Myogenic cell cultures derived from muscle biopsies are excellent models for human cell differentiation. We report the first comprehensive analysis of myogenesis-specific DNA hyper- and hypo-methylation throughout the genome for human muscle progenitor cells (both myoblasts and myotubes) and skeletal muscle tissue vs. 30 non-muscle samples using reduced representation bisulfite sequencing. We also focused on four genes with extensive hyper- or hypo-methylation in the muscle lineage (PAX3, TBX1, MYH7B/MIR499 and OBSCN) to compare DNA methylation, DNaseI hypersensitivity, histone modification, and CTCF binding profiles. We found that myogenic hypermethylation was strongly associated with homeobox or T-box genes and muscle hypomethylation with contractile fiber genes. Nonetheless, there was no simple relationship between differential gene expression and myogenic differential methylation, rather only for subsets of these genes, such as contractile fiber genes. Skeletal muscle retained ~30% of the hypomethylated sites but only ~3% of hypermethylated sites seen in myogenic progenitor cells. By enzymatic assays, skeletal muscle was 2-fold enriched globally in genomic 5-hydroxymethylcytosine (5-hmC) vs. myoblasts or myotubes and was the only sample type enriched in 5-hmC at tested myogenic hypermethylated sites in PAX3/CCDC140 andTBX1. TET1 and TET2 RNAs, which are involved in generation of 5-hmC and DNA demethylation, were strongly upregulated in myoblasts and myotubes. Our findings implicate de novo methylation predominantly before the myoblast stage and demethylation before and after the myotube stage in control of transcription and co-transcriptional RNA processing. They also suggest that, in muscle, TET1 or TET2 are involved in active demethylation and in formation of stable 5-hmC residues.

BackgroundFacioscapulohumeral muscular dystrophy (FSHD) is a dominant disease linked to contraction of an array of tandem 3.3-kb repeats (D4Z4) at 4q35. Within each repeat unit is a gene, DUX4, that can encode a protein containing two homeodomains. A DUX4 transcript derived from the last repeat unit in a contracted array is associated with pathogenesis but it is unclear how.MethodsUsing exon-based microarrays, the expression profiles of myogenic precursor cells were determined. Both undifferentiated myoblasts and myoblasts differentiated to myotubes derived from FSHD patients and controls were studied after immunocytochemical verification of the quality of the cultures. To further our understanding of FSHD and normal myogenesis, the expression profiles obtained were compared to those of 19 non-muscle cell types analyzed by identical methods.ResultsMany of the ~17,000 examined genes were differentially expressed (> 2-fold, p < 0.01) in control myoblasts or myotubes vs. non-muscle cells (2185 and 3006, respectively) or in FSHD vs. control myoblasts or myotubes (295 and 797, respectively). Surprisingly, despite the morphologically normal differentiation of FSHD myoblasts to myotubes, most of the disease-related dysregulation was seen as dampening of normal myogenesis-specific expression changes, including in genes for muscle structure, mitochondrial function, stress responses, and signal transduction. Other classes of genes, including those encoding extracellular matrix or pro-inflammatory proteins, were upregulated in FSHD myogenic cells independent of an inverse myogenesis association. Importantly, the disease-linked DUX4 RNA isoform was detected by RT-PCR in FSHD myoblast and myotube preparations only at extremely low levels. Unique insights into myogenesis-specific gene expression were also obtained. For example, all four Argonaute genes involved in RNA-silencing were significantly upregulated during normal (but not FSHD) myogenesis relative to non-muscle cell types.ConclusionsDUX4's pathogenic effect in FSHD may occur transiently at or before the stage of myoblast formation to establish a cascade of gene dysregulation. This contrasts with the current emphasis on toxic effects of experimentally upregulated DUX4 expression at the myoblast or myotube stages. Our model could explain why DUX4's inappropriate expression was barely detectable in myoblasts and myotubes but nonetheless linked to FSHD.

Epstein-Barr virus (EBV) is associated with roughly 10% of gastric carcinomas worldwide (EBVaGC). Although previous investigations provide a strong link between EBV and gastric carcinomas, these studies were performed using selected EBV gene probes. Using a cohort of gastric carcinoma RNA-seq data sets from The Cancer Genome Atlas (TCGA), we performed a quantitative and global assessment of EBV gene expression in gastric carcinomas and assessed EBV associated cellular pathway alterations. EBV transcripts were detected in 17% of samples but these samples varied significantly in EBV coverage depth. In four samples with the highest EBV coverage (hiEBVaGC – high EBV associated gastric carcinoma), transcripts from the BamHI A region comprised the majority of EBV reads. Expression of LMP2, and to a lesser extent, LMP1 were also observed as was evidence of abortive lytic replication. Analysis of cellular gene expression indicated significant immune cell infiltration and a predominant IFNG response in samples expressing high levels of EBV transcripts relative to samples expressing low or no EBV transcripts. Despite the apparent immune cell infiltration, high levels of the cytotoxic T-cell (CTL) and natural killer (NK) cell inhibitor, IDO1, was observed in the hiEBVaGCs samples suggesting an active tolerance inducing pathway in this subgroup. These results were confirmed in a separate cohort of 21 Vietnamese gastric carcinoma samples using qRT-PCR and on tissue samples using in situ hybridization and immunohistochemistry. Lastly, a panel of tumor suppressors and candidate oncogenes were expressed at lower levels in hiEBVaGC versus EBV-low and EBV-negative gastric cancers suggesting the direct regulation of tumor pathways by EBV.

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Background: Tight regulation of homeobox genes is essential for vertebrate development. In a study of genome-wide differential methylation, we recently found that homeobox genes, including those in the HOX gene clusters, were highly overrepresented among the genes with hypermethylation in the skeletal muscle lineage. Methylation was analyzed by reduced representation bisulfite sequencing (RRBS) of postnatal myoblasts, myotubes and adult skeletal muscle tissue and 30 types of non-muscle-cell cultures or tissues. Results: In this study, we found that myogenic hypermethylation was present in specific subregions of all four HOX gene clusters and was associated with various chromatin epigenetic features. Although the 3′ half of the HOXD cluster was silenced and enriched in polycomb repression-associated H3 lysine 27 trimethylation in most examined cell types, including myoblasts and myotubes, myogenic samples were unusual in also displaying much DNA methylation in this region. In contrast, both HOXA and HOXC clusters displayed myogenic hypermethylation bordering a central region containing many genes preferentially expressed in myogenic progenitor cells and consisting largely of chromatin with modifications typical of promoters and enhancers in these cells. A particularly interesting example of myogenic hypermethylation was HOTAIR, a HOXC noncoding RNA gene, which can silence HOXD genes in trans via recruitment of polycomb proteins. In myogenic progenitor cells, the preferential expression of HOTAIR was associated with hypermethylation immediately downstream of the gene. Other HOX gene regions also displayed myogenic DNA hypermethylation despite being moderately expressed in myogenic cells. Analysis of representative myogenic hypermethylated sites for 5-hydroxymethylcytosine revealed little or none of this base, except for an intragenic site in HOXB5 which was specifically enriched in this base in skeletal muscle tissue, whereas myoblasts had predominantly 5-methylcytosine at the same CpG site. Conclusions: Our results suggest that myogenic hypermethylation of HOX genes helps fine-tune HOX sense and antisense gene expression through effects on 5′ promoters, intragenic and intergenic enhancers and internal promoters. Myogenic hypermethylation might also affect the relative abundance of different RNA isoforms, facilitate transcription termination, help stop the spread of activation-associated chromatin domains and stabilize repressive chromatin structures.

High-throughput RNA sequencing (RNA-seq) has become an instrumental assay for the analysis of multiple aspects of an organism's transcriptome. Further, the analysis of a biological specimen's associated microbiome can also be performed using RNA-seq data and this application is gaining interest in the scientific community. There are many existing bioinformatics tools designed for analysis and visualization of transcriptome data. Despite the availability of an array of next generation sequencing (NGS) analysis tools, the analysis of RNA-seq data sets poses a challenge for many biomedical researchers who are not familiar with command-line tools. Here we present RNA CoMPASS, a comprehensive RNA-seq analysis pipeline for the simultaneous analysis of transcriptomes and metatranscriptomes from diverse biological specimens. RNA CoMPASS leverages existing tools and parallel computing technology to facilitate the analysis of even very large datasets. RNA CoMPASS has a web-based graphical user interface with intrinsic queuing to control a distributed computational pipeline. RNA CoMPASS was evaluated by analyzing RNA-seq data sets from 45 B-cell samples. Twenty-two of these samples were derived from lymphoblastoid cell lines (LCLs) generated by the infection of naïve B-cells with the Epstein Barr virus (EBV), while another 23 samples were derived from Burkitt's lymphomas (BL), some of which arose in part through infection with EBV. Appropriately, RNA CoMPASS identified EBV in all LCLs and in a fraction of the BLs. Cluster analysis of the human transcriptome component of the RNA CoMPASS output clearly separated the BLs (which have a germinal center-like phenotype) from the LCLs (which have a blast-like phenotype) with evidence of activated MYC signaling and lower interferon and NF-kB signaling in the BLs. Together, this analysis illustrates the utility of RNA CoMPASS in the simultaneous analysis of transcriptome and metatranscriptome data. RNA CoMPASS is freely available at http://rnacompass.sourceforge.net/.

We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.

Atherosclerosis involves phenotypic modulation and transdifferentiation of vascular smooth muscle cells (SMCs). Data are given in tabular or figure format that illustrate genome-wide DNA methylation alterations in atherosclerotic vs. control aorta (athero DMRs). Data based upon publicly available chromatin state profiles are also shown for normal aorta, monocyte, and skeletal muscle tissue-specific DMRs and for aorta-specific chromatin features (enhancer chromatin, promoter chromatin, repressed chromatin, actively transcribed chromatin). Athero hypomethylated and hypermethylated DMRs as well as epigenetic and transcription profiles are described for the following genes: ACTA2, MYH10, MYH11 (SMC-associated genes); SMAD3 (a signaling gene for SMCs and other cell types); CD79B and SH3BP2 (leukocyte-associated genes); and TBX20 and genes in the HOXA, HOXB, HOXC , and HOXD clusters (T-box and homeobox developmental genes). The data reveal strong correlations between athero hypermethylated DMRs and regions of enhancer chromatin in aorta, which are discussed in the linked research article “Atherosclerosis-associated differentially methylated regions can reflect the disease phenotype and are often at enhancers” (M. Lacey et al., 2019).