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Epigenetic changes are required for normal development, yet the nature and respective contribution of factors that drive epigenetic variation in humans remain to be fully characterized. Here, we assessed how the blood DNA methylome of 884 adults is affected by DNA sequence variation, age, sex and 139 factors relating to life habits and immunity. Furthermore, we investigated whether these effects are mediated or not by changes in cellular composition, measured by deep immunophenotyping. We show that DNA methylation differs substantially between naïve and memory T cells, supporting the need for adjustment on these cell-types. By doing so, we find that latent cytomegalovirus infection drives DNA methylation variation and provide further support that the increased dispersion of DNA methylation with aging is due to epigenetic drift. Finally, our results indicate that cellular composition and DNA sequence variation are the strongest predictors of DNA methylation, highlighting critical factors for medical epigenomics studies. Many studies assess epigenetic marks in white blood cells, but it is unclear how much immune factors affect the epigenome. Here, the authors show that fine-scale blood cell composition and cytomegalovirus infection affect the DNA methylome of adults.

Cell identity is determined by the selective activation or silencing of specific genes via transcription factor binding and epigenetic modifications on the genome. Chromatin immunoprecipitation (ChIP) has been the standard technique for mapping the sites of transcription factor binding and histone modification. Recently, alternative methods to ChIP have been developed for addressing the increasing demands for low-input epigenomic profiling. Chromatin integration labeling (ChIL) followed by sequencing (ChIL-seq) has been demonstrated to be particularly useful for epigenomic profiling of low-input samples or even single cells because the technique amplifies the target genomic sequence before cell lysis. After labeling the target protein or modification in situ with an oligonucleotide-conjugated antibody (ChIL probe), the nearby genome sequence is amplified by Tn5 transposase-mediated transposition followed by T7 RNA polymerase-mediated transcription. ChIL-seq enables the detection of the antibody target localization under a fluorescence microscope and at the genomic level. Here we describe the detailed protocol of ChIL-seq with assessment methods for the key steps, including ChIL probe reaction, transposition, in situ transcription and sequencing library preparation. The protocol usually takes 3 d to prepare the sequencing library, including overnight incubations for the ChIL probe reaction and in situ transcription. The ChIL probe can be separately prepared and stored for several months, and its preparation and evaluation protocols are also documented in detail. An optional analysis for multiple targets (multitarget ChIL-seq) is also described. We anticipate that the protocol presented here will make the ChIL technique more widely accessible for analyzing precious samples and facilitate further applications. The authors describe detailed procedures for an epigenomic profiling method suitable for low-input samples that is based on in situ labeling with an oligonucleotide-conjugated antibody.

PurposeThe interindividual variable response to weight-loss treatments requires the search for new predictive biomarkers for improving the success of weight-loss programs. The aim of this study is to identify novel genes that distinguish individual responses to a weight-loss dietary treatment by using the integrative analysis of mRNA expression and DNA methylation arrays.MethodsSubjects from Metabolic Syndrome Reduction in Navarra (RESMENA) project were classified as low (LR) or high (HR) responders depending on their weight loss. Transcriptomic (n = 24) and epigenomic (n = 47) patterns were determined by array-based genome-wide technologies in human white blood cells at the baseline of the treatment period. CD44 expression was validated by qRT-PCR and methylation degree of CpGs of the gene was validated by MassARRAY® EpiTYPER™ in a subsample of 47 subjects. CD44 protein levels were measured by ELISA in human plasma.ResultsDifferent expression and DNA methylation profiles were identified in LR in comparison to HR. The integrative analysis of both array data identified four genes: CD44, ITPR1, MTSS1 and FBXW5 that were differently methylated and expressed between groups. CD44 showed higher expression and lower DNA methylation levels in LR than in HR. Although differences in CD44 protein levels between LR and HR were not statistically significant, a positive association was observed between CD44 mRNA expression and protein levels.ConclusionsIn summary, the combination of a genome-wide methylation and expression array dataset can be a useful strategy to identify novel genes that might be considered as predictors of the dietary response. CD44 gene transcription and methylation may be a possible candidate biomarker for weight-loss prediction.

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Single-cell multi-omics has recently emerged as a powerful technology by which different layers of genomic output—and hence cell identity and function—can be recorded simultaneously. Integrating various components of the epigenome into multi-omics measurements allows for studying cellular heterogeneity at different time scales and for discovering new layers of molecular connectivity between the genome and its functional output. Measurements that are increasingly available range from those that identify transcription factor occupancy and initiation of transcription to long-lasting and heritable epigenetic marks such as DNA methylation. Together with techniques in which cell lineage is recorded, this multilayered information will provide insights into a cell’s past history and its future potential. This will allow new levels of understanding of cell fate decisions, identity, and function in normal development, physiology, and disease.

DNA and RNA modifications have important functions, including the regulation of gene expression. Existing methods based on short-read sequencing for the detection of modifications show difficulty in determining the modification patterns of single chromosomes or an entire transcript sequence. Furthermore, the kinds of modifications for which detection methods are available are very limited. The Nanopore sequencer is a single-molecule, long-read sequencer that can directly sequence RNA as well as DNA. Moreover, the Nanopore sequencer detects modifications on long DNA and RNA molecules. In this review, we mainly focus on base modification detection in the DNA and RNA of mammals using the Nanopore sequencer. We summarize current studies of modifications using the Nanopore sequencer, detection tools using statistical tests or machine learning, and applications of this technology, such as analyses of open chromatin, DNA replication, and RNA metabolism.

Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N 6 -methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement. Sequencing and genomic diversification of five allopolyploid cotton species provide insights into polyploid genome evolution and epigenetic landscapes for cotton improvement.

This protocol describes how to use ChromHMM, a robust open-source software package that enables the learning of chromatin states, annotates their occurrences across the genome, and facilitates their biological interpretation. Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

Infant gliomas have paradoxical clinical behavior compared to those in children and adults: low-grade tumors have a higher mortality rate, while high-grade tumors have a better outcome. However, we have little understanding of their biology and therefore cannot explain this behavior nor what constitutes optimal clinical management. Here we report a comprehensive genetic analysis of an international cohort of clinically annotated infant gliomas, revealing 3 clinical subgroups. Group 1 tumors arise in the cerebral hemispheres and harbor alterations in the receptor tyrosine kinases ALK , ROS1 , NTRK and MET . These are typically single-events and confer an intermediate outcome. Groups 2 and 3 gliomas harbor RAS/MAPK pathway mutations and arise in the hemispheres and midline, respectively. Group 2 tumors have excellent long-term survival, while group 3 tumors progress rapidly and do not respond well to chemoradiation. We conclude that infant gliomas comprise 3 subgroups, justifying the need for specialized therapeutic strategies. Infant gliomas behave differently to their childhood or adult counterparts. Here, the authors perform a large-scale genetic analysis of these tumours, revealing genetic alterations which may offer therapeutic opportunities.

We show that epigenome- and transcriptome-wide association studies (EWAS and TWAS) are prone to significant inflation and bias of test statistics, an unrecognized phenomenon introducing spurious findings if left unaddressed. Neither GWAS-based methodology nor state-of-the-art confounder adjustment methods completely remove bias and inflation. We propose a Bayesian method to control bias and inflation in EWAS and TWAS based on estimation of the empirical null distribution. Using simulations and real data, we demonstrate that our method maximizes power while properly controlling the false positive rate. We illustrate the utility of our method in large-scale EWAS and TWAS meta-analyses of age and smoking.