Ionizing radiations induce shared epigenomic signatures unraveling adaptive mechanisms of cancerous cell lines with or without methionine dependency.

BACKGROUND: Although radiation therapy represents a core cancer treatment modality, its efficacy is hampered by radioresistance. The effect of ionizing radiations (IRs) is well known regarding their ability to induce genetic alterations; however, their impact on the epigenome landscape in cancer, notably at the CpG dinucleotide resolution, remains to be further deciphered. In addition, no evidence is available regarding the effect of IRs on the DNA methylome profile according to the methionine dependency phenotype, which represents a hallmark of metabolic adaptation in cancer. METHODS: We used a case-control study design with a fractionated irradiation regimen on four cancerous cell lines representative of HCC (HepG2), melanoma (MeWo and MeWo-LC1, which exhibit opposed methionine dependency phenotypes), and glioblastoma (U251). We performed high-resolution genome-wide DNA methylome profiling using the MethylationEPIC BeadChip on baseline conditions, irradiated cell lines (cumulative dose of 10 Gy), and non-irradiated counterparts. We performed epigenome-wide association studies to assess the effect of IRs and methionine-dependency-oriented analysis by carrying out epigenome-wide conditional logistic regression. We looked for epigenome signatures at the locus and single-probe (CpG dinucleotide) levels and through enrichment analyses of gene ontologies (GO). The EpiMet project was registered under the ID#AAP-BMS_003_211. RESULTS: EWASs revealed shared GO annotation pathways associated with increased methylation signatures for several biological processes in response to IRs, including blood circulation, plasma membrane-bounded cell projection organization, cell projection organization, multicellular organismal process, developmental process, and animal organ morphogenesis. Epigenome-wide conditional logistic regression analysis on the methionine dependency phenotype highlighted several epigenome signatures related to cell cycle and division and responses to IR and ultraviolet light. CONCLUSIONS: IRs generated a variation in the methylation level of a high number of CpG probes with shared biological pathways, including those associated with cell cycle and division, responses to IRs, sustained angiogenesis, tissue invasion, and metastasis. These results provide insight on shared adaptive mechanisms of the epigenome in cancerous cell lines in response to IR. Future experiments should focus on the tryptic association between IRs, the initiation of a radioresistance phenotype, and their interaction with methionine dependency as a hallmark of metabolic adaptation in cancer.

Epigenetic quantification of circulating immune cells in peripheral blood of triple-negative breast cancer patients.

BACKGROUND: A shift in the proportions of blood immune cells is a hallmark of cancer development. Here, we investigated whether methylation-derived immune cell type ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) are associated with triple-negative breast cancer (TNBC). METHODS: Leukocyte subtype-specific unmethylated/methylated CpG sites were selected, and methylation levels at these sites were used as proxies for immune cell type proportions and mdNLR estimation in 231 TNBC cases and 231 age-matched controls. Data were validated using the Houseman deconvolution method. Additionally, the natural killer (NK) cell ratio was measured in a prospective sample set of 146 TNBC cases and 146 age-matched controls. RESULTS: The mdNLRs were higher in TNBC cases compared with controls and associated with TNBC (odds ratio (OR) range (2.66-4.29), all Padj. < 1e-04). A higher neutrophil ratio and lower ratios of NK cells, CD4 + T cells, CD8 + T cells, monocytes, and B cells were associated with TNBC. The strongest association was observed with decreased NK cell ratio (OR range (1.28-1.42), all Padj. < 1e-04). The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls (P = 0.019). CONCLUSION: This immunomethylomic study shows that a shift in the ratios/proportions of leukocyte subtypes is associated with TNBC, with decreased NK cell showing the strongest association. These findings improve our knowledge of the role of the immune system in TNBC and point to the possibility of using NK cell level as a non-invasive molecular marker for TNBC risk assessment, early detection, and prevention.

Epigenetic modelling of former, current and never smokers.

BACKGROUND: DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. RESULTS: We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. CONCLUSIONS: Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary.

Interpretation of omics data analyses.

Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

New molecular approaches to Alzheimer's disease.

Alzheimer's disease is a neurodegenerative disorder and the most common and devastating form of dementia. It affects mainly older people, accounting for 50-80% of dementia cases. The age is the main associated risk factor and based on the onset age, early-onset (EOAD) or late-onset (LOAD) forms are distinguished. AD has a strong impact both on the life-style of patients and their families and on the society, due to the high costs related to social and medical care. So far, despite the great advances in understanding of the AD pathogenesis, there is no a cure for this form of dementia and current available treatments are limited to temporarily relieve symptoms. In this review, firstly we give an overview of the current knowledge of the genetic basis of both forms of AD with a particular emphasis on the insights in the understanding of the pathogenic mechanisms of this disorder. Then we discuss the promising relevance of "omics sciences" and the open challenges of the application of Big Data in promoting precision medicine for AD.

Considerations when processing and interpreting genomics data of the placenta.

The application of genomic approaches to placental research has opened exciting new avenues to help us understand basic biological properties of the placenta, improve prenatal screening/diagnosis, and measure effects of in utero exposures on child health outcomes. In the last decade, such large-scale genomic data (including epigenomics and transcriptomics) have become more easily accessible to researchers from many disciplines due to the increasing ease of obtaining such data and the rapidly evolving computational tools available for analysis. While the potential of large-scale studies has been widely promoted, less attention has been given to some of the challenges associated with processing and interpreting such data. We hereby share some of our experiences in assessing data quality, reproducibility, and interpretation in the context of genome-wide studies of the placenta, with the aim to improve future studies. There is rarely a single "best" approach, as that can depend on the study question and sample cohort. However, being consistent, thoroughly assessing potential confounders in the data, and communicating key variables in the methods section of the manuscript are critically important to help researchers to collaborate and build on each other's work.

MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions.

Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the three methods combination, we implemented both the iterative method by Qu et al. [Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): "Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation," Bioinformatics, 29, 2645-2646.], and also a novel non iterative approximation using Lagrange multipliers. The newly proposed non iterative solutions greatly decrease computational time, common bottlenecks when processing high-throughput data. The MLML2R package is flexible as it takes as input both, preprocessed intensities from Infinium Methylation arrays and counts from Next Generation Sequencing technologies. The MLML2R package is freely available at https://CRAN.R-project.org/package=MLML2R.

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition.

The Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenomics Project seek to characterize the epigenome in diverse cell types using assays that identify, for example, genomic regions with modified histones or accessible chromatin. These efforts have produced thousands of datasets but cannot possibly measure each epigenomic factor in all cell types. To address this, we present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to computationally impute missing experiments. PREDICTD leverages an elegant model called "tensor decomposition" to impute many experiments simultaneously. Compared with the current state-of-the-art method, ChromImpute, PREDICTD produces lower overall mean squared error, and combining the two methods yields further improvement. We show that PREDICTD data captures enhancer activity at noncoding human accelerated regions. PREDICTD provides reference imputed data and open-source software for investigating new cell types, and demonstrates the utility of tensor decomposition and cloud computing, both promising technologies for bioinformatics.

An Efficient Approach to Screening Epigenome-Wide Data.

Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk.

Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies.

In epigenome-wide association studies (EWAS), different methylation profiles of distinct cell types may lead to false discoveries. We introduce ReFACTor, a method based on principal component analysis (PCA) and designed for the correction of cell type heterogeneity in EWAS. ReFACTor does not require knowledge of cell counts, and it provides improved estimates of cell type composition, resulting in improved power and control for false positives in EWAS. Corresponding software is available at http://www.cs.tau.ac.il/~heran/cozygene/software/refactor.html.

Epigenetic and genetic variation at SKA2 predict suicidal behavior and post-traumatic stress disorder.

Traumatic stress results in hypothalamic pituitary adrenal (HPA) axis abnormalities and an increased risk to both suicidal behaviors and post-traumatic stress disorder (PTSD). Previous work out of our laboratory identified SKA2 DNA methylation associations with suicidal behavior in the blood and brain of multiple cohorts. Interaction of SKA2 with stress predicted suicidal behavior with ~80% accuracy. SKA2 is hypothesized to reduce the ability to suppress cortisol following stress, which is of potentially high relevance in traumatized populations. Our objective was to investigate the interaction of SKA2 and trauma exposure on HPA axis function, suicide attempt and PTSD. SKA2 DNA methylation at Illumina HM450 probe cg13989295 was assessed for association with suicidal behavior and PTSD metrics in the context of Child Trauma Questionnaire (CTQ) scores in 421 blood and 61 saliva samples from the Grady Trauma Project (GTP) cohort. Dexamethasone suppression test (DST) data were evaluated for a subset of 209 GTP subjects. SKA2 methylation interacted with CTQ scores to predict lifetime suicide attempt in saliva and blood with areas under the receiver operator characteristic curve (AUCs) of 0.76 and 0.73 (95% confidence interval (CI): 0.6-0.92, P = 0.003, and CI: 0.65-0.78, P < 0.0001) and to mediate the suppression of cortisol following DST (ß = 0.5 ± 0.19, F = 1.51, degrees of freedom (df) = 12/167, P = 0.0096). Cumulatively, the data suggest that epigenetic variation at SKA2 mediates vulnerability to suicidal behaviors and PTSD through dysregulation of the HPA axis in response to stress.

Integrative modeling of multi-platform genomic data under the framework of mediation analysis.

Given the availability of genomic data, there have been emerging interests in integrating multi-platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi-square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path-specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path-specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies.

Applications of alignment-free methods in epigenomics.

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

The dilemma of choosing the ideal permutation strategy while estimating statistical significance of genome-wide enrichment.

Integrative analyses of genomic, epigenomic and transcriptomic features for human and various model organisms have revealed that many such features are nonrandomly distributed in the genome. Significant enrichment (or depletion) of genomic features is anticipated to be biologically important. Detection of genomic regions having enrichment of certain features and estimation of corresponding statistical significance rely on the expected null distribution generated by a permutation model. We discuss different genome-wide permutation approaches, present examples where the permutation strategy affects the null model and show that the confidence in estimating statistical significance of genome-wide enrichment might depend on the choice of the permutation approach. In those cases, where biologically relevant constraints are unclear, it is preferable to examine whether key conclusions are consistent, irrespective of the choice of the randomization strategy.

Genome-wide DNA methylation analysis in alcohol dependence.

Genetic, epigenetic, and environmental factors influence the development of alcohol dependence (AD). Recent studies have shown that DNA methylation markers in peripheral blood may serve as risk markers for AD. Yet a genome-wide epigenomic approach investigating the role of DNA methylation in AD has yet to be performed. We conducted a population-based, case-control study of genome-wide DNA methylation to determine if alterations in gene-specific methylation were associated with AD in a Chinese population. Using the Illumina Infinium Human Methylation27 BeadChip, we assessed gene-specific methylation in over 27 000 CpG sites from DNA isolated from lymphocytes in 63 male AD in-patients and 65 male healthy controls. Using a multi-factorial statistical model, we observed differential methylation between cases and controls at multiple CpG sites with the majority of the methylated CpG sites being hypomethylated. Analyses with the online gene set analysis toolkit WebGestalt revealed that the genes of interest were enriched in multiple biological processes involved in AD development. Gene Ontology function annotation showed that stress, immune response and signal transduction were highly associated with AD. Further analysis by the Kyoto Encyclopedia of Genes and Genomes revealed associations with multiple pathways involved in metabolism through cytochrome P450, cytokine-cytokine receptor interaction and calcium signaling. Associations with canonical pathways previously shown to be involved in AD were also observed, such as dehydrogenases 1A (ADH1A), ADH7, aldehyde dehydrogenases 3B2 (ALDH3B2) and cytochrome P450 2A13. We present evidence that alterations in DNA methylation may be associated with AD, which is consistent with epigenetic theory.

The genetic epidemiology of nonalcoholic fatty liver disease: toward a personalized medicine.

The understanding of the genetic bases of complex diseases such as nonalcoholic fatty liver disease opens new opportunities and challenges. This article explores new tools designed toward moving genomic data into clinical medicine, providing putative answers to more practical questions.

Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.

The analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.

Biologic and epigenetic impact of commuting to work by car or using public transportation: a case-control study.

BACKGROUND AND AIMS: Commuting by public transportation (PT) entails more physical activity and energy expenditure than by cars, but its biologic consequences are unknown. METHODS: In 2009-2010, we randomly sampled New York adults, usually commuting either by car (n=79) or PT (n=101). Measures comprised diet and physical activity questionnaires, weight and height, white blood cell (WBC) count, C reactive protein, (CRP) gene-specific methylation (IL-6), and global genomic DNA methylation (LINE-1 methylation). RESULTS: Compared to the 101 PT commuters, the 79 car drivers were about 9 years older, 2 kg/m(2) heavier, more often non-Hispanic whites, and ate more fruits and more meats. The 2005 guidelines for physical activity were met by more car drivers than PT users (78.5% vs. 65.0%). There were no differences in median levels of CRP (car vs. PT: 0.6 vs. 0.5mg/dl), mean levels of WBC (car vs. PT: 6.7 vs. 6.5 cells/mm(3)), LINE-1 methylation (car vs. PT: 78.0% vs. 78.3%), and promoter methylation of IL-6 (car vs. PT: 56.1% vs. 58.0%). CONCLUSIONS: PT users were younger and lighter than car drivers, but their commute mode did not translate into a lower inflammatory response or a higher DNA methylation, maybe because, overall, car drivers were more physically active.

Investigation of maternal effects, maternal-fetal interactions and parent-of-origin effects (imprinting), using mothers and their offspring.

Many complex genetic effects, including epigenetic effects, may be expected to operate via mechanisms in the inter-uterine environment. A popular design for the investigation of such effects, including effects of parent-of-origin (imprinting), maternal genotype, and maternal-fetal genotype interactions, is to collect DNA from affected offspring and their mothers (case/mother duos) and to compare with an appropriate control sample. An alternative design uses data from cases and both parents (case/parent trios) but does not require controls. In this study, we describe a novel implementation of a multinomial modeling approach that allows the estimation of such genetic effects using either case/mother duos or case/parent trios. We investigate the performance of our approach using computer simulations and explore the sample sizes and data structures required to provide high power for detection of effects and accurate estimation of the relative risks conferred. Through the incorporation of additional assumptions (such as Hardy-Weinberg equilibrium, random mating and known allele frequencies) and/or the incorporation of additional types of control sample (such as unrelated controls, controls and their mothers, or both parents of controls), we show that the (relative risk) parameters of interest are identifiable and well estimated. Nevertheless, parameter interpretation can be complex, as we illustrate by demonstrating the mathematical equivalence between various different parameterizations. Our approach scales up easily to allow the analysis of large-scale genome-wide association data, provided both mothers and affected offspring have been genotyped at all variants of interest.