H3K36 dimethylation shapes the epigenetic interaction landscape by directing repressive chromatin modifications in embryonic stem cells.

Epigenetic modifications on the chromatin do not occur in isolation. Chromatin-associated proteins and their modification products form a highly interconnected network, and disturbing one component may rearrange the entire system. We see this increasingly clearly in epigenetically dysregulated cancers. It is important to understand the rules governing epigenetic interactions. Here, we use the mouse embryonic stem cell (mESC) model to describe in detail the relationships within the H3K27-H3K36-DNA methylation subnetwork. In particular, we focus on the major epigenetic reorganization caused by deletion of the histone 3 lysine 36 methyltransferase NSD1, which in mESCs deposits nearly all of the intergenic H3K36me2. Although disturbing the H3K27 and DNA methylation (DNAme) components also affects this network to a certain extent, the removal of H3K36me2 has the most drastic effect on the epigenetic landscape, resulting in full intergenic spread of H3K27me3 and a substantial decrease in DNAme. By profiling DNMT3A and CHH methylation (mCHH), we show that H3K36me2 loss upon Nsd1-KO leads to a massive redistribution of DNMT3A and mCHH away from intergenic regions and toward active gene bodies, suggesting that DNAme reduction is at least in part caused by redistribution of de novo methylation. Additionally, we show that pervasive acetylation of H3K27 is regulated by the interplay of H3K36 and H3K27 methylation. Our analysis highlights the importance of H3K36me2 as a major determinant of the developmental epigenome and provides a framework for further consolidating our knowledge of epigenetic networks.

The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape.

Enzymes that catalyse CpG methylation in DNA, including the DNA methyltransferases 1 (DNMT1), 3A (DNMT3A) and 3B (DNMT3B), are indispensable for mammalian tissue development and homeostasis1-4. They are also implicated in human developmental disorders and cancers5-8, supporting the critical role of DNA methylation in the specification and maintenance of cell fate. Previous studies have suggested that post-translational modifications of histones are involved in specifying patterns of DNA methyltransferase localization and DNA methylation at promoters and actively transcribed gene bodies9-11. However, the mechanisms that control the establishment and maintenance of intergenic DNA methylation remain poorly understood. Tatton-Brown-Rahman syndrome (TBRS) is a childhood overgrowth disorder that is defined by germline mutations in DNMT3A. TBRS shares clinical features with Sotos syndrome (which is caused by haploinsufficiency of NSD1, a histone methyltransferase that catalyses the dimethylation of histone H3 at K36 (H3K36me2)8,12,13), which suggests that there is a mechanistic link between these two diseases. Here we report that NSD1-mediated H3K36me2 is required for the recruitment of DNMT3A and maintenance of DNA methylation at intergenic regions. Genome-wide analysis shows that the binding and activity of DNMT3A colocalize with H3K36me2 at non-coding regions of euchromatin. Genetic ablation of Nsd1 and its paralogue Nsd2 in mouse cells results in a redistribution of DNMT3A to H3K36me3-modified gene bodies and a reduction in the methylation of intergenic DNA. Blood samples from patients with Sotos syndrome and NSD1-mutant tumours also exhibit hypomethylation of intergenic DNA. The PWWP domain of DNMT3A shows dual recognition of H3K36me2 and H3K36me3 in vitro, with a higher binding affinity towards H3K36me2 that is abrogated by TBRS-derived missense mutations. Together, our study reveals a trans-chromatin regulatory pathway that connects aberrant intergenic CpG methylation to human neoplastic and developmental overgrowth.

Depletion of H3K36me2 recapitulates epigenomic and phenotypic changes induced by the H3.3K36M oncohistone mutation.

Hotspot histone H3 mutations have emerged as drivers of oncogenesis in cancers of multiple lineages. Specifically, H3 lysine 36 to methionine (H3K36M) mutations are recurrently identified in chondroblastomas, undifferentiated sarcomas, and head and neck cancers. While the mutation reduces global levels of both H3K36 dimethylation (H3K36me2) and trimethylation (H3K36me3) by dominantly inhibiting their respective specific methyltransferases, the relative contribution of these methylation states to the chromatin and phenotypic changes associated with H3K36M remains unclear. Here, we specifically deplete H3K36me2 or H3K36me3 in mesenchymal cells, using CRISPR-Cas9 to separately knock out the corresponding methyltransferases NSD1/2 or SETD2. By profiling and comparing the epigenomic and transcriptomic landscapes of these cells with cells expressing the H3.3K36M oncohistone, we find that the loss of H3K36me2 could largely recapitulate H3.3K36M's effect on redistribution of H3K27 trimethylation (H3K27me3) and gene expression. Consistently, knockout of Nsd1/2, but not Setd2, phenocopies the differentiation blockade and hypersensitivity to the DNA-hypomethylating agent induced by H3K36M. Together, our results support a functional divergence between H3K36me2 and H3K36me3 and their nonredundant roles in H3K36M-driven oncogenesis.

Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development.

MOTIVATION: Transcriptional regulatory networks controlling cell fate decisions in mammalian embryonic development remain elusive despite a long time of research. The recent emergence of single-cell RNA profiling technology raises hope for new discovery. Although experimental works have obtained intriguing insights into the mouse early development, a holistic and systematic view is still missing. Mathematical models of cell fates tend to be concept-based, not designed to learn from real data. To elucidate the regulatory mechanisms behind cell fate decisions, it is highly desirable to synthesize the data-driven and knowledge-driven modeling approaches. RESULTS: We propose a novel method that integrates the structure of a cell lineage tree with transcriptional patterns from single-cell data. This method adopts probabilistic Boolean network (PBN) for network modeling, and genetic algorithm as search strategy. Guided by the 'directionality' of cell development along branches of the cell lineage tree, our method is able to accurately infer the regulatory circuits from single-cell gene expression data, in a holistic way. Applied on the single-cell transcriptional data of mouse preimplantation development, our algorithm outperforms conventional methods of network inference. Given the network topology, our method can also identify the operational interactions in the gene regulatory network (GRN), corresponding to specific cell fate determination. This is one of the first attempts to infer GRNs from single-cell transcriptional data, incorporating dynamics of cell development along a cell lineage tree. AVAILABILITY AND IMPLEMENTATION: Implementation of our algorithm is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide analysis in Plasmodium falciparum reveals early and late phases of RNA polymerase II occupancy during the infectious cycle.

BACKGROUND: Over the course of its intraerythrocytic developmental cycle (IDC), the malaria parasite Plasmodium falciparum tightly orchestrates the rise and fall of transcript levels for hundreds of genes. Considerable debate has focused on the relative importance of transcriptional versus post-transcriptional processes in the regulation of transcript levels. Enzymatically active forms of RNAPII in other organisms have been associated with phosphorylation on the serines at positions 2 and 5 of the heptad repeats within the C-terminal domain (CTD) of RNAPII. We reasoned that insight into the contribution of transcriptional mechanisms to gene expression in P. falciparum could be obtained by comparing the presence of enzymatically active forms of RNAPII at multiple genes with the abundance of their associated transcripts. RESULTS: We exploited the phosphorylation state of the CTD to detect enzymatically active forms of RNAPII at most P. falciparum genes across the IDC. We raised highly specific monoclonal antibodies against three forms of the parasite CTD, namely unphosphorylated, Ser5-P and Ser2/5-P, and used these in ChIP-on-chip type experiments to map the genome-wide occupancy of RNAPII. Our data reveal that the IDC is divided into early and late phases of RNAPII occupancy evident from simple bi-phasic RNAPII binding profiles. By comparison to mRNA abundance, we identified sub-sets of genes with high occupancy by enzymatically active forms of RNAPII and relatively low transcript levels and vice versa. We further show that the presence of active and repressive histone modifications correlates with RNAPII occupancy over the IDC. CONCLUSIONS: The simple early/late occupancy by RNAPII cannot account for the complex dynamics of mRNA accumulation over the IDC, suggesting a major role for mechanisms acting downstream of RNAPII occupancy in the control of gene expression in this parasite.

H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs.

Polycomb Repressive Complex 2 (PRC2)-mediated histone H3K27 tri-methylation (H3K27me3) recruits canonical PRC1 (cPRC1) to maintain heterochromatin. In early development, polycomb-regulated genes are connected through long-range 3D interactions which resolve upon differentiation. Here, we report that polycomb looping is controlled by H3K27me3 spreading and regulates target gene silencing and cell fate specification. Using glioma-derived H3 Lys-27-Met (H3K27M) mutations as tools to restrict H3K27me3 deposition, we show that H3K27me3 confinement concentrates the chromatin pool of cPRC1, resulting in heightened 3D interactions mirroring chromatin architecture of pluripotency, and stringent gene repression that maintains cells in progenitor states to facilitate tumor development. Conversely, H3K27me3 spread in pluripotent stem cells, following neural differentiation or loss of the H3K36 methyltransferase NSD1, dilutes cPRC1 concentration and dissolves polycomb loops. These results identify the regulatory principles and disease implications of polycomb looping and nominate histone modification-guided distribution of reader complexes as an important mechanism for nuclear compartment organization. Highlights: The confinement of H3K27me3 at PRC2 nucleation sites without its spreading correlates with increased 3D chromatin interactions.The H3K27M oncohistone concentrates canonical PRC1 that anchors chromatin loop interactions in gliomas, silencing developmental programs.Stem and progenitor cells require factors promoting H3K27me3 confinement, including H3K36me2, to maintain cPRC1 loop architecture.The cPRC1-H3K27me3 interaction is a targetable driver of aberrant self-renewal in tumor cells.

Bayesian Data Fusion of Gene Expression and Histone Modification Profiles for Inference of Gene Regulatory Network.

Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior.

H3K27M in Gliomas Causes a One-Step Decrease in H3K27 Methylation and Reduced Spreading within the Constraints of H3K36 Methylation.

The discovery of H3K27M mutations in pediatric gliomas marked a new chapter in cancer epigenomics. Numerous studies have investigated the effect of this mutation on H3K27 trimethylation, but only recently have we started to realize its additional effects on the epigenome. Here, we use isogenic glioma H3K27M+/- cell lines to investigate H3K27 methylation and its interaction with H3K36 and H3K9 modifications. We describe a "step down" effect of H3K27M on the distribution of H3K27 methylation: me3 is reduced to me2, me2 is reduced to me1, whereas H3K36me2/3 delineates the boundaries for the spread of H3K27me marks. We also observe a replacement of H3K27me2/3 silencing by H3K9me3. Using a computational simulation, we explain our observations by reduced effectiveness of PRC2 and constraints imposed on the deposition of H3K27me by antagonistic H3K36 modifications. Our work further elucidates the effects of H3K27M in gliomas as well as the general principles of deposition in H3K27 methylation.

H3K27M induces defective chromatin spread of PRC2-mediated repressive H3K27me2/me3 and is essential for glioma tumorigenesis.

Lys-27-Met mutations in histone 3 genes (H3K27M) characterize a subgroup of deadly gliomas and decrease genome-wide H3K27 trimethylation. Here we use primary H3K27M tumor lines and isogenic CRISPR-edited controls to assess H3K27M effects in vitro and in vivo. We find that whereas H3K27me3 and H3K27me2 are normally deposited by PRC2 across broad regions, their deposition is severely reduced in H3.3K27M cells. H3K27me3 is unable to spread from large unmethylated CpG islands, while H3K27me2 can be deposited outside these PRC2 high-affinity sites but to levels corresponding to H3K27me3 deposition in wild-type cells. Our findings indicate that PRC2 recruitment and propagation on chromatin are seemingly unaffected by K27M, which mostly impairs spread of the repressive marks it catalyzes, especially H3K27me3. Genome-wide loss of H3K27me3 and me2 deposition has limited transcriptomic consequences, preferentially affecting lowly-expressed genes regulating neurogenesis. Removal of H3K27M restores H3K27me2/me3 spread, impairs cell proliferation, and completely abolishes their capacity to form tumors in mice.

Ryegrass uptake of soil Cu/Zn induced by EDTA/EDDS together with a vertical direct-current electrical field.

This paper summarizes a study on the application of vertical electric direct-current (DC) to control the migration of metal complexes in soil columns when EDTA/[S,S]-ethylenediaminedisuccinic acid (EDDS) is used to enhance ryegrass uptake of Cu/Zn from contaminated soil. The results show that application of EDTA/EDDS significantly increased ryegrass uptake of Cu/Zn when compared with no EDTA/EDDS application and the Cu/Zn concentrations in all soil solutions sampled at three different levels of the soil column, i.e. 15, 30 and 50 cm. Application of vertical DC electrical field (1.0 V cm(-1)) through top anode (close to the surface) and bottom cathode caused redistribution of Cu/Zn concentrations. Copper/Zn concentrations significantly decreased in soil pore fluid that were sampled in the bottom sections of the column, suggesting an efficient control of the leaching risk of the Cu/Zn complexes when the vertical electrical field is applied. The shoot Cu concentration of ryegrass in the treatments with EDTA/EDDS and electrical field together was 0.46/0.61 times higher than that in treatments without electrical fields.

Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses.

BACKGROUND: The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution. METHODS: We obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the "observed" mutations in different years. RESULTS: The sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites. CONCLUSIONS: Our study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.

The Max-Min High-Order Dynamic Bayesian Network for Learning Gene Regulatory Networks with Time-Delayed Regulations.

Accurately reconstructing gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationships between genes. We applied sensitivity analysis on our model to study the performance variation along different parameter settings. The result provides hints on the setting of parameters of MMHO-DBN.

Deciphering histone code of transcriptional regulation in malaria parasites by large-scale data mining.

Histone modifications play a major role in the regulation of gene expression. Accumulated evidence has shown that histone modifications mediate biological processes such as transcription cooperatively. This has led to the hypothesis of 'histone code' which suggests that combinations of different histone modifications correspond to unique chromatin states and have distinct functions. In this paper, we propose a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. Our approach can output rules with statistical significance. Some of the discovered rules are supported by literature of experimental results. Moreover, we have also discovered de novo rules which can guide further research in epigenetic regulation of transcription. Based on our association rules we build a model to predict gene expression, which outperforms a published Bayesian network model for gene expression prediction by histone modifications. The results of our study reveal mechanisms for histone modifications to regulate transcription in large-scale. Among our findings, the cooperation among histone modifications provides new evidence for the hypothesis of histone code. Furthermore, the rules output by our method can be used to predict the change of gene expression.

Highly sensitive inference of time-delayed gene regulation by network deconvolution.

BACKGROUND: Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN. RESULTS: In this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs. CONCLUSION: By taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.