Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

Consistent DNA Hypomethylations in Prostate Cancer

With approximately 1.4 million men annually diagnosed with prostate cancer (PCa) worldwide, PCa remains a dreaded threat to life and source of devastating morbidity. In recent decades, a significant decrease in age-specific PCa mortality has been achieved by increasing prostate-specific antigen (PSA) screening and improving treatments. Nevertheless, upcoming, augmented recommendations against PSA screening underline an escalating disproportion between the benefit and harm of current diagnosis/prognosis and application of radical treatment standards. Undoubtedly, new potent diagnostic and prognostic tools are urgently needed to alleviate this tensed situation. They should allow a more reliable early assessment of the upcoming threat, in order to enable applying timely adjusted and personalized therapy and monitoring. Here, we present a basic study on an epigenetic screening approach by Methylated DNA Immunoprecipitation (MeDIP). We identified genes associated with hypomethylated CpG islands in three PCa sample cohorts. By adjusting our computational biology analyses to focus on single CpG-enriched 60-nucleotide-long DNA probes, we revealed numerous consistently differential methylated DNA segments in PCa. They were associated among other genes with NOTCH3, CDK2AP1, KLK4, and ADAM15. These can be used for early discrimination, and might contribute to a new epigenetic tumor classification system of PCa. Our analysis shows that we can dissect short, differential methylated CpG-rich DNA fragments and combinations of them that are consistently present in all tumors. We name them tumor cell-specific differential methylated CpG dinucleotide signatures (TUMS).

BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2³¹ bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis.

Fast and exact quantification of motif occurrences in biological sequences

BACKGROUND

Identification of motifs and quantification of their occurrences are important for the study of genetic diseases, gene evolution, transcription sites, and other biological mechanisms. Exact formulae for estimating count distributions of motifs under Markovian assumptions have high computational complexity and are impractical to be used on large motif sets. Approximated formulae, e.g. based on compound Poisson, are faster, but reliable p value calculation remains challenging. Here, we introduce 'motif_prob', a fast implementation of an exact formula for motif count distribution through progressive approximation with arbitrary precision. Our implementation speeds up the exact calculation, usually impractical, making it feasible and posit to substitute currently employed heuristics.

RESULTS

We implement motif_prob in both Perl and C+ + languages, using an efficient error-bound iterative process for the exact formula, providing comparison with state-of-the-art tools (e.g. MoSDi) in terms of precision, run time benchmarks, along with a real-world use case on bacterial motif characterization. Our software is able to process a million of motifs (13-31 bases) over genome lengths of 5 million bases within the minute on a regular laptop, and the run times for both the Perl and C+ + code are several orders of magnitude smaller (50-1000× faster) than MoSDi, even when using their fast compound Poisson approximation (60-120× faster). In the real-world use cases, we first show the consistency of motif_prob with MoSDi, and then how the p-value quantification is crucial for enrichment quantification when bacteria have different GC content, using motifs found in antimicrobial resistance genes. The software and the code sources are available under the MIT license at https://github.com/DataIntellSystLab/motif_prob .

CONCLUSIONS

The motif_prob software is a multi-platform and efficient open source solution for calculating exact frequency distributions of motifs. It can be integrated with motif discovery/characterization tools for quantifying enrichment and deviation from expected frequency ranges with exact p values, without loss in data processing efficiency.

Effects of lifestyle interventions on epigenetic signatures of liver fat: Central randomized controlled trial

BACKGROUND AND AIMS

In the CENTRAL trial context, we found diverse liver fat dynamics in response to different dietary interventions. Epigenetic mechanisms may contribute to the intraindividual variation. Moreover, genetic factors are involved in developing nonalcoholic fatty-liver disease (NAFLD), a disease reflected by an increase in intrahepatic fat (IHF). In this exploratory analysis, we primarily aimed to examine the effect of lifestyle interventions on DNA-methylation of NAFLD related genes associated with IHF.

METHODS

For 120 participants from the CENTRAL trial, an 18-month regimen of either low-fat (LF) or Mediterranean-low carbohydrate (MED/LC) diets, with or without physical activity (PA+/PA-), was instructed. Magnetic resonance imaging was used to measure IHF%, which was analysed for association with CpG specific DNA-methylation levels of 41 selected candidate genes. Single-nucleotide polymorphisms known to be associated with NAFLD within the studied genes were genotyped by TaqMan assays.

RESULTS

At baseline, participants (92% men; body mass index = 30.2 kg/m² ) had mean IHF of 10.7% (59% NAFLD). Baseline-IHF% was inversely correlated with DNA-methylation at individual CpGs within AC074286.1, CRACR2A, A2MP1, FARP1 (P < .05 for all multivariate models). FARP1 rs9584805 showed association with IHF, with the prevalence of NAFLD and baseline methylation level of the CpG site (cg00071727) associated with IHF%. Following 18-month lifestyle intervention, differential DNA-methylation patterns were observed between diets at cg14335324 annotated to A2MP1 (P = .04, LF vs. MED/LC), and differential DNA-methylation between PA groups within AC074286.1, CRACR2A, and FARP1 CpGs (P < .05 for all, PA-vs. PA+).

CONCLUSIONS

This study suggests epigenetic markers for IHF and potential epigenetic remodeling after long-term lifestyle interventions.

DNA methylation patterns expose variations in enhancer-chromatin modifications during embryonic stem cell differentiation

In mammals, cellular identity is defined through strict regulation of chromatin modifications and DNA methylation that control gene expression. Methylation of cytosines at CpG sites in the genome is mainly associated with suppression; however, the reason for enhancer-specific methylation is not fully understood. We used sequential ChIP-bisulfite-sequencing for H3K4me1 and H3K27ac histone marks. By collecting data from the same genomic region, we identified enhancers differentially methylated between these two marks. We observed a global gain of CpG methylation primarily in H3K4me1-marked nucleosomes during mouse embryonic stem cell differentiation. This gain occurred largely in enhancer regions that regulate genes critical for differentiation. The higher levels of DNA methylation in H3K4me1- versus H3K27ac-marked enhancers, despite it being the same genomic region, indicates cellular heterogeneity of enhancer states. Analysis of single-cell RNA-seq profiles demonstrated that this heterogeneity correlates with gene expression during differentiation. Furthermore, heterogeneity of enhancer methylation correlates with transcription start site methylation. Our results provide insights into enhancer-based functional variation in complex biological systems.

Cellular dynamics are underlined by numerous regulatory layers. The regulatory mechanism of interest in this work are enhancers. Enhancers are regulatory regions responsible, mainly, for increasing the possibility of transcription of a certain gene. Enhancers are marked by two distinct chemical groups-H3K4me1 and H3K27ac on the tail of histones. Histones are the proteins responsible for DNA packaging into condensed chromatin structure. In contrast, DNA methylation is a chemical modification often found on enhancers, and is traditionally associated with repression. A long-debated question revolves around the functional relevance of DNA methylation in the context of enhancers. Here, we combined the two regulatory layers, histone marks and DNA methylation, to a single measurement that can highlight DNA methylation separately on each histone mark but at the same genomic region. When isolated with H3K4me1, enhancers showed higher levels of methylation compared to H3K27ac. As we measured the same genomic locations, we show that differences of DNA methylation between these marks can only be explained by cellular heterogeneity. We also demonstrated that these enhancers tend to play roles in stem cell differentiation and expression levels of the genes they control correlate with cell-to-cell variation.

Polymorphisms in Neuronal Growth Regulator 1 and Otoancorin Alternate the Susceptibility to Lung Cancer in Chinese Nonsmoking Females

Cell adhesion molecules (CAMs) play crucial roles in the genesis and progress of tumor. We investigated the effects of single nucleotide polymorphisms (SNPs) of CAMs, neuronal growth regulator 1 (NEGR1), and Otoancorin (OTOA) on lung cancer susceptibility in Chinese nonsmoking females. Logistic regression and Cox regression analyses were conducted to investigate the effects of SNPs and environmental factors. For rs3102911, genotype TT carriers decreased the risk of lung cancer with an odds ratio (OR) of 0.635. AA genotypes of rs741718 increased the risk of lung cancer with an OR of 3.527. In stratified analysis, genotype AA carriers of rs741718 had a high susceptibility to lung adenocarcinoma compared with GG and AG genotypes. Analyses of association between SNPs and clinical characteristics revealed that rs3102911 as a protective factor and rs741718 as a risk factor influenced the lung cancer occurrence and progression in nonsmoking females.

Epigenetic diversity of Saccharum spp. accessions assessed by methylation-sensitive amplification polymorphism (MSAP)

The epigenetic diversity of six genotype groups (commercial cultivars, S. officinarum, S. spontaneum, S. robustum, S. barberi, and Erianthus sp.) was assessed through methylation-sensitive amplification polymorphism (MSAP). A total of 1341 MSAP loci were analyzed, of which 1117 (83.29%) were susceptible to cytosine methylation and responsible for a higher proportion of overall diversity among genotypes. The MSAP selective primer combinations captured different proportions of internal and external cytosine methylation loci across genotype groups, while the average external cytosine frequency was higher for all genotype groups. The genotypes were divided into two subpopulations with a high differentiation index (φst = 0.086) based on epigenetic loci. The genotypes were clustered in three subgroups for both methylated and unmethylated loci, considering dissimilarity values. Four methylated fragments (MFs) were randomly selected and subsequently sequenced and compared with sugarcane public databases using BLASTN. MF alignments suggest that cytosine methylation occurs in sugarcane near CpG islands and tandem repeats within transcribed regions and putative cis-regulatory sequences, which assigned functions are associated with stress adaptation. These results provide the first insights about the distribution of this epigenetic mark in sugarcane genome, and suggest a biological relevance of methylated loci.

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment

DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic “deserts”. In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.

Identification of DNA motifs that regulate DNA methylation

DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with "somatic QTLs" (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.

P3BSseq: parallel processing pipeline software for automatic analysis of bisulfite sequencing data

Motivation

Bisulfite sequencing (BSseq) processing is among the most cumbersome next generation sequencing (NGS) applications. Though some BSseq processing tools are available, they are scattered, require puzzling parameters and are running-time and memory-usage demanding.

Results

We developed P3BSseq, a parallel processing pipeline for fast, accurate and automatic analysis of BSseq reads that trims, aligns, annotates, records the intermediate results, performs bisulfite conversion quality assessment, generates BED methylome and report files following the NIH standards. P3BSseq outperforms the known BSseq mappers regarding running time, computer hardware requirements (processing power and memory use) and is optimized to process the upcoming, extended BSseq reads. We optimized the P3BSseq parameters for directional and non-directional libraries, and for single-end and paired-end reads of Whole Genome and Reduced Representation BSseq. P3BSseq is a user-friendly streamlined solution for BSseq upstream analysis, requiring only basic computer and NGS knowledge.

Availability and Implementation

P3BSseq binaries and documentation are available at: http://sourceforge.net/p/p3bsseq/wiki/Home/

Contact

mararabra@yahoo.co.uk

Supplimentary Information

Supplementary data are available at Bioinformatics online.

Quasi-Stem Cells Derived from Human Somatic Cells by Chemically Modified Carbon Nanotubes

Surface modification of micro- and nanotopography was employed to alter the surface properties of scaffolds for controlling cell attachment, proliferation, and differentiation. This study reports a method for generating multinucleated colonies as evidenced by spherical colony formation through nanotopography-induced expression of reprogramming factors in human dermal fibroblasts. Colony formation was achieved by subjecting the cells to specific environments such as culturing with single-walled carbon nanotubes and poly-l-lysine (PLL-SWCNTs). We obtained encouraging results showing that PLL-SWCNT treatment transformed fibroblast cells, and the transformed cells expressed the pluripotency-associated factors OCT4, NANOG, and SOX2 in addition to TRA-1-60 and SSEA-4, which are characteristic stem cell markers. Downregulation of lamin A/C, HDAC1, HDAC6, Bcl2, cytochrome c, p-FAK, p-ERK, and p-JNK and upregulation of H3K4me3 and p-p38 were confirmed in the generated colonies, indicating reprogramming of cells. This protocol increases the possibility of successfully reprogramming somatic cells into induced pluripotent stem cells (iPSCs), thereby overcoming the difficulties in iPSC generation such as genetic mutations, carcinogenesis, and undetermined risk factors.

DNA methylation regulates discrimination of enhancers from promoters through a H3K4me1-H3K4me3 seesaw mechanism

Background

DNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers.

Results

We investigated the relationship between DNA methylation and active chromatin marks through genome-wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized “seesaw” dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state. We summarize these observations into a rule of thumb of one-out-of-three methylation marks: “In each genomic region only one out of these three methylation marks {DNA methylation, H3K4me1, H3K4me3} is high. If it is the DNA methylation, the region is inactive. If it is H3K4me1, the region is an enhancer, and if it is H3K4me3, the region is a promoter”. To test our model, we used available genome-wide datasets of H3K4 methyltransferases knockouts. Our analysis suggests that CXXC proteins, as readers of non-methylated CpGs would regulate the “seesaw” mechanism that focuses H3K4me3 to unmethylated sites, while being repulsed from H3K4me1 decorated enhancers and CpG island shores.

Conclusions

Our results show that DNA methylation discriminates promoters from enhancers through H3K4me1-H3K4me3 seesaw mechanism, and suggest its possible function in the inheritance of chromatin marks after cell division. Our analyses suggest aberrant formation of promoter-like regions and ectopic transcription of hypomethylated regions of DNA. Such mechanism process can have important implications in biological process in where it has been reported abnormal DNA methylation status such as cancer and aging.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4353-7) contains supplementary material, which is available to authorized users.

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.

Comparative Analysis of Genome and Epigenome in Closely Related Medaka Species Identifies Conserved Sequence Preferences for DNA Hypomethylated Domains

The genomes of vertebrates are globally methylated, but a small portion of genomic regions are known to be hypomethylated. Although hypomethylated domains (HMDs) have been implicated in transcriptional regulation in various ways, how a HMD is determined in a particular genomic region remains elusive. To search for DNA motifs essential for the formation of HMDs, we performed the genome-wide comparative analysis of genome and DNA methylation patterns of the two medaka inbred lines, Hd-rRII1 and HNI-II, which are derived from northern and southern subpopulations of Japan and exhibit high levels of genetic variations (SNP, ∼ 3%). We successfully mapped > 70% of HMDs in both genomes and found that the majority of those mapped HMDs are conserved between the two lines (common HMDs). Unexpectedly, the average genetic variations are similar in the common HMD and other genome regions. However, we identified short well-conserved motifs that are specifically enriched in HMDs, suggesting that they may play roles in the establishment of HMDs in the medaka genome.

SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

BACKGROUND

The molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. The information generated by each type of assay individually gives an insight into the state of the cells tested. What should be possible is to add the information derived from separate, complementary assays to gain higher-confidence insights into cellular states. At present, the analysis of multi-dimensional, massive genome-wide data requires an initial pruning step to create manageable subsets of observations that are then used for integration, which decreases the sizes of the intersecting data sets and the potential for biological insights. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution.

RESULTS

SMITE combines p-values by accounting for the correlation between non-independent values within data sets, allowing genes and gene modules in an interaction network to be assigned significance values. The contribution of each type of genomic data can be weighted, permitting integration of individually under-powered data sets, increasing the overall ability to detect effects within modules of genes. We apply SMITE to a complex genomic data set including the epigenomic and transcriptomic effects of Toxoplasma gondii infection on human host cells and demonstrate that SMITE is able to identify novel subnetworks of dysregulated genes. Additionally, we show that SMITE outperforms Functional Epigenetic Modules (FEM), the current paradigm of using the spin-glass algorithm to integrate gene expression and epigenetic data.

CONCLUSIONS

SMITE represents a flexible, scalable tool that allows integration of transcriptional and epigenetic regulatory data from genome-wide assays to boost confidence in finding gene modules reflecting altered cellular states.

Survivin Improves Reprogramming Efficiency of Human Neural Progenitors by Single Molecule OCT4

Induced pluripotent stem (iPS) cells have been generated from human somatic cells by ectopic expression of four Yamanaka factors. Here, we report that Survivin, an apoptosis inhibitor, can enhance iPS cells generation from human neural progenitor cells (NPCs) together with one factor OCT4 (1F-OCT4-Survivin). Compared with 1F-OCT4, Survivin accelerates the process of reprogramming from human NPCs. The neurocyte-originated induced pluripotent stem (NiPS) cells generated from 1F-OCT4-Survivin resemble human embryonic stem (hES) cells in morphology, surface markers, global gene expression profiling, and epigenetic status. Survivin keeps high expression in both iPS and ES cells. During the process of NiPS cell to neural cell differentiation, the expression of Survivin is rapidly decreased in protein level. The mechanism of Survivin promotion of reprogramming efficiency from NPCs may be associated with stabilization of β-catenin in WNT signaling pathway. This hypothesis is supported by experiments of RT-PCR, chromatin immune-precipitation, and Western blot in human ES cells. Our results showed overexpression of Survivin could improve the efficiency of reprogramming from NPCs to iPS cells by one factor OCT4 through stabilization of the key molecule, β-catenin.

DNA methylation dynamics: identification and functional annotation

DNA methylation is an epigenetic modification of cytosines that undergoes dynamic changes in a temporal, spatial and cell-type-specific manner. Recent advances in technology have permitted the profiling of high-throughput methylomes in large numbers of biological samples. Various computational tools have been developed to identify and analyze DNA methylation dynamics in a variety of critical biological processes. As DNA methylation is becoming increasingly viewed as a dynamic process, the mechanisms governing DNA methylation dynamics and its roles in the transcriptional regulatory network are of great interest. It has been reported that DNA methylation dynamics plays essential roles in multiple biological processes, including development and cancer. As a functional event, the dynamics of DNA methylation have become increasingly relevant to many researchers. Here, we review state-of-the-art advances at three levels (genome-wide identification, regulatory mechanism investigation and the functional annotation) in the field of DNA methylation dynamics, as well as the future perspective of DNA methylation dynamics.

Does mouse embryo primordial germ cell activation start before implantation as suggested by single-cell transcriptomics dynamics?

STUDY HYPOTHESIS

Does primordial germ cell (PGC) activation start before mouse embryo implantation, and does the possible regulation of the DNA (cytosine-5-)-methyltransferase 3-like (Dnmt3l) by transcription factor AP-2, gamma (TCFAP2C) have a role in this activation and in the primitive endoderm (PE)-epiblast (EPI) lineage specification?

STUDY FINDING

A burst of expression of PGC markers, such as Dppa3/Stella, Ifitm2/Fragilis, Fkbp6 and Prdm4, is observed from embryonic day (E) 3.25, and some of them, together with the late germ cell markers Zp3, Mcf2 and Morc1, become restricted to the EPI subpopulation at E4.5, while the dynamics analysis of the PE-EPI transitions in the single-cell data suggests that TCFAP2C transitorily represses Dnmt3l in EPI cells at E3.5 and such repression is withdrawn with reactivation of Dnmt3l expression in PE and EPI cells at E4.5.

WHAT IS KNOWN ALREADY

In the mouse preimplantation embryo, cells with the same phenotype take different fates based on the orchestration between topological clues (cell polarity, positional history and division orientation) and gene regulatory rules (at transcriptomics and epigenomics level), prompting the proposal of positional, stochastic and combined models explaining the specification mechanism. PGC specification starts at E6.0-6.5 post-implantation. In view of the important role of DNA methylation in developmental events, the cross-talk between some transcription factors and DNA methyltransferases is of particular relevance. TCFAP2C has a CpG DNA methylation motif that is not methylated in pluripotent cells and that could potentially bind on DNMT3L, the stimulatory DNA methyltransferase co-factor that assists in the process of de novo DNA methylation. Chromatin-immunoprecipitation analysis has demonstrated that Dnmt3l is indeed a target of TCFAP2C.

STUDY DESIGN, SAMPLES/MATERIALS, METHODS

We aimed to assess the timing of early preimplantation events and to understand better the segregation of the inner cell mass (ICM) into PE and EPI. We designed a single-cell transcriptomics dynamics computational study to identify markers of the PE-EPI bifurcation in ICM cells through searching for statistically significant (using the Student's t-test method) differently expressed genes (DEGs) between PE and EPI cells from E3.5 to E4.5. The DEGs common for E3.5 and E4.5 were used as the markers defining the steady states. We collected microarray and next-generation sequencing transcriptomics data from public databases from bulk populations and single cells from mice at E3.25, E3.5 and E4.5. The results are based on three independent single-cell transcriptomics data sets, with a fold change of 3 and P-value <0.01 for the DEG selection.

MAIN RESULTS AND THE ROLE OF CHANCE

The dynamics analysis revealed new transitory E3.5 and steady PE and EPI markers. Among the transitory E3.5 PE markers (Dnmt3l, Dusp4, Cpne8, Akap13, Dcaf12l1, Aaed1, B4galt6, BC100530, Rnpc3, Tfpi, Lgalsl, Ckap4 and Fbxl20), several (Dusp4, Akap13, Cpn8, Dcaf12l1 and Tfpi) are related to the extracellular regulated kinase pathway. We also identified new transitory E3.5 EPI markers (Sgk1, Mal, Ubxn2a, Atg16l2, Gm13102, Tcfap2c, Hexb, Slc1a1, Svip, Liph and Mier3), six new stable PE markers (Sdc4, Cpn1, Dkk1, Havcr1, F2r/Par1 and Slc7a6os) as well as three new stable EPI markers (Zp3, Mcf2 and Hexb), which are known to be late stage germ cell markers. We found that mouse PGC marker activation starts at least at E3.25 preimplantation. The transcriptomics dynamics analyses support the regulation of Dnmt3l expression by TCFAP2C.

LIMITATIONS, REASONS FOR CAUTION

Since the regulation of Dnmt3l by TCFAP2C is based on computational prediction of DNA methylation motifs, Chip-Seq and transcriptomics data, functional studies are required to validate this result.

WIDER IMPLICATIONS OF THE FINDINGS

We identified a collection of previously undescribed E3.5-specific PE and EPI markers, and new steady PE and EPI markers. Identification of these genes, many of which encode cell membrane proteins, will facilitate the isolation and characterization of early PE and EPI populations. Since it is so well established in the literature that mouse PGC specification is a post-implantation event, it was surprising for us to see activation of PGC markers as early as E3.25 preimplantation, and identify the newly found steady EPI markers as late germ cell markers. The discovery of such early activation of PGC markers has important implications in the derivation of germ cells from pluripotent cells (embryonic stem cells or induced pluripotent stem cells), since the initial stages of such derivation resemble early development. The early activation of PGC markers points out the difficulty of separating PGC cells from pluripotent populations. Collectively, our results suggest that the combining of the precision of single-cell omics data with dynamic analysis of time-series data can establish the timing of some developmental stages as earlier than previously thought.

LARGE-SCALE DATA

Not applicable.

STUDY FUNDING AND COMPETING INTERESTS

This work was supported by grants DFG15/14 and DFG15/020 from Diputación Foral de Gipuzkoa (Spain), and grant II14/00016 from I + D + I National Plan 2013-2016 (Spain) and FEDER funds. The authors declare no conflict of interest.

CellMethy: Identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues

DNA methylation patterns may serve as a key in determining cell phenotypes and functions. Adjacent CpG patterns may provide insight into methylation functional mechanisms. Some regions display different DNA methylation patterns between normal and cancer tissues, but the same average methylation level. Here, we developed a method (CellMethy) to infer a region in which all CpGs exhibit concordant methylation (CM) and to quantify the extent of CM in the region. Using simulation data, CellMethy showed high performance in discovering the concordant methylation patterns (AUC = 0.89). CellMethy was then applied to RRBS data including 11 normal tissues and 12 tumors. We found that the extent of CM exhibited wider differentials among tissues than did the average methylation levels from the CM regions, with 45% of CM regions occurring specifically in one tissue and mainly in tumors. Then, we identified CM regions through genome wide bisulfite sequencing (GWBS) data on breast cancer. Approximately 82% of CM regions revealed a significantly different extent of CM between cancer and normal tissues. CellMethy can accurately describe concordantly methylated regions, and the results suggest that CM might also serve as a stable marker of cell sub-populations.

Genome-wide DNA methylation analysis identifies a metabolic memory profile in patient-derived diabetic foot ulcer fibroblasts

Diabetic foot ulcers (DFUs) are a serious complication of diabetes. Previous exposure to hyperglycemic conditions accelerates a decline in cellular function through metabolic memory despite normalization of glycemic control. Persistent, hyperglycemia-induced epigenetic patterns are considered a central mechanism that activates metabolic memory; however, this has not been investigated in patient-derived fibroblasts from DFUs. We generated a cohort of patient-derived lines from DFU fibroblasts (DFUF), and site- and age-matched diabetic foot fibroblasts (DFF) and non-diabetic foot fibroblasts (NFF) to investigate global and genome-wide DNA methylation patterns using liquid chromatography/mass spectrometry and the Illumina Infinium HumanMethylation450K array. DFFs and DFUFs demonstrated significantly lower global DNA methylation compared to NFFs (p = 0.03). Hierarchical clustering of differentially methylated probes (DMPs, p = 0.05) showed that DFFs and DFUFs cluster together and separately from NFFs. Twenty-five percent of the same probes were identified as DMPs when individually comparing DFF and DFUF to NFF. Functional annotation identified enrichment of DMPs associated with genes critical to wound repair, including angiogenesis (p = 0.07) and extracellular matrix assembly (p = 0.035). Identification of sustained DNA methylation patterns in patient-derived fibroblasts after prolonged passage in normoglycemic conditions demonstrates persistent metabolic memory. These findings suggest that epigenetic-related metabolic memory may also underlie differences in wound healing phenotypes and can potentially identify therapeutic targets.

Regulators associated with clinical outcomes revealed by DNA methylation data in breast cancer

The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

Modeling the relationship of epigenetic modifications to transcription factor binding

Transcription factors (TFs) and epigenetic modifications play crucial roles in the regulation of gene expression, and correlations between the two types of factors have been discovered. However, methods for quantitatively studying the correlations remain limited. Here, we present a computational approach to systematically investigating how epigenetic changes in chromatin architectures or DNA sequences relate to TF binding. We implemented statistical analyses to illustrate that epigenetic modifications are predictive of TF binding affinities, without the need of sequence information. Intriguingly, by considering genome locations relative to transcription start sites (TSSs) or enhancer midpoints, our analyses show that different locations display various relationship patterns. For instance, H3K4me3, H3k9ac and H3k27ac contribute more in the regions near TSSs, whereas H3K4me1 and H3k79me2 dominate in the regions far from TSSs. DNA methylation plays relatively important roles when close to TSSs than in other regions. In addition, the results show that epigenetic modification models for the predictions of TF binding affinities are cell line-specific. Taken together, our study elucidates highly coordinated, but location- and cell type-specific relationships between epigenetic modifications and binding affinities of TFs.

Computational Biology Methods for Characterization of Pluripotent Cells

Pluripotent cells are a powerful tool for regenerative medicine and drug discovery. Several techniques have been developed to induce pluripotency, or to extract pluripotent cells from different tissues and biological fluids. However, the characterization of pluripotency requires tedious, expensive, time-consuming, and not always reliable wet-lab experiments; thus, an easy, standard quality-control protocol of pluripotency assessment remains to be established. Here to help comes the use of high-throughput techniques, and in particular, the employment of gene expression microarrays, which has become a complementary technique for cellular characterization. Research has shown that the transcriptomics comparison with an Embryonic Stem Cell (ESC) of reference is a good approach to assess the pluripotency. Under the premise that the best protocol is a computer software source code, here I propose and explain line by line a software protocol coded in R-Bioconductor for pluripotency assessment based on the comparison of transcriptomics data of pluripotent cells with an ESC of reference. I provide advice for experimental design, warning about possible pitfalls, and guides for results interpretation.

Epigenetic modifications of gene promoter DNA in the liver of adult female mice masculinized by testosterone

Testosterone (T) is known to masculinize the female phenotype of the liver, evidenced as up- and down-regulated expressions of male- and female-predominant genes, respectively, involved in hepatic metabolism. This study is aimed at identifying epigenetic modifications of promoters of these differently expressed genes in the liver after masculinization by T of adult female C57BL/6 mice using methylated DNA immunoprecipitation and NimbleGen microarrays. Among the 17,354 promoters examined, 82 promoters in the liver have been identified to be significantly changed by T (p<0.05), with 47 and 35 promoters exhibiting increased and decreased DNA methylation, respectively. Most of these promoters display the changes of DNA methylation in their Ups-regions, which are between +500 and +2000 bp upstream from the transcription start site (TSS) of the genes. Less T-induced modifications have been detected in the Cor-regions of the promoters, i.e., +500 to -500 bp around the TSS. Only 13 and 7 Cor-promoters are hyper- and hypo-methylated, respectively, among which are 10 hyper- and 5 hypo-methylated promoters of genes with annotated functions. Surprisingly, the promoters are largely unmethylated in those genes whose expression has been previously found to be permanently deregulated by T in the liver, as e.g. the T-upregulated male-predominant genes Cyp7b1, Cyp2d9, Cyp4a10, Ugt2b1, Ugt2b38, Hsd3b5, Slco1a1 as well as the T-downregulated female-predominant genes Cyp2b9, Cyp2b13, Cyp3a41, Cyp3a44, Fmo3, Sult2a2, respectively. Though methylatable, the promoter DNA of Ar, Esr1, and Esr2 remained unaffected by T. However, T decreases DNA-methylation of the Cor-promoter region of Ddc encoding the AR-coactivator dopa decarboxylase. Among the identified 15 Cor-promoters of genes with annotated functions are also those of Defb43, Cst11, and Sele involved in innate immunity. Our data support the view that T may exert long-lasting epigenetic effects on functions of the liver-inherent immune system.

Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development

During differentiation of embryonic stem cells, chromatin reorganizes to establish cell type-specific expression programs. Here, we have dissected the linkages between DNA methylation (5mC), hydroxymethylation (5hmC), nucleosome repositioning, and binding of the transcription factor CTCF during this process. By integrating MNase-seq and ChIP-seq experiments in mouse embryonic stem cells (ESC) and their differentiated counterparts with biophysical modeling, we found that the interplay between these factors depends on their genomic context. The mostly unmethylated CpG islands have reduced nucleosome occupancy and are enriched in cell type-independent binding sites for CTCF. The few remaining methylated CpG dinucleotides are preferentially associated with nucleosomes. In contrast, outside of CpG islands most CpGs are methylated, and the average methylation density oscillates so that it is highest in the linker region between nucleosomes. Outside CpG islands, binding of TET1, an enzyme that converts 5mC to 5hmC, is associated with labile, MNase-sensitive nucleosomes. Such nucleosomes are poised for eviction in ESCs and become stably bound in differentiated cells where the TET1 and 5hmC levels go down. This process regulates a class of CTCF binding sites outside CpG islands that are occupied by CTCF in ESCs but lose the protein during differentiation. We rationalize this cell type-dependent targeting of CTCF with a quantitative biophysical model of competitive binding with the histone octamer, depending on the TET1, 5hmC, and 5mC state.