Condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome

Deciphering genetic regulatory codes remains a challenge. Here, we present an effective approach to identifying in vivo condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome. A resampling-based algorithm was adopted to cluster our microarray data of a stress response, which generated 35 tight clusters with unique expression patterns containing 811 genes of 5652 genes significantly altered. Database searches identified many known motifs within the 3-kb regulatory regions of 40 genes from 3 clusters and modules with six to nine motifs that were commonly shared by 60-100% of these genes. The upstream regulatory region contained the highest frequency of these common motifs. CisModule program predictions were comparable with the results from database searches and found four potentially novel motifs. This result indicates that these motifs and modules could be responsible for gene coregulation of the stress response in the lacrimal gland.     

Detection of Deregulated Modules Using Deregulatory Linked Path

The identification of deregulated modules (such as induced by oncogenes) is a crucial step for exploring the pathogenic process of complex diseases. Most of the existing methods focus on deregulation of genes rather than the links of the path among them. In this study, we emphasize on the detection of deregulated links, and develop a novel and effective regulatory path-based approach in finding deregulated modules. Observing that a regulatory pathway between two genes might involve in multiple rather than a single path, we identify condition-specific core regulatory path (CCRP) to detect the significant deregulation of regulatory links. Using time-series gene expression, we define the regulatory strength within each gene pair based on statistical dependence analysis. The CCRPs in regulatory networks can then be identified using the shortest path algorithm. Finally, we derive the deregulated modules by integrating the differential edges (as deregulated links) of the CCRPs between the case and the control group. To demonstrate the effectiveness of our approach, we apply the method to expression data associated with different states of Human Epidermal Growth Factor Receptor 2 (HER2). The experimental results show that the genes as well as the links in the deregulated modules are significantly enriched in multiple KEGG pathways and GO biological processes, most of which can be validated to suffer from impact of this oncogene based on previous studies. Additionally, we find the regulatory mechanism associated with the crucial gene SNAI1 significantly deregulated resulting from the activation of HER2. Hence, our method provides not only a strategy for detecting the deregulated links in regulatory networks, but also a way to identify concerning deregulated modules, thus contributing to the target selection of edgetic drugs.     

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.     

Integrating genetic and network analysis to characterize genes related to mouse weight

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.     

结合基因表达数据和ChIP-chip数据的酵母转录调控模块的识别

活细胞依赖其众多的转录调控模块来实现复杂的生物功能,识别转录调控模块对深入理解细胞的功能及其转录机制有着重要的意义。本文结合酵母基因表达数据和ChIP-chip数据,提出了一种转录调控模块识别算法。该算法通过采用不同的P值阈值分别得到了核心集和粗糙集,然后对核心集和粗糙集进行判别,最后对基因进行扩展之后得到基因转录调控模块。将该算法运用到两个酵母基因表达数据中,得到了一些具有显著生物学意义的基因转录调控模块。与其它算法相比,该算法不仅可以识别含有较多基因的转录调控模块,而且可以识别一些其它算法不能识别的基因转录调控模块。识别得到的基因转录调控模块有着不同的生物学功能,并且有助于进一步理解酵母的转录调控机制。     

Discovery of microRNA-mRNA modules via population-based probabilistic learning

MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gene regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e. computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.     

An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS''s multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

Functional interaction between an insulator and a Pc-dependent silencer by the example of the Mcp boundary of the <Emphasis Type="Italic">Drosophila melanogaster Abd-B</Emphasis> gene

Insulators are cis-regulatory elements that prevent improper gene activation and heterochromatin spreading. As shown previously, the boundary element, Mcp, from the regulatory region of the Drosophila melanogaster Abd-B gene, contains insulator. Here, we studied the boundary function of the Mcp insulator and showed that this function is provided by two modules. One is responsible for long-distance interactions and the capability of blocking enhancers. The other is essential for blocking Pc-dependent repression. It was observed for the first time that an insulator increased the repressor activity of a neighbor silencer.     

Comparative genomics using <Emphasis Type="Italic">Fugu</Emphasis> reveals insights into regulatory subfunctionalization

Background  

A major mechanism for the preservation of gene duplicates in the genome is thought to be mediated via loss or modification of cis-regulatory subfunctions between paralogs following duplication (a process known as regulatory subfunctionalization). Despite a number of gene expression studies that support this mechanism, no comprehensive analysis of regulatory subfunctionalization has been undertaken at the level of the distal cis-regulatory modules involved. We have exploited fish-mammal genomic alignments to identify and compare more than 800 conserved non-coding elements (CNEs) that associate with genes that have undergone fish-specific duplication and retention.