microRNA调控的生物网络

生物网络是生物体内各种分子通过相互作用来完成各种复杂的生物功能的一个体系。网络水平的研究,有助于我们从整体上理解生物体内各种复杂事件发生的内在机制。microRNA（miRNA）是一类在转录后水平调控基因表达的小RNA分子。研究结果表明,miRNA调控的靶基因分布范围很广,因此必然与目前所研究的生物网络有着各种各样的联系。对这种关系的揭示,将对阐明miRNA的调控规律起到重要的作用。本文重点讨论了miRNA调控的基因调控网络、蛋白质相互作用网络以及细胞信号传导网络的特征。此外,还总结了miRNA调控的网络模体（motif）和miRNA协同作用网络的特征。     

MicroRNA在环境毒理学中的研究进展

MicroRNA（miRNA）是一类长为20～24nt的非编码单链小分子RNA,主要存在于真核生物中,具有组织特异性、无开放阅读框等特点,在转录或翻译水平调控基因表达,参与细胞增殖、分化、凋亡,并与炎症、肿瘤等疾病发生、发展密切相关。环境毒理学研究表明,当生物暴露于环境化学物质时,会引起相关miRNA表达发生变化,进而导致其靶基因表达发生改变。因此,有必要明确环境化学物质、miRNA和相关靶基因三者问的作用关系。在环境毒理学研究和环境监测中,miRNA可作为识别环境中化学物质基因毒性和致癌性的生物标记物,并可用于预测环境化学物质对生物体的毒性。     

基于微阵列数据的基因网络预测方法研究进展

DNA微阵列技术可同时定量测定成千上万个基因在生物样本中的表达水平,从这一技术获得的全基因组范围表达数据为揭示基因间复杂调控关系提供了可能。研究人员试图通过数学和计算方法来构建遗传互作的模型,这些基因调控网络模型有聚类法、布尔网络、贝叶斯网络、微分方程等。文章对网络重建计算方法的研究现状进行了较为全面的综述,比较了不同模型的优缺点,并对该领域进一步的研究趋势进行了展望。     

基于随机游走的风险致病基因预测研究进展

风险致病基因预测有助于揭示癌症等复杂疾病发生、发展机理,提高现有复杂疾病检测、预防及治疗水平,为药物设计提供靶标.全基因组关联分析(GWAS)和连锁分析等传统方法通常会产生数百种候选致病基因,采用生物实验方法进一步验证这些候选致病基因往往成本高、费时费力,而通过计算方法预测风险致病基因,并对其进行排序,可有效减少候选致病基因数量,帮助生物学家优化实验验证方案.鉴于目前随机游走算法在风险致病基因预测方面的卓越表现,本文从单元分子网络、多重分子网络和异构分子网络出发,对基于随机游走预测风险致病基因研究进展进行较全面的综述分析,讨论其所存在的计算问题,展望未来可能的研究方向.     

生物标志物研究与早期药物研发基因表达分析功能学检测罗氏RealTime ready应用

前言 个体化医疗（PHC）根据基因信息,针对个体的遗传独特性,对个体进行卫生保健、疾病预防与治疗服务,满足个体需要。对特定生物状态指示剂的生物标志物分子的识别与鉴定,在个体化医疗中具有重要作用（1）。因此,生物标志物是药物从靶点鉴定至药物应用生命周期的核心元素。     

微小RNA起源及功能和动植物基因寻找计算方法

微小RNAs（microRNAs,miRNAs）是长度约为22个核苷酸（nt）的内源性非编码小分子RNA。miRNA作为重要的基因调节因子,通过多种机制抑制其靶mRNA的表达。miRNA的表达和／或功能异常与人类多种疾病密切相关。因此,近年miR—NA与人类疾病的相关研究备受关注,寻找miRNA基因显得尤为重要。过去对miRNA基因进行研究的范围较为局限,获得的新miRNA基因很少。目前,对miRNA基因目录的补充主要依赖于复杂计算工具的发展,随着计算工具的发展获得多种简易的寻找miRNA基因的方法,但对miRNA基因目录的补充仍未能起有效作用。本文在简单介绍动植物miRNA生物起源和功能及作用机制的基础上,主要关注动植物miRNA基因寻找的计算方法,可望为探索动植物miRNAs基因寻找的新的计算方法提供有价值的参考。     

突变肠杆菌株NRS-1中5个草甘膦逆境应答基因的克隆与功能研究

【目的】微生物对草甘膦的抗性受复杂的遗传体系调控,涉及靶基因和大量相关调控基因。对肠杆菌属细菌NRS-1突变菌株在高浓度草甘膦逆境下的5个重要差异表达基因进行功能研究,以期深入了解非靶标基因在抗草甘膦微生物的作用特点,为发掘优异基因资源,服务抗草甘膦转基因生物育种提供参考。【方法】NRS-1的差异表达基因可能在蛋白质合成、代谢、细胞膜等水平发挥作用,保护细胞免受高浓度草甘膦逆境,因此分别选取易位酶延伸因子fus A、丁二酸脱氢酶sdh A、胸苷磷酸化酶deo A、鸟氨酸氨甲酰转移酶arg F、周质蛋白osm Y进行克隆,采用大肠杆菌原核表达、转化拟南芥实验研究其功能,并通过细菌双杂及KEGG pathway分析基因间互作特点。【结果】在明确5个基因结构特点基础上,通过大肠杆菌原核表达及转基因拟南芥鉴定,发现这5个基因及草甘膦的靶基因5-烯醇式丙酮莽草酸-3-磷酸合成酶基因aro A对提高2种生物的草甘膦耐性均有不同程度的作用,其中arg F、deo A的抗性较好,与aro A相当,表明在应对草甘膦逆境时,芳香族、含有胸腺嘧啶氨基酸及精氨酸的合成代谢通路可能起重要作用;利用基因互作与KEGG分析发现5个基因与靶基因aro A间形成复杂的调控网络,但无直接的蛋白互作。【结论】NRS-1的5个差异表达基因对草甘膦逆境具有抗性,arg F、deo A优于其他3个基因,其与靶基因aro A间表现复杂的基因互作关系。     

遗传风险评分在复杂疾病遗传学研究中的应用

心血管疾病、2型糖尿病、原发性高血压、哮喘、肥胖、肿瘤等复杂疾病在全球范围内流行,并成为人类死亡的主要原因。越来越多的人开始关注遗传易感性在复杂疾病发病机制中的作用。至今,与复杂疾病相关的易感基因和基因序列变异仍未完全清楚。人们希望通过遗传关联研究来阐明复杂疾病的遗传基础。近年来,全基因组关联研究和候选基因研究发现了大量与复杂疾病有关的基因序列变异。这些与复杂疾病有因果和(或)关联关系的基因序列变异的发现促进了复杂疾病预测和防治方法的产生和发展。遗传风险评分(Genetic risk score,GRS)作为探索单核苷酸多态(Single nucleotide polymorphisms,SNPs)与复杂疾病临床表型之间关系的新兴方法,综合了若干SNPs的微弱效应,使基因多态对疾病的预测性大幅度提升。该方法在许多复杂疾病遗传学研究中得到成功应用。本文重点介绍了GRS的计算方法和评价标准,简要列举了运用GRS取得的系列成果,并对运用过程中所存在的局限性进行了探讨,最后对遗传风险评分的未来发展方向进行了展望。     

应用DNA芯片数据挖掘复杂疾病相关基因的集成决策方法

DNA芯片技术的迅速发展, 可同时检测成千上万个基因的表达谱数据, 为生命科学家们从一个全新的角度阐明生命的本质提供了可能性. 目前, 基因表达谱分析的工作大多集中在对癌症等疾病分类、疾病亚型识别等方面, 而从这些基因表达谱信息中挖掘反映疾病本质特征的相关基因, 是一项在后基因组时代更具挑战意义的科学研究, 基因挖掘由于缺少理想的数据挖掘技术而被忽视. 我们提出了一种新颖的特征基因挖掘的集成决策方法, 目的在于解决三个重要的生物学问题: 生物学分类及疾病分型、复杂疾病相关基因深度挖掘和目标驱使的基因网络构建. 我们成功地将此集成决策方法应用于一套结肠癌DNA表达谱数据, 结果显示这一新颖的特征基因挖掘技术在应用DNA芯片数据分析、挖掘复杂疾病相关基因等方面具有很高的价值.     

基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。     

Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling

Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks. An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia phenotype, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore, the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

Systems biology strategy to study lipotoxicity and the metabolic syndrome

Systems biology views and studies the biological systems in the context of complex interactions between their building blocks and processes. Given its multi-level complexity, metabolic syndrome (MetS) makes a strong case for adopting the systems biology approach. Despite many MetS traits being highly heritable, it is becoming evident that the genetic contribution to these traits is mediated via gene–gene and gene–environment interactions across several spatial and temporal scales, and that some of these traits such as lipotoxicity may even be a product of long-term dynamic changes of the underlying genetic and molecular networks. This presents several conceptual as well as methodological challenges and may demand a paradigm shift in how we study the undeniably strong genetic component of complex diseases such as MetS. The argument is made here that for adopting systems biology approaches to MetS an integrative framework is needed which glues the biological processes of MetS with specific physiological mechanisms and principles and that lipotoxicity is one such framework. The metabolic phenotypes, molecular and genetic networks can be modeled within the context of such integrative framework and the underlying physiology.     

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.     

Inference of biological pathway from gene expression profiles by time delay boolean networks

One great challenge of genomic research is to efficiently and accurately identify complex gene regulatory networks. The development of high-throughput technologies provides numerous experimental data such as DNA sequences, protein sequence, and RNA expression profiles makes it possible to study interactions and regulations among genes or other substance in an organism. However, it is crucial to make inference of genetic regulatory networks from gene expression profiles and protein interaction data for systems biology. This study will develop a new approach to reconstruct time delay Boolean networks as a tool for exploring biological pathways. In the inference strategy, we will compare all pairs of input genes in those basic relationships by their corresponding [Formula: see text]-scores for every output gene. Then, we will combine those consistent relationships to reveal the most probable relationship and reconstruct the genetic network. Specifically, we will prove that [Formula: see text] state transition pairs are sufficient and necessary to reconstruct the time delay Boolean network of [Formula: see text] nodes with high accuracy if the number of input genes to each gene is bounded. We also have implemented this method on simulated and empirical yeast gene expression data sets. The test results show that this proposed method is extensible for realistic networks.     

Examining Emergence of Functional Gene Clustering in a Simulated Evolution

Recent research suggests that rather than being random, gene order may be coupled with gene functionality. These findings may be explained by mechanisms that require physical proximity such as co-expression and co-regulation. Alternatively, they may be due to evolutionary-dynamics forces, as expressed in genetic drift or linkage disequilibrium. This paper proposes a biologically plausible model for evolutionary development. Using the model, which includes natural selection and the development of gene networks and cellular organisms, the co-evolution of recombination rate and gene functionality is examined. The results presented here are compatible with previous biological findings showing that functionally related genes are clustered. These results imply that evolutionary pressure in a complex environment is sufficient for the emergence of gene order that is coupled with functionality. They shed further light on the mechanisms that may cause such gene clusters.