DNMT inhibitors reverse a specific signature of aberrant promoter DNA methylation and associated gene silencing in AML

<b>Background</b>. Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) are neoplastic disorders of hematopoietic stem cells. DNA methyltransferase inhibitors (DNMTi), 5-azacytidine (AzaC) and 5-aza-2’-deoxycytidine (Decitabine), benefit some MDS/AML patients. However, the role of DNMTi-induced DNA hypomethylation in regulation of gene expression in AML is unclear.<p></p> <b>Results. </b> We compared the effects of AzaC on DNA methylation and gene expression using whole-genome single-nucleotide bisulfite-sequencing (WGBS) and RNA-sequencing in OCI-AML3 (AML3) cells. For data analysis, we used an approach recently developed for discovery of differential patterns of DNA methylation associated with changes in gene expression, that is tailored to single-nucleotide bisulfite-sequencing data (Washington University Interpolated Methylation Signatures (WIMSi)). By this approach, a subset of genes upregulated by AzaC was found to be characterized by AzaC-induced signature methylation loss flanking the transcription start site. These genes are enriched for genes increased in methylation and decreased in expression in AML3 cells compared to normal hematopoietic stem and progenitor cells. Moreover, these genes are preferentially upregulated by Decitabine in human primary AML blasts, and control cell proliferation, death and development. <p></p> <b>Conclusions.</b> Our WGBS and WIMSi data analysis approach has identified a set of genes whose is methylation and silencing in AML is reversed by DNMTi. These genes are good candidates for direct regulation by DNMTi, and their reactivation by DNMTi may contribute to therapeutic activity. This study also demonstrates the ability of WIMSi to reveal relationships between DNA methylation and gene expression, based on single-nucleotide bisulfite-sequencing and RNA-seq data.<p></p&gt

DNMT gene expression and methylome in Marek’s disease resistant and susceptible chickens prior to and following infection by MDV

Marek’s disease (MD) is characterized as a T cell lymphoma induced by a cell-associated α-herpesvirus, Marek’s disease virus type 1 (MDV1). As with many viral infectious diseases, DNA methylation variations were observed in the progression of MD; these variations are thought to play an important role in host-virus interactions. We observed that DNA methyltransferase 3a (DNMT3a) and 3b (DNMT3b) were differentially expressed in chicken MD-resistant line 6(3) and MD-susceptible line 7(2) at 21 d after MDV infection. To better understand the role of methylation variation induced by MDV infection in both chicken lines, we mapped the genome-wide DNA methylation profiles in each line using Methyl-MAPS (methylation mapping analysis by paired-end sequencing). Collectively, the data sets collected in this study provide a more comprehensive picture of the chicken methylome. Overall, methylation levels were reduced in chickens from the resistant line 6(3) after MDV infection. We identified 11,512 infection-induced differential methylation regions (iDMRs). The number of iDMRs was larger in line 7(2) than in line 6(3), and most of iDMRs found in line 6(3) were overlapped with the iDMRs found in line 7(2). We further showed that in vitro methylation levels were associated with MDV replication, and found that MDV propagation in the infected cells was restricted by pharmacological inhibition of DNA methylation. Our results suggest that DNA methylation in the host may be associated with disease resistance or susceptibility. The methylation variations induced by viral infection may consequentially change the host transcriptome and result in diverse disease outcomes

Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes

Methylation of the CpG-rich region (CpG island) overlapping a gene’s promoter is a generally accepted mechanism for silencing expression. While recent technological advances have enabled measurement of DNA methylation and expression changes genome-wide, only modest correlations between differential methylation at gene promoters and expression have been found. We hypothesize that stronger associations are not observed because existing analysis methods oversimplify their representation of the data and do not capture the diversity of existing methylation patterns. Recently, other patterns such as CpG island shore methylation and long partially hypomethylated domains have also been linked with gene silencing. Here, we detail a new approach for discovering differential methylation patterns associated with expression change using genome-wide high-resolution methylation data: we represent differential methylation as an interpolated curve, or signature, and then identify groups of genes with similarly shaped signatures and corresponding expression changes. Our technique uncovers a diverse set of patterns that are conserved across embryonic stem cell and cancer data sets. Overall, we find strong associations between these methylation patterns and expression. We further show that an extension of our method also outperforms other approaches by generating a longer list of genes with higher quality associations between differential methylation and expression