Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

Transcriptome Analysis Reveals the Effect of PdhR in Plesiomonas shigelloides

The pyruvate dehydrogenase complex regulator (PdhR) was originally identified as a repressor of the pdhR-aceEF-lpd operon, which encodes the pyruvate dehydrogenase complex (PDHc) and PdhR itself. According to previous reports, PdhR plays a regulatory role in the physiological and metabolic pathways of bacteria. At present, the function of PdhR in Plesiomonas shigelloides is still poorly understood. In this study, RNA sequencing (RNA-Seq) of the wild-type strain and the ΔpdhR mutant strains was performed for comparison to identify the PdhR-controlled pathways, revealing that PdhR regulates ~7.38% of the P. shigelloides transcriptome. We found that the deletion of pdhR resulted in the downregulation of practically all polar and lateral flagella genes in P. shigelloides; meanwhile, motility assay and transmission electron microscopy (TEM) confirmed that the ΔpdhR mutant was non-motile and lacked flagella. Moreover, the results of RNA-seq and quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) showed that PdhR positively regulated the expression of the T3SS cluster, and the ΔpdhR mutant significantly reduced the ability of P. shigelloides to infect Caco-2 cells compared with the WT. Consistent with previous research, pyruvate-sensing PdhR directly binds to its promoter and inhibits pdhR-aceEF-lpd operon expression. In addition, we identified two additional downstream genes, metR and nuoA, that are directly negatively regulated by PdhR. Furthermore, we also demonstrated that ArcA was identified as being located upstream of pdhR and lpdA and directly negatively regulating their expression. Overall, we revealed the function and regulatory pathway of PdhR, which will allow for a more in-depth investigation into P. shigelloides pathogenicity as well as the complex regulatory network.

Homology-based reconstruction of regulatory networks for bacterial and archaeal genomes

Gene regulation is a key process for all microorganisms, as it allows them to adapt to different environmental stimuli. However, despite the relevance of gene expression control, for only a handful of organisms is there related information about genome regulation. In this work, we inferred the gene regulatory networks (GRNs) of bacterial and archaeal genomes by comparisons with six organisms with well-known regulatory interactions. The references we used are: Escherichia coli K-12 MG1655, Bacillus subtilis 168, Mycobacterium tuberculosis, Pseudomonas aeruginosa PAO1, Salmonella enterica subsp. enterica serovar typhimurium LT2, and Staphylococcus aureus N315. To this end, the inferences were achieved in two steps. First, the six model organisms were contrasted in an all-vs-all comparison of known interactions based on Transcription Factor (TF)-Target Gene (TG) orthology relationships and Transcription Unit (TU) assignments. In the second step, we used a guilt-by-association approach to infer the GRNs for 12,230 bacterial and 649 archaeal genomes based on TF-TG orthology relationships of the six bacterial models determined in the first step. Finally, we discuss examples to show the most relevant results obtained from these inferences. A web server with all the predicted GRNs is available at https://regulatorynetworks.unam.mx/ or http://132.247.46.6/.

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acid- and jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid- and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

A transcriptional regulatory network of Rsv3-mediated extreme resistance against Soybean mosaic virus

Resistance genes are an effective means for disease control in plants. They predominantly function by inducing a hypersensitive reaction, which results in localized cell death restricting pathogen spread. Some resistance genes elicit an atypical response, termed extreme resistance, where resistance is not associated with a hypersensitive reaction and its standard defense responses. Unlike hypersensitive reaction, the molecular regulatory mechanism(s) underlying extreme resistance is largely unexplored. One of the few known, naturally occurring, instances of extreme resistance is resistance derived from the soybean Rsv3 gene, which confers resistance against the most virulent Soybean mosaic virus strains. To discern the regulatory mechanism underlying Rsv3-mediated extreme resistance, we generated a gene regulatory network using transcriptomic data from time course comparisons of Soybean mosaic virus-G7-inoculated resistant (L29, Rsv3-genotype) and susceptible (Williams82, rsv3-genotype) soybean cultivars. Our results show Rsv3 begins mounting a defense by 6 hpi via a complex phytohormone network, where abscisic acid, cytokinin, jasmonic acid, and salicylic acid pathways are suppressed. We identified putative regulatory interactions between transcription factors and genes in phytohormone regulatory pathways, which is consistent with the demonstrated involvement of these pathways in Rsv3-mediated resistance. One such transcription factor identified as a putative transcriptional regulator was MYC2 encoded by Glyma.07G051500. Known as a master regulator of abscisic acid and jasmonic acid signaling, MYC2 specifically recognizes the G-box motif ("CACGTG"), which was significantly enriched in our data among differentially expressed genes implicated in abscisic acid- and jasmonic acid-related activities. This suggests an important role for Glyma.07G051500 in abscisic acid- and jasmonic acid-derived defense signaling in Rsv3. Resultantly, the findings from our network offer insights into genes and biological pathways underlying the molecular defense mechanism of Rsv3-mediated extreme resistance against Soybean mosaic virus. The computational pipeline used to reconstruct the gene regulatory network in this study is freely available at https://github.com/LiLabAtVT/rsv3-network.

Genomic and transcriptomic analyses reveal distinct biological functions for cold shock proteins (VpaCspA and VpaCspD) in Vibrio parahaemolyticus CHN25 during low-temperature survival

Background

Vibrio parahaemolyticus causes serious seafood-borne gastroenteritis and death in humans. Raw seafood is often subjected to post-harvest processing and low-temperature storage. To date, very little information is available regarding the biological functions of cold shock proteins (CSPs) in the low-temperature survival of the bacterium. In this study, we determined the complete genome sequence of V. parahaemolyticus CHN25 (serotype: O5:KUT). The two main CSP-encoding genes (VpacspA and VpacspD) were deleted from the bacterial genome, and comparative transcriptomic analysis between the mutant and wild-type strains was performed to dissect the possible molecular mechanisms that underlie low-temperature adaptation by V. parahaemolyticus.

Results

The 5,443,401-bp V. parahaemolyticus CHN25 genome (45.2% G + C) consisted of two circular chromosomes and three plasmids with 4,724 predicted protein-encoding genes. One dual-gene and two single-gene deletion mutants were generated for VpacspA and VpacspD by homologous recombination. The growth of the ΔVpacspA mutant was strongly inhibited at 10 °C, whereas the VpacspD gene deletion strongly stimulated bacterial growth at this low temperature compared with the wild-type strain. The complementary phenotypes were observed in the reverse mutants (ΔVpacspA-com, and ΔVpacspD-com). The transcriptome data revealed that 12.4% of the expressed genes in V. parahaemolyticus CHN25 were significantly altered in the ΔVpacspA mutant when it was grown at 10 °C. These included genes that were involved in amino acid degradation, secretion systems, sulphur metabolism and glycerophospholipid metabolism along with ATP-binding cassette transporters. However, a low temperature elicited significant expression changes for 10.0% of the genes in the ΔVpacspD mutant, including those involved in the phosphotransferase system and in the metabolism of nitrogen and amino acids. The major metabolic pathways that were altered by the dual-gene deletion mutant (ΔVpacspAD) radically differed from those that were altered by single-gene mutants. Comparison of the transcriptome profiles further revealed numerous differentially expressed genes that were shared among the three mutants and regulators that were specifically, coordinately or antagonistically modulated by VpaCspA and VpaCspD. Our data also revealed several possible molecular coping strategies for low-temperature adaptation by the bacterium.

Conclusions

This study is the first to describe the complete genome sequence of V. parahaemolyticus (serotype: O5:KUT). The gene deletions, complementary insertions, and comparative transcriptomics demonstrate that VpaCspA is a primary CSP in the bacterium, while VpaCspD functions as a growth inhibitor at 10 °C. These results have improved our understanding of the genetic basis for low-temperature survival by the most common seafood-borne pathogen worldwide.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3784-5) contains supplementary material, which is available to authorized users.

Computational inference of gene regulatory networks: Approaches, limitations and opportunities

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Cautions about the reliability of pairwise gene correlations based on expression data

BACKGROUND

Rapid growth in the availability of genome-wide transcript abundance levels through gene expression microarrays and RNAseq promises to provide deep biological insights into the complex, genome-wide transcriptional behavior of single-celled organisms. However, this promise has not yet been fully realized.

RESULTS

We find that computation of pairwise gene associations (correlation; mutual information) across a set of 2782 total genome-wide expression samples from six diverse bacteria produces unexpectedly large variation in estimates of pairwise gene association-regardless of the metric used, the organism under study, or the number and source of the samples. We pinpoint the cause to sampling bias. In particular, in repositories of expression data (e.g., Gene Expression Omnibus, GEO), many individual genes show small differences in absolute gene expression levels across the set of samples. We demonstrate that these small differences are due mainly to "noise" instead of "signal" attributable to environmental or genetic perturbations. We show that downstream analysis using gene expression levels of genes with small differences yields biased estimates of pairwise association.

CONCLUSIONS

We propose flagging genes with small differences in absolute, RMA-normalized, expression levels (e.g., standard deviation less than 0.5), as potentially yielding biased pairwise association metrics. This strategy has the potential to substantially improve the confidence in genome-wide conclusions about transcriptional behavior in bacterial organisms. Further work is needed to further refine strategies to identify genes with small difference in expression levels prior to computing gene-gene association metrics.

Data- and knowledge-based modeling of gene regulatory networks: an update

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.

A new regulatory mechanism for bacterial lipoic acid synthesis

Lipoic acid, an essential enzyme cofactor, is required in three domains of life. In the past 60 years since its discovery, most of the pathway for lipoic acid synthesis and metabolism has been elucidated. However, genetic control of lipoic acid synthesis remains unclear. Here, we report integrative evidence that bacterial cAMP-dependent signaling is linked to lipoic acid synthesis in Shewanella species, the certain of unique marine-borne bacteria with special ability of metal reduction. Physiological requirement of protein lipoylation in γ-proteobacteria including Shewanella oneidensis was detected using Western blotting with rabbit anti-lipoyl protein primary antibody. The two genes (lipB and lipA) encoding lipoic acid synthesis pathway were proved to be organized into an operon lipBA in Shewanella, and the promoter was mapped. Electrophoretic mobility shift assays confirmed that the putative CRP-recognizable site (AAGTGTGATCTATCTTACATTT) binds to cAMP-CRP protein with origins of both Escherichia coli and Shewanella. The native lipBA promoter of Shewanella was fused to a LacZ reporter gene to create a chromosome lipBA-lacZ transcriptional fusion in E. coli and S. oneidensis, allowing us to directly assay its expression level by β-galactosidase activity. As anticipated, the removal of E. coli crp gene gave above fourfold increment of lipBA promoter-driven β-gal expression. The similar scenario was confirmed by both the real-time quantitative PCR and the LacZ transcriptional fusion in the crp mutant of Shewanella. Furthermore, the glucose effect on the lipBA expression of Shewanella was evaluated in the alternative microorganism E. coli. As anticipated, an addition of glucose into media effectively induces the transcriptional level of Shewanella lipBA in that the lowered cAMP level relieves the repression of lipBA by cAMP-CRP complex. Therefore, our finding might represent a first paradigm mechanism for genetic control of bacterial lipoic acid synthesis.

PdhR, the pyruvate dehydrogenase repressor, does not regulate lipoic acid synthesis

Lipoic acid is a covalently-bound enzyme cofactor required for central metabolism all three domains of life. In the last 20 years the pathway of lipoic acid synthesis and metabolism has been established in Escherichia coli. Expression of the genes of the lipoic acid biosynthesis pathway was believed to be constitutive. However, in 2010 Kaleta and coworkers (BMC Syst. Biol. 4:116) predicted a binding site for the pyruvate dehydrogenase operon repressor, PdhR (referred to lipA site 1) upstream of lipA, the gene encoding lipoic acid synthase and concluded that PdhR regulates lipA transcription. We report in vivo and in vitro evidence that lipA is not controlled by PdhR and that the putative regulatory site deduced by the prior workers is nonfunctional and physiologically irrelevant. E. coli PdhR was purified to homogeneity and used for electrophoretic mobility shift assays. The lipA site 1 of Kaleta and coworkers failed to bind PdhR. The binding detected by these workers is due to another site (lipA site 3) located far upstream of the lipA promoter. Relative to the canonical PdhR binding site lipA site 3 is a half-palindrome and as expected had only weak PdhR binding ability. Manipulation of lipA site 3 to construct a palindrome gave significantly enhanced PdhR binding affinity. The native lipA promoter and the version carrying the artificial lipA3 palindrome were transcriptionally fused to a LacZ reporter gene to directly assay lipA expression. Deletion of pdhR gave no significant change in lipA promoter-driven β-galactosidase activity with either the native or constructed palindrome upstream sequences, indicating that PdhR plays no physiological role in regulation of lipA expression.

Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation

Background

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network.

Results

Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1.

Conclusions

Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.

Reverse engineering cellular networks with information theoretic methods

Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.

Inference of the genetic network regulating lateral root initiation in Arabidopsis thaliana

Regulation of gene expression is crucial for organism growth, and it is one of the challenges in systems biology to reconstruct the underlying regulatory biological networks from transcriptomic data. The formation of lateral roots in Arabidopsis thaliana is stimulated by a cascade of regulators of which only the interactions of its initial elements have been identified. Using simulated gene expression data with known network topology, we compare the performance of inference algorithms, based on different approaches, for which ready-to-use software is available. We show that their performance improves with the network size and the inclusion of mutants. We then analyze two sets of genes, whose activity is likely to be relevant to lateral root initiation in Arabidopsis, and assess causality of their regulatory interactions by integrating sequence analysis with the intersection of the results of the best performing methods on time series and mutants. The methods applied capture known interactions between genes that are candidate regulators at early stages of development. The network inferred from genes significantly expressed during lateral root formation exhibits distinct scale free, small world and hierarchical properties and the nodes with a high out-degree may warrant further investigation.

Evolution of transcriptional regulation in closely related bacteria

BACKGROUND

The exponential growth of the number of fully sequenced genomes at varying taxonomic closeness allows one to characterize transcriptional regulation using comparative-genomics analysis instead of time-consuming experimental methods. A transcriptional regulatory unit consists of a transcription factor, its binding site and a regulated gene. These units constitute a graph which contains so-called "network motifs", subgraphs of a given structure. Here we consider genomes of closely related Enterobacteriales and estimate the fraction of conserved network motifs and sites as well as positions under selection in various types of non-coding regions.

RESULTS

Using a newly developed technique, we found that the highest fraction of positions under selection, approximately 50%, was observed in synvergon spacers (between consecutive genes from the same strand), followed by ~45% in divergon spacers (common 5'-regions), and ~10% in convergon spacers (common 3'-regions). The fraction of selected positions in functional regions was higher, 60% in transcription factor-binding sites and ~45% in terminators and promoters. Small, but significant differences were observed between Escherichia coli and Salmonella enterica. This fraction is similar to the one observed in eukaryotes.The conservation of binding sites demonstrated some differences between types of regulatory units. In E. coli, strains the interactions of the type "local transcriptional factor gene" turned out to be more conserved in feed-forward loops (FFLs) compared to non-motif interactions. The coherent FFLs tend to be less conserved than the incoherent FFLs. A natural explanation is that the former imply functional redundancy.

CONCLUSIONS

A naïve hypothesis that FFL would be highly conserved turned out to be not entirely true: its conservation depends on its status in the transcriptional network and also from its usage. The fraction of positions under selection in intergenic regions of bacterial genomes is roughly similar to that of eukaryotes. Known regulatory sites explain 20±5% of selected positions.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

Background

Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed.

Results

We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size.

Conclusions

Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

A workflow for genome-wide mapping of archaeal transcription factors with ChIP-seq

Deciphering the structure of gene regulatory networks across the tree of life remains one of the major challenges in postgenomic biology. We present a novel ChIP-seq workflow for the archaea using the model organism Halobacterium salinarum sp. NRC-1 and demonstrate its application for mapping the genome-wide binding sites of natively expressed transcription factors. This end-to-end pipeline is the first protocol for ChIP-seq in archaea, with methods and tools for each stage from gene tagging to data analysis and biological discovery. Genome-wide binding sites for transcription factors with many binding sites (TfbD) are identified with sensitivity, while retaining specificity in the identification the smaller regulons (bacteriorhodopsin-activator protein). Chromosomal tagging of target proteins with a compact epitope facilitates a standardized and cost-effective workflow that is compatible with high-throughput immunoprecipitation of natively expressed transcription factors. The Pique package, an open-source bioinformatics method, is presented for identification of binding events. Relative to ChIP-Chip and qPCR, this workflow offers a robust catalog of protein–DNA binding events with improved spatial resolution and significantly decreased cost. While this study focuses on the application of ChIP-seq in H. salinarum sp. NRC-1, our workflow can also be adapted for use in other archaea and bacteria with basic genetic tools.

Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a Systems Biology approach

BACKGROUND

In System Biology, iterations of wet-lab experiments followed by modelling approaches and model-inspired experiments describe a cyclic workflow. This approach is especially useful for the inference of gene regulatory networks based on high-throughput gene expression data. Experiments can verify or falsify the predicted interactions allowing further refinement of the network model. Aspergillus fumigatus is a major human fungal pathogen. One important virulence trait is its ability to gain sufficient amounts of iron during infection process. Even though some regulatory interactions are known, we are still far from a complete understanding of the way iron homeostasis is regulated.

RESULTS

In this study, we make use of a reverse engineering strategy to infer a regulatory network controlling iron homeostasis in A. fumigatus. The inference approach utilizes the temporal change in expression data after a change from iron depleted to iron replete conditions. The modelling strategy is based on a set of linear differential equations and offers the possibility to integrate known regulatory interactions as prior knowledge. Moreover, it makes use of important selection criteria, such as sparseness and robustness. By compiling a list of known regulatory interactions for iron homeostasis in A. fumigatus and softly integrating them during network inference, we are able to predict new interactions between transcription factors and target genes. The proposed activation of the gene expression of hapX by the transcriptional regulator SrbA constitutes a so far unknown way of regulating iron homeostasis based on the amount of metabolically available iron. This interaction has been verified by Northern blots in a recent experimental study. In order to improve the reliability of the predicted network, the results of this experimental study have been added to the set of prior knowledge. The final network includes three SrbA target genes. Based on motif searching within the regulatory regions of these genes, we identify potential DNA-binding sites for SrbA. Our wet-lab experiments demonstrate high-affinity binding capacity of SrbA to the promoters of hapX, hemA and srbA.

CONCLUSIONS

This study presents an application of the typical Systems Biology circle and is based on cooperation between wet-lab experimentalists and in silico modellers. The results underline that using prior knowledge during network inference helps to predict biologically important interactions. Together with the experimental results, we indicate a novel iron homeostasis regulating system sensing the amount of metabolically available iron and identify the binding site of iron-related SrbA target genes. It will be of high interest to study whether these regulatory interactions are also important for close relatives of A. fumigatus and other pathogenic fungi, such as Candida albicans.

Reverse engineering: a key component of systems biology to unravel global abiotic stress cross-talk

Understanding the global abiotic stress response is an important stepping stone for the development of universal stress tolerance in plants in the era of climate change. Although co-occurrence of several stress factors (abiotic and biotic) in nature is found to be frequent, current attempts are poor to understand the complex physiological processes impacting plant growth under combinatory factors. In this review article, we discuss the recent advances of reverse engineering approaches that led to seminal discoveries of key candidate regulatory genes involved in cross-talk of abiotic stress responses and summarized the available tools of reverse engineering and its relevant application. Among the universally induced regulators involved in various abiotic stress responses, we highlight the importance of (i) abscisic acid (ABA) and jasmonic acid (JA) hormonal cross-talks and (ii) the central role of WRKY transcription factors (TF), potentially mediating both abiotic and biotic stress responses. Such interactome networks help not only to derive hypotheses but also play a vital role in identifying key regulatory targets and interconnected hormonal responses. To explore the full potential of gene network inference in the area of abiotic stress tolerance, we need to validate hypotheses by implementing time-dependent gene expression data from genetically engineered plants with modulated expression of target genes. We further propose to combine information on gene-by-gene interactions with data from physical interaction platforms such as protein-protein or TF-gene networks.

More than just a metabolic regulator--elucidation and validation of new targets of PdhR in Escherichia coli

BACKGROUND

The pyruvate dehydrogenase regulator protein (PdhR) of Escherichia coli acts as a transcriptional regulator in a pyruvate dependent manner to control central metabolic fluxes. However, the complete PdhR regulon has not yet been uncovered. To achieve an extended understanding of its gene regulatory network, we combined large-scale network inference and experimental verification of results obtained by a systems biology approach.

RESULTS

22 new genes contained in two operons controlled by PdhR (previously only 20 regulatory targets in eight operons were known) were identified by analysing a large-scale dataset of E. coli from the Many Microbes Microarray Database and novel expression data from a pdhR knockout strain, as well as a PdhR overproducing strain. We identified a regulation of the glycolate utilization operon glcDEFGBA using chromatin immunoprecipitation and gel shift assays. We show that this regulation could be part of a cross-induction between genes necessary for acetate and pyruvate utilisation controlled through PdhR. Moreover, a link of PdhR regulation to the replication machinery of the cell via control of the transcription of the dcw-cluster was verified in experiments. This augments our knowledge of the functions of the PdhR-regulon and demonstrates its central importance for further cellular processes in E. coli.

CONCLUSIONS

We extended the PdhR regulon by 22 new genes contained in two operons and validated the regulation of the glcDEFGBA operon for glycolate utilisation and the dcw-cluster for cell division proteins experimentally. Our results provide, for the first time, a plausible regulatory link between the nutritional status of the cell and cell replication mediated by PdhR.

Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities

Background

Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element.

Results

This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network.

Conclusions

The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions.

Inference of gene regulatory networks from time series by Tsallis entropy

Background

The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed.

Results

In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes.

Conclusions

A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.