Non-specific filtering of beta-distributed data

Perfluorooctanesulfonic acid (PFOS) induced cancer related DNA methylation alterations in human breast cells: A whole genome methylome study

DNA methylation plays a pivotal role in cancer. The ubiquitous contaminant perfluorooctanesulfonic acid (PFOS) has been epidemiologically associated with breast cancer, and can induce proliferation and malignant transformation of normal human breast epithelial cells (MCF-10A), but the information about its effect on DNA methylation is sparse. The aim of this study was to characterize the whole-genome methylome effects of PFOS in our breast cell model and compare the findings with previously demonstrated DNA methylation alterations in breast tumor tissues. The DNA methylation profile was assessed at single CpG resolution in MCF-10A cells treated with 1 μM PFOS for 72 h by using Enzymatic Methyl sequencing (EM-seq). We found 12,591 differentially methylated CpG-sites and 13,360 differentially methylated 100 bp tiles in the PFOS exposed breast cells. These differentially methylated regions (DMRs) overlapped with 2406 genes of which 494 were long non-coding RNA and 1841 protein coding genes. We identified 339 affected genes that have been shown to display altered DNA methylation in breast cancer tissue and several other genes related to cancer development. This includes hypermethylation of GACAT3, DELEC1, CASC2, LCIIAR, MUC16, SYNE1 and hypomethylation of TTN and KMT2C. DMRs were also found in estrogen receptor genes (ESR1, ESR2, ESRRG, ESRRB, GREB1) and estrogen responsive genes (GPER1, EEIG1, RERG). The gene ontology analysis revealed pathways related to cancer phenotypes such as cell adhesion and growth. These findings improve the understanding of PFOS's potential role in breast cancer and illustrate the value of whole-genome methylome analysis in uncovering mechanisms of chemical effects, identifying biomarker candidates, and strengthening epidemiological associations, potentially impacting risk assessment.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc ^Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

Differential regulation of the DNA methylome in adults born during the Great Chinese Famine in 1959-1961

BACKGROUND

Extensive epidemiological studies have established the association between exposure to early-life adversity and health status and diseases in adults. Epigenetic regulation is considered as a key mediator for this phenomenon but analysis on humans is sparse. The Great Chinese Famine lasting from 1958 to 1961 is a natural string of disasters offering a precious opportunity for elucidating the underlying epigenetic mechanism of the long-term effect of early adversity.

METHODS

Using a high-throughput array platform for DNA methylome profiling, we conducted a case-control epigenome-wide association study on early-life exposure to Chinese famine in 79 adults born during 1959-1961 and compared to 105 unexposed subjects born 1963-1964.

RESULTS

The single CpG site analysis of whole epigenome revealed a predominant pattern of decreased DNA methylation levels associated with fetal exposure to famine. Four CpG sites were detected with p < 1e-06 (linked to EHMT1, CNR1, UBXN7 and ESM1 genes), 16 CpGs detected with 1e-06 < p < 1e-05 and 157 CpGs with 1e-05 < p < 1e-04, with a predominant pattern of hypomethylation. Functional annotation to genes and their enriched biological pathways mainly involved neurodevelopment, neuropsychological disorders and metabolism. Multiple sites analysis detected two top-rank differentially methylated regions harboring RNF39 on chromosome 6 and PTPRN2 on chromosome 7, both showing epigenetic association with stress-related conditions.

CONCLUSION

Early-life exposure to famine could mediate DNA methylation regulations that persist into adulthood with broad impacts in the activities of genes and biological pathways. Results from this study provide new clues to the epigenetic embedding of early-life adversity and its impacts on adult health.

Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative k-Medoids Clustering Algorithm (T-CLUST)

Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k-means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed. In this study, we introduce three related algorithms for a complete high-throughput methylation analysis: a variance-based dimension reduction algorithm to handle high-dimensionality in data, an outlier detection algorithm to identify the outliers of data, and an advanced Tabu-based iterative k-medoids clustering algorithm (T-CLUST) to reduce the impact of initial solutions on the performance of conventional k-medoids algorithm. The performance of the proposed algorithms is demonstrated on nine different real DNA methylation datasets obtained from the Gene Expression Omnibus DataSets database. The accuracy of the cluster identification obtained by our proposed algorithms is higher than those of hierarchical and k-means clustering, as well as the conventional methods. The algorithms are implemented in MATLAB, and available at: http://www.coe.miami.edu/simlab/tclust.html.

DNA methylation of chronic lymphocytic leukemia with differential response to chemotherapy

Acquired resistance to chemotherapy is an important clinical problem and can also occur without detectable cytogenetic aberrations or gene mutations. Chronic lymphocytic leukemia (CLL) is molecularly well characterized and has been elemental for establishing central paradigms in oncology. This prompted us to check whether specific epigenetic changes at the level of DNA methylation might underlie development of treatment resistance. We used Illumina Infinium HumanMethylation450 BeadChips to obtain DNA methylation profiles of 71 CLL patients with differential responses. Thirty-six patients were categorized as relapsed/refractory after treatment with fludarabine or bendamustine and 21 of them had genetic aberrations of TP53. The other 35 patients were untreated at the time of sampling and 15 of them had genetic aberration of TP53. Although we could not correlate chemoresistance with epigenetic changes, the patients were comprehensively characterized regarding relevant prognostic and molecular markers (e.g. IGHV mutation status, chromosome aberrations, TP53 mutation status, clinical parameters), which makes our dataset a unique and valuable resource that can be used by researchers to test alternative hypotheses.

Measurement(s)	DNA methylation
Technology Type(s)	methylation profiling by array
Factor Type(s)	TP53 mutation status • response to fludarabine or bendamustine • chromosomal aberration • IGHV mutation status
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12006624

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing

BACKGROUND

Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data.

RESULTS

In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts.

CONCLUSIONS

Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.

TOAST: improving reference-free cell composition estimation by cross-cell type differential analysis

In the analysis of high-throughput data from complex samples, cell composition is an important factor that needs to be accounted for. Except for a limited number of tissues with known pure cell type profiles, a majority of genomics and epigenetics data relies on the "reference-free deconvolution" methods to estimate cell composition. We develop a novel computational method to improve reference-free deconvolution, which iteratively searches for cell type-specific features and performs composition estimation. Simulation studies and applications to six real datasets including both DNA methylation and gene expression data demonstrate favorable performance of the proposed method. TOAST is available at https://bioconductor.org/packages/TOAST .

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis

Bisulfite treatment-based methylation microarray (mainly Illumina 450K Infinium array) and next-generation sequencing (reduced representation bisulfite sequencing, Agilent SureSelect Human Methyl-Seq, NimbleGen SeqCap Epi CpGiant or whole-genome bisulfite sequencing) are commonly used for base resolution DNA methylome research. Although multiple tools and methods have been developed and used for the data preprocessing and analysis, confusions remains for these platforms including how and whether the 450k array should be normalized; which platform should be used to better fit researchers' needs; and which statistical models would be more appropriate for differential methylation analysis. This review presents the commonly used platforms and compares the pros and cons of each in methylome profiling. We then discuss approaches to study design, data normalization, bias correction and model selection for differentially methylated individual CpGs and regions.