additionally, we measured regional SNPs, along with CD4+ T cell expression of total IL32, and the γ and β isoforms. Several SNPs were associated with methylation. Two SNPs were also associated with JIA, and we found evidence of interaction such that methylation was only associated with JIA in minor allele carriers (e.g. rs10431961 p(interaction) = 0.011). Methylation at one measured CpG was inversely correlated with total IL32 expression (Spearman r = −0.73, p = 0.0009), but this was not a JIA-associated CpG. Overall, our data further confirms that reduced IL32 methylation is associated with JIA, and that SNPs play an interactive role

The potential of epigenetic therapy to target the 3D epigenome in endocrine-resistant breast cancer

Three-dimensional (3D) epigenome remodeling is an important mechanism of gene deregulation in cancer. However, its potential as a target to counteract therapy resistance remains largely unaddressed. Here, we show that epigenetic therapy with decitabine (5-Aza-mC) suppresses tumor growth in xenograft models of pre-clinical metastatic estrogen receptor positive (ER+) breast tumor. Decitabine-induced genome-wide DNA hypomethylation results in large-scale 3D epigenome deregulation, including de-compaction of higher-order chromatin structure and loss of boundary insulation of topologically associated domains. Significant DNA hypomethylation associates with ectopic activation of ER-enhancers, gain in ER binding, creation of new 3D enhancer–promoter interactions and concordant up-regulation of ER-mediated transcription pathways. Importantly, long-term withdrawal of epigenetic therapy partially restores methylation at ER-enhancer elements, resulting in a loss of ectopic 3D enhancer–promoter interactions and associated gene repression. Our study illustrates the potential of epigenetic therapy to target ER+ endocrine-resistant breast cancer by DNA methylation-dependent rewiring of 3D chromatin interactions, which are associated with the suppression of tumor growth

The epigenetic landscape of paediatric acute myeloid leukaemia

© 2019 Braydon Ashley MeyerPaediatric acute myeloid leukaemia (AML) is a cancer of the blood and bone marrow. It is currently one of the leading causes of cancer-related mortality in children. While induction therapy is largely successful in achieving patient remission, the relatively high mortality rate is driven by the large genetic heterogeneity of AML and recurrence of disease. Disease relapse rate is higher than other childhood leukaemias, is fast acting and often chemotherapy resistant. While much of the genetic contribution to disease has been described, there is still a component of AML pathogenesis that has yet to be discovered. Many of the genetic lesions found in adult AML directly affect epigenetic modifying genes, however this is not the case in children. Despite this, previous research has shown vast epigenetic alteration in paediatric AML. As such it is possible that some of the unexplained pathogenesis in childhood AML can be elucidated by modulation of gene activity via aberrant changes in the most widely studied epigenetic process, DNA methylation (DNAm). Few studies have comprehensively interrogated the DNA methylome of paediatric AML, nor has the prognostic utility or biomarker potential of DNAm been explored. In this study, we explored the global methylation profile of paediatric AML in comparison to non-leukaemic controls and subtype-dependant and independent biomarkers of disease that may have functional relevance. Furthermore, we described DNAm signatures with potential prognostic utility, to accurately identify predisposition to relapse at diagnosis. Genome-wide DNAm was interrogated via the HumanMethylation450 BeadChip Array (HM450K) on a cohort comprising of 128 archival and fresh bone marrow tissue sourced from multiple hospitals around Australia. This data was then combined with the TARGET AML cohort comprising of a further 231 bone marrow samples. Targeted replication and validation of findings was undertaken on a reduced cohort using SEQUENOM MassArray EpiTYPER. Bioinformatic and machine learning analyses were undertaken in R. The findings revealed subtype-independent genome-wide average methylation (GWAM) to be increased in diagnostic samples compared to non-leukaemic controls. This was further verified by differences in the global methylation proxy genes known as LINE1 and Alu. Deeper interrogation of these differences demonstrated wide-spread differential methylation in previously implicated genes in AML pathogenesis including WT1 and DGKG, both of which were validated in an independent cohort. Other genes identified to be differentially methylated included ZSCAN1, REC8 and IRX1. Subtype analysis validated previous studies showing inv(16)-specific differential methylation in MN1 and MEIS1. Finally, DNAm was used as the primary feature for a machine learning model designed to predict patient relapse at diagnosis. The final model achieved an area under the curve (AUC) of 94% with correct identification of 91% of all cases involved (F-measure=0.914). To date, this study represents the largest and most comprehensive insight into aberrant DNAm in paediatric AML. Results have increased our understanding of genes that are differentially methylated and highlight the potential utility of DNAm as a future prognostic biomarker. It is anticipated that these findings will serve as a foundation for future functional studies aimed at delivering truly personalised treatment regimens for children with AML

Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling

Abstract Background The Illumina family of Infinium Methylation BeadChip microarrays has been widely used over the last 15 years for genome-wide DNA methylation profiling, including large-scale and population-based studies, due to their ease of use and cost effectiveness. Succeeding the popular HumanMethylationEPIC BeadChip (EPICv1), the recently released Infinium MethylationEPIC v2.0 BeadChip (EPICv2) claims to extend genomic coverage to more than 935,000 CpG sites. Here, we comprehensively characterise the reproducibility, reliability and annotation of the EPICv2 array, based on bioinformatic analysis of both manifest data and new EPICv2 data from diverse biological samples. Results We find a high degree of reproducibility with EPICv1, evidenced by comparable sensitivity and precision from empirical cross-platform comparison incorporating whole genome bisulphite sequencing (WGBS), and high correlation between technical sample replicates, including between samples with DNA input levels below the manufacturer’s recommendation. We provide a full assessment of probe content, evaluating genomic distribution and changes from previous array versions. We characterise EPICv2’s new feature of replicated probes and provide recommendations as to the superior probes. In silico analysis of probe sequences demonstrates that probe cross-hybridisation remains a significant problem in EPICv2. By mapping the off-target sites at single nucleotide resolution and comparing with WGBS we show empirical evidence for preferential off-target binding. Conclusions Overall, we find EPICv2 a worthy successor to the previous Infinium methylation microarrays, however some technical issues remain. To support optimal EPICv2 data analysis we provide an expanded version of the EPICv2 manifest to aid researchers in understanding probe design, data processing, choosing appropriate probes for analysis and for integration with methylation datasets from previous versions of the Infinium Methylation BeadChip

Additional file 1 of Characterisation and reproducibility of the HumanMethylationEPIC v2.0 BeadChip for DNA methylation profiling

Additional file 1: Table S1. Summary of probes on EPICv2, divided by probe type and Infinium design type. Table S2. Details of nv probes and their matched variant within the COSMIC census database. Table S3. Summary of control probes on EPICv2. Table S4. Discrepant probes between Illumina manifest and script to recompute sequence from Illumina manifest 'Forward sequence' and 'IlmnID'. Table S5. Summary of number of probes per a) exact-replicate, b) location-replicate and c) sequence-only-replicate probe set. Table S6. Examples of a) exact-replicate, b) location-replicate and c) sequence-only-replicate probe sets. Table S7. Lists of IlmnIDs for probes that have different types of replicate. Table S8. Lists of IlmnIDs for probes that have different types of replicate, grouped by probe set. Table S9. Matches between EPICv2 probes and probes on older versions of the microarray based on 1) probe name, 2) target location (hg38) and 3) probe sequence of sesame manifests. Table S10. Number of replicate probes within older arrays (excluding control probes). Table S11. Number and percentage of sites targeted on each chromosome for each probe category. Table S12. Distribution of probes relative to different genomic features. Table S13. Details of samples profiled on EPICv2. Table S14. Number of probes with detection p-value >0.05 per sample. Table S15. Probes with no BLAT hits. Table S16. BLAT hit locations for probes that do not map to their target location in the Illumina manifest. Table S17. Results of competitive evaluation of location replicates

Proceedings Of The 23Rd Paediatric Rheumatology European Society Congress: Part Two

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