Construction Of Gene Regulatory Networks Research Articles

Inferring Gene Regulatory Networks from a Population of Yeast Segregants

Constructing gene regulatory networks is crucial to unraveling the genetic architecture of complex traits and to understanding the mechanisms of diseases. On the basis of gene expression and single nucleotide polymorphism data in the yeast, Saccharomyces cerevisiae, we constructed gene regulatory networks using a two-stage penalized least squares method. A large system of structural equations via optimal prediction of a set of surrogate variables was established at the first stage, followed by consistent selection of regulatory effects at the second stage. Using this approach, we identified subnetworks that were enriched in gene ontology categories, revealing directional regulatory mechanisms controlling these biological pathways. Our mapping and analysis of expression-based quantitative trait loci uncovered a known alteration of gene expression within a biological pathway that results in regulatory effects on companion pathway genes in the phosphocholine network. In addition, we identify nodes in these gene ontology-enriched subnetworks that are coordinately controlled by transcription factors driven by trans-acting expression quantitative trait loci. Altogether, the integration of documented transcription factor regulatory associations with subnetworks defined by a system of structural equations using quantitative trait loci data is an effective means to delineate the transcriptional control of biological pathways.

GeNeCK: a web server for gene network construction and visualization

BackgroundReverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and user-friendly online tools is still lagging.ResultsWe developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation-, likelihood-, Bayesian- and mutual information-based methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.ConclusionsGeNeCK is an efficient and easy-to-use web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck.

A method for design of expression tracking controllers for gene regulatory networks

As the synthetic biology community continues to refine experimental techniques for construction of gene regulatory networks, there is continued need for computational design tools to efficiently achieve desired performance. Tracking controllers play a fundamental role in a range of control engineering tasks. In this paper we address the design of expression tracking controllers for gene regulatory networks. We employ a class of simple networks as controllers and describe gene expression dynamics via a piecewise affine approximation of general nonlinear model formulations. We propose a method for the design of tracking controllers. In the case of regulation of the activity of a single gene, we derive an analytic description of a parameterization of tracking controllers. The results are illustrated with numerical examples.

Identification of Gene Regulatory Networks by Integrating Genetic Programming With Particle Filtering

Gene regulatory network can help to analyze and understand the underlying regulatory mechanism and the interaction among genes, and it plays a central role in morphogenesis of complex diseases such as cancer. DNA sequencing technology has efficiently produced a large amount of data for constructing gene regulatory networks. However, measured gene expression data usually contain uncertain noise, and inference of gene regulatory network model under non-Gaussian noise is a challenging issue which needs to be addressed. In this study, a joint algorithm integrating genetic programming and particle filter is presented to infer the ordinary differential equations model of gene regulatory network. The strategy uses genetic programming to identify the terms of ordinary differential equations, and applies particle filtering to estimate the parameters corresponding to each term. We systematically discuss the convergence and complexity of the proposed algorithm, and verify the efficiency and effectiveness of the proposed method compared to the existing approaches. Furthermore, we show the utility of our inference algorithm using a real HeLa dataset. In summary, a novel algorithm is proposed to infer the gene regulatory networks under non-Gaussian noise and the results show that this method can achieve more accurate models compared to the existing inference algorithms based on biological datasets.

Constructing tissue-specific transcriptional regulatory networks via a Markov random field

BackgroundRecent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.ResultsWe propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn’s disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/.ConclusionsCSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets.

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.

Optimized time-lag differential method for constructing gene regulatory network

One major molecular genetics control mechanism is transcriptional gene regulation where regulatory genes control other genes over time. The biological approaches to determine the Gene Regulatory Networks (GRN), require costly experimental setup and time for generating voluminous time series gene expression data. Computational model has been developed to generate GRN by analyzing time series gene expression data. Sometimes gene to gene interaction not performing in order due to change of activity in the cell, resulting regulatory error, creating difficulty to identify the target genes. Searching of regulatory genes with permissible error, producing maximum number of target genes is the real challenge for predicting the most important and instant regulating pathway of the human GRNs. The aim of the paper is to establish a computational methodology to reconstruct the GRN from short time series gene expression data with an objective to explain the observed behavior of biological systems. We employ nondominated sorting genetic algorithm (NSGA-II) to develop the proposed Optimized time-lag differential (OTD) algorithm, which identifies maximum number of target genes for the selected regulatory genes within minimum regulating time. Experiments have been conducted using eight different time series microarray data set showing better performance compare to others.

Induced Protein-RNA Interactions in the 5’ UTR Lead to Conformational Changes that Alter Translation

We study translational regulation by a 5’ UTR sequence encoding the binding site of an RNA-binding protein (RBP) in bacteria, using a reporter assay and selective 2′-hydroxyl acylation analysed by primer extension sequencing (SHAPE-Seq). We tested constructs containing a single hairpin, based on the binding sites of the coat RBPs of bacteriophages GA, MS2, PP7, and Qβ, positioned in the 5’ UTR of a reporter gene. With specifically-bound RBP present, either weak repression or up-regulation is observed, depending on the binding site and its flanking sequence. SHAPE-Seq data for a representative construct exhibiting up-regulation, indicates a partially-folded hairpin and non-reactive upstream and downstream flanking region, which we attribute to intermediate structures that apparently block translation. RBP binding stabilizes the fully-folded hairpin state facilitating translation, suggesting that the up-regulating constructs function as protein-sensing regulatory elements. This finding is further supported by lengthening the binding-site stem, which in turn destabilizes the translationally-inactive state, and abolishes the up-regulating behavior. Finally, we find that the combination of two binding sites, positioned in the 5’ UTR and the ribosomal initiation region within the CDS of the same transcript, can yield a cooperative regulatory response. Together, we show that the interaction of an RBP with its RNA target facilitates structural changes in the RNA, which is reflected by a controllable range of binding affinities and dose response behaviors. Thus, demonstrating that RNA-RBP interactions can provide a platform for constructing gene regulatory networks that are based on translational, rather than transcriptional, regulation.

Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach.

How to mine the gene regulatory relationship and construct gene regulatory network (GRN) is of utmost interest within the whole biological community, however, which has been consistently a challenging problem since the tremendous complexity in cellular systems. In present work, we construct gene regulatory network using an improved three-phase dependency analysis algorithm (TPDA) Bayesian network learning method, which includes the steps of Drafting, Thickening, and Thinning. In order to solve the problem of learning result is not reliable due to the high order conditional independence test, we use the entropy estimation approach of Gaussian kernel probability density estimator to calculate the (conditional) mutual information between genes. The experiment on the public benchmark data sets show the improved method outperforms the other nine kinds of Bayesian network learning methods when to process the data with large sample size, with small number of discrete values, and the frequency of different discrete values is about same. In addition, the improved TPDA method was further applied on a real large gene expression data set on RNA-seq from a global collection with 368 elite maize inbred lines. Experiment results show it performs better than the original TPDA method and the other nine kinds of Bayesian network learning algorithms significantly.

Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis.

The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering, and biomedicine, the traditional methods for causal inference require at least three variables. However, quantitative genetic analysis such as QTL, eQTL, mQTL, and genomic-imaging data analysis requires exploring the causal relationships between two variables. This paper will focus on bivariate causal discovery with continuous variables. We will introduce independence of cause and mechanism (ICM) as a basic principle for causal inference, algorithmic information theory and additive noise model (ANM) as major tools for bivariate causal discovery. Large-scale simulations will be performed to evaluate the feasibility of the ANM for bivariate causal discovery. To further evaluate their performance for causal inference, the ANM will be applied to the construction of gene regulatory networks. Also, the ANM will be applied to trait-imaging data analysis to illustrate three scenarios: presence of both causation and association, presence of association while absence of causation, and presence of causation, while lack of association between two variables. Telling cause from effect between two continuous variables from observational data is one of the fundamental and challenging problems in omics and imaging data analysis. Our preliminary simulations and real data analysis will show that the ANMs will be one of choice for bivariate causal discovery in genomic and imaging data analysis.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review.

: System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Gene regulatory network construction identified NFYA as a diffuse subtype-specific prognostic factor in gastric cancer.

Lauren classification is a pathology-based gastric cancer (GC) subtyping system, which is widely used in the clinical treatment of patients with GC. However, genome- scale molecular characteristics to distinguish between diffuse (DF) and intestinal (IT) GC remain incompletely characterized, particularly at the transcriptional regulatory level. In the present study, gene regulatory networks were constructed using the Passing Attributes between Networks for Data Assimilation (PANDA) algorithm for DF, IT and mixed GC. The results indicated that >85% of transcription factor (TF)-target edges were shared among all three GC subtypes. In TF enrichment analysis, 13 TFs, including nuclear transcription factor Y subunit α (NFYA) and forkhead box L1, were activated in DF GC, whereas 8 TFs, including RELA proto-oncogene and T-cell leukemia homeobox 1 (TLX1), were activated in IT GC. Out of these identified TFs, NFYA [Hazard ratio (HR) (95% confidence interval, CI)=0.560 (0.349, 0.900), P=0.017] and sex determining region Y [HR (95% CI)=0.603 (0.375, 0.969), P=0.037] were identified as independent prognostic factors in DF GC, but not in IT GC, whereas TLX1 [HR (95% CI)=0.547 (0.321, 0.9325), P=0.027] was identified as an independent prognostic factor in IT GC, but not in DF GC. Verification at the cellular level was also performed; interference of NFYA expression using small interfering RNA in MGC803 cells (DF GC-derived cells) markedly inhibited cell growth and colony formation. Similar effects were also detected in SGC-7901 cells (IT GC-derived cells), but to a lesser extent. In conclusion, identified gene regulatory networks differed between distinct GC subtypes, in which the same TFs had different biological effects. Specifically, NFYA was identified as a DF subtype- specific independent prognostic factor in GC.

Abstract 347: Mechanistic Basis of Neonatal Heart Regeneration Revealed by Transcriptome and Histone Modification Profiling

The adult mammalian heart has very limited capacity to regenerate upon injury. Following myocardial infarction, cardiomyocytes are extensively lost and fibroblasts are activated to form a scar, resulting in compromised cardiac function. In contrast, the heart of a neonatal mouse has full capacity to regenerate after myocardial infarction, although such regenerative ability is lost by postnatal (P) day 7. The molecular properties of neonatal hearts that enable regeneration remain unknown. To uncover the mechanisms of neonatal heart regeneration, we induced myocardial infarction (MI) in P1 and P8 hearts, and profiled transcriptomic and epigenomic dynamics by RNA-Seq, H3K27ac ChIP-Seq and H3K27me3 ChIP-Seq at various time points following the surgery. Analysis of differentially expressed genes and groups of enhancers that acquire active status post-MI revealed distinct post-infarction responses between P1 and P8 hearts. Dramatic differences in immune response and developmental gene programs were identified in regenerative P1 versus non-regenerative P8 hearts. Furthermore, we analyzed transcription factors involved in the injury response post MI and constructed gene regulatory networks during neonatal cardiac injury. This work provides insights into the molecular basis of neonatal heart regeneration, and identifies potential genes and cis-regulatory elements that can be modulated to facilitate heart regeneration.

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model — spaceMap — to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations (CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast (n=77) and ovarian (n=174) cancer studies. Each cancer exhibits disruption of ‘ion transmembrane transport’ and ‘regulation from RNA polymerase II promoter’ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap — including vignettes and documentation — is hosted on https://topherconley.github.io/spacemap.

Transcription Factors in Regulatory and Protein Subnetworks during Generation of Neural Stem Cells and Neurons from Direct Reprogramming of Non-fibroblastic Cell Sources

Direct reprogramming of non-fibroblastic cells to the neuronal cell types including induced neurons (iNs) and induced neural stem cells (iNSCs) has provided an alternative approach for the direct reprogramming of fibroblasts to those cells. However, to increase the efficiency of the reprogramming process the underlying mechanisms should be clarified. In the current study, we analyzed the gene expression profiles of five different cellular conversions to understand the most significant molecular mechanisms and transcription factors (TFs) underlying each conversion. For each conversion, we found the list of differentially expressed genes (DEGs) and the list of differentially expressed TFs (DE-TFs) which regulate expression of DEGs. Moreover, we constructed gene regulatory networks based on the TF-binding sites’ data and found the most central regulators and the most active part of the networks. Furthermore, protein complexes were identified from constructed protein–protein interaction networks for DE-TFs. Finally, we proposed a list of main regulators for each conversion; for example, in the direct conversion of epithelial-like cells (ECs) to iNSCs, combination of centrality with active modules or protein complex analyses highlighted the role of POU3F2, BACH1, AR, PBX1, SOX2 and NANOG genes in this conversion. To the best of our knowledge, this study is the first one that analyzed the direct conversion of non-fibroblastic cells toward iNs and iNSCs and we believe that the expression manipulation of identified genes may increase efficiency of these processes.

Shark EvoDevOmics: Linking Teeth and Taste Buds to the Evolution of Tooth Regeneration

The shark dentition is continuously regenerative, with teeth forming from a continuous and permanent epithelial dental lamina. We have identified an intriguing link between the regenerative potential of the shark dentition and the oral taste bud field, both of which form from a common epithelia. We performed RNAseq analyses on specific cell compartments within the continuous dental lamina and cells from the taste‐tooth (lamina) junction, associated with progenitor cells linked to tooth regeneration. We have identified a set of candidate genes involved in the initiation and maintenance of continuous dental regeneration. Expression analyses have verified these candidates and their relationship with specific compartments of the dental lamina associated with tooth regeneration. Our data provide a framework from which we can further investigate stem/progenitor candidates crucial for the process of tooth regeneration, in addition to providing new information on the timing and cyclicity of the shark tooth system. The dental lamina in sharks is an ideal regenerative organ in which to investigate perpetual tooth development, and to further our understanding of the origin of teeth. With these data we can start to construct gene regulatory networks as a tool to further investigate the functional role of these novel candidates. Our findings shed light on the developmental regulation of both the timing of continued dental initiation and the maintenance of stem progenitors for rapid tooth regeneration in an exciting and emerging EvoDevOmic model, the shark.Support or Funding InformationThis research was supported by the following research grants: Natural Environment Research Council (NERC) Standard Grant NE/K014595/1 (to G.J.F); and Leverhulme Trust Research Grant RPG‐211 (to G.J.F.).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Construction of diagnosis system and gene regulatory networks based on microarray analysis

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

DiffGRN: differential gene regulatory network analysis.

Identification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementation, the approach lacks in the representation of causal effects and multivariate effects between genes. In this paper, we propose an approach named Differential Gene Regulatory Network (DiffGRN) that infers differential gene regulation between two groups. We infer gene regulatory networks of two groups using Random LASSO, and then we identify differential gene regulations by the proposed significance test. The advantages of DiffGRN are to capture multivariate effects of genes that regulate a gene simultaneously, to identify causality of gene regulations, and to discover differential gene regulators between regression-based gene regulatory networks. We assessed DiffGRN by simulation experiments and showed its outstanding performance than the current state-of-the-art correlation-based method, DINGO. DiffGRN is applied to gene expression data in asthma. The DiNA with asthma data showed a number of gene regulations, such as ADAM12 and RELB, reported in biological literature.

Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships by integrating messenger ribonucleic acid (mRNA) gene expression and deoxyribonucleic acid (DNA) level information including copy number and methylation. Data integration allows for inference on the directionality of certain regulatory relationships, which would be otherwise indistinguishable due to Markov equivalence. Efficient inference is developed based on simultaneous equation models. Bayesian model selection techniques are adopted to estimate the graph structure. We illustrate our approach by simulations and application in colon adenocarcinoma pathway analysis.

Identification of Effective Genes from HIV-1 VPR Microarray Datasets after Constructing GA-Optimized Genetic Network

Identification of effective genes from microarray dataset is important for obtaining relationship between that gene and a particular disease. For this, Gene Regulatory Network (GRN) construction is important. In this paper, GA-optimized GRNs are constructed using radial basis neural network (RBN) approach within cluster center matrix of human HIV-1 Vpr mutant microarray time series gene expression data. After this, effective genes from each microarray dataset are found by calculating minimum Euclidean distance between optimized output value and corresponding dataset.