Predictive value of plasma copeptin level for the risk and mortality of heart failure: a meta‐analysis

Epidemiologic studies are inconsistent regarding the association between plasma copeptin level and heart failure (HF). The aim of this study was to perform a meta‐analysis to determine whether high level of copeptin is correlated with incidence of HF and mortality in patients with HF. We searched PUBMED and EMBASE databases for studies conducted from 1966 through May 2016 to identify studies reporting hazard ratio (HR) estimates with 95% confidence intervals (CIs) for the association between plasma copeptin level and HF. A random‐effects model was used to combine study‐specific risk estimates. A total of 13 studies were included in the meta‐analysis, with five studies on the incidence of HF and eight studies on the mortality of patients with HF. For incidence of HF, the summary HR indicated a borderline positive association of high plasma copeptin level with HF risk (HR, 1.60; 95% CI, 0.90–2.85). Furthermore, an increase of 1 standard deviation in log copeptin level was associated with a 17% increase in the risk of incident HF (HR, 1.17; 95% CI, 1.02–1.33). For all‐cause mortality of patients with HF, we also found a significant association between elevated plasma copeptin level and increased mortality of HF (HR, 1.76; 95% CI, 1.33–2.33). Our dose–response analysis indicated that an increment in copeptin level of 1 pmol/l was associated with a 3% increase in all‐cause mortality (HR, 1.03; 95% CI, 1.01–1.05). In conclusion, our results suggest that elevated plasma copeptin level is associated with an increased risk of HF and all‐cause mortality in patients with HF.     

Integrative analysis of competing endogenous RNA networks reveals the functional lncRNAs in heart failure

Heart failure has become one of the top causes of death worldwide. It is increasing evidence that lncRNAs play important roles in the pathology processes of multiple cardiovascular diseases. Additionally, lncRNAs can function as ceRNAs by sponging miRNAs to affect the expression level of mRNAs, implicating in numerous biological processes. However, the functional roles and regulatory mechanisms of lncRNAs in heart failure are still unclear. In our study, we constructed a heart failure‐related lncRNA‐mRNA network by integrating probe re‐annotation pipeline and miRNA‐target interactions. Firstly, some lncRNAs that had the central topological features were found in the heart failure‐related lncRNA‐mRNA network. Then, the lncRNA‐associated functional modules were identified from the network, using bidirectional hierarchical clustering. Some lncRNAs that involved in modules were demonstrated to be enriched in many heart failure‐related pathways. To investigate the role of lncRNA‐associated ceRNA crosstalks in certain disease or physiological status, we further identified the lncRNA‐associated dysregulated ceRNA interactions. And we also performed a random walk algorithm to identify more heart failure‐related lncRNAs. All these lncRNAs were verified to show a strong diagnosis power for heart failure. These results will help us to understand the mechanism of lncRNAs in heart failure and provide novel lncRNAs as candidate diagnostic biomarkers or potential therapeutic targets.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Proteomic analysis of mitochondria reveals a metabolic switch from fatty acid oxidation to glycolysis in the failing heart

This work characterizes the mitochondrial proteomic profile in the failing heart and elucidates the molecular basis of mitochondria in heart failure. Heart failure was induced in rats by myocardial infarction, and mitochondria were isolated from hearts by differential centrifugation. Using two-dimen- sional gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry, a system biology approach was employed to investigate differences in mitochondrial proteins between normal and failing hearts. Mass spectrometry identified 27 proteins differentially expressed that involved in energy metabolism. Among those, the up-regulated proteins included tricarboxylic acid cycle enzymes and pyruvate dehydrogenase complex subunits while the down-regulated proteins were involved in fatty acid oxidation and the OXPHOS complex. These results suggest a substantial metabolic switch from free fatty acid oxidation to glycolysis in heart failure and provide molecular evidence for alterations in the structural and functional parameters of mitochondria that may contribute to cardiac dysfunction during ischemic injury.     

Insights from the clustering of microarray data associated with the heart disease

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines'' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified.     

Systematic analysis on multiple Gene Expression Omnibus data sets reveals fierce immune response in hepatitis B virus‐related acute liver failure

Acute liver failure (ALF) caused by hepatitis B virus (HBV) is common type of liver failure in the world, with high morbidity and mortality rates. However, the prevalence, genetic background and factors determining the development of HBV‐related ALF are rarely studied. In this study, we examined three Gene Expression Omnibus (GEO) data sets by bioinformatics analysis to identify differentially expressed genes (DEGs), key biological processes and pathways. Immune infiltration analysis showed high immune cells infiltration in HBV‐related ALF tissue. We then confirmed natural killer cells and macrophages infiltration in clinical samples by immunohistochemistry assay, implying these cells play a significant role in HBV‐ALF. We found 1277 genes were co‐up‐regulated and that 1082 genes were co‐down‐regulated in the 3 data sets. Inflammation‐related pathways were enriched in the co‐up‐regulated genes and synthetic metabolic pathways were enriched in the co‐down‐regulated genes. WGCNA also revealed a key module enriching in immune inflammation response and identified 10 hub genes, differentially expressed in an independent data set. In conclusion, we identified fierce immune inflammatory response to elucidate the immune‐driven mechanism of HBV‐ALF and 10 hub genes based on gene expression profiles.     

生信分析筛选心衰氧化应激相关关键[]基因并预测候选药物

为探究氧化应激相关基因在心力衰竭发生发展中的作用,并发现核心基因进行靶基因药物预测。从GEO数据库下载GSE120895基因表达图谱,通过GEO2R筛选差异表达基因,将差异表达基因与GeneCard数据库中筛选的氧化应激相关基因取交集,得到心力衰竭氧化应激相关差异表达基因,利用R软件对差异表达基因进行GO及KEGG分析,利用Cytoscape进行PPI网络的模块以及关键基因的筛选。之后在GSE17800基因表达图谱中验证关键基因的表达,并针对关键基因进行相互作用药物预测。差异表达基因与氧化应激相关基因取交集后,共筛选出52个上调的氧化应激相关差异表达基因,在此基础上,筛选出ACTB,STAT3,FN1,EDN1,CAT共5个关键基因,在GSE17800基因表达图谱中验证后,针对4个关键基因预测了19个靶基因潜在药物。总之,本研究通过生物信息学方法鉴定关键基因,并预测潜在治疗药物,从而为了解心力衰竭的分子机制及其诊治方法提供新的见解。     

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.     

Partitioning the variation in a plot‐by‐species data matrix that is related to n sets of explanatory variables

Abstract. Variation partitioning by (partial) constrained ordination is a popular method for exploratory data analysis, but applications are mostly restricted to simple ecological questions only involving two or three sets of explanatory variables, such as climate and soil, this because of the rapid increase in complexity of calculations and results with an increasing number of explanatory variable sets. The existence is demonstrated of a unique algorithm for partitioning the variation in a set of response variables on n sets of explanatory variables; it is shown how the 2n– 1 non‐overlapping components of variation can be calculated. Methods for evaluation and presentation of variation partitioning results are reviewed, and a recursive algorithm is proposed for distributing the many small components of variation over simpler components. Several issues related to the use and usefulness of variation partitioning with n sets of explanatory variables are discussed with reference to a worked example.     

家蚕P450基因CYP18A1的克隆、序列分析及转录活性

蜕皮激素对昆虫生长、发育和繁殖有重要调控作用,尤其对蜕皮和变态过程。利用GenBank上登录的蜕皮激素C₂₆羟基化酶候选基因CYP18A1的氨基酸序列对家蚕Bombyx mori全基因组数据库进行BLASTP比对,发现了家蚕直向同源基因(ortholog),其完全编码序列经RT-PCR检测和克隆、测序验证后,再以此为信息探针检索家蚕表达序列标签(expressed sequence tags,EST)数据库进行拼接延伸,获得了一条包括5′非翻译区在内的长度为1 737 bp的cDNA序列,验证结果也表明与电子克隆序列完全一致(GenBank登录号为EF421988,P450命名委员会将其命名为CYP18A1)。该基因的开放阅读框为1 623 bp,编码541个氨基酸,含有包括P450s特征结构域在内的所有昆虫P450基因的5个保守结构域,其推定的分子量为61.67 kD,等电点为 8.54。将该基因cDNA序列与家蚕基因组序列进行比对,结果表明该基因具有6个外显子,5个内含子,外显子／内含子边界符合经典的GT-AG规则。同源性分析也发现家蚕CYP18A1与其他昆虫的直向同源基因具有较高相似性。用RT-PCR方法对家蚕主要发育变态时期与组织进行检测,显示出该基因的转录表达不仅具有时空特异性,而且在表达时期上与已报道的蚕体内蜕皮激素含量变化有紧密的一致性。该研究进一步证实了CYP18A1基因与昆虫体内蜕皮激素代谢平衡相关联。     

The combination of two‐dimensional and three‐dimensional analysis methods contributes to the understanding of glioblastoma spatial heterogeneity

Heterogeneity is regarded as the major factor leading to the poor outcomes of glioblastoma (GBM) patients. However, conventional two‐dimensional (2D) analysis methods, such as immunohistochemistry and immunofluorescence, have limited capacity to reveal GBM spatial heterogeneity. Thus, we sought to develop an effective analysis strategy to increase the understanding of GBM spatial heterogeneity. Here, 2D and three‐dimensional (3D) analysis methods were compared for the examination of cell morphology, cell distribution and large intact structures, and both types of methods were employed to dissect GBM spatial heterogeneity. The results showed that 2D assays showed only cross‐sections of specimens but provided a full view. To visualize intact GBM specimens in 3D without sectioning, the optical tissue clearing methods CUBIC and iDISCO+ were used to clear opaque specimens so that they would become more transparent, after which the specimens were imaged with a two‐photon microscope. The 3D analysis methods showed specimens at a large spatial scale at cell‐level resolution and had overwhelming advantages in comparison to the 2D methods. Furthermore, in 3D, heterogeneity in terms of cell stemness, the microvasculature, and immune cell infiltration within GBM was comprehensively observed and analysed. Overall, we propose that 2D and 3D analysis methods should be combined to provide much greater detail to increase the understanding of GBM spatial heterogeneity.