DNA N-methyladenine (6mA) is a non-canonical DNA modification that is present at low levels in different eukaryotes, but its prevalence and genomic function in higher plants are unclear. Using mass spectrometry, immunoprecipitation and validation with analysis of single-molecule real-time sequencing, we observed that about 0.2% of all adenines are 6mA methylated in the rice genome. 6mA occurs most frequently at GAGG motifs and is mapped to about 20% of genes and 14% of transposable elements. In promoters, 6mA marks silent genes, but in bodies correlates with gene activity. 6mA overlaps with 5-methylcytosine (5mC) at CG sites in gene bodies and is complementary to 5mC at CHH sites in transposable elements. We show that OsALKBH1 may be potentially involved in 6mA demethylation in rice. The results suggest that 6mA is complementary to 5mC as an epigenomic mark in rice and reinforce a distinct role for 6mA as a gene expression-associated epigenomic mark in eukaryotes.

DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation. We present a quantitative approach, quantitative differentially methylated regions (QDMRs), to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. QDMR was applied to synthetic methylation patterns and methylation profiles detected by methylated DNA immunoprecipitation microarray (MeDIP-chip) in human tissues/cells. This approach can give a reasonable quantitative measure of methylation difference across multiple samples. Then DMR threshold was determined from methylation probability model. Using this threshold, QDMR identified 10 651 tissue DMRs which are related to the genes enriched for cell differentiation, including 4740 DMRs not identified by the method developed by Rakyan et al. QDMR can also measure the sample specificity of each DMR. Finally, the application to methylation profiles detected by reduced representation bisulphite sequencing (RRBS) in mouse showed the platform-free and species-free nature of QDMR. This approach provides an effective tool for the high-throughput identification of potential functional regions involved in epigenetic regulation.

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease–gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease–gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease–disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases.

No abstract

Super-enhancers are large clusters of transcriptional enhancers regarded as having essential roles in driving the expression of genes that control cell identity during development and tumorigenesis. The construction of a genome-wide super-enhancer database is urgently needed to better understand super-enhancer-directed gene expression regulation for a given biology process. Here, we present a specifically designed web-accessible database, Super-Enhancer Archive (SEA, http://sea.edbc.org). SEA focuses on integrating super-enhancers in multiple species and annotating their potential roles in the regulation of cell identity gene expression. The current release of SEA incorporates 83 996 super-enhancers computationally or experimentally identified in 134 cell types/tissues/diseases, including human (75 439, three of which were experimentally identified), mouse (5879, five of which were experimentally identified), Drosophila melanogaster (1774) and Caenorhabditis elegans (904). To facilitate data extraction, SEA supports multiple search options, including species, genome location, gene name, cell type/tissue and super-enhancer name. The response provides detailed (epi)genetic information, incorporating cell type specificity, nearby genes, transcriptional factor binding sites, CRISPR/Cas9 target sites, evolutionary conservation, SNPs, H3K27ac, DNA methylation, gene expression and TF ChIP-seq data. Moreover, analytical tools and a genome browser were developed for users to explore super-enhancers and their roles in defining cell identity and disease processes in depth.

Expression of biosynthetic pathways in heterologous hosts is an emerging approach to expedite production improvement and biosynthetic modification of natural products derived from microbial secondary metabolites. Herein we describe the development of a versatile Escherichia coli-Streptomyces shuttle Bacterial Artificial Chromosomal (BAC) conjugation vector, pSBAC, to facilitate the cloning, genetic manipulation, and heterologous expression of actinomycetes secondary metabolite biosynthetic gene clusters. The utility of pSBAC was demonstrated through the rapid cloning and heterologous expression of one of the largest polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) biosynthetic pathways: the meridamycin biosynthesis gene cluster (mer). The entire mer gene cluster ( approximately 90 kb) was captured in a single pSBAC clone through a straightforward restriction enzyme digestion and cloning approach and transferred into Streptomyces lividans. The production of meridamycin (1) in the heterologous host was achieved after replacement of the original promoter with an ermE* promoter and was enhanced by feeding with a biosynthetic precursor. The success of heterologous expression of such a giant gene cluster demonstrates the versatility of BAC cloning technology and paves the road for future exploration of expression of the meridamycin biosynthetic pathway in various hosts, including strains that have been optimized for polyketide production.

Shigella flexneri serotype 2 variant (II:3,4,7,8) was isolated in 2008 and first reported in China in 2013. In the present study, epidemiological surveillance from 2003 to 2013 in China suggested that this serotype first appeared in Guangxi in 2003; it then emerged in Shanghai and Xinjiang in 2004 and in Henan in 2008. Of the 1813 S. flexneri isolates, 58 S. flexneri serotype 2 variant strains were identified. Serotype 2 variant has emerged as a prominent serotype in recent years, with 2a (32.6%), X variant (25.2%), 1a (9.4%), X (6.3%), 2b (5.4%), and 1b (3.6%). According to phenotypic and genotypic analysis, the serotype 2 variant originated from 2a to 2b. A higher antibiotic resistance rate was observed between 2009 and 2013 than that between 2003 and 2008. Among 22 cephalosporin-resistant isolates, blaTEM-1, blaOXA-1, blaCTX-3, blaCTX-14, and blaCTX-79 were detected. Among 22 fluoroquinolone-resistant isolates, a Ser80Ile mutation in parC was present in all of the isolates. Moreover, 21 isolates had three gyrA point mutations (Ser83Leu, His211Tyr, Asp87Asn, or Gly) and one isolate had two gyrA point mutations (Ser83Leu and His211Tyr). The prevalence of His211Tyr in the fluoroquinolone-resistant isolates is concerning, and the mutation was first reported in China. Besides, 22 isolates harbored the aac(6′)-Ib-cr gene, and two isolates harbored qnrS1. In view of the increased epidemic frequency and multidrug-resistant strain emergence, continuous surveillance will be needed to understand the actual disease burden and provide guidance for shigellosis.

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis‐subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal‐like subtype into two different prognosis‐subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.