基于microRNA共调控网络的microRNA调控机制研究

已有研究通过计算和实验的手段,证明了不同的microRNA(miRNA)通过相互之间的合作,来共同调控它们所共有的靶基因。对miRNA之间这种合作行为的特性的研究,能够帮助我们更好的理解miRNA的调控机理。本文建立了一个网络来描述miRNA之间的合作关系,并通过对该网络的分析,得出了四点关于miRNA调控机制的性质。第一,基因靶标数目越多的miRNA倾向于与越多的miRNA伙伴进行合作。第二,进化上保守的miRNA所具有的共调控伙伴的数目显著多于非保守的miRNA。第三,以上的性质是跨物种的存在的(人与小鼠)。第四,miRNA与蛋白质在系统层面性质存在一定的相似。     

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer

Background

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model??s prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

Results

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via ??GR constraints??. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

Conclusions

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.     

Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours

Background

The availability of hundreds of bacterial genomes allowed a comparative genomic study of the Type VI Secretion System (T6SS), recently discovered as being involved in pathogenesis. By combining comparative and phylogenetic approaches using more than 500 prokaryotic genomes, we characterized the global T6SS genetic structure in terms of conservation, evolution and genomic organization.

Results

This genome wide analysis allowed the identification of a set of 13 proteins constituting the T6SS protein core and a set of conserved accessory proteins. 176 T6SS loci (encompassing 92 different bacteria) were identified and their comparison revealed that T6SS-encoded genes have a specific conserved genetic organization. Phylogenetic reconstruction based on the core genes showed that lateral transfer of the T6SS is probably its major way of dissemination among pathogenic and non-pathogenic bacteria. Furthermore, the sequence analysis of the VgrG proteins, proposed to be exported in a T6SS-dependent way, confirmed that some C-terminal regions possess domains showing similarities with adhesins or proteins with enzymatic functions.

Conclusion

The core of T6SS is composed of 13 proteins, conserved in both pathogenic and non-pathogenic bacteria. Subclasses of T6SS differ in regulatory and accessory protein content suggesting that T6SS has evolved to adapt to various microenvironments and specialized functions. Based on these results, new functional hypotheses concerning the assembly and function of T6SS proteins are proposed.     

Comparative transcriptome analysis reveals the regulation network for fiber strength in cotton

Biotechnology Letters - Determine the effect of secondary cell wall (SCW) thickness and microcrystalline cellulose content (MCC) on mature fiber strength (FS) and reveal through comparative...     

Gene regulation network analysis reveals core genes associated with survival in glioblastoma multiforme

Glioblastoma multiforme (GBM) is a very serious mortality of central nervous system cancer. The microarray data from GSE2223 , GSE4058 , GSE4290 , GSE13276 , GSE68848 and GSE70231 (389 GBM tumour and 67 normal tissues) and the RNA‐seq data from TCGA‐GBM dataset (169 GBM and five normal samples) were chosen to find differentially expressed genes (DEGs). RRA (Robust rank aggregation) method was used to integrate seven datasets and calculate 133 DEGs (82 up‐regulated and 51 down‐regulated genes). Subsequently, through the PPI (protein‐protein interaction) network and MCODE/ cytoHubba methods, we finally filtered out ten hub genes, including FOXM1, CDK4, TOP2A, RRM2, MYBL2, MCM2, CDC20, CCNB2, MYC and EZH2, from the whole network. Functional enrichment analyses of DEGs were conducted to show that these hub genes were enriched in various cancer‐related functions and pathways significantly. We also selected CCNB2, CDC20 and MYBL2 as core biomarkers, and further validated them in CGGA, HPA and CCLE database, suggesting that these three core hub genes may be involved in the origin of GBM. All these potential biomarkers for GBM might be helpful for illustrating the important role of molecular mechanisms of tumorigenesis in the diagnosis, prognosis and targeted therapy of GBM cancer.     

Epigenetic regulation of prostate cancer

Prostate cancer is a commonly diagnosed cancer in men and a leading cause of cancer deaths. Whilst the underlying mechanisms leading to prostate cancer are still to be determined, it is evident that both genetic and epigenetic changes contribute to the development and progression of this disease. Epigenetic changes involving DNA hypo- and hypermethylation, altered histone modifications and more recently changes in microRNA expression have been detected at a range of genes associated with prostate cancer. Furthermore, there is evidence that particular epigenetic changes are associated with different stages of the disease. Whilst early detection can lead to effective treatment, and androgen deprivation therapy has a high response rate, many tumours develop towards hormone-refractory prostate cancer, for which there is no successful treatment. Reliable markers for early detection and more effective treatment strategies are, therefore, needed. Consequently, there is a considerable interest in the potential of epigenetic changes as markers or targets for therapy in prostate cancer. Epigenetic modifiers that demethylate DNA and inhibit histone deacetylases have recently been explored to reactivate silenced gene expression in cancer. However, further understanding of the mechanisms and the effects of chromatin modulation in prostate cancer are required. In this review, we examine the current literature on epigenetic changes associated with prostate cancer and discuss the potential use of epigenetic modifiers for treatment of this disease.     

microRNA155在动脉粥样硬化发生过程中的网络调控机制

动脉粥样硬化(atherosclerosis,AS)是一种慢性进行性的血管炎症性疾病,其发病机制主要包括内皮细胞损伤,脂质浸润及炎症介质分泌等。microRNA155(miR-155)是参与AS炎性调控、免疫和自噬信号等通路的微小非编码RNA。系统性研究miR-155及其靶基因的网络调控机制,能全面理解miR-155在AS中的作用,促进其在临床诊断中的应用开发。利用miRNA靶基因预测数据库miRDB、miRmap和Starbase获取miR-155的靶基因集。R语言分析基因表达综合数据库(gene expression omnibus,GEO)共享平台动脉粥样硬化斑块差异表达基因(GSE24702),筛选出18 076个差异表达基因。利用基因集富集分析(gene set enrichment analysis,GSEA)分析,观察这些差异表达基因共同富集在IL6-JAK-STAT3信号通路、炎症反应和TNFα等炎症信号通路。与miR-155靶基因交叉匹配得到371个交集mRNA,其中159个在动脉粥样硬化斑块中上调,212个在动脉粥样硬化斑块中下调。基因本体(gene ontology,GO)及基因组数据库(kyoto encyclopedia of genes and genomes,KEGG)分析研究基因功能,GO富集分析371个差异基因主要富集炎症和凋亡信号通路的负调控等功能,KEGG分析371个差异基因主要富集TGFβ等炎症信号通路。蛋白相互作用网络(protein-protein interaction networks,PPI)分析获得关键节点基因是ARRB2、FBXO11、SOCS1、FBXO22、FBXO30、KRAS、RNF19A、TRIM32、HERC4、PJA1、RCHY1和DET1。本研究表明,miR-155主要通过调控炎症反应等相关信号通路影响斑块细胞炎症、自噬及凋亡等功能,进而影响动脉粥样硬化的各个进程。     

Integrative network analysis reveals active microRNAs and their functions in gastric cancer

Background

Ralstonia eutropha H16, found in both soil and water, is a Gram-negative lithoautotrophic bacterium that can utillize CO₂ and H₂ as its sources of carbon and energy in the absence of organic substrates. R. eutropha H16 can reach high cell densities either under lithoautotrophic or heterotrophic conditions, which makes it suitable for a number of biotechnological applications. It is the best known and most promising producer of polyhydroxyalkanoates (PHAs) from various carbon substrates and is an environmentally important bacterium that can degrade aromatic compounds. In order to make R. eutropha H16 a more efficient and robust biofactory, system-wide metabolic engineering to improve its metabolic performance is essential. Thus, it is necessary to analyze its metabolic characteristics systematically and optimize the entire metabolic network at systems level.

Results

We present the lithoautotrophic genome-scale metabolic model of R. eutropha H16 based on the annotated genome with biochemical and physiological information. The stoichiometic model, RehMBEL1391, is composed of 1391 reactions including 229 transport reactions and 1171 metabolites. Constraints-based flux analyses were performed to refine and validate the genome-scale metabolic model under environmental and genetic perturbations. First, the lithoautotrophic growth characteristics of R. eutropha H16 were investigated under varying feeding ratios of gas mixture. Second, the genome-scale metabolic model was used to design the strategies for the production of poly[R-(-)-3hydroxybutyrate] (PHB) under different pH values and carbon/nitrogen source uptake ratios. It was also used to analyze the metabolic characteristics of R. eutropha when the phosphofructokinase gene was expressed. Finally, in silico gene knockout simulations were performed to identify targets for metabolic engineering essential for the production of 2-methylcitric acid in R. eutropha H16.

Conclusion

The genome-scale metabolic model, RehMBEL1391, successfully represented metabolic characteristics of R. eutropha H16 at systems level. The reconstructed genome-scale metabolic model can be employed as an useful tool for understanding its metabolic capabilities, predicting its physiological consequences in response to various environmental and genetic changes, and developing strategies for systems metabolic engineering to improve its metabolic performance.     

Comprehensive gene expression analysis reveals multiple signal pathways associated with prostate cancer

Prostate cancer (PC) depends on androgenic signaling for growth and survival. To data, the exact molecular mechanism of hormone controlling proliferation and tumorigenesis in the PC remains unclear. Therefore, in this study, we explored the differentially expressed genes (DEGs) and identified featured genes related to hormone stimulus from PC. Two sets of gene expression data, including PC and normal control sample, were downloaded from Gene Expression Omnibus (GEO) database. The t-test was used to identify DEGs between PC and controls. Gene ontology (GO) functional annotation was applied to analyze the function of DEGs and screen hormone-related DEGs. Then these hormone-related DEGs were further analyzed in constructed cancer network and Human Protein Reference Database to screen important signaling pathways they participated in. A total of 912 DEGs were obtained which included 326 up-regulated genes and 586 down-regulated genes. GO functional enrichment analysis identified 50 hormone-related DEGs associated with PC. After pathway and PPI network analysis, we found these hormone-related DEGs participated in several important signaling pathways including TGF-β (TGFB2, TGFB3 and TGFBR2), MAPK (TGFB2, TGFB3 and TGFBR2), insulin (PIK3R3, SHC1 and EIF4EBP1), and p53 signaling pathways (CCND2 and CDKN1A). In addition, a total of five hormone-related DEGs (SHC1, CAV1, RXRA, CDKN1A and SRF) were located in the center of PPI network and 12 hormone-related DEGs formed six protein modules. These important signal pathways and hormone-related DEGs may provide potential therapeutic targets for PC.     

Global microRNA level regulation of EGFR‐driven cell‐cycle protein network in breast cancer

The EGFR‐driven cell‐cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large‐scale miRNA screening approach with a high‐throughput proteomic readout and network‐based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3′‐UTR of target genes. Furthermore, the novel network‐analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co‐regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR‐124, miR‐147 and miR‐193a‐3p) as novel tumor suppressors that co‐target EGFR‐driven cell‐cycle network proteins and inhibit cell‐cycle progression and proliferation in breast cancer.