Multilocus loss of DNA methylation in individuals with mutations in the histone H3 Lysine 4 Demethylase KDM5C

Background

A number of neurodevelopmental syndromes are caused by mutations in genes encoding proteins that normally function in epigenetic regulation. Identification of epigenetic alterations occurring in these disorders could shed light on molecular pathways relevant to neurodevelopment.

Results

Using a genome-wide approach, we identified genes with significant loss of DNA methylation in blood of males with intellectual disability and mutations in the X-linked KDM5C gene, encoding a histone H3 lysine 4 demethylase, in comparison to age/sex matched controls. Loss of DNA methylation in such individuals is consistent with known interactions between DNA methylation and H3 lysine 4 methylation. Further, loss of DNA methylation at the promoters of the three top candidate genes FBXL5, SCMH1, CACYBP was not observed in more than 900 population controls. We also found that DNA methylation at these three genes in blood correlated with dosage of KDM5C and its Y-linked homologue KDM5D. In addition, parallel sex-specific DNA methylation profiles in brain samples from control males and females were observed at FBXL5 and CACYBP.

Conclusions

We have, for the first time, identified epigenetic alterations in patient samples carrying a mutation in a gene involved in the regulation of histone modifications. These data support the concept that DNA methylation and H3 lysine 4 methylation are functionally interdependent. The data provide new insights into the molecular pathogenesis of intellectual disability. Further, our data suggest that some DNA methylation marks identified in blood can serve as biomarkers of epigenetic status in the brain.     

MOABS: model based analysis of bisulfite sequencing data

Background

Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age.

Results

We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged.

Conclusions

Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.     

Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns

Background

Aberrant CpG island promoter DNA hypermethylation is frequently observed in cancer and is believed to contribute to tumor progression by silencing the expression of tumor suppressor genes. Previously, we observed that promoter hypermethylation in breast cancer reflects cell lineage rather than tumor progression and occurs at genes that are already repressed in a lineage-specific manner. To investigate the generality of our observation we analyzed the methylation profiles of 1,154 cancers from 7 different tissue types.

Results

We find that 1,009 genes are prone to hypermethylation in these 7 types of cancer. Nearly half of these genes varied in their susceptibility to hypermethylation between different cancer types. We show that the expression status of hypermethylation prone genes in the originator tissue determines their propensity to become hypermethylated in cancer; specifically, genes that are normally repressed in a tissue are prone to hypermethylation in cancers derived from that tissue. We also show that the promoter regions of hypermethylation-prone genes are depleted of repetitive elements and that DNA sequence around the same promoters is evolutionarily conserved. We propose that these two characteristics reflect tissue-specific gene promoter architecture regulating the expression of these hypermethylation prone genes in normal tissues.

Conclusions

As aberrantly hypermethylated genes are already repressed in pre-cancerous tissue, we suggest that their hypermethylation does not directly contribute to cancer development via silencing. Instead aberrant hypermethylation reflects developmental history and the perturbation of epigenetic mechanisms maintaining these repressed promoters in a hypomethylated state in normal cells.     

Variations in genomic DNA methylation during the long-term in vitro proliferation of oil palm embryogenic suspension cultures

Key message

The long-term proliferation of embryogenic cell suspensions of oil palm is associated with changes in both genomic methylation rates and embryogenic capacities.

Abstract

In the aim of exploring the relationship between epigenetic stability and the long-term in vitro proliferation of plant tissues, we have studied changes in genomic DNA methylation levels in embryogenic suspensions of oil palm (Elaeis guineensis Jacq.). Five embryogenic callus lines were obtained from selected hybrid seeds and then proliferated as suspension cultures. Each clonal line obtained from a single genotype was subdivided into three independent subclonal lines. Once established, cultures proliferated for 12 months and genomic DNA was sampled at 4 months intervals for the estimation of global DNA methylation rates through high performance liquid chromatography (HPLC) quantitation of deoxynucleosides. Our results show that in vitro proliferation induces DNA hypermethylation in a time-dependent fashion. Moreover, this trend is statistically significant in several clonal lines and shared between subclonal lines originating from the same genotype. Interestingly, the only clonal line undergoing loss of genomic methylation in the course of proliferation has been found unable to generate somatic embryos. We discuss the possible implications of genome-wide DNA methylation changes in proliferating cells with a view to the maintenance of genomic and epigenomic stability.     

Modeling genomic data with type attributes, balancing stability and maintainability

Background

The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.

Results

We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation.

Conclusion

Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.     

Improved reduced representation bisulfite sequencing for epigenomic profiling of clinical samples

Background

DNA methylation plays crucial roles in epigenetic gene regulation in normal development and disease pathogenesis. Efficient and accurate quantification of DNA methylation at single base resolution can greatly advance the knowledge of disease mechanisms and be used to identify potential biomarkers. We developed an improved pipeline based on reduced representation bisulfite sequencing (RRBS) for cost-effective genome-wide quantification of DNA methylation at single base resolution. A selection of two restriction enzymes (Taq^αI and MspI) enables a more unbiased coverage of genomic regions of different CpG densities. We further developed a highly automated software package to analyze bisulfite sequencing results from the Solexa GAIIx system.

Results

With two sequencing lanes, we were able to quantify ~1.8 million individual CpG sites at a minimum sequencing depth of 10. Overall, about 76.7% of CpG islands, 54.9% of CpG island shores and 52.2% of core promoters in the human genome were covered with at least 3 CpG sites per region.

Conclusions

With this new pipeline, it is now possible to perform whole-genome DNA methylation analysis at single base resolution for a large number of samples for understanding how DNA methylation and its changes are involved in development, differentiation, and disease pathogenesis.     

Resveratrol retards progression of diabetic nephropathy through modulations of oxidative stress, proinflammatory cytokines, and AMP-activated protein kinase

Background

The Cdc42-interacting protein-4, Trip10 (also known as CIP4), is a multi-domain adaptor protein involved in diverse cellular processes, which functions in a tissue-specific and cell lineage-specific manner. We previously found that Trip10 is highly expressed in estrogen receptor-expressing (ER⁺) breast cancer cells. Estrogen receptor depletion reduced Trip10 expression by progressively increasing DNA methylation. We hypothesized that Trip10 functions as a tumor suppressor and may be involved in the malignancy of ER-negative (ER^-) breast cancer. To test this hypothesis and evaluate whether Trip10 is epigenetically regulated by DNA methylation in other cancers, we evaluated DNA methylation of Trip10 in liver cancer, brain tumor, ovarian cancer, and breast cancer.

Methods

We applied methylation-specific polymerase chain reaction and bisulfite sequencing to determine the DNA methylation of Trip10 in various cancer cell lines and tumor specimens. We also overexpressed Trip10 to observe its effect on colony formation and in vivo tumorigenesis.

Results

We found that Trip10 is hypermethylated in brain tumor and breast cancer, but hypomethylated in liver cancer. Overexpressed Trip10 was associated with endogenous Cdc42 and huntingtin in IMR-32 brain tumor cells and CP70 ovarian cancer cells. However, overexpression of Trip10 promoted colony formation in IMR-32 cells and tumorigenesis in mice inoculated with IMR-32 cells, whereas overexpressed Trip10 substantially suppressed colony formation in CP70 cells and tumorigenesis in mice inoculated with CP70 cells.

Conclusions

Trip10 regulates cancer cell growth and death in a cancer type-specific manner. Differential DNA methylation of Trip10 can either promote cell survival or cell death in a cell type-dependent manner.     

Direct interaction between the catalytic subunit of Protein Phosphatase 1 and pRb

Background

Among the epigenetic alterations occurring in cancer, DNA hypermethylation and histone hypoacetylation are the focus of intense research because their pharmacological inhibition has shown to produce antineoplastic activity in a variety of experimental models. The objective of this study was to evaluate the combined antineoplastic effect of the DNA methylation inhibitor hydralazine and the histone deacetylase inhibitor valproic acid in a panel of cancer cell lines.

Results

Hydralazine showed no growth inhibitory effect on cervical, colon, breast, sarcoma, glioma, and head & neck cancer cell lines when used alone. On the contrary, valproic acid showed a strong growth inhibitory effect that is potentiated by hydralazine in some cell lines. Individually, hydralazine and valproic acid displayed distinctive effects upon global gene over-expression but the number of genes over-expressed increased when cells were treated with the combination. Treatment of HeLa cells with hydralazine and valproic acid lead to an increase in the cytotoxicity of gemcitabine, cisplatin and adriamycin. A higher antitumor effect of adriamycin was observed in mice xenografted with human fibrosarcoma cells when the animals were co-treated with hydralazine and valproic acid.

Conclusion

Hydralazine and valproic acid, two widely used drugs for cardiovascular and neurological conditions respectively have promising antineoplastic effects when used concurrently and may increase the antitumor efficacy of current cytotoxic agents.     

Plasticity of DNA methylation in mouse T cell activation and differentiation

Background

Circulating CD4⁺ T helper cells are activated through interactions with antigen presenting cells and undergo differentiation into specific T helper cell subsets depending on the type of antigen encountered. In addition, the relative composition of the circulating CD4⁺ T cell population changes as animals mature with an increased percentage of the population being memory/effector type cells.

Results

Here, we report on the highly plastic nature of DNA methylation at the genome-wide level as T cells undergo activation, differentiation and aging. Of particular note were the findings that DNA demethylation occurred rapidly following T cell activation and that all differentiated T cell populations displayed lower levels of global methylation than the non-differentiated population. In addition, T cells from older mice had a reduced level of DNA methylation, most likely explained by the increase in the memory/effector cell fraction. Although significant genome-wide changes were observed, changes in DNA methylation at individual genes were restricted to specific cell types. Changes in the expression of enzymes involved in DNA methylation and demethylation reflect in most cases the changes observed in the genome-wide DNA methylation status.

Conclusion

We have demonstrated that DNA methylation is dynamic and flexible in CD4+ T cells and changes rapidly both in a genome-wide and in a targeted manner during T cell activation, differentiation. These changes are accompanied by parallel changes in the enzymatic complexes that have been implicated in DNA methylation and demethylation implying that the balance between these opposing activities may play a role in the maintaining the methylation profile of a given cell type but also allow flexibility in a cell population that needs to respond rapidly to environmental signals.     

The MiAge Calculator: a DNA methylation-based mitotic age calculator of human tissue types

Cell division is important in human aging and cancer. The estimation of the number of cell divisions (mitotic age) of a given tissue type in individuals is of great interest as it allows not only the study of biological aging (using a new molecular aging target) but also the stratification of prospective cancer risk. Here, we introduce the MiAge Calculator, a mitotic age calculator based on a novel statistical framework, the MiAge model. MiAge is designed to quantitatively estimate mitotic age (total number of lifetime cell divisions) of a tissue using the stochastic replication errors accumulated in the epigenetic inheritance process during cell divisions. With the MiAge model, the MiAge Calculator was built using the training data of DNA methylation measures of 4,020 tumor and adjacent normal tissue samples from eight TCGA cancer types and was tested using the testing data of DNA methylation measures of 2,221 tumor and adjacent normal tissue samples of five other TCGA cancer types. We showed that within each of the thirteen cancer types studied, the estimated mitotic age is universally accelerated in tumor tissues compared to adjacent normal tissues. Across the thirteen cancer types, we showed that worse cancer survivals are associated with more accelerated mitotic age in tumor tissues. Importantly, we demonstrated the utility of mitotic age by showing that the integration of mitotic age and clinical information leads to improved survival prediction in six out of the thirteen cancer types studied. The MiAge Calculator is available at http://www.columbia.edu/～sw2206/softwares.htm.