Genome-wide quantitative assessment of variation in DNA methylation patterns

DNA methylation heterogeneity attributable to a complex tumor immune microenvironment prompts prognostic risk in glioma

Gliomas are malignant tumours of the human nervous system with different World Health Organization (WHO) classifications, glioblastoma (GBM) with higher grade and are more malignant than lower-grade glioma (LGG). To dissect how the DNA methylation heterogeneity in gliomas is influenced by the complex cellular composition of the tumour immune microenvironment, we first compared the DNA methylation profiles of purified human immune cells and bulk glioma tissue, stratifying three tumour immune microenvironmental subtypes for GBM and LGG samples from The Cancer Genome Atlas (TCGA). We found that more intermediate methylation sites were enriched in glioma tumour tissues, and used the Proportion of sites with Intermediate Methylation (PIM) to compare intertumoral DNA methylation heterogeneity. A larger PIM score reflected stronger DNA methylation heterogeneity. Enhanced DNA methylation heterogeneity was associated with stronger immune cell infiltration, better survival rates, and slower tumour progression in glioma patients. We then created a Cell-type-associated DNA Methylation Heterogeneity Contribution (CMHC) score to explore the impact of different immune cell types on heterogeneous CpG site (CpGct) in glioma tissues. We identified eight prognosis-related CpGct to construct a risk score: the Cell-type-associated DNA Methylation Heterogeneity Risk (CMHR) score. CMHR was positively correlated with cytotoxic T-lymphocyte infiltration (CTL), and showed better predictive performance for IDH status (AUC = 0.96) and glioma histological phenotype (AUC = 0.81). Furthermore, DNA methylation alterations of eight CpGct might be related to drug treatments of gliomas. In conclusion, we indicated that DNA methylation heterogeneity is associated with a complex tumour immune microenvironment, glioma phenotype, and patient's prognosis.

Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or "disorder" in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

Quantitative Investigation of Methylation Heterogeneity by Digital Melting Curve Analysis on a SlipChip for Atrial Fibrillation

Methylation is an essential epigenetic modification involved in regulating gene expression and maintaining genome stability. Methylation patterns can be heterogeneous, exhibiting variations in both level and density. However, current methods of methylation analysis, including sequencing, methylation-specific PCR, and high-resolution melting curve analysis (HRM), face limitations of high cost, time-consuming workflows, and the difficulty of both accurate heterogeneity analysis and precise quantification. Here, a droplet array SlipChip-based (da-SlipChip-based) digital melting curve analysis (MCA) method was developed for the accurate quantification of both methylation level (ratio of methylated molecules to total molecules) and methylation density (ratio of methylated CpG sites to total CpG sites). The SlipChip-based digital MCA system supplements an in situ thermal cycler with a fluorescence imaging module for real-time MCA. The da-SlipChip can generate 10,656 droplets of 1 nL each, which can be separated into four lanes, enabling the simultaneous analysis of four samples. This method's clinical application was demonstrated by analyzing samples from ten healthy individuals and twenty patients with atrial fibrillation (AF), the most common arrhythmia. This method can distinguish healthy individuals from those with AF of both the paroxysmal and persistent types. It also holds potential for broader application in various research and clinical settings requiring methylation analysis.

Loss of epigenetic suppression of retrotransposons with oncogenic potential in aging mammary luminal epithelial cells

A primary function of DNA methylation in mammalian genomes is to repress transposable elements (TEs). The widespread methylation loss that is commonly observed in cancer cells results in the loss of epigenetic repression of TEs. The aging process is similarly characterized by changes to the methylome. However, the impact of these epigenomic alterations on TE silencing and the functional consequences of this have remained unclear. To assess the epigenetic regulation of TEs in aging, we profiled DNA methylation in human mammary luminal epithelial cells (LEps)-a key cell lineage implicated in age-related breast cancers-from younger and older women. We report here that several TE subfamilies function as regulatory elements in normal LEps, and a subset of these display consistent methylation changes with age. Methylation changes at these TEs occurred at lineage-specific transcription factor binding sites, consistent with loss of lineage specificity. Whereas TEs mainly showed methylation loss, CpG islands (CGIs) that are targets of the Polycomb repressive complex 2 (PRC2) show a gain of methylation in aging cells. Many TEs with methylation loss in aging LEps have evidence of regulatory activity in breast cancer samples. We furthermore show that methylation changes at TEs impact the regulation of genes associated with luminal breast cancers. These results indicate that aging leads to DNA methylation changes at TEs that undermine the maintenance of lineage specificity, potentially increasing susceptibility to breast cancer.

There's more to it: uncovering genomewide DNA methylation heterogeneity

Tweetable abstract Monitoring changes in methylation heterogeneity can be powerful in detecting disease progression early. This editorial highlights the importance of profiling methylation heterogeneity and identifies existing measures and research gaps.

Specific Methyl-CpG Configurations Define Cell Identity through Gene Expression Regulation

Cell identity is determined by the chromatin structure and profiles of gene expression, which are dependent on chromatin accessibility and DNA methylation of the regions critical for gene expression, such as enhancers and promoters. These epigenetic modifications are required for mammalian development and are essential for the establishment and maintenance of the cellular identity. DNA methylation was once thought to be a permanent repressive epigenetic mark, but systematic analyses in various genomic contexts have revealed a more dynamic regulation than previously thought. In fact, both active DNA methylation and demethylation occur during cell fate commitment and terminal differentiation. To link methylation signatures of specific genes to their expression profiles, we determined the methyl-CpG configurations of the promoters of five genes switched on and off during murine postnatal brain differentiation by bisulfite-targeted sequencing. Here, we report the structure of significant, dynamic, and stable methyl-CpG profiles associated with silencing or activation of the expression of genes during neural stem cell and brain postnatal differentiation. Strikingly, these methylation cores mark different mouse brain areas and cell types derived from the same areas during differentiation.

High-resolution Nanopore methylome-maps reveal random hyper-methylation at CpG-poor regions as driver of chemoresistance in leukemias

Aberrant DNA methylation at CpG dinucleotides is a cancer hallmark that is associated with the emergence of resistance to anti cancer treatment, though molecular mechanisms and biological significance remain elusive. Genome scale methylation maps by currently used methods are based on chemical modification of DNA and are best suited for analyses of methylation at CpG rich regions (CpG islands). We report the first high coverage whole-genome map in cancer using the long read nanopore technology, which allows simultaneous DNA-sequence and -methylation analyses on native DNA. We analyzed clonal epigenomic/genomic evolution in Acute Myeloid Leukemias (AMLs) at diagnosis and relapse, after chemotherapy. Long read sequencing coupled to a novel computational method allowed definition of differential methylation at unprecedented resolution, and showed that the relapse methylome is characterized by hypermethylation at both CpG islands and sparse CpGs regions. Most differentially methylated genes, however, were not differentially expressed nor enriched for chemoresistance genes. A small fraction of under-expressed and hyper-methylated genes at sparse CpGs, in the gene body, was significantly enriched in transcription factors (TFs). Remarkably, these few TFs supported large gene-regulatory networks including 50% of all differentially expressed genes in the relapsed AMLs and highly-enriched in chemoresistance genes. Notably, hypermethylated regions at sparse CpGs were poorly conserved in the relapsed AMLs, under-represented at their genomic positions and showed higher methylation entropy, as compared to CpG islands. Analyses of available datasets confirmed TF binding to their target genes and conservation of the same gene-regulatory networks in large patient cohorts. Relapsed AMLs carried few patient specific structural variants and DNA mutations, apparently not involved in drug resistance. Thus, drug resistance in AMLs can be mainly ascribed to the selection of random epigenetic alterations at sparse CpGs of a few transcription factors, which then induce reprogramming of the relapsing phenotype, independently of clonal genomic evolution.

Dynamic antagonism between key repressive pathways maintains the placental epigenome

DNA and Histone 3 Lysine 27 methylation typically function as repressive modifications and operate within distinct genomic compartments. In mammals, the majority of the genome is kept in a DNA methylated state, whereas the Polycomb repressive complexes regulate the unmethylated CpG-rich promoters of developmental genes. In contrast to this general framework, the extra-embryonic lineages display non-canonical, globally intermediate DNA methylation levels, including disruption of local Polycomb domains. Here, to better understand this unusual landscape's molecular properties, we genetically and chemically perturbed major epigenetic pathways in mouse trophoblast stem cells. We find that the extra-embryonic epigenome reflects ongoing and dynamic de novo methyltransferase recruitment, which is continuously antagonized by Polycomb to maintain intermediate, locally disordered methylation. Despite its disorganized molecular appearance, our data point to a highly controlled equilibrium between counteracting repressors within extra-embryonic cells, one that can seemingly persist indefinitely without bistable features typically seen for embryonic forms of epigenetic regulation.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Epigenetics as a mediator of plasticity in cancer

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

The impact of spatial correlation on methylation entropy with application to mouse brain methylome

BACKGROUND

With the advance of bisulfite sequencing technologies, massive amount of methylation data have been generated, which provide unprecedented opportunities to study the epigenetic mechanism and its relationship to other biological processes. A commonly seen feature of the methylation data is the correlation between nearby CpG sites. Although such a spatial correlation was utilized in several epigenetic studies, its interaction to other characteristics of the methylation data has not been fully investigated.

RESULTS

We filled this research gap from an information theoretic perspective, by exploring the impact of the spatial correlation on the methylation entropy (ME). With the spatial correlation taken into account, we derived the analytical relation between the ME and another key parameter, the methylation probability. By comparing it to the empirical relation between the two corresponding statistics, the observed ME and the mean methylation level, genomic loci under strong epigenetic control can be identified, which may serve as potential markers for cell-type specific methylation. The proposed method was validated by simulation studies, and applied to analyze a published dataset of mouse brain methylome.

CONCLUSIONS

Compared to other sophisticated methods developed in literature, the proposed method provides a simple but effective way to detect CpG segments under strong epigenetic control (e.g., with bipolar methylation pattern). Findings from this study shed light on the identification of cell-type specific genes/pathways based on methylation data from a mixed cell population.

Inferring the Cancer Cellular Epigenome Heterogeneity via DNA Methylation Patterns

Tumor cells evolve through space and time, generating genetically and phenotypically diverse cancer cell populations that are continually subjected to the selection pressures of their microenvironment and cancer treatment.

Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane

Background

Sugarcane is the most important sugar crop, contributing > 80% of global sugar production. High sucrose content is a key target of sugarcane breeding, yet sucrose improvement in sugarcane remains extremely slow for decades. Molecular breeding has the potential to break through the genetic bottleneck of sucrose improvement. Dissecting the molecular mechanism(s) and identifying the key genetic elements controlling sucrose accumulation will accelerate sucrose improvement by molecular breeding. In our previous work, a proteomics dataset based on 12 independent samples from high- and low-sugar genotypes treated with ethephon or water was established. However, in that study, employing conventional analysis, only 25 proteins involved in sugar metabolism were identified .

Results

In this work, the proteomics dataset used in our previous study was reanalyzed by three different statistical approaches, which include a logistic marginal regression, a penalized multiple logistic regression named Elastic net, as well as a Bayesian multiple logistic regression method named Stochastic search variable selection (SSVS) to identify more sugar metabolism-associated proteins. A total of 507 differentially abundant proteins (DAPs) were identified from this dataset, with 5 of them were validated by western blot. Among the DAPs, 49 proteins were found to participate in sugar metabolism-related processes including photosynthesis, carbon fixation as well as carbon, amino sugar, nucleotide sugar, starch and sucrose metabolism. Based on our studies, a putative network of key proteins regulating sucrose accumulation in sugarcane is proposed, with glucose-6-phosphate isomerase, 2-phospho-D-glycerate hydrolyase, malate dehydrogenase and phospho-glycerate kinase, as hub proteins.

Conclusions

The sugar metabolism-related proteins identified in this work are potential candidates for sucrose improvement by molecular breeding. Further, this work provides an alternative solution for omics data processing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08768-2.

mHapTk: a comprehensive toolkit for the analysis of DNA methylation haplotypes

SUMMARY

Bisulfite sequencing remains the gold standard technique to detect DNA methylation profiles at single-nucleotide resolution. The DNA methylation status of CpG sites on the same fragment represents a discrete methylation haplotype (mHap). The mHap-level metrics were demonstrated to be promising cancer biomarkers and explain more gene expression variation than average methylation. However, most existing tools focus on average methylation and neglect mHap patterns. Here, we present mHapTk, a comprehensive python toolkit for the analysis of DNA mHap. It calculates eight mHap-level summary statistics in predefined regions or across individual CpG in a genome-wide manner. It identifies methylation haplotype blocks, in which methylations of pairwise CpGs are tightly correlated. Furthermore, mHap patterns can be visualized with the built-in functions in mHapTk or external tools such as IGV and deepTools.

AVAILABILITY AND IMPLEMENTATION

https://jiantaoshi.github.io/mhaptk/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Epigenetics, DNA damage, and aging

Over the course of a human lifespan, genome integrity erodes, leading to an increased abundance of several types of chromatin changes. The abundance of DNA lesions (chemical perturbations to nucleotides) increases with age, as does the number of genomic mutations and transcriptional disruptions caused by replication or transcription of those lesions, respectively. At the epigenetic level, precise DNA methylation patterns degrade, likely causing increasingly stochastic variations in gene expression. Similarly, the tight regulation of histone modifications begins to unravel. The genomic instability caused by these mechanisms allows transposon element reactivation and remobilization, further mutations, gene dysregulation, and cytoplasmic chromatin fragments. This cumulative genomic instability promotes cell signaling events that drive cell fate decisions and extracellular communications known to disrupt tissue homeostasis and regeneration. In this Review, we focus on age-related epigenetic changes and their interactions with age-related genomic changes that instigate these events.

Transcriptomic changes induced by applications of a commercial extract of Ascophyllum nodosum on tomato plants

Extracts of Ascophyllum nodosum are commonly used as commercial biostimulants in crop production. To further understand the seaweed extract-induced phenomena in plants, a transcriptomic study was conducted. RNA-seq differential gene expression analysis of tomato plants treated with a commercial A. nodosum extract formulation (Stimplex) revealed the up-regulation of 635 and down-regulation of 456 genes. Ontology enrichment analysis showed three gene categories were augmented, including biological processes, cellular components, and molecular functions. KEGG pathway analysis revealed that the extract had a strong influence on the expression of genes involved in carbon fixation, secondary metabolism, MAPK-signalling, plant hormone signal transduction, glutathione metabolism, phenylpropanoid and stilbenoid metabolism, and plant-pathogen interactions. qRT-PCR validation analysis using 15 genes established a strong correlation with the RNA sequencing results. The activities of defence enzymes were also significantly enhanced by seaweed extract treatment. Furthermore, AN-SWE treated tomato plants had significantly higher chlorophyll and growth hormone content and showed improved plant growth parameters and nutrient profiles than the control. It is postulated that seaweed extract-induced gene regulation was responsible for favourable plant responses that enabled better growth and tolerance to stress conditions. This study provides evidence at the transcriptomic level for the positive effects of foliar application of the Ascophyllum nodosum extract (Stimplex) observed in treated tomato plants.

Genetic loci and metabolic states associated with murine epigenetic aging

Changes in DNA methylation (DNAm) are linked to aging. Here, we profile highly conserved CpGs in 339 predominantly female mice belonging to the BXD family for which we have deep longevity and genomic data. We use a 'pan-mammalian' microarray that provides a common platform for assaying the methylome across mammalian clades. We computed epigenetic clocks and tested associations with DNAm entropy, diet, weight, metabolic traits, and genetic variation. We describe the multifactorial variance of methylation at these CpGs and show that high-fat diet augments the age-related changes. Entropy increases with age. The progression to disorder, particularly at CpGs that gain methylation over time, was predictive of genotype-dependent life expectancy. The longer-lived BXD strains had comparatively lower entropy at a given age. We identified two genetic loci that modulate epigenetic age acceleration (EAA): one on chromosome (Chr) 11 that encompasses the Erbb2/Her2 oncogenic region, and the other on Chr19 that contains a cytochrome P450 cluster. Both loci harbor genes associated with EAA in humans, including STXBP4, NKX2-3, and CUTC. Transcriptome and proteome analyses revealed correlations with oxidation-reduction, metabolic, and immune response pathways. Our results highlight concordant loci for EAA in humans and mice, and demonstrate a tight coupling between the metabolic state and epigenetic aging.

Genetic variation at mouse and human ribosomal DNA influences associated epigenetic states

BACKGROUND

Ribosomal DNA (rDNA) displays substantial inter-individual genetic variation in human and mouse. A systematic analysis of how this variation impacts epigenetic states and expression of the rDNA has thus far not been performed.

RESULTS

Using a combination of long- and short-read sequencing, we establish that 45S rDNA units in the C57BL/6J mouse strain exist as distinct genetic haplotypes that influence the epigenetic state and transcriptional output of any given unit. DNA methylation dynamics at these haplotypes are dichotomous and life-stage specific: at one haplotype, the DNA methylation state is sensitive to the in utero environment, but refractory to post-weaning influences, whereas other haplotypes entropically gain DNA methylation during aging only. On the other hand, individual rDNA units in human show limited evidence of genetic haplotypes, and hence little discernible correlation between genetic and epigenetic states. However, in both species, adjacent units show similar epigenetic profiles, and the overall epigenetic state at rDNA is strongly positively correlated with the total rDNA copy number. Analysis of different mouse inbred strains reveals that in some strains, such as 129S1/SvImJ, the rDNA copy number is only approximately 150 copies per diploid genome and DNA methylation levels are < 5%.

CONCLUSIONS

Our work demonstrates that rDNA-associated genetic variation has a considerable influence on rDNA epigenetic state and consequently rRNA expression outcomes. In the future, it will be important to consider the impact of inter-individual rDNA (epi)genetic variation on mammalian phenotypes and diseases.

OUP accepted manuscript

Motivation

Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.

Results

We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.

Availability and implementation

MeConcord is available at https://github.com/WangLabTHU/MeConcord.

Supplementary information

Supplementary data are available at Bioinformatics online.

Correlation between small-scale methylation changes and gene expression during the development of myopia

Visually induced changes in the expression of early growth response-1 (EGR1), FBJ osteosarcoma oncogene (FOS), and NGFI-A binding protein-2 (NAB2) appear to form a part of a retinal network fundamental to ocular growth regulation, and thus, the development of myopia (short-sightedness). However, it is unclear how environmental (visual) cues are translated into these molecular changes. One possibility is through epigenetic modifications such as DNA methylation, a known regulator of such processes. By sequencing bisulfite-converted DNA amplicons, this study examined whether changes in DNA methylation occur within specific regulatory and promoter regions of EGR1, FOS, and NAB2 during the periods of increased and decreased ocular growth in chicks. Visually induced changes in ocular growth rates were associated with single-point, but not large-scale, shifts in methylation levels within the investigated regions. Analysis of methylation pattern variability (entropy) demonstrated that the observed methylation changes are occurring within small subpopulations of retinal cells. This concurs with previous observations that EGR1 and FOS are differentially regulated at the peptide level within specific retinal cell types. Together, the findings of this study support a potential role for DNA methylation in the translation of external visual cues into molecular changes critical for ocular growth regulation and myopia development.

RLM: Fast and simplified extraction of Read-Level Methylation metrics from bisulfite sequencing data

 

Bisulfite sequencing data provide value beyond the straightforward methylation assessment by analyzing single-read patterns. Over the past years, various informative metrics have been established to explore this information. However, limited compatibility with alignment tools, reference genomes or the measurements they provide present a bottleneck for most groups to include this information as standard analysis. To address this, we developed RLM, a fast and scalable tool for the computation of frequently used Read-Level Methylation statistics. RLM supports several common alignment tools, works independently of the reference genome and handles all frequently used sequencing experiment designs. RLM can process large input files with a billion reads in just a few hours on common workstations.

AVAILABILITY

https://github.com/sarahet/RLM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An atherosclerotic plaque-targeted single-chain antibody for MR/NIR-II imaging of atherosclerosis and anti-atherosclerosis therapy

Background

Oxidation-specific epitopes (OSEs) are rich in atherosclerotic plaques. Innate and adaptive immune responses to OSEs play an important role in atherosclerosis. The purpose of this study was to develop novel human single-chain variable fragment (scFv) antibody specific to OSEs to image and inhibit atherosclerosis.

Results

Here, we screened a novel scFv antibody, named as ASA6, from phage-displayed human scFv library. ASA6 can bind to oxidized LDL (Ox-LDL) and atherosclerotic plaques. Meanwhile, ASA6 can also inhibit the uptake of Ox-LDL into macrophage to reduce macrophage apoptosis. The atherosclerotic lesion area of ApoE^−/− mice administrated with ASA6 antibody was significantly reduced. Transcriptome analysis reveals the anti-atherosclerosis effect of ASA6 is related to the regulation of fatty acid metabolism and inhibition of M1 macrophage polarization. Moreover, we conjugated ASA6 antibody to NaNdF₄@NaGdF₄ nanoparticles for noninvasive imaging of atherosclerotic plaques by magnetic resonance (MR) and near-infrared window II (NIR-II) imaging.

Conclusions

Together, these data demonstrate the potential of ASA6 antibody in targeted therapy and noninvasive imaging for atherosclerosis.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-021-01047-4.

Tracing and tracking epiallele families in complex DNA populations

DNA methylation is a stable epigenetic modification, extremely polymorphic and driven by stochastic and deterministic events. Most of the current techniques used to analyse methylated sequences identify methylated cytosines (mCpGs) at a single-nucleotide level and compute the average methylation of CpGs in the population of molecules. Stable epialleles, i.e. CpG strings with the same DNA sequence containing a discrete linear succession of phased methylated/non-methylated CpGs in the same DNA molecule, cannot be identified due to the heterogeneity of the 5'-3' ends of the molecules. Moreover, these are diluted by random unstable methylated CpGs and escape detection. We present here MethCoresProfiler, an R-based tool that provides a simple method to extract and identify combinations of methylated phased CpGs shared by all components of epiallele families in complex DNA populations. The methylated cores are stable over time, evolve by acquiring or losing new methyl sites and, ultimately, display high information content and low stochasticity. We have validated this method by identifying and tracing rare epialleles and their families in synthetic or in vivo complex cell populations derived from mouse brain areas and cells during postnatal differentiation. MethCoresProfiler is written in R language. The software is freely available at https://github.com/84AP/MethCoresProfiler/.

Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers

BACKGROUND

Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification.

RESULTS

We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers.

CONCLUSIONS

Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

Single-Cell Genomics and Epigenomics: Technologies and Applications in Plants

The development of genomics and epigenomics has allowed rapid advances in our understanding of plant biology. However, conventional bulk analysis dilutes cell-specific information by providing only average information, thereby limiting the resolution of genomic and functional genomic studies. Recent advances in single-cell sequencing technology concerning genomics and epigenomics open new avenues to dissect cell heterogeneity in multiple biological processes. Recent applications of these approaches to plants have provided exciting insights into diverse biological questions. We highlight the methodologies underlying the current techniques of single-cell genomics and epigenomics before covering their recent applications, potential significance, and future perspectives in plant biology.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

ZCWPW1 is recruited to recombination hotspots by PRDM9 and is essential for meiotic double strand break repair

During meiosis, homologous chromosomes pair and recombine, enabling balanced segregation and generating genetic diversity. In many vertebrates, double-strand breaks (DSBs) initiate recombination within hotspots where PRDM9 binds, and deposits H3K4me3 and H3K36me3. However, no protein(s) recognising this unique combination of histone marks have been identified. We identified Zcwpw1, containing H3K4me3 and H3K36me3 recognition domains, as having highly correlated expression with Prdm9. Here, we show that ZCWPW1 has co-evolved with PRDM9 and, in human cells, is strongly and specifically recruited to PRDM9 binding sites, with higher affinity than sites possessing H3K4me3 alone. Surprisingly, ZCWPW1 also recognises CpG dinucleotides. Male Zcwpw1 knockout mice show completely normal DSB positioning, but persistent DMC1 foci, severe DSB repair and synapsis defects, and downstream sterility. Our findings suggest ZCWPW1 recognition of PRDM9-bound sites at DSB hotspots is critical for synapsis, and hence fertility.

Sexual reproduction – that is, the combination of sex cells from two different individuals to produce an embryo – is one of the many mechanisms that have evolved to maintain genetic diversity. Most human cells contain 23 pairs of chromosomes, with each chromosome in a pair carrying either a paternal or maternal copy of the same gene. To form an embryo with the right number of chromosomes, each sex cell (the egg or sperm cell) must only contain one chromosome from each pair.

Sex cells are produced from parent cells containing two sets of paternal and maternal chromosomes: these cells then divide twice to form four sex cells which contain only one chromosome from each pair. Before the parent cell divides, a process known as ‘recombination’ takes place, which allows chromosomes in a pair to exchange bits of genetic information. This reshuffling ensures that each chromosome in a sex cell is unique. A protein called PRDM9 helps control which sections of genetic information are recombined by modifying proteins attached to the chromosomes, marking them as locations for exchange. The DNA at each of these sites is then broken and repaired using the genetic sequence of the chromosome it is paired with as a template, thus causing the two chromosomes to swap genes.

In 2019, a group of researchers found a set of genes in the testis of mice that are expressed at the same time as the gene for PRDM9. This suggested that another protein called ZCWPW1 is likely involved in recombination, but the precise role of this protein was unclear. To answer this question, Wells, Bitoun et al. – including many of the researchers involved in the 2019 study – examined human cells grown in the laboratory to determine where ZCWPW1 binds to in the chromosome.

This revealed that ZCWPW1 can be found at the same sites as PRDM9, which is responsible for bringing it there. Furthermore, cells from male mice lacking the gene for ZCWPW1 cannot complete the exchange of genetic information between chromosomes, meaning that the mice are infertile. As such, ZCWPW1 seems to connect location selection by PRDM9 to the DNA repair mechanisms needed for gene exchange between chromosomes.

Infertility is a significant issue for humans affecting as many as one in every six couples. Fertility is complex and many of the biological mechanisms involved are not fully understood. This work suggests that both PRDM9 and ZCWPW1 are key to the production of sex cells and may be worth investigating as factors that affect fertility in humans.

Quantitative comparison of within-sample heterogeneity scores for DNA methylation data

DNA methylation is an epigenetic mark with important regulatory roles in cellular identity and can be quantified at base resolution using bisulfite sequencing. Most studies are limited to the average DNA methylation levels of individual CpGs and thus neglect heterogeneity within the profiled cell populations. To assess this within-sample heterogeneity (WSH) several window-based scores that quantify variability in DNA methylation in sequencing reads have been proposed. We performed the first systematic comparison of four published WSH scores based on simulated and publicly available datasets. Moreover, we propose two new scores and provide guidelines for selecting appropriate scores to address cell-type heterogeneity, cellular contamination and allele-specific methylation. Most of the measures were sensitive in detecting DNA methylation heterogeneity in these scenarios, while we detected differences in susceptibility to technical bias. Using recently published DNA methylation profiles of Ewing sarcoma samples, we show that DNA methylation heterogeneity provides information complementary to the DNA methylation level. WSH scores are powerful tools for estimating variance in DNA methylation patterns and have the potential for detecting novel disease-associated genomic loci not captured by established statistics. We provide an R-package implementing the WSH scores for integration into analysis workflows.

Virtual methylome dissection facilitated by single-cell analyses

Background

Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm.

Results

In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld).

Conclusions

We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

Modeling the Hematopoietic Landscape

Some time ago, we proposed a continuum-like view of the lineages open to hematopoietic stem cells (HSCs); each HSC self-renews or chooses from the spectrum of all end-cell options and can then "merely" differentiate. Having selected a cell lineage, an individual HSC may still "step sideways" to an alternative, albeit closely related, fate: HSC and their progeny therefore remain versatile. The hematopoietic cytokines erythropoietin, granulocyte colony-stimulating factor, macrophage colony-stimulating factor, granulocyte/macrophage colony-stimulating factor and ligand for the fms-like tyrosine kinase 3 instruct cell lineage. Sub-populations of HSCs express each of the cytokine receptors that are positively auto-regulated upon cytokine binding. Many years ago, Waddington proposed that the epigenetic landscape played an important role in cell lineage choice. This landscape is dynamic and unstable especially regarding DNA methylation patterns across genomic DNA. This may underlie the receptor diversity of HSC and their decision-making.

HeteroMeth: A Database of Cell-to-cell Heterogeneity in DNA Methylation

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.

MeDIP combined with in-solution targeted enrichment followed by NGS: Inter-individual methylation variability of fetal-specific biomarkers and their implementation in a proof of concept study for NIPT

DNA methylation is the most characterized epigenetic process exhibiting stochastic variation across different tissues and individuals. In non-invasive prenatal testing (NIPT) fetal specific methylated regions can potentially be used as biomarkers for the accurate detection of fetal aneuploidies. The aim of this study was the investigation of inter-individual methylation variability of previously reported fetal-specific markers and their implementation towards the development of a novel NIPT assay for the detection of trisomies 13, 18, and 21. Methylated DNA Immunoprecipitation (MeDIP) combined with in-solution targeted enrichment followed by NGS was performed in 29 CVS and 27 female plasma samples to assess inter-individual methylation variability of 331 fetal-specific differentially methylated regions (DMRs). The same approach was implemented for the NIPT of trisomies 13, 18 and 21 using spiked-in (n = 6) and pregnancy samples (n = 44), including one trisomy 13, one trisomy 18 and four trisomy 21. Despite the variability of DMRs, CVS samples showed statistically significant hypermethylation (p<2e-16) compared to plasma samples. Importantly, our assay correctly classified all euploid and aneuploid cases without any false positive results (n = 44). This work provides the starting point for the development of a NIPT assay based on a robust set of fetal specific biomarkers for the detection of fetal aneuploidies. Furthermore, the assay’s targeted nature significantly reduces the analysis cost per sample while providing high read depth at regions of interest increasing significantly its accuracy.

Dynamic transcriptome landscape of Asian domestic honeybee (Apis cerana) embryonic development revealed by high-quality RNA sequencing

Background

Honeybee development consists of four stages: embryo, larva, pupa and adult. Embryogenesis, a key process of cell division and differentiation, takes 3 days in honeybees. However, the embryonic transcriptome and the dynamic regulation of embryonic transcription are still largely uncharacterized in honeybees, especially in the Asian honeybee (Apis cerana). Here, we employed high-quality RNA-seq to explore the transcriptome of Asian honeybee embryos at three ages, approximately 24, 48 and 72 h (referred to as Day1, Day2 and Day3, respectively).

Results

Nine embryo samples, three from each age, were collected for RNA-seq. According to the staging scheme of honeybee embryos and the morphological features we observed, our Day1, Day2 and Day3 embryos likely corresponded to the late stage four, stage eight and stage ten development stages, respectively. Hierarchical clustering and principal component analysis showed that same-age samples were grouped together, and the Day2 samples had a closer relationship with the Day3 samples than the Day1 samples. Finally, a total of 18,284 genes harboring 55,646 transcripts were detected in the A. cerana embryos, of which 44.5% consisted of the core transcriptome shared by all three ages of embryos. A total of 4088 upregulated and 3046 downregulated genes were identified among the three embryo ages, of which 2010, 3177 and 1528 genes were upregulated and 2088, 2294 and 303 genes were downregulated from Day1 to Day2, from Day1 to Day3 and from Day2 to Day3, respectively. The downregulated genes were mostly involved in cellular, biosynthetic and metabolic processes, gene expression and protein localization, and macromolecule modification; the upregulated genes mainly participated in cell development and differentiation, tissue, organ and system development, and morphogenesis. Interestingly, several biological processes related to the response to and detection of light stimuli were enriched in the first-day A. cerana embryogenesis but not in the Apis mellifera embryogenesis, which was valuable for further investigations.

Conclusions

Our transcriptomic data substantially expand the number of known transcribed elements in the A. cerana genome and provide a high-quality view of the transcriptome dynamics of A. cerana embryonic development.

Electronic supplementary material

The online version of this article (10.1186/s12861-018-0169-1) contains supplementary material, which is available to authorized users.

Whole genome DNA methylation: beyond genes silencing

The combination of DNA bisulfite treatment with high-throughput sequencing technologies has enabled investigation of genome-wide DNA methylation at near base pair level resolution, far beyond that of the kilobase-long canonical CpG islands that initially revealed the biological relevance of this covalent DNA modification. The latest high-resolution studies have revealed a role for very punctual DNA methylation in chromatin plasticity, gene regulation and splicing. Here, we aim to outline the major biological consequences of DNA methylation recently discovered. We also discuss the necessity of tuning DNA methylation resolution into an adequate scale to ease the integration of the methylome information with other chromatin features and transcription events such as gene expression, nucleosome positioning, transcription factors binding dynamic, gene splicing and genomic imprinting. Finally, our review sheds light on DNA methylation heterogeneity in cell population and the different approaches used for its assessment, including the contribution of single cell DNA analysis technology.

Considerations for Design and Analysis of DNA Methylation Studies

The number of epigenetic studies is exponentially increasing. There is anticipation that DNA methylation may close gaps in our understanding of disease etiology, and how certain risk factors affect health and disease, but also that it has potential as a biomarker for disease. Human DNA methylation studies require careful considerations for design and analysis including population and tissue selection, population stratification, cell heterogeneity, confounding, temporality, sample size, appropriate statistical analysis, and validation of results. In this chapter, we discuss relevant aspects for the design of DNA methylation studies and delineate essential steps for their analysis. Specifically, we summarize methods used to extricate biologic signals from technical noise, and statistical approaches to capture meaningful variability based on the research hypothesis.

Prediction of genome-wide DNA methylation in repetitive elements

DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in genome is widely used to quantify global methylation. However, methylation may vary in specific RE and play diverse roles in disease development, thus averaging methylation across RE may lose significant biological information. The ambiguous mapping of short reads by and high cost of current bisulfite sequencing platforms make them impractical for quantifying locus-specific RE methylation. Although microarray-based approaches (particularly Illumina's Infinium methylation arrays) provide cost-effective and robust genome-wide methylation quantification, the number of interrogated CpGs in RE remains limited. We report a random forest-based algorithm (and corresponding R package, REMP) that can accurately predict genome-wide locus-specific RE methylation based on Infinium array profiling data. We validated its prediction performance using alternative sequencing and microarray data. Testing its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more comprehensively extended locus-specific RE methylation information that can be readily applied to large human studies in a cost-effective manner. Our work has the potential to improve our understanding of the role of global methylation in human diseases, especially cancer.

Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection

BACKGROUND

Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called 'epialleles' - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually.

RESULTS

We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder.

CONCLUSIONS

Detection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity. R code is available at github.com/james-e-barrett.

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Adjacent CpG sites in mammalian genomes can be co-methylated due to the processivity of methyltransferases or demethylases. Yet discordant methylation patterns have also been observed, and found related to stochastic or uncoordinated molecular processes. We focused on a systematic search and investigation of regions in the full human genome that exhibit highly coordinated methylation. We defined 147,888 blocks of tightly coupled CpG sites, called methylation haplotype blocks (MHBs) with 61 sets of whole genome bisulfite sequencing (WGBS) data, and further validated with 101 sets of reduced representation bisulfite sequencing (RRBS) data and 637 sets of methylation array data. Using a metric called methylation haplotype load (MHL), we performed tissue-specific methylation analysis at the block level. Subsets of informative blocks were further identified for deconvolution of heterogeneous samples. Finally, we demonstrated quantitative estimation of tumor load and tissue-of-origin mapping in the circulating cell-free DNA of 59 cancer patients using methylation haplotypes.

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.

NGSmethDB 2017: enhanced methylomes and differential methylation

The 2017 update of NGSmethDB stores whole genome methylomes generated from short-read data sets obtained by bisulfite sequencing (WGBS) technology. To generate high-quality methylomes, stringent quality controls were integrated with third-part software, adding also a two-step mapping process to exploit the advantages of the new genome assembly models. The samples were all profiled under constant parameter settings, thus enabling comparative downstream analyses. Besides a significant increase in the number of samples, NGSmethDB now includes two additional data-types, which are a valuable resource for the discovery of methylation epigenetic biomarkers: (i) differentially methylated single-cytosines; and (ii) methylation segments (i.e. genome regions of homogeneous methylation). The NGSmethDB back-end is now based on MongoDB, a NoSQL hierarchical database using JSON-formatted documents and dynamic schemas, thus accelerating sample comparative analyses. Besides conventional database dumps, track hubs were implemented, which improved database access, visualization in genome browsers and comparative analyses to third-part annotations. In addition, the database can be also accessed through a RESTful API. Lastly, a Python client and a multiplatform virtual machine allow for program-driven access from user desktop. This way, private methylation data can be compared to NGSmethDB without the need to upload them to public servers. Database website: http://bioinfo2.ugr.es/NGSmethDB.

Statistical challenges in analyzing methylation and long-range chromosomal interaction data

With the rapid development of high throughput technologies such as array and next generation sequencing (NGS), genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.

DNA methylation: conducting the orchestra from exposure to phenotype?

DNA methylation, through 5-methyl- and 5-hydroxymethylcytosine (5mC and 5hmC), is considered to be one of the principal interfaces between the genome and our environment, and it helps explain phenotypic variations in human populations. Initial reports of large differences in methylation level in genomic regulatory regions, coupled with clear gene expression data in both imprinted genes and malignant diseases, provided easily dissected molecular mechanisms for switching genes on or off. However, a more subtle process is becoming evident, where small (<10 %) changes to intermediate methylation levels are associated with complex disease phenotypes. This has resulted in two clear methylation paradigms. The latter “subtle change” paradigm is rapidly becoming the epigenetic hallmark of complex disease phenotypes, although we are currently hampered by a lack of data addressing the true biological significance and meaning of these small differences.

Our initial expectation of rapidly identifying mechanisms linking environmental exposure to a disease phenotype led to numerous observational/association studies being performed. Although this expectation remains unmet, there is now a growing body of literature on specific genes, suggesting wide ranging transcriptional and translational consequences of such subtle methylation changes. Data from the glucocorticoid receptor (NR3C1) has shown that a complex interplay between DNA methylation, extensive 5′UTR splicing, and microvariability gives rise to the overall level and relative distribution of total and N-terminal protein isoforms generated. Additionally, the presence of multiple AUG translation initiation codons throughout the complete, processed mRNA enables translation variability, hereby enhancing the translational isoforms and the resulting protein isoform diversity, providing a clear link between small changes in DNA methylation and significant changes in protein isoforms and cellular locations. Methylation changes in the NR3C1 CpG island alters the NR3C1 transcription and eventually protein isoforms in the tissues, resulting in subtle but visible physiological variability.

This review addresses the current pathophysiological and clinical associations of such characteristically small DNA methylation changes, the ever-growing roles of DNA methylation and the evidence available, particularly from the glucocorticoid receptor of the cascade of events initiated by such subtle methylation changes, as well as addressing the underlying question as to what represents a genuine biologically significant difference in methylation.

Methods for genome-wide DNA methylation analysis in human cancer

Aberrant DNA methylation is considered to be one of the most common hallmarks of cancer. Several recent advances in assessing the DNA methylome provide great promise for deciphering the cancer-specific DNA methylation patterns. Herein, we present the current key technologies used to detect high-throughput genome-wide DNA methylation, and the available cancer-associated methylation databases. Additionally, we focus on the computational methods for preprocessing, analyzing and interpreting the cancer methylome data. It not only discusses the challenges of the differentially methylated region calling and the prediction model construction but also highlights the biomarker investigation for cancer diagnosis, prognosis and response to treatment. Finally, some emerging challenges in the computational analysis of cancer methylome data are summarized.

The application of information theory for the research of aging and aging-related diseases

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

Information Thermodynamics of Cytosine DNA Methylation

Cytosine DNA methylation (CDM) is a stable epigenetic modification to the genome and a widespread regulatory process in living organisms that involves multicomponent molecular machines. Genome-wide cytosine methylation patterning participates in the epigenetic reprogramming of a cell, suggesting that the biological information contained within methylation positions may be amenable to decoding. Adaptation to a new cellular or organismal environment also implies the potential for genome-wide redistribution of CDM changes that will ensure the stability of DNA molecules. This raises the question of whether or not we would be able to sort out the regulatory methylation signals from the CDM background (“noise”) induced by thermal fluctuations. Here, we propose a novel statistical and information thermodynamic description of the CDM changes to address the last question. The physical basis of our statistical mechanical model was evaluated in two respects: 1) the adherence to Landauer’s principle, according to which molecular machines must dissipate a minimum energy ε = k_BT ln2 at each logic operation, where k_B is the Boltzmann constant, and T is the absolute temperature and 2) whether or not the binary stretch of methylation marks on the DNA molecule comprise a language of sorts, properly constrained by thermodynamic principles. The study was performed for genome-wide methylation data from 152 ecotypes and 40 trans-generational variations of Arabidopsis thaliana and 93 human tissues. The DNA persistence length, a basic mechanical property altered by CDM, was estimated with values from 39 to 66.9 nm. Classical methylome analysis can be retrieved by applying information thermodynamic modelling, which is able to discriminate signal from noise. Our finding suggests that the CDM signal comprises a language scheme properly constrained by molecular thermodynamic principles, which is part of an epigenomic communication system that obeys the same thermodynamic rules as do current human communication systems.

HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis

The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download.