基因调控网络的模块化组织研究

基因调控网络表现的是大量基因受到转录因子的调控而最终转录翻译为蛋白质进而实现生物功能的复杂信息, 是人们理解生物过程和基因功能的重要内容。为了理解基因调控网络中的调控机理, 网络的拓扑结构及其组织方式是极其重要的研究内容之一。它不仅能说明网络的局部特征, 并且能揭示调控网络的构造方法, 同时还能对调控信号通路进行全面系统的分析。调控网络可分为4层结构: 调控元件、Motif、模块和整个网络。当前, 这种层次结构受到人们越来越多的认可。文中重点讨论motif和模块两层, 比较分析了近年来对网络组织结构的多方面研究内容, 阐述了各个研究结果与结论具有的生物学意义, 并指出了其中存在的问题。在此基础上, 文中还针对这些问题提出了可能存在的研究方向, 并展望了基因调控网络模块化组织的研究前景。     

Network Based Prediction Model for Genomics Data Analysis

Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. We employ the gradient descent boosting procedure to build an additive tree model and propose a new algorithm to utilize the network structure in fitting small tree weak learners. We illustrate by simulation studies and a real data example that, by making use of the network information, NetBoosting outperforms a few existing methods in terms of accuracy of prediction and variable selection.     

Inverse system perturbations as a new methodology for identifying transcriptomic signaling participants in balanced biological processes

We devise a novel, systems-biology approach for identifying genetic participants in homeostatic biological processes. The central idea is that genes which are inversely regulated in alignment with positive and negative system perturbation are strong candidates for significant regulatory involvement in a given homeostatic process. This allows us to integrate known genetic participants together with hitherto unknown ones into a signaling network. We illustrate this concept and justify the underlying rationale in the exemplary case of the formation of blood vessels (angiogenesis) in the progression of pancreatic cancer where we have introduced a gene regulatory network governing the shift from a non angiogenic phenotype to an angiogenic phenotype in pancreatic tissue (‘angiogenic switch’). The envisaged pay-off of our approach is an improved understanding of signaling networks as well as the discovery of yet unknown genetic agents for diagnostic and therapeutic purposes. Subject to mild constraints, the same algorithm for the identification of signalling components can in principle be implemented across a wide spectrum of homeostatic processes including, e.g., apoptosis and fibrogenesis.     

Arabidopsis paves the way: genomic and network analyses in crops

Arabidopsis genomic and network analyses have facilitated crop research towards the understanding of many biological processes of fundamental importance for agriculture. Genes that were identified through genomic analyses in Arabidopsis have been used to manipulate crop traits such as pathogen resistance, yield, water-use efficiency, and drought tolerance, with the effects being tested in field conditions. The integration of diverse Arabidopsis genome-wide datasets in probabilistic functional networks has been demonstrated as a feasible strategy to associate novel genes with traits of interest, and novel genomic methods continue to be developed. The combination of genome-wide location studies, using ChIP-Seq, with gene expression profiling data is affording a genome-wide view of regulatory networks previously delineated through genetic and molecular analyses, leading to the identification of novel components and of new connections within these networks.     

Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.     

Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.     

基于ceRNA网络识别登革热诊断标志物

通过比较登革热患者和健康人群转录组数据,识别差异基因,构建失调ceRNA网络,筛选关键基因富集分析,解析潜在生物学功能,助力登革热诊断标志物的研究。从GEO数据库下载登革热外周血芯片数据,识别差异基因并进行富集分析。结合miRNA-mRNA互作数据,利用超几何算法和皮尔森相关性计算方法识别登革热失调ceRNA互作对,使用Cytoscape软件可视化ceRNA网络与模块挖掘,对网络模块进行功能富集及外部数据验证表达模式。筛选出251个差异基因,发现其富集在细胞周期等生物学通路中。经外部数据验证,网络模块基因的表达趋势与训练集数据大致相同,表明模块基因在登革热疾病中的潜在诊断效能。本研究可为确定有效的疾病诊断分子标志物提供思路。     

一种基于基因表达模型识别酵母细胞周期条件特异调控子网的方法

由高通量微阵列技术产生的数据集可以用于解释生物系统基因调控的未知机制.生物过程是动态的,所以很有必要关注某些条件下特异的基因调控子网络.细胞周期是一个基本的细胞过程,识别酵母的细胞周期特异调控子网是理解细胞周期过程的基础,并且有助于揭示其他细胞条件的基因调控机理.使用一个基因表达微分方程模型(GEDEM),从静态网络中识别了动态的细胞周期相关调控关系.与已经报道的细胞周期相关调控相互作用相比,该方法识别了更多的真实存在的条件特异调控关系,取得了比当前的方法更好的性能.在大数据集上,GEDEM 识别了具有高敏感性和特异性的调控子网.组合调控的深入分析显示,条件特异调控子网的转录因子之间的相关性呈现出比静态网络中转录因子相关性更强,这说明条件特异网络比静态网络更加接近真实情况.另外,GEDEM 方法还识别更多潜在的共调控转录因子.     

RNA binding protein sex-lethal (Sxl) and control of Drosophila sex determination and dosage compensation.

In the past two decades, scientists have elucidated the molecular mechanisms behind Drosophila sex determination and dosage compensation. These two processes are controlled essentially by two different sets of genes, which have in common a master regulatory gene, Sex-lethal (Sxl). Sxl encodes one of the best-characterized members of the family of RNA binding proteins. The analysis of different mechanisms involved in the regulation of the three identified Sxl target genes (Sex-lethal itself, transformer, and male specific lethal-2) has contributed to a better understanding of translation repression, as well as constitutive and alternative splicing. Studies using the Drosophila system have identified the features of the protein that contribute to its target specificity and regulatory functions. In this article, we review the existing data concerning Sxl protein, its biological functions, and the regulation of its target genes.