Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Background

MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.

Results

We proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.

Conclusions

Our network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.     

The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways

There is great interest in chromosome- and pathway-based techniques for genomics data analysis in the current work in order to understand the mechanism of disease. However, there are few studies addressing the abilities of machine learning methods in incorporating pathway information for analyzing microarray data. In this paper, we identified the characteristic pathways by combining the classification error rates of out-of-bag (OOB) in random forests with pathways information. At each characteristic pathway, the correlation of gene expression was studied and the co-regulated gene patterns in different biological conditions were mined by Mining Attribute Profile (MAP) algorithm. The discovered co-regulated gene patterns were clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same characteristic pathway were approximate. Furthermore, two characteristic pathways were discovered to present co-regulated gene patterns in which one contained 108 patterns and the other contained one pattern. The results of cluster analysis showed that the smallest similarity coefficient of clusters was more than 0.623, which indicated that the co-regulated patterns in different biological conditions were more approximate at the same characteristic pathway. The methods discussed in this paper can provide additional insight into the study of microarray data.     

The evolutionary potential of the Drosophila sex determination gene network

The evolution of sex determination mechanisms is known to be relatively rapid, though recent evidence indicates that certain parts of the mechanism may be more highly conserved. These characteristics establish the sex determination mechanism as a good candidate for the theoretical study of gene network evolution, particularly of networks involved in development. We investigate the short-term evolutionary potential of the sex determination mechanism in Drosophila melanogaster with the aid of a synchronous logical model. We introduce general theoretical concepts such as a network-specific form of mutation, and a notion of functional equivalence between networks. We apply this theoretical framework to the sex determination mechanism and compare it to a population of random networks, enabling us to find features both general to sex determination networks, and particular to the Drosophila network. In general, sex determination networks exist within large sets of functionally equivalent networks all of which satisfy the sex determination task. These large sets are in turn composed of subsets which are mutationally related, suggesting a high degree of flexibility is available without compromising the core functionality. Two particular characteristics of the Drosophila network are found: (a) a parsimonious use of gene interactions, and (b) the network structure can produce a relatively large number of dynamical pattern variations through single network mutations.     

Kolmogorov complexity of epithelial pattern formation: The role of regulatory network configuration

The tissues of multicellular organisms are made of differentiated cells arranged in organized patterns. This organization emerges during development from the coupling of dynamic intra- and intercellular regulatory networks. This work applies the methods of information theory to understand how regulatory network structure both within and between cells relates to the complexity of spatial patterns that emerge as a consequence of network operation. A computational study was performed in which undifferentiated cells were arranged in a two dimensional lattice, with gene expression in each cell regulated by identical intracellular randomly generated Boolean networks. Cell–cell contact signalling between embryonic cells is modeled as coupling among intracellular networks so that gene expression in one cell can influence the expression of genes in adjacent cells. In this system, the initially identical cells differentiate and form patterns of different cell types. The complexity of network structure, temporal dynamics and spatial organization is quantified through the Kolmogorov-based measures of normalized compression distance and set complexity. Results over sets of random networks that operate in the ordered, critical and chaotic domains demonstrate that: (1) ordered and critical networks tend to create the most information-rich patterns; (2) signalling configurations in which cell-to-cell communication is non-directional mostly produce simple patterns irrespective of the internal network domain; and (3) directional signalling configurations, similar to those that function in planar cell polarity, produce the most complex patterns, but only when the intracellular networks function in non-chaotic domains.     

一种基于时间自动机的基因网络逻辑模型

最近几年来国外基因组（基因网络）系统逻辑行为的研究新进展——基于有限状态自动机模型的方法,针对该方法的局限性,提出了一种基于时间自动机的基因网络模型,以描述网络行为的时间约束。     

芹菜NHL-like蛋白基因克隆与表达分析

植物生长发育过程中会遭遇各种病原物的攻击,植物为了应对这些危害并适应外界的生存竞争,逐渐演化出了复杂的防御机制。NHL基因家族庞大,部分NHL基因受病原物诱导后会过量表达,增强植物对多种病原菌的抗性,是与植物防御机制密切相关的蛋白。本研究以津南实芹和美国西芹2个芹菜品种为试验材料,分别克隆出NHL-like蛋白基因AgNHL。序列分析表明,上述2种芹菜的AgNHL基因序列均含636 bp的开放阅读框,编码211个氨基酸。2种芹菜碱基序列,只有第36位不同,津南实芹为T,美国西芹为C,2种碱基序列相似性高达99.84%,编码的氨基酸序列相同。进化分析显示,2种芹菜的NHL-like蛋白与葡萄、大豆等植物的相似度较高,在第80~185氨基酸间含一个LEA-2蛋白保守结构域。荧光定量PCR结果表明,AgNHL基因主要在芹菜茎中表达,根中表达量最低,有明显组织特异性,品种差异也很显著。对2种芹菜分别进行4℃低温、38℃高温、20%PEG处理、0.2 mol/L NaCl处理2 h表达分析显示,低温、盐处理下该基因表达量明显上升。     

毛竹谷胱甘肽过氧化物酶基因分子特征及其表达模式分析

利用生物信息学以及分子生物学方法对毛竹(Phyllostachys edulis (Carriere) J. Houzeau)谷胱甘肽过氧化物酶基因(GPX)的分子特征以及表达模式进行分析。结果显示,在毛竹中共鉴定出9个GPX家族成员基因(PeGPX1-PeGPX9),均具有6个外显子以及5个内含子,PeGPXs编码蛋白的长度为168~235 aa,相对分子量在18.41~25.54 kD,等电点范围为5.88~9.48。亚细胞定位预测结果发现,除PeGPX5定位在线粒体上,其他PeGPXs都定位在叶绿体上。在PeGPXs启动子中含有多种与胁迫和激素相关的顺式作用元件。qRT-PCR分析结果表明,强光、低温、GA₃、NAA和MeJA处理均可引起毛竹叶片中PeGPXs表达量发生明显变化。     

Involvement of nitrogen in storage root growth and related gene expression in sweet potato (Ipomoea batatas)

• Nitrogen (N) could affect storage root growth and development of sweet potato. To manage external N concentration fluctuations, plants have developed a wide range of strategies, such as growth changes and gene expression.
• Five sweet potato cultivars were used to analyse the functions of N in regulating storage root growth. Growth responses and physiological indicators were measured to determine the physiological changes regulated by different N concentrations. Expression profiles of related genes were analysed via microarray hybridization data and qRT‐PCR analysis to reveal the molecular mechanisms of storage root growth regulated by different N concentrations.
• The growth responses and physiological indicators of the five cultivars were changed by N concentration. The root fresh weight of two of the sweet potato cultivars, SS19 and GS87, was higher under low N concentrations compared with the other cultivars. SS19 and GS87 were found to be having greater tolerance to low N concentration. The expression of N metabolism and storage root growth related genes was regulated by N concentration in sweet potato.
• These results reveal that N significantly regulated storage root growth. SS19 and GS87 were more tolerant to low N concentration and produced greater storage root yield (at 30 days). Furthermore, several N response genes were involved in both N metabolism and storage root growth.

     

Identification of dilated cardiomyopathy signature genes through gene expression and network data integration

Dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF) and cardiac transplantations in Western countries. Single-source gene expression analysis studies have identified potential disease biomarkers and drug targets. However, because of the diversity of experimental settings and relative lack of data, concerns have been raised about the robustness and reproducibility of the predictions. This study presents the identification of robust and reproducible DCM signature genes based on the integration of several independent data sets and functional network information. Gene expression profiles from three public data sets containing DCM and non-DCM samples were integrated and analyzed, which allowed the implementation of clinical diagnostic models. Differentially expressed genes were evaluated in the context of a global protein–protein interaction network, constructed as part of this study. Potential associations with HF were identified by searching the scientific literature. From these analyses, classification models were built and their effectiveness in differentiating between DCM and non-DCM samples was estimated. The main outcome was a set of integrated, potentially novel DCM signature genes, which may be used as reliable disease biomarkers. An empirical demonstration of the power of the integrative classification models against single-source models is also given.     

富集液中六六六（γ-BHC）脱氯基因的克隆、表达及降解试验

从长期受六六六污染的土壤中获得高效降解六六六的富集液 ,其对六六六 4种异构体的降解效果均为10 0 % ,但至今未能获得纯培养。根据国外报道的六六六脱氯基因linA序列 ,设计并合成一对引物 ,通过PCR技术从富集液总DNA中扩增了 4 71bp的基因片段 ,命名为linN ,测序结果表明linN与报道的脱氯基因linA和linA2的同源性达 99% ,与linA1的同源性达 97%。将linN定向克隆到pET 2 9α表达载体中 ,转化至E .coliBL2 1,经IPTG诱导后可表达分子量约 17kD的蛋白 ,表达产物占菌体总蛋白的 30 %左右 ;诱导后转化子的降解能力明显提高 ,粗酶也有很好的降解效果 ,为进一步分离纯培养和构建多功能农药残留降解菌提供了基础     

Prediction of future gene expression profile by analyzing its past variation pattern

A number of initial Hematopoietic Stem Cells (HSC) are considered in a container that are able to divide into HSCs or differentiate into various types of descendant cells. In this paper, a method is designed to predict an approximate gene expression profile (GEP) for future descendant cells resulted from HSC division/differentiation. First, the GEP prediction problem is modeled into a multivariate time series prediction problem. A novel method called EHSCP (Extended Hematopoietic Stem Cell Prediction) is introduced which is an artificial neural machine to solve the problem. EHSCP accepts the initial sequence of measured GEPs as input and predicts GEPs of future descendant cells. This prediction can be performed for multiple stages of cell division/differentiation. EHSCP considers the GEP sequence as time series and computes correlation between input time series. Two novel artificial neural units called PLSTM (Parametric Long Short Term Memory) and MILSTM (Multi-Input LSTM) are designed. PLSTM makes EHSCP able to consider this correlation in output prediction. Since there exist thousands of time series in GEP prediction, a hierarchical encoder is proposed that computes this correlation using 101 MILSTMs. EHSCP is trained using 155 datasets and is evaluated on 39 test datasets. These evaluations show that EHSCP surpasses existing methods in terms of prediction accuracy and number of correctly-predicted division/differentiation stages. In these evaluations, number of correctly-predicted stages in EHSCP was 128 when as many as 8 initial stages were given.     

Discovery in toxicology: mediation by gene expression array technology

Toxicogenomics is a term that represents the merging of toxicology with novel genomics techniques. Data generated in the new-age era of toxicology is relatively complex, requires new bioinformatics tools for adequate interpretation, and allows for the rapid generation of testable hypotheses. Hazard identification and risk assessment processes will advance from the use of genomics techniques, which will lead to greater understanding of mechanism(s) of action of toxicants, development of novel biomarkers of exposure and effect, and better identification of sensitive subpopulations.