Identifying candidate genes for wood formation in poplar based on microarray network analysis and graph theory

In poplar, genetic research on wood properties is very important for the improvement of wood quality. Studies of wood formation genes at each developmental stage using modern biotechnology have often been limited to several genes or gene families. Because of the complex regulatory network involved in the co-expression and interactions of thousands of genes, however, the genetic mechanisms of wood formation must be surveyed on a genome-wide scale. In this study, we identified wood formation-related genes using a differentially co-expressed (DCE) gene subset approach based on biological networks inferred from microarray data. Gene co-expression networks in leaf, root, and wood tissues were first constructed and topologically analyzed using microarray data collected from the Gene Expression Omnibus. The DCE gene modules in wood-forming tissue were then detected based on graph theory, which was followed by gene ontology (GO) enrichment analysis and GO annotation of probe sets. Finally, 72 probe sets were identified in the largest cohesive subgroup of the DCE gene network in wood tissue, with most of the probe sets associated with wood formation-related biological processes and GO cellular component categories. The approach described in this paper provides an effective strategy to identify wood formation genes in poplar and should contribute to the better understanding of the genetic and molecular mechanisms underlying wood properties in trees.     

Concordance of Gene Expression and Functional Correlation Patterns across the NCI-60 Cell Lines and the Cancer Genome Atlas Glioblastoma Samples

Background

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. We recently clustered genes based on correlation of expression profiles across the NCI-60. Many of the resulting clusters were characterized by cancer-associated biological functions. The set of curated glioblastoma (GBM) gene expression data from the Cancer Genome Atlas (TCGA) initiative has recently become available. Thus, we are now able to determine which of the processes are robustly shared by both the immortalized cell lines and clinical cancers.

Results

Our central observation is that some sets of highly correlated genes in the NCI-60 expression data are also highly correlated in the GBM expression data. Furthermore, a “double fishing” strategy identified many sets of genes that show Pearson correlation ≥0.60 in both the NCI-60 and the GBM data sets relative to a given “bait” gene. The number of such gene sets far exceeds the number expected by chance.

Conclusion

Many of the gene-gene correlations found in the NCI-60 do not reflect just the conditions of cell lines in culture; rather, they reflect processes and gene networks that also function in vivo. A number of gene network correlations co-occur in the NCI-60 and GBM data sets, but there are others that occur only in NCI-60 or only in GBM. In sum, this analysis provides an additional perspective on both the utility and the limitations of the NCI-60 in furthering our understanding of cancers in vivo.     

Characterization of Differentially Expressed Genes Involved in Pathways Associated with Gastric Cancer

To explore the patterns of gene expression in gastric cancer, a total of 26 paired gastric cancer and noncancerous tissues from patients were enrolled for gene expression microarray analyses. Limma methods were applied to analyze the data, and genes were considered to be significantly differentially expressed if the False Discovery Rate (FDR) value was < 0.01, P-value was <0.01 and the fold change (FC) was >2. Subsequently, Gene Ontology (GO) categories were used to analyze the main functions of the differentially expressed genes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we found pathways significantly associated with the differential genes. Gene-Act network and co-expression network were built respectively based on the relationships among the genes, proteins and compounds in the database. 2371 mRNAs and 350 lncRNAs considered as significantly differentially expressed genes were selected for the further analysis. The GO categories, pathway analyses and the Gene-Act network showed a consistent result that up-regulated genes were responsible for tumorigenesis, migration, angiogenesis and microenvironment formation, while down-regulated genes were involved in metabolism. These results of this study provide some novel findings on coding RNAs, lncRNAs, pathways and the co-expression network in gastric cancer which will be useful to guide further investigation and target therapy for this disease.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

Molecular interaction maps of bioregulatory networks: a general rubric for systems biology

A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. "Heuristic" MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. "Explicit" MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network.     

基于高通量测序的阿尔泰蝠蛾(鳞翅目:蝙蝠蛾科)幼虫转录组分析

本研究采用Illumina HiSeq TM 2500测序平台对阿尔泰蝠蛾Hepialus altaicola Wang幼虫进行转录组测序及生物信息学分析.经序列拼接后共获得100133个Unigenes,总长度86319112 bp,平均长度862 bp,N50长度1628 bp.将Unigenes与NR、COG/KOG、Pfam、Swiss-Prot、GO、KEGG数据库比对,共获得38198条Unigenes,其中Nr数据库注释的Unigenes最多,为32381条,占32.34％.通过GO功能分类,共有13216个Unigenes在GO数据库中细胞组分、分子功能和生物学过程等3大类57个分支中找到注释;KEGG通路分析,共有15058条Unigenes被注释,归属于305条代谢通路.CDS预测发现54002条序列可被编码,占全部基因的53.93％.基因注释进一步获得311个与冷适应相关的代谢调节基因,并用FPKM值对基因表达量进行评估.本研究获得的转录组信息及分析结果,为进一步研究阿尔泰蝠蛾的基因功能及低温生态适应性奠定分子基础.     

Global/temporal gene expression analysis of Escherichia coli in the early stages of symbiotic relationship development with the cellular slime mold Dictyostelium discoideum

Escherichia coli and the cellular slime mold Dictyostelium discoideum form stable viscous symbiotic colonies in the laboratory. To examine changes in E. coli gene expression during establishment of this symbiotic relationship, cells of symbiotic co-cultures and monocultures at various time points were subjected to microarrays analysis. Genes changed significantly over time compared to the initial gene expression level were determined as characteristics of GO function categories. The categories that appeared significantly at the same sampling time points between the two cultures were also identified. Up-regulation of genes from several GO categories associated with polysaccharide synthesis, cell wall degradation, and iron acquisition as well as down-regulation of genes from GO categories associated with biosynthesis through starvation response were observed in co-cultures, indicating exchange of molecules between the two organisms. Up-regulation of genes from several GO categories associated with anaerobic respiration and flagella biosynthesis were also observed, indicating that the environment inside symbiotic colonies was similar to that in developed biofilms. Up-regulation of genes associated with energy-generating systems indicated that E. coli prolonged survival within the symbiotic colony. Thus, E. coli showed not only molecule exchange but also altered expression of various genes in symbiosis with D. discoideum.     

Navigating the cell

Changes in cell shape are associated with a variety of processes including cell migration, axon outgrowth, cell division, and vesicle trafficking. C. elegans UNC-53 and its vertebrate homologs, the Navigators, are required for the migration of cells and the outgrowth of neuronal processes. The identification of novel molecular interactions and live imaging studies have revealed that UNC-53/NAVs are signal transducers associated with actin filaments, microtubules, and intermediate filaments. In addition to modulating cytoskeletal dynamics at the leading edge of migrating or outgrowing cells, both UNC-53 and the navigators are expressed in adult cells, conspicuously those with specialized roles in endocytosis or secretion. Collectively, these results suggest that UNC-53/NAVs may be a central regulator of cytoskeletal dynamics, responsible for integrating signaling cues to multiple components of the cytoskeleton to coordinate rearrangement during cell outgrowth or trafficking.     

Charting the genetic interaction map of a cell

Genome sequencing projects have revealed a massive catalog of genes and astounding genetic diversity in a variety of organisms. We are now faced with the formidable challenge of assigning functions to thousands of genes, and how to use this information to understand how genes interact and coordinate cell function. Studies indicate that the majority of eukaryotic genes are dispensable, highlighting the extensive buffering of genomes against genetic and environmental perturbations. Such robustness poses a significant challenge to those seeking to understand the wiring diagram of the cell. Genome-scale screens for genetic interactions are an effective means to chart the network that underlies this functional redundancy. A complete atlas of genetic interactions offers the potential to assign functions to most genes identified by whole genome sequencing projects and to delineate a functional wiring diagram of the cell. Perhaps more importantly, mapping genetic networks on a large-scale will shed light on the general principles and rules governing genetic networks and provide valuable information regarding the important but elusive relationship between genotype and phenotype.     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

Comparative analysis of gene expression profiles of acute hepatic failure and that of liver regeneration in rat

To explore the relevance of rat liver regeneration (LR) to acute hepatic failure (AHF), Rat Genome 230 2.0 Array was used to detect their gene expression profiles in this study, and the reliability of the detection results was confirmed by real-time-PCR. 1012 genes were found to be significantly changed in AHF occurrence and 948 genes in LR. Hierarchical clustering analysis showed that physiological activities of AHF and those of LR had no time correlation. Hierarchical clustering analysis (which is performed to group genes based on the similarity of expression patterns) showed that physiological activities of AHF and those of LR had no time correlation. K-means clustering analysis (which is used to check the difference in the relevant predictor variables between different groups is significant or not) demonstrated that gene expression trend of C1 group (genes relate to categories of stimulus–response and cell apoptosis, etc.) in AHF and in LR was extremely similar, that those of their C2 group (categories of regulation of homeostasis and hormone stimulation, etc.) were contrary, and that those of their C3 (material and energy metabolism and oxidation reduction, etc.), C4 (Cell cycle-related genes) and C5 (cell proliferation-related genes) groups were also similar with the gene expression changes of LR more abundant. GO classifications and functional clustering analysis (which was used to statistics the numbers or composition of proteins or genes at a function level) revealed that cellular processes including immune response, inflammatory reaction, cell migration and adhesion, etc. were increased both in AHF and in LR, whereas material and energy metabolism were decreased. Of them, stimulus response, inflammatory reaction and regulation of apoptosis, etc. were stronger in AHF occurrence than in LR, but ion homeostasis, hormonal response, regulation of cell division and proliferation, etc. were weaker in AHF occurrence. Gene expression changes and physiological activities of AHF and those of LR not only have similarities but also differences.