灵长类主要组织相容性复合体Ⅰ类基因进化概述

主要组织相容性复合体（MHC）是和免疫反应直接相关的基因群。MHC I类分子的多态性和病原体的多变性相对应,它是个体在重大传染疾病中存活下来的重要依据。在灵长类动物进化过程中,由于分化时间的差异和生存压力的不同,造成了各物种MHC I类基因不同的存在状态,使它们的MHC I 类基因在基因数量和基因功能上有所差异,同时还产生了物种特异性基因。本文描述了灵长类MHC I类基因的总体变化特征,并着重讨论了6个典型MHC I 类基因在各典型灵长类物种中的特点及关联性。     

基于转录组数据的网络分析挖掘鼻咽癌与口腔鳞癌的共享功能模块

鼻咽癌和口腔鳞癌是两种在临床上高度相关的疾病,从分子层面系统性研究这两种疾病的相互关系却鲜见报道。本研究通过大规模的转录组数据分析识别鼻咽癌和口腔鳞癌的共享功能模块及其核心基因(一因多效模块和基因),以期阐明这两种疾病共享的分子机制。从GEO数据库获取这两种癌症的两套转录组数据,应用倍数法和经验贝叶斯方法筛选出鼻咽癌差异表达基因1279个,口腔鳞癌差异表达基因1293个,其中两者共享基因278个。以共享基因为种子,通过蛋白质-蛋白质互作知识引导构建基因网络,其中最大子网包含1290个基因和1766互作对。应用Newman算法提取了15个共享功能模块。对这些模块进行拓扑学分析,挖掘出58个核心基因,包括已知的与鼻咽癌或口腔鳞癌相关的基因(如PCNA、CDK1、STAT1、CCL5和MMP1等)和鲜有报道的基因(如MELK、NME1、RACGAP1、INHBA和NID1等)。通路富集分析发现鼻咽癌和口腔鳞癌的共享功能模块参与多个生物学通路,包括p53信号通路、ECM受体相互作用、黏着斑、细胞周期等。本研究表明鼻咽癌和口腔鳞癌具有相似的致癌机制,所挖掘的共享模块可能是这两种疾病演化的核心分子相互作用机制。     

与实验条件相关的基因功能模块聚类分析方法

针对细胞内基因功能模块化的现象,定义了“基因功能模块”和“特征功能模块”两个概念,并基于这两个概念提出一种“与实验条件相关的基因功能模块聚类算法”。该算法综合利用基因功能知识与基因表达谱信息,将基因聚类为与实验条件相关的基因功能模块。向基因表达谱中加入水平逐渐升高的数据噪音,根据基因功能模块对数据噪音的抵抗力,确定最稳定的基因功能模块,即特征功能模块。加噪音实验显示,在基因芯片技术可能发生的噪音范围内,该算法对噪音的稳健性优于层次聚类和模糊C均值聚类。将模块聚类算法应用在NCI60数据集上,发现了8个与实验条件高度相关的特征功能模块。     

尼罗鳄MHCⅡ类B基因第二外元的序列变异分析

利用一对简并引物扩增了尼罗鳄MHCⅡ类分子B基因第二外元的部分片段,并对PCR产物进行了克隆和测序,结果得到8种不同的序列,序列长度为166 bp;经分析,序列中有56个变异位点,核苷酸的非同义替换多于同义替换,造成30个位点氨基酸的改变,氨基酸的替换趋于集中在假定的抗原结合位点附近.核苷酸和氨基酸序列与已报道的扬子鳄和密河鳄的MHCⅡ类B基因第二外元序列有较高的同源性,利用PAUP4.0软件构建的NJ树显示,鳄类的MHCⅡ类B基因存在跨种多态性现象.     

乌龟MHCⅡ类B基因第二外显子的克隆及序列分析

利用一对兼并性引物扩增了乌龟MHCⅡ类分子B基因第二外显子的部分片段,并对PCR产物进行了克隆和测序,结果得到8种长度为166 bp的不同序列。经分析,序列中有84个变异位点,核苷酸的非同义替换(dN)多于同义替换(dS),造成39个位点氨基酸的改变。氨基酸的替换趋于集中在假定的抗原结合位点附近。利用MEGA、PAUP软件分别构建NJ树和MP树,两种树极为相似,均分为两支。同一个体中出现有多种序列,提示乌龟MHCⅡ类分子B基因可能存在着座位重复。研究表明:乌龟MHCⅡ类分子B基因第二外显子有较高的多态性,有利于乌龟野生种群的遗传保护。     

犬类MHC复合体Ⅱ类基因研究进展

概述了犬类MHCⅡ类基因的物理结构图谱,对基因的结构、功能及表达、基因分型方法、基因多态性、基因与疾病的关联研究等作了详细的综述,指明了对犬类MHCⅡ类基因的研究利于其抗病育种,对人类自身免疫性疾病和器官移植等方面具有重要现实意义。     

抗原加工和提呈的“操纵子”

在人类及鼠的主要组织相容性复合物（MHC）II类在基因区域内,人们新发现4个位置靠近的基因涉及MHC I类分子所结合的内源性免疫原肽段的加工和提呈,LMP－2和MLP－7基因产物与细胞内构成蛋白酶复合体的某些亚基具有很大的同源性,可能与降解产生内源性免疫原肽段有关,而TAP－1及TAP－2基因编码产物形成的异二聚体蛋白复合物在内质网上构成穿膜通道,负责将加工好的内源性免疫原肽段转运入内质网,与MHC I类分子相互作用,本就这两种4个基因及其产物的结构与功能给予介绍。     

Ⅱ类反式激活因子与免疫调节

Ⅱ类反式激活因子(class Ⅱ trans-activator,CIITA)为非DNA结合蛋白,在MHC Ⅱ类基因的转录激活过程中以协同激活分子的形式发挥主导开关的作用。CIITA还可以调节其他与抗原递呈相关的基因,如H-2M基因、Ia相关恒定链(Ii chain)基因等。结构上,CIITA分子又是NOD样受体(NOD-likereceptor,NLR)家族成员之一,其功能与固有免疫密切相关。除此之外,CIITA在T细胞分化、FasL介导的细胞死亡、胶原的合成等方面也发挥着重要的调节作用。     

兴国红鲤MHCⅡ类基因多态性、表达及其与鱼体抗病力关系的分析

对41尾感病和22尾抗病兴国红鲤的308个有效克隆进行测序,获得171条不同的MHC Ⅱ类基因编码序列,分属26个不同的等位基因,其中Cyca-DXA24Cyca-DXA36为新发现的13个等位基因。MHCⅡ类基因片段的长度为624 bp,包括第14个外显子,分别编码信号肽、1和2结构域及连接肽/跨膜区。1结构域的变异明显大于2结构域,表现在1结构域中核苷酸和氨基酸变异位点比例（55.16%和79.76%）明显高于2结构域的变异位点比例（45.96%和68.42%）。1结构域的PBR区的非同义碱基替换率（dN）与同义碱基替换率（dS）的比值 （=dN/dS）为5.742,远远高于非抗原结合位点（non-PBR）及2结构域的0.755、0.592,揭示兴国红鲤MHC Ⅱ类基因的1结构域在进化过程中受到正向选择作用。等位基因Cyca-DXA24 （P0.01）与兴国红鲤对嗜水气单胞菌的抗性相关,等位基因Cyca-DXA3 （P0.05）、Cyca-DXA4 （P0.01）、Cyca-DXA6 （P0.05）、Cyca-DXA33 （P0.05）与兴国红鲤对嗜水气单胞菌的易感性相关。荧光定量PCR结果表明,MHCⅡ类基因在健康兴国红鲤的肾、肝、鳃等10个组织均能普遍表达。人工感染嗜水气单胞菌后,肾、肝、脾3个组织中的MHCⅡ类基因的表达量均发生了不同程度的变化,表明MHCⅡ类分子在兴国红鲤的免疫反应中起到重要作用。       

Functional modules with disease discrimination abilities for various cancers

Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers. However, the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases, which would introduce low reproducibility of gene lists selected by statistical methods. Here, by analyzing seven cancer datasets, we showed that, in each cancer, a wide range of functional modules have altered gene expressions and thus have high disease classification abilities. The results also showed that seven modules are shared across diverse cancers, suggesting hints about the common mechanisms of cancers. Therefore, instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments, we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.     

Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.     

The mRNA related ceRNA–ceRNA landscape and significance across 20 major cancer types

Cross-talk between competitive endogenous RNAs (ceRNAs) through shared miRNAs represents a novel layer of gene regulation that plays important roles in the physiology and development of cancers. However, a global view of their system-level properties across various types of cancers is still unknown. Here, we constructed the mRNA related ceRNA–ceRNA interaction landscape across 20 cancer types by systematically analyzing molecular profiles of 5203 tumors and miRNA regulations. Our study highlights the conserved features shared by pan-cancer and higher similarity within similar origin cell type. Moreover, a core ceRNA network was identified. Function analysis identified a common theme of cancer hallmarks, however they exhibit phenotype-specific connectivity patterns. Besides, we found a marked rewiring in the ceRNA program between various cancers, and further revealed conserved and rewired network ceRNA hubs in each cancer, which were tensely competitive interactions to constitute conserved and cancer-specific modules. By providing mechanistic linkage between known cancer miRNAs, their mediated ceRNA–ceRNA interactions, and the associations with known cancer hallmarks, the inferred cancer ceRNA–ceRNA interaction landscape will serve as a powerful public resource for further biological discoveries of tumorigenesis.     

Comparisons of gene coexpression network modules in breast cancer and ovarian cancer

Background

Breast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules.

Methods

We applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. Enrichment analysis on Gene Ontology (GO), KEGG pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) in the identified modules was performed.

Results

We identified 43 modules that are enriched by at least one of the four types of enrichments. 31, 25, and 18 modules are enriched by GO terms, KEGG pathways, and DO terms, respectively. The structure of 29 modules in both cancers is significantly different with p-values less than 0.05, of which 25 modules have larger densities in ovarian cancer. One module was found to be significantly enriched by the terms related to breast cancer from GO, KEGG and DO enrichment. One module was found to be significantly enriched by ovarian cancer related terms.

Conclusion

Breast cancer and ovarian cancer share some common properties on the module level. Integration of both cancers helps identifying the potential cancer associated modules.

     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer

Gene coexpression network analysis is a powerful “data-driven” approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types. The analysis was conducted using an elaborate weighted gene coexpression network (WGCNA) methodology and identified over 3,000 robust gene coexpression modules. The modules covered a range of known tumor features, such as proliferation, extracellular matrix remodeling, hypoxia, inflammation, angiogenesis, tumor differentiation programs, specific signaling pathways, genomic alterations, and biomarkers of individual tumor subtypes. To prioritize genes with respect to those tumor features, we ranked genes within each module by connectivity, leading to identification of module-specific functionally prominent hub genes. To showcase the utility of this network information, we positioned known cancer drug targets within the coexpression networks and predicted that Anakinra, an anti-rheumatoid therapeutic agent, may be promising for development in colorectal cancer. We offer a comprehensive, normalized and well documented collection of >3000 gene coexpression modules in a variety of cancers as a rich data resource to facilitate further progress in cancer research.     

TXNIP (VDUP-1, TBP-2): a major redox regulator commonly suppressed in cancer by epigenetic mechanisms

TXNIP (also named as VDUP-1 or TBP-2) was originally isolated in HL60 cells treated with Vitamin D3. Subsequently, it has been identified as a major redox regulator and a Tumor Suppressor Gene (TSG) in various solid tumors and hematological malignancies. In the present review, we will first provide an overview of TXNIP gene and protein structures, followed by a summary of the studies that have demonstrated its frequent repression in human cancers and relevant clinical significance, as well as functional characterization in animal models. We will then highlight our current knowledge of TXNIP signaling and biological functions. Next, we will discuss the evidence that clearly have demonstrated that the epigenetic silencing of TXNIP in cancer through various molecular mechanisms. The therapeutic use of small molecular inhibitors to reactivate TXNIP expression for cancer treatment will also be discussed in this review.     

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer''s disease (AD), Parkinson''s disease (PD) and Huntington''s disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.