Expression and metabolism profiles of CVT associated with inflammatory responses and oxygen carrier ability in the brain.

AIM: As the main type of stroke, the incidence of cerebral venous thrombosis (CVT) has been rising. However, the comprehensive mechanisms behind it remain unclear. Thus, the multi-omics study is required to investigate the mechanism after CVT and elucidate the characteristic pathology of venous stroke and arterial stroke. METHODS: Adult rats were subjected to CVT and MCAO models. Whole-transcriptome sequencing (RNA-seq) and untargeted metabolomics analysis were performed to construct the transcriptome and metabolism profiles of rat brains after CVT and also MCAO. The difference analysis, functional annotation, and enrichment analysis were also performed. RESULTS: Through RNA-seq analysis, differentially expressed genes (DEGs) were screened. 174 CVT specific genes including Il1a, Ccl9, Cxxl6, Tnfrsf14, etc., were detected. The hemoglobin genes, including both Hba and Hbb, were significantly downregulated after CVT, compared both to the MCAO and Sham groups. Metabolism analysis showed that CVT had higher heterogeneity of metabolism compared to MCAO. Metabolites including N-stearoyltyrosine, 5-methoxy-3-indoleaceate, Afegostat, pipecolic acid, etc. were specially regulated in CVT. Through the immune infiltration analysis, it was found that CVT had a higher immune response, with the abundance of certain types of immune cells increased, especially T helper cells. It was important to find the prevalence of the activation of inflammatory chemokine, cytokine, NOD-like pathway, and neutrophil extracellular trap. CONCLUSION: We explored and analyzed the gene expression and metabolomic characteristics of CVT, revealed the specific inflammatory reaction mechanism of CVT and found the markers in transcriptome and metabolism levels. It points out the direction for CVT early diagnosis and treatment.

CircPDS5B Reduction Improves Angiogenesis Following Ischemic Stroke by Regulating MicroRNA-223-3p/NOTCH2 Axis.

Background and Objectives: Ischemic stroke (IS) is responsible for major causes of global death and disability, for which promoting angiogenesis is a promising therapeutic strategy. This study analyzed circular RNA PDS5B (circPDS5B) and its related mechanisms in angiogenesis in IS. Methods: In the permanent middle cerebral artery occlusion (pMCAO) mouse model, circPDS5B, microRNA (miR)-223-3p, and NOTCH2 levels were checked. By testing neurologic function, neuronal apoptosis, and expression of angiogenesis-related proteins in pMCAO mice, the protective effects of circPDS5B knockdown were probed. In human brain microvascular endothelial cells (HBMECs) under oxygen-glucose deprivation (OGD) conditions, the effects of circPDS5B, miR-223-3p, and NOTCH2 on angiogenesis were studied by measuring cellular activities. Results: The increase of circPDS5B and NOTCH2 expression and the decrease of miR-223-3p expression were examined in pMCAO mice. Reducing circPDS5B expression indicated protection against neurologic dysfunction, apoptosis, and angiogenesis impairment. For circPDS5B-depleted or miR-223-3p-restored HBMECs under OGD treatment, angiogenesis was promoted. MiR-223-3p inhibition-associated reduction of angiogenesis could be counteracted by knocking down NOTCH2. CircPDS5B depletion-induced angiogenesis in OGD-conditioned HBMECs was repressed after overexpressing NOTCH2. Discussion: In IS, the expression of circPDS5B was upregulated, and miR-223-3p inhibited HBMECs activity and promoted NOTCH2 expression, thus promoting IS. CircPDS5B reduction improves angiogenesis following ischemic stroke by regulating microRNA-223-3p/NOTCH2 axis.

A chromosome-level genome assembly for Erianthus fulvus provides insights into its biofuel potential and facilitates breeding for improvement of sugarcane.

Erianthus produces substantial biomass, exhibits a good Brix value, and shows wide environmental adaptability, making it a potential biofuel plant. In contrast to closely related sorghum and sugarcane, Erianthus can grow in degraded soils, thus releasing pressure on agricultural lands used for biofuel production. However, the lack of genomic resources for Erianthus hinders its genetic improvement, thus limiting its potential for biofuel production. In the present study, we generated a chromosome-scale reference genome for Erianthus fulvus Nees. The genome size estimated by flow cytometry was 937 Mb, and the assembled genome size was 902 Mb, covering 96.26% of the estimated genome size. A total of 35 065 protein-coding genes were predicted, and 67.89% of the genome was found to be repetitive. A recent whole-genome duplication occurred approximately 74.10 million years ago in the E. fulvus genome. Phylogenetic analysis showed that E. fulvus is evolutionarily closer to S. spontaneum and diverged after S. bicolor. Three of the 10 chromosomes of E. fulvus formed through rearrangements of ancestral chromosomes. Phylogenetic reconstruction of the Saccharum complex revealed a polyphyletic origin of the complex and a sister relationship of E. fulvus with Saccharum sp., excluding S. arundinaceum. On the basis of the four amino acid residues that provide substrate specificity, the E. fulvus SWEET proteins were classified as mono- and disaccharide sugar transporters. Ortho-QTL genes identified for 10 biofuel-related traits may aid in the rapid screening of E. fulvus populations to enhance breeding programs for improved biofuel production. The results of this study provide valuable insights for breeding programs aimed at improving biofuel production in E. fulvus and enhancing sugarcane introgression programs.

Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer.

IMPORTANCE: The utility of buccal cells as an epithelial source tissue for epigenome-wide association studies (EWASs) remains to be demonstrated. Given the direct exposure of buccal cells to potent carcinogens such as smoke, epigenetic changes in these cells may provide insights into the development of smoke-related cancers. OBJECTIVE: To perform an EWAS in buccal and blood cells to assess the relative effect of smoking on the DNA methylation (DNAme) patterns in these cell types and to test whether these DNAme changes are also seen in epithelial cancer. DESIGN, SETTING, AND PARTICIPANTS: In 2013, we measured DNAme at more than 480,000 CpG sites in buccal samples provided in 1999 by 790 women (all aged 53 years in 1999) from the United Kingdom Medical Research Council National Survey of Health and Development. This included matched blood samples from 152 women. We constructed a DNAme-based smoking index and tested its sensitivity and specificity to discriminate normal from cancer tissue in more than 5000 samples. MAIN OUTCOMES AND MEASURES: CpG sites whose DNAme level correlates with smoking pack-years, and construction of an associated sample-specific smoking index, which measures the mean deviation of DNAme at smoking-associated CpG sites from a normal reference. RESULTS: In a discovery set of 400 women, we identified 1501 smoking-associated CpG sites at a genome-wide significance level of P < 10-7, which were validated in an independent set of 390 women. This represented a 40-fold increase of differentially methylated sites in buccal cells compared with matched blood samples. Hypermethylated sites were enriched for bivalently marked genes and binding sites of transcription factors implicated in DNA repair and chromatin architecture (P < 10-10). A smoking index constructed from the DNAme changes in buccal cells was able to discriminate normal tissue from cancer tissue with a mean receiver operating characteristic area under the curve of 0.99 (range, 0.99-1.00) for lung cancers and of 0.91 (range, 0.71-1.00) for 13 other organs. The corresponding area under the curve of a smoking signature derived from blood cells was lower than that derived from buccal cells in 14 of 15 cancer types (Wilcoxon signed rank test, P = .001). CONCLUSIONS AND RELEVANCE: These data point toward buccal cells as being a more appropriate source of tissue than blood to conduct EWASs for smoking-related epithelial cancers.

An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging.

Recent studies have demonstrated that the DNA methylome changes with age. This epigenetic drift may have deep implications for cellular differentiation and disease development. However, it remains unclear how much of this drift is functional or caused by underlying changes in cell subtype composition. Moreover, no study has yet comprehensively explored epigenetic drift at different genomic length scales and in relation to regulatory elements. Here we conduct an in-depth analysis of epigenetic drift in blood tissue. We demonstrate that most of the age-associated drift is independent of the increase in the granulocyte to lymphocyte ratio that accompanies aging and that enrichment of age-hypermethylated CpG islands increases upon adjustment for cellular composition. We further find that drift has only a minimal impact on in-cis gene expression, acting primarily to stabilize pre-existing baseline expression levels. By studying epigenetic drift at different genomic length scales, we demonstrate the existence of mega-base scale age-associated hypomethylated blocks, covering approximately 14% of the human genome, and which exhibit preferential hypomethylation in age-matched cancer tissue. Importantly, we demonstrate the feasibility of integrating Illumina 450k DNA methylation with ENCODE data to identify transcription factors with key roles in cellular development and aging. Specifically, we identify REST and regulatory factors of the histone methyltransferase MLL complex, whose function may be disrupted in aging. In summary, most of the epigenetic drift seen in blood is independent of changes in blood cell type composition, and exhibits patterns at different genomic length scales reminiscent of those seen in cancer. Integration of Illumina 450k with appropriate ENCODE data may represent a fruitful approach to identify transcription factors with key roles in aging and disease.

RNA-Seq-based transcriptome analysis of methicillin-resistant Staphylococcus aureus biofilm inhibition by ursolic acid and resveratrol.

Bacterial biofilms are particularly problematic since they become resistant to most available antibiotics. Hence, novel potential antagonists to inhibit biofilm formation are urgent. Here the influences of two natural products, ursolic acid and resveratrol, on biofilm of the clinical methicillin-resistant Staphylococcus aureus (MRSA) isolate were investigated using RNA-seq, and the differentially expressed genes were analyzed using Cuffdiff. The results showed that ursolic acid inhibition of biofilm formation may reduce amino acids metabolism and adhesins expression and resveratrol may disturb quorum sensing (QS) and the synthesis of surface proteins and capsular polysaccharides. In addition, the transcriptome analysis of resveratrol and the combination of resveratrol with vancomycin inhibition of established biofilm revealed that resveratrol would disturb the expression of genes related to QS, surface and secreted proteins, and capsular polysaccharides. These findings suggest that ursolic acid and resveratrol could be useful to be adjunct therapies for the treatment of MRSA biofilm-involved infections.

A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control.

MOTIVATION: There is a growing number of studies generating matched Illumina Infinium HumanMethylation450 and gene expression data, yet there is a corresponding shortage of statistical tools aimed at their integrative analysis. Such integrative tools are important for the discovery of epigenetically regulated gene modules or molecular pathways, which play key roles in cellular differentiation and disease. RESULTS: Here, we present a novel functional supervised algorithm, called Functional Epigenetic Modules (FEM), for the integrative analysis of Infinium 450k DNA methylation and matched or unmatched gene expression data. The algorithm identifies gene modules of coordinated differential methylation and differential expression in the context of a human interactome. We validate the FEM algorithm on simulated and real data, demonstrating how it successfully retrieves an epigenetically deregulated gene, previously known to drive endometrial cancer development. Importantly, in the same cancer, FEM identified a novel epigenetically deregulated hotspot, directly upstream of the well-known progesterone receptor tumour suppressor pathway. In the context of cellular differentiation, FEM successfully identifies known endothelial cell subtype-specific gene expression markers, as well as a novel gene module whose overexpression in blood endothelial cells is mediated by DNA hypomethylation. The systems-level integrative framework presented here could be used to identify novel key genes or signalling pathways, which drive cellular differentiation or disease through an underlying epigenetic mechanism. AVAILABILITY AND IMPLEMENTATION: FEM is freely available as an R-package from http://sourceforge.net/projects/funepimod.

Computational identification of protein-protein interactions in rice based on the predicted rice interactome network.

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.

PRIN: a predicted rice interactome network.

BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. RESULTS: To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. CONCLUSIONS: PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/.