Expression Pattern, Interaction Network, and Functional Analysis of the Arabidopsis Botrytis Susceptible1 Interactor

E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Theory Analysis of the Handover Challenge in Express Train Access Networks （ETAN）

To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.

Identification of key genes underlying clinical features of hepatocellular carcinoma based on weighted gene co‑expression network analysis and bioinformatics analysis

Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.

纤维肌痛综合征生物标记物的筛选及免疫细胞浸润分析

背景:纤维肌痛综合征作为常见风湿病,其发病与中枢敏化及免疫异常有关,但具体过程尚未阐明,缺乏特异性诊断标志物,不断探索该病的发病机制具有重要的临床意义。目的:基于加权基因共表达网络分析(WGCNA)等生物信息学方法和机器学习算法筛选纤维肌痛综合征潜在的诊断相关标志基因,并分析其免疫细胞浸润特征。方法:对来自基因表达综合数据库(GEO)的纤维肌痛综合征数据集转录谱进行差异分析和WGCNA分析,整合筛选出差异共表达基因,进一步采用机器学习套索回归(LASSO)算法、支持向量机递归特征消除(SVM-RFE)机器学习算法来识别核心生物标志物,并绘制受试者工作特征(ROC)曲线以评估诊断价值。最后,采用单样本基因集富集分析(ssGSEA)和基因集富集分析(GSEA)评估纤维肌痛综合征的免疫细胞浸润情况及通路富集。结果与结论:①对GSE67311数据集按照log2|(FC)|>0,P<0.05的条件进行差异分析后获得8个下调的差异表达基因;进行WGCNA分析后获得正相关性最高(r=0.22,P=0.04)的模块(MEdarkviolet)内含基因497个,负相关性最高(r=-0.41,P=6×10-5)的模块(MEsalmon2)内含基因19个;将差异表达基因与WGCNA的2个高相关性模块基因取交集,获得7个基因。②对上述7个基因进行LASSO回归算法筛选出4个基因,进行SVM-RFE机器学习算法筛选出5个基因,两者取交集后确定了3个核心基因,分别为重组1号染色体开放阅读框150蛋白(germinal center associated signaling and motility like,GCSAML)、整合素β8(Integrin beta-8,ITGB8)和羧肽酶A3(carboxypeptidase A3,CPA3);绘制3个核心基因的ROC曲线下面积分别为0.744,0.739,0.734,提示均具有很好的诊断价值,可作为纤维肌痛综合征的生物标志物。③免疫浸润分析结果显示,与对照组相比纤维肌痛综合征患者记忆B细胞、CD56 bright NK细胞和肥大细胞显著下调(P<0.05),且与上述3个生物标志物显著正相关(P<0.05)。④富集分析结果提示,纤维肌痛综合征的富集途径包括9条,主要与嗅觉传导、神经活性配体-受体相互作用及感染等通路密切相关。⑤上述结果显示,纤维肌痛综合征的发生发展与多基因参与、免疫调节异常及多个通路失调有关,但这些基因与免疫细胞之间的相互作用,以及它们与各通路之间的关系尚需进一步研究。

Quantitative Expression of Heat Flow versus Tectonic Deformation in the China Continent: The Effects of Plastic-Flow Network and Stable Block

Based on the heat flow data published in 1990 and 2001, a study of the factors influencing the terrestrial heat flow distribution in the China continent and its quantitative expression is carried out using the ＂Netlike Plastic-Flow＂ continental dynamics model and the methods of statistic analysis and optimum fitting. The result indicates that the factors influencing the heat flow distribution is classified into two groups, i.e. background and tectonic ones, in which the former mainly involves the non- uniform distribution of mantle heat flow, heat production of radioactive dements in the crust, heattransfer media and hydrothermal circulation, while the latter mainly involves plastic-flow networks and relatively-stable blocks. The plastic-flow network is a manifestation of shear localization in the netlike plastic-flow process in the lower lithosphere, which is composed of two sets of plastic-flow belts （PFBs） intersecting each other and, as one of the basic action regimes, controls the intraplate tectonic deformation. Relatively stable blocks （RSBs）, which are the tectonic units with relatively-high viscosities existing in the netlike plastic-flow field, as one of the principal origins, result in the development of large-seale compressional basins. PFB and RSB, as the active and quiet states of tectonic deformation, give rise to the higher and lower heat flow values, respectivdy. The provincial average heat flow in continent can be estimated using the expression qav = q0 ＋ a Pbt-c Pbk, where the three terms of the right side are background heat flow, PFB-positive contribution and RSB-negative contribution, Pbt and Pbk are the PFB- and RSB-coverage ratios, respectively, a is the coefficient of PFB- positive contribution depending mainly on the strain in the lower lithosphere, and c is the coefficient of RSB-negative contribution related mainly to the thickness of the lithosphere, the aseismic-area ratio and the tectonic age. For the major portion of the China continent excluding some of the southeastern region of China, the confidence interval of the provincial average background heat flow is qo=57.25±24.8 mW/m^2 and the PFB-positive- and RSB-negative-contribution coefficients are a=14.8-71.9 mW/m^2 and c=0-25.6 mW/m^2, respectively. The concepts of PFB and RSB effects and the heat flow expression suggested provide a new choice of the approach to the quantitative description of the characteristics of heat flow distribution in continent and their physical mechanisms.

HMM-Based Photo-Realistic Talking Face Synthesis Using Facial Expression Parameter Mapping with Deep Neural Networks

This paper proposes a technique for synthesizing a pixel-based photo-realistic talking face animation using two-step synthesis with HMMs and DNNs. We introduce facial expression parameters as an intermediate representation that has a good correspondence with both of the input contexts and the output pixel data of face images. The sequences of the facial expression parameters are modeled using context-dependent HMMs with static and dynamic features. The mapping from the expression parameters to the target pixel images are trained using DNNs. We examine the required amount of the training data for HMMs and DNNs and compare the performance of the proposed technique with the conventional PCA-based technique through objective and subjective evaluation experiments.

骨关节炎中枢纽基因及在免疫浸润中作用的生物信息学分析与鉴定

背景:低度慢性炎症被认为在骨关节炎的发病机制中起着核心作用,但是具体的分子机制目前仍不清楚。目的:筛选和探讨骨关节炎中的潜在枢纽基因及免疫细胞的浸润情况。方法:将来自GPL570平台的GSE206848数据集与来自GPL96平台的GSE55235和GSE55457数据集合形成原始数据集。使用加权基因共表达网络分析去除离群样本,随后鉴定差异表达基因,并对差异表达基因进行功能富集分析。此外,构建差异表达基因的蛋白质-蛋白质相互作用网络,并使用Cytoscape软件中的两种不同算法筛选枢纽基因。利用CIBERSORT算法评估骨关节炎样本与正常对照之间免疫细胞浸润比例的差异。通过对收集到的骨关节炎患者滑膜组织样本进行定量反转录聚合酶链反应(RT-qPCR)实验,并结合来自GPL96测序平台的GSE12021数据集作为独立数据集,验证枢纽基因对骨关节炎的诊断能效。结果与结论:①在去除5个离群样本后,共鉴定出340个差异表达基因,其中包括159个上调基因和181个下调基因。通过加权基因共表达网络分析和Cytoscape共获得了6个枢纽基因。②CIBERSORT分析显示,骨关节炎组织中多种类型免疫细胞浸润比例与正常组织相比存在差异,6个枢纽基因的表达水平与骨关节炎中多种免疫细胞的相对比例密切相关。③RT-qPCR结果表明,6个基因的相对表达水平相对于正常组织呈下调趋势,但是NFKBIA和PTGS2的表达差异不显著(P>0.05);原始和外部数据集中的ROC曲线表明,6个枢纽基因对骨关节炎具有较强的诊断能力(AUC>0.8)。④结论:最终确定了4个枢纽基因,分别为CDKN1A、MYC、CXC趋化因子配体2和血管内皮生长因子A,可能通过介导免疫应答和炎症反应成为未来治疗骨关节炎的分子靶点。

加权共表达网络分析与机器学习识别类风湿关节炎滑膜中的关键基因

背景:类风湿关节炎是一种全身的免疫相关性疾病,主要病理特点是关节滑膜炎性增生及关节软骨的破坏,其发病机制目前尚不明确,迫切需要发现新的具有高度敏感性和特异性的诊断标志物。目的:联合使用生物信息学技术及计算机学习算法,识别并筛选类风湿关节炎患者滑膜中的关键基因,构建类风湿关节炎预测模型并进行验证。方法:从基因表达综合数据库中下载3个包含类风湿关节炎患者滑膜的数据集(GSE77298、GSE55235、GSE55457),GSE77298和GSE55235作为训练集,GSE55457作为测试集,共纳入66个样本,其中类风湿关节炎患者滑膜样本39个,正常滑膜样本27个。应用R语言筛选训练集中的差异基因,然后使用加权共表达网络将训练集中的基因模块化,选出关键模块中的特征基因,将差异表达基因和特征基因取交集,交集基因进入下一步机器学习。采用3种机器学习方法:最小绝对值收敛和选择算子算法、支持向量机-递归特征消除和随机森林算法对交集基因进一步分析获得枢纽基因,将枢纽基因再次相交即得到类风湿关节炎滑膜中的关键基因。以关键基因为变量构建预测类风湿关节炎的列线图模型,推测患者发生类风湿关节炎的危险程度,使用受试者工作特征曲线确定类风湿关节炎预测模型及其关键基因的诊断价值。结果与结论:①通过差异分析,训练集中共筛选出差异基因730个,加权共表达网络分析得到特征基因185个,两者交集基因159个;②最小绝对值收敛和选择算子发现枢纽基因4个,支持向量机-递归特征消除发现枢纽基因11个,随机森林发现枢纽基因5个,取交集后获得关键基因2个(TNS3、SDC1);③基于2个关键基因,在训练集及测试集种构建列线图,其校准预测曲线与标准曲线贴合较好,且预测类风湿关节炎发生的临床效能良好;④上述结果证实,基于生物信息及机器学习算法获得的TNS3和SDC1有可能成为类风湿关节炎诊断和治疗的关键靶点。

LabVIEW 7 Express平台上的液压测试系统

为了满足工程实践对先进液压测试技术的需要,研究了基于美国NI公司最新推出的LabVIEW7Express虚拟仪 器开发平台的液压测试系统的软硬件结构,介绍了测试系统实现网络通讯、数据库链接和多种编程语言联合作业的方法, 提出了现代液压测试系统的技术方案。

基于APM Express的网络教学平台的设计与实现

Internet技术的广泛应用和Web技术的不断发展,对传统的教学方式产生了深远的影响。基于网络的教学平台成为当今计算机应用的一个热点,利用APM Express开发组件,采用B/S体系结构搭建一个网络教学平台,详细描述了系统的功能、设计和实现,体现了网络教学平台的开放性、交互性和自主性等特点。

骨形态发生蛋白/Wnt信号通路调控成骨:揭示骨骼形成和重塑的分子机制

背景:成骨细胞是负责骨骼形成和重塑的主要细胞类型,其功能的正常发挥受到多种信号通路的精密调控,其中,骨形态发生蛋白和Wnt信号通路在成骨过程中起着关键作用。目的:综述了骨形态发生蛋白/Wnt信号通路在调控成骨细胞功能中的作用,并分析其在不同生理和病理条件下的变化,以期进一步揭示骨骼形成和重塑的分子机制。方法:以中文检索词“BMP信号通路,Wnt信号通路,成骨”及英文检索词“BMP signaling pathway,Wnt signaling pathway,osteogenesis”在中国知网、万方和PubMed数据库中检索研究原著,检索时限为各数据库建库至2023年6月的相关文献,最终筛选61篇文献进行分析总结。采用文献综述的方法,对骨形态发生蛋白/Wnt信号通路调控成骨作用的研究进行梳理和分析。结果与结论:①骨形态发生蛋白和Wnt信号通路在成骨细胞的分化、增殖和成熟过程中发挥重要作用。骨形态发生蛋白信号通路主要通过激活Smad蛋白来调控成骨相关基因的表达,Smad蛋白被激活后进入细胞核,调控与成骨相关的基因表达,与骨形态发生蛋白不同,Wnt信号通路主要依赖于β-catenin的激活来发挥生物学效应。②不同生理和病理状态下,骨形态发生蛋白/Wnt信号通路的调控作用会受到多种因素的影响。生长因子及激素及机械应力等会在一定程度上影响骨形态发生蛋白/Wnt信号通路的活性。③骨形态发生蛋白/Wnt信号通路在调控成骨过程中与其他信号通路相互作用,共同构成复杂的调控网络。④中药和天然化合物可以通过调控信号通路来促进骨骼健康,为中药治疗骨骼疾病提供了新的可能性。⑤未来研究可进一步探讨骨形态发生蛋白/Wnt信号通路与其他信号通路的相互作用以及其在不同生理和病理条件下的变化,解析此复杂网络中的关键节点和调控机制,为骨骼相关疾病的治疗提供更精确的靶点,也为揭示骨骼形成和重塑的分子机制提供新的思路。

Quantitative identification of compounds-dependent on-modules and differential allosteric modules from homologous ischemic networks

Module-based methods have made much progress in deconstructing biological networks.However,it is a great challenge to quantitatively compare the topological structural variations of modules(allosteric modules,AMs)under different situations.A total of 23,42 and 15co-expression modules were identified in baicalin(BA),jasminoidin(JA)and ursodeoxycholic acid(UA)in a global anti-ischemic mice network,respectively.Then,we integrated the methods of module-based consensus ratio(MCR)and modified Z summary module statistic to validate 12 BA,22 JA and 8 UA on-modules based on comparing with vehicle.The MCRs for pairwise comparisons were 1.55%(BA vs JA),1.45%(BA vs UA),and1.27%(JA vs UA),respectively.Five conserved allosteric modules(CAMs)and 17 unique allosteric modules(UAMs)were identified among these groups.In conclusion,module-centric analysis may provide us a unique approach to understand multiple pharmacological mechanisms associated with differential phenotypes in the era of modular pharmacology.

Identification of pathogenic genes of pterygium based on the Gene Expression Omnibus database

AIM: To identify the pathogenic genes in pterygium.METHODS: We obtained m RNA expression profiles from the Gene Expression Omnibus database(GEO) to identify differentially expressed genes(DEGs) between pterygium tissues and normal conjunctiva tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction(PPI) network and transcription factors(TFs)-target gene regulatory network was performed to understand the function of DEGs. The expression of selected DEGs were validated by the quantitative real-time polymerase chain reaction(qRT-PCR).RESULTS: A total of 557 DEGs were identified between pterygium and normal individual. In PPI network, several genes were with high degrees such as FN1, KPNB1, DDB1, NF2 and BUB3. SSH1, PRSS23, LRP5L, MEOX1, RBM14, ABCA1, JOSD1, KRT6 A and UPK1B were the most downstream genes regulated by TFs. q RT-PCR results showed that FN1, PRSS23, ABCA1, KRT6A, ECT2 and SPARC were significantly up-regulated in pterygium and MEOX1 and MMP3 were also up-regulated with no significance, which was consistent with the our integrated analysis. CONCLUSION: The deregulated genes might be involved in the pathology of pterygium and could be used as treatment targets for pterygium.

利用Nport Express实现单片机上网

就如何利用 MOXA公司的 Nport

酒精性肝炎自噬关键基因的筛选及生物信息学分析

目的筛选酒精性肝炎(AH)的自噬关键基因,探讨AH潜在的生物标志物和治疗靶点。方法采用基因表达综合数据库(GEO)中的2个AH基因芯片和从MSigDB、GeneCards数据库中获得的自噬相关数据集,通过加权基因共表达网络分析(WGCNA)获取关键基因。对筛选的关键基因进行基因本体(GO)、京都基因和基因组百科全书(KEGG)功能富集分析,蛋白质相互作用(PPI)分析,免疫浸润分析,构建信使RNA(mRNA)-微小RNA(miRNA)网络,进行酒精性肝病不同分期的自噬相关关键基因的表达差异分析,并进一步通过实时荧光定量逆转录聚合酶链反应(RT-qPCR)在AH患者和小鼠肝脏组织中验证。结果本研究筛选得到了11个与AH自噬相关的基因(EEF1A2、CFTR、SOX4、TREM2、CTHRC1、HSPB8、TUBB3、PRKAA2、RNASE1、MTCL1、HGF),均为上调基因。在AH患者和小鼠肝脏组织中,SOX4、TREM2、HSPB8、PRKAA2在AH组中的相对表达量均高于对照组。结论SOX4、TREM2、HSPB8、PRKAA2可能是AH潜在的生物标志物和治疗靶点。

Challenges Analyzing RNA-Seq Gene Expression Data

The analysis of messenger Ribonucleic acid obtained through sequencing techniques (RNA-se- quencing) data is very challenging. Once technical difficulties have been sorted, an important choice has to be made during pre-processing: Two different paths can be chosen: Transform RNA- sequencing count data to a continuous variable or continue to work with count data. For each data type, analysis tools have been developed and seem appropriate at first sight, but a deeper analysis of data distribution and structure, are a discussion worth. In this review, open questions regarding RNA-sequencing data nature are discussed and highlighted, indicating important future research topics in statistics that should be addressed for a better analysis of already available and new appearing gene expression data. Moreover, a comparative analysis of RNAseq count and transformed data is presented. This comparison indicates that transforming RNA-seq count data seems appropriate, at least for differential expression detection.

Modeling of gene regulatory networks: A review

Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics applications. In this paper we tried to review the different methods for reconstructing gene regulatory networks.