Partially observed bipartite network analysis to identify predictive connections in transcriptional regulatory networks
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Messenger RNA expression is regulated by a complex interplay of different regulatory proteins. Unfortunately, directly measuring the individual activity of these regulatory proteins is difficult, leaving us with only the resulting gene expression pattern as a marker for the underlying regulatory network or regulator-gene associations. Furthermore, traditional methods to predict these regulator-gene associations do not define the relative importance of each association, leading to a large number of connections in the global regulatory network that, although true, are not useful. Here we present a Bayesian method that identifies which known transcriptional relationships in a regulatory network are consistent with a given body of static gene expression data by eliminating the non-relevant ones. The Partially Observed Bipartite Network (POBN) approach developed here is tested using E. coli expression data and a transcriptional regulatory network derived from RegulonDB. When the regulatory network for E. coli was integrated with 266 E. coli gene chip observations, POBN identified 93 out of 570 connections that were either inconsistent or not adequately supported by the expression data. POBN provides a systematic way to integrate known transcriptional networks with observed gene expression data to better identify which transcriptional pathways are likely responsible for the observed gene expression pattern.Keywords:
Gene regulatory network
Mathematical modeling of biological networks is an essential part of Systems Biology. Developing and using such models in order to understand gene regulatory networks is a major challenge. We present an algorithm that determines the smallest perturbations required for manipulating the dynamics of a network formulated as a Petri net, in order to cause or avoid a specified phenotype. By modifying McMillan's unfolding algorithm, we handle partial knowledge and reduce computation cost. The methodology is demonstrated on a glioma network. Out of the single gene perturbations, activation of glutathione S-transferase P (GSTP1) gene was by far the most effective in blocking the cancer phenotype. Among pairs of perturbations, NFkB and TGF-β had the largest joint effect, in accordance with their role in the EMT process. Our method allows perturbation analysis of regulatory networks and can overcome incomplete information. It can help in identifying drug targets and in prioritizing perturbation experiments.
Gene regulatory network
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The transcriptional regulator TtgR belongs to the TetR family of transcriptional repressors. It depresses the transcription of the TtgABC operon and itself and thus regulates the extrusion of noxious chemicals with efflux pumps in bacterial cells. As the ligand-binding domain of TtgR is rather flexible, it can bind with a number of structurally diverse ligands, such as antibiotics, flavonoids and aromatic solvents. In the current work, we perform equilibrium and nonequilibrium alchemical free energy simulation to predict the binding affinities of a series of ligands targeting the TtgR protein and an agreement between the theoretical prediction and the experimental result is observed. End-point methods MM/PBSA and MM/GBSA are also employed for comparison. We further study the interaction maps and contacts between the protein and the ligand and identify important interactions in the protein-ligand binding cases. The dynamics fluctuation and secondary structures are also investigated. The current work sheds light on atomic and thermodynamic understanding of the TtgR-ligand interactions.
TetR
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Occidiofungin is a highly effective antifungal glycopeptide produced by certain Burkholderia strains. The ocf gene cluster, responsible for occidiofungin biosynthesis, is regulated by the cluster-specific regulators encoded by an ambR homolog(s) within the same gene cluster, while the extent to which occidiofungin biosynthesis is connected with the core regulation network remains unknown. Here, we report that the LysR-type regulator BysR acts as a pleiotropic regulator and is essential for occidiofungin biosynthesis. Magnaporthe oryzae was used as an antifungal target in this study, and deletion of bysR and ocfE abolished the antagonistic activity against M. oryzae in Burkholderia sp. strain JP2-270. The ΔbysR defect can be recovered by constitutively expressing bysR or ambR1, but not ambR2. Electrophoretic mobility shift assays (EMSAs) collectively showed that BysR regulates ambR1 by directly binding to its promoter region. In addition, transcriptomic analysis revealed altered expression of 350 genes in response to bysR deletion, and the genes engaged in flagellar assembly and bacterial chemotaxis constitute the most enriched pathways. Also, 400 putative BysR-targeted loci were identified by DNA affinity purification sequencing (DAP-seq) in JP2-270. These loci include not only genes engaged in key metabolic pathways but also those involved in secondary metabolic pathways. To conclude, the occidiofungin produced by JP2-270 is the main substance inhibiting M. oryzae, and BysR controls occidiofungin production by directly targeting ambR1, an intracluster transcriptional regulatory gene that further activates the transcription of the ocf gene cluster. IMPORTANCE We report for the first time that occidiofungin production is regulated by the global transcriptional factor BysR, by directly targeting the specific regulator ambR1, which further promotes the transcription of ocf genes. BysR also acts as a pleiotropic regulator that controls various cellular processes in Burkholderia sp. strain JP2-270. This study provides insight into the regulatory mechanism of occidiofungin synthesis and enhances our understanding of the regulatory patterns of the LysR-type regulator.
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Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN. In this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs. By taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.
Gene regulatory network
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Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli , transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the challenge of inferring activities of unknown regulators by generating de novo (binding) motifs and integrating with expression data. We identify a number of key regulators active in the metabolic switch, including PhoP with its associated directed repeat PHO box, candidate motifs for two SARPs, a CRP family regulator, an iron response regulator and that for LexA. Experimental validation for some of our predictions was obtained using gel-shift assays. Our analysis is applicable to any organism for which there is a reasonable amount of complementary expression data and for which motifs (either over represented or evolutionary conserved) can be identified in the genome.
Repressor lexA
Response regulator
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Regulon
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Abstract Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.
Gene regulatory network
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Single-Cell Analysis
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Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.
Gene regulatory network
Reductionism
Proteome
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Abstract Brasilicardin A (BraA) is a promising immunosuppressive compound produced naturally by the pathogenic bacterium Nocardia terpenica IFM 0406. Heterologous host expression of brasilicardin gene cluster showed to be efficient to bypass the safety issues, low production levels and lack of genetic tools related with the use of native producer. Further improvement of production yields requires better understanding of gene expression regulation within the BraA biosynthetic gene cluster (Bra‐BGC); however, the only so far known regulator of this gene cluster is Bra12. In this study, we discovered the protein LysRNt, a novel member of the LysR‐type transcriptional regulator family, as a regulator of the Bra‐BGC. Using in vitro approaches, we identified the gene promoters which are controlled by LysRNt within the Bra‐BGC. Corresponding genes encode enzymes involved in BraA biosynthesis as well as the key Bra‐BGC regulator Bra12. Importantly, we provide in vivo evidence that LysRNt negatively affects production of brasilicardin congeners in the heterologous host Amycolatopsis japonicum . Finally, we demonstrate that some of the pathway related metabolites, and their chemical analogs, can interact with LysRNt which in turn affects its DNA‐binding activity.
Gene cluster
Heterologous
Heterologous expression
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Gene regulatory network
Functional Genomics
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