CellMethy: Identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues

Relationship between STAP1 methylation in peripheral blood T cells and the clinicopathological characteristics and prognosis of patients within 5-cm diameter HCC

BACKGROUND

The aim of this study is to explore the methylation of signal transduction adaptor protein 1 (STAP1) in peripheral blood T cells as a prognostic marker for hepatocellular carcinoma (HCC) ≤5 cm.

METHODS

A total of 66 HCC patients who visited our hospital from November 2012 to June 2016 were retrospectively analyzed, and 55 patients who met the inclusion and exclusion criteria were studied. Clinical and pathological data were collected from all patients to detect STAP1 methylation. STAP1 methylation expression was analyzed in HCC patients ≤5 cm with different clinicopathological features; univariate and independent prognostic factors were analyzed in HCC patients; and the relationship between STAP1 methylation expression and prognosis was analyzed in HCC patients.

RESULTS

There was no significant difference in STAP1 methylation expression between patients with different gender, age, history of alcoholism, history of liver cirrhosis, recurrence, 3-year OS, 5-year OS, treatment, number of tumors, tumor diameter, HBV-DNA, HBSAg, Hbe-Ag expression, and AFP level (P>0.05); however, there was significant difference in STAP1 methylation expression between patients with different survival, 3-year DFS, and 5-year DFS (P<0.05). Multivariate Cox regression analysis showed that recurrence and STAP1 methylation were independent factors for OS and DFS (P<0.05). Kaplan-Meier survival curve results showed that the median survival time, OS, and DFS of STAP1 hypermethylation expression were shorter than those of hypomethylation (P<0.05).

CONCLUSIONS

STAP1 methylation in peripheral blood T cells serves as a potential prognostic marker for HCC ≤5 cm.

Roles of TET and TDG in DNA demethylation in proliferating and non-proliferating immune cells

BACKGROUND

TET enzymes mediate DNA demethylation by oxidizing 5-methylcytosine (5mC) in DNA to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC). Since these oxidized methylcytosines (oxi-mCs) are not recognized by the maintenance methyltransferase DNMT1, DNA demethylation can occur through "passive," replication-dependent dilution when cells divide. A distinct, replication-independent ("active") mechanism of DNA demethylation involves excision of 5fC and 5caC by the DNA repair enzyme thymine DNA glycosylase (TDG), followed by base excision repair.

RESULTS

Here by analyzing inducible gene-disrupted mice, we show that DNA demethylation during primary T cell differentiation occurs mainly through passive replication-dependent dilution of all three oxi-mCs, with only a negligible contribution from TDG. In addition, by pyridine borane sequencing (PB-seq), a simple recently developed method that directly maps 5fC/5caC at single-base resolution, we detect the accumulation of 5fC/5caC in TDG-deleted T cells. We also quantify the occurrence of concordant demethylation within and near enhancer regions in the Il4 locus. In an independent system that does not involve cell division, macrophages treated with liposaccharide accumulate 5hmC at enhancers and show altered gene expression without DNA demethylation; loss of TET enzymes disrupts gene expression, but loss of TDG has no effect. We also observe that mice with long-term (1 year) deletion of Tdg are healthy and show normal survival and hematopoiesis.

CONCLUSIONS

We have quantified the relative contributions of TET and TDG to cell differentiation and DNA demethylation at representative loci in proliferating T cells. We find that TET enzymes regulate T cell differentiation and DNA demethylation primarily through passive dilution of oxi-mCs. In contrast, while we observe a low level of active, replication-independent DNA demethylation mediated by TDG, this process does not appear to be essential for immune cell activation or differentiation.

Converging genetic and epigenetic drivers of paediatric acute lymphoblastic leukaemia identified by an information-theoretic analysis

In cancer, linking epigenetic alterations to drivers of transformation has been difficult, in part because DNA methylation analyses must capture epigenetic variability, which is central to tumour heterogeneity and tumour plasticity. Here, by conducting a comprehensive analysis, based on information theory, of differences in methylation stochasticity in samples from patients with paediatric acute lymphoblastic leukaemia (ALL), we show that ALL epigenomes are stochastic and marked by increased methylation entropy at specific regulatory regions and genes. By integrating DNA methylation and single-cell gene-expression data, we arrived at a relationship between methylation entropy and gene-expression variability, and found that epigenetic changes in ALL converge on a shared set of genes that overlap with genetic drivers involved in chromosomal translocations across the disease spectrum. Our findings suggest that an epigenetically driven gene-regulation network, with UHRF1 (ubiquitin-like with PHD and RING finger domains 1) as a central node, links genetic drivers and epigenetic mediators in ALL.

Hierarchical cooperation of transcription factors from integration analysis of DNA sequences, ChIP-Seq and ChIA-PET data

Background

Chromosomal architecture, which is constituted by chromatin loops, plays an important role in cellular functions. Gene expression and cell identity can be regulated by the chromatin loop, which is formed by proximal or distal enhancers and promoters in linear DNA (1D). Enhancers and promoters are fundamental non-coding elements enriched with transcription factors (TFs) to form chromatin loops. However, the specific cooperation of TFs involved in forming chromatin loops is not fully understood.

Results

Here, we proposed a method for investigating the cooperation of TFs in four cell lines by the integrative analysis of DNA sequences, ChIP-Seq and ChIA-PET data. Results demonstrate that the interaction of enhancers and promoters is a hierarchical and dynamic complex process with cooperative interactions of different TFs synergistically regulating gene expression and chromatin structure. The TF cooperation involved in maintaining and regulating the chromatin loop of cells can be regulated by epigenetic factors, such as other TFs and DNA methylation.

Conclusions

Such cooperation among TFs provides the potential features that can affect chromatin’s 3D architecture in cells. The regulation of chromatin 3D organization and gene expression is a complex process associated with the hierarchical and dynamic prosperities of TFs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5535-2) contains supplementary material, which is available to authorized users.

Identifying CpG sites with different differential methylation frequencies in colorectal cancer tissues based on individualized differential methylation analysis

A big challenge to clinical diagnosis and therapy of colorectal cancer (CRC) is its extreme heterogeneity, and thus it would be of special importance if we could find common biomarkers besides subtype-specific biomarkers for CRC. Here, with DNA methylation data produced by different laboratories, we firstly revealed that the relative methylation-level orderings (RMOs) of CpG sites within colorectal normal tissues are highly stable but widely disrupted in the CRC tissues. This finding provides the basis for using the RankComp algorithm to identify differentially methylated (DM) CpG sites in every individual CRC sample through comparing the RMOs within the individual sample with the stable RMOs predetermined in normal tissues. For 75 CRC samples, RankComp detected averagely 4,062 DM CpG sites per sample and reached an average precision of 91.34% in terms that the hypermethylation or hypomethylation states of the DM CpG sites detected for each cancer sample were consistent with the observed differences between this cancer sample and its paired adjacent normal sample. Finally, we applied RankComp to identify DM CpG sites for each of the 268 CRC samples from The Cancer Genome Atlas and found 26 and 143 genes whose promoter regions included CpG sites that were hypermethylated and hypomethylated, respectively, in more than 95% of the 268 CRC samples. Individualized pathway analysis identified six pathways that were significantly enriched with DM genes in more than 90% of the CRC tissues. These universal DNA methylation biomarkers could be important diagnostic makers and therapy targets for CRC.