Regulatory Interaction Networks Research Articles

ChemInform Abstract: Synthetic Mammalian Gene Networks as a Blueprint for the Design of Interactive Biohybrid Materials

AbstractReview: 83 refs.

PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing

Small RNAs (sRNAs) are a class of short (20–25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete ‘sRNAome’ we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Crosstalk between transcription factors and microRNAs in human protein interaction network

BackgroundGene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear.MethodsIn this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network.ResultsAmong the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions.ConclusionsAltogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.

Network Based Prediction Model for Genomics Data Analysis

Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. We employ the gradient descent boosting procedure to build an additive tree model and propose a new algorithm to utilize the network structure in fitting small tree weak learners. We illustrate by simulation studies and a real data example that, by making use of the network information, NetBoosting outperforms a few existing methods in terms of accuracy of prediction and variable selection.

CsrA modulates luxR transcript levels in Vibrio fischeri

The quorum-sensing and CsrA regulons of Vibrios control overlapping cellular functions during growth. Hence, the potential exists for regulatory network interactions between the pathways that enable them to be coordinately controlled. In Vibrio cholerae, CsrA indirectly modulates the activity of LuxO in the quorum-sensing signaling pathway. In this study, it was demonstrated that in Vibrio fischeri, CsrA causes an increase in the transcript levels of a downstream quorum-sensing regulatory gene, luxR, which does not exist in the V.cholerae system. In V.fischeri, the increase in luxR transcripts caused by CsrA does not depend on the LitR transcriptional activator nor does the CsrA effect seem to occur through the global regulator cAMP-CRP. Thus, there appears to be more than one mechanism whereby the CsrA and quorum-sensing pathways integrate regulatory outputs in Vibrios.

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

BackgroundThe use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment.ResultsOur findings demonstrate that a miRNA’s functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set.ConclusionsWe describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.

Systems Biochemistry and Structural Biology of Microtubule End Tracking

The microtubule cytoskeleton performs essential mechano-chemical tasks in eukaryotic cells. It is crucial for the internal organisation of the cell, intracellular trafficking and for the separation of the genetic material during cell division. These complex processes require the coordinated activity of dynamic microtubules, molecular motors, their regulators, and other proteins linking microtubules to intracellular substructures. All these proteins form a dynamically interconnected microtubule cytoskeleton whose distinct biological function is intimately linked to its overall organisation and dynamic state. How this network operates as an integrative system and how its large-scale behaviour depends on the combinatorial action of its nano-scale biochemical constituents is a major open question. Biochemical reconstitutions of cytoskeletal subsystems mimicking in vivo behaviour, in combination with quantitative fluorescence microscopy and cryo-electron microscopy, have become a powerful approach to extract the underlying rules of how the microtubule cytoskeleton acts as a dynamic system. For example, through this approach the hierarchical functioning of a regulatory protein interaction network at growing microtubule ends, formed around so-called end binding (EB) proteins, can now be understood from the atomic to the micrometer scale. In the future, the challenge will be to reconstitute even more complex systems to be able to test directly our understanding of higher-order cytoskeletal functions.

Synthetic mammalian gene networks as a blueprint for the design of interactive biohybrid materials

Synthetic biology aims at the rational design and construction of devices, systems and organisms with desired functionality based on modular well-characterized biological building blocks. Based on first proof-of-concept studies in bacteria a decade ago, synthetic biology strategies have rapidly entered mammalian cell technology providing novel therapeutic solutions. Here we review how biological building blocks can be rewired to interactive regulatory genetic networks in mammalian cells and how these networks can be transformed into open- and closed-loop control configurations for autonomously managing disease phenotypes. In the second part of this tutorial review we describe how the regulatory biological sensors and switches can be transferred from mammalian cell synthetic biology to materials sciences in order to develop interactive biohybrid materials with similar (therapeutic) functionality as their synthetic biological archetypes. We develop a perspective of how the convergence of synthetic biology with materials sciences might contribute to the development of truly interactive and adaptive materials for autonomous operation in a complex environment.

Genome-Wide Scale-Free Network Inference for Candida albicans.

Discovery of essential genes in pathogenic organisms is an important step in the development of new medication. Despite a growing number of genome data available, little is known about C. albicans, a major fungal pathogen. Most of the human population carries C. albicans as commensal, but it can cause systemic infection that may lead to the death of the host if the immune system has deteriorated. In many organisms central nodes in the interaction network (hubs) play a crucial role for information and energy transport. Knock-outs of such hubs often lead to lethal phenotypes making them interesting drug targets. To identify these central genes via topological analysis, we inferred gene regulatory networks that are sparse and scale-free. We collected information from various sources to complement the limited expression data available. We utilized a linear regression algorithm to infer genome-wide gene regulatory interaction networks. To evaluate the predictive power of our approach, we used an automated text-mining system that scanned full-text research papers for known interactions. With the help of the compendium of known interactions, we also optimize the influence of the prior knowledge and the sparseness of the model to achieve the best results. We compare the results of our approach with those of other state-of-the-art network inference methods and show that we outperform those methods. Finally we identify a number of hubs in the genome of the fungus and investigate their biological relevance.

Viruses and microRNAs: a toolbox for systematic analysis

RNA silencing is emerging as a novel layer of regulation of virus-host interaction. Since individual small RNAs can probably repress dozens if not hundreds of target mRNA molecules, and transcripts, on the other hand, may be recognized by multiple regulatory small RNAs, a dense and complex interaction network of microRNAs (miRNAs) and their targets arises. A comprehensive analysis of miRNA functions thus not only requires systematic approaches employing high-throughput technologies but also calls for the development of improved experimental technologies and a profound bioinformatic analysis. Integration of complementary approaches will enhance our understanding of the mutual regulation of virus and host. Focusing on herpesviruses, we here describe currently available technologies and summarize present results obtained by high-throughput approaches. These techniques can be broadly applied to other virus families and pathways employing other classes of small regulatory RNAs and therefore are powerful universal tools for research on virus-host interaction.

Whole blood genome-wide expression profiling and network analysis suggest MELAS master regulators

Background: The heteroplasmic mitochondrial DNA (mtDNA) mutation A3243G causes the mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome as one of the most frequent mitochondrial diseases. The process of reconfiguration of nuclear gene expression profile to accommodate cellular processes to the functional status of mitochondria might be a key to MELAS disease manifestation and could contribute to its diverse phenotypic presentation.Objective: To determine master regulatory protein networks and disease-modifying genes in MELAS syndrome.Methods: Analyses of whole blood transcriptomes from 10 MELAS patients using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses.Results: Hierarchical cluster analysis elucidated that the relative abundance of mutant mtDNA molecules is decisive for the nuclear gene expression response. Further analyses confirmed not only transcription factors already known to be involved in mitochondrial diseases (such as TFAM), but also detected the hypoxia-inducible factor 1 complex, nuclear factor Y and cAMP responsive element-binding protein-related transcription factors as novel master regulators for reconfiguration of nuclear gene expression in response to the MELAS mutation. Correlation analyses of gene alterations and clinico-genetic data detected significant correlations between A3243G-induced nuclear gene expression changes and mutant mtDNA load as well as disease characteristics. These potential disease-modifying genes influencing the expression of the MELAS phenotype are mainly related to clusters primarily unrelated to cellular energy metabolism, but important for nucleic acid and protein metabolism, and signal transduction.Discussion: Our data thus provide a framework to search for new pathogenetic concepts and potential therapeutic approaches to treat the MELAS syndrome.

Analysis of the Human Endogenous Coregulator Complexome

Elucidation of endogenous cellular protein-protein interactions and their networks is most desirable for biological studies. Here we report our study of endogenous human coregulator protein complex networks obtained from integrative mass spectrometry-based analysis of 3290 affinity purifications. By preserving weak protein interactions during complex isolation and utilizing high levels of reciprocity in the large dataset, we identified many unreported protein associations, such as a transcriptional network formed by ZMYND8, ZNF687, and ZNF592. Furthermore, our work revealed a tiered interplay within networks that share common proteins, providing a conceptual organization of a cellular proteome composed of minimal endogenous modules (MEMOs), complex isoforms (uniCOREs), and regulatory complex-complex interaction networks (CCIs). This resource will effectively fill a void in linking correlative genomic studies with an understanding of transcriptional regulatory protein functions within the proteome for formulation and testing of future hypotheses.

Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks

Topological network motifs represent functional relationships within and between regulatory and protein-protein interaction networks. Enriched motifs often aggregate into self-contained units forming functional modules. Theoretical models for network evolution by duplication-divergence mechanisms and for network topology by hierarchical scale-free networks have suggested a one-to-one relation between network motif enrichment and aggregation, but this relation has never been tested quantitatively in real biological interaction networks. Here we introduce a novel method for assessing the statistical significance of network motif aggregation and for identifying clusters of overlapping network motifs. Using an integrated network of transcriptional, posttranslational and protein-protein interactions in yeast we show that network motif aggregation reflects a local modularity property which is independent of network motif enrichment. In particular our method identified novel functional network themes for a set of motifs which are not enriched yet aggregate significantly and challenges the conventional view that network motif enrichment is the most basic organizational principle of complex networks.

Regulation of Central Metabolism Genes of Mycobacterium tuberculosis by Parallel Feed-Forward Loops Controlled by Sigma Factor E (σ E )

Cells respond to external stimuli through networks of regulatory interactions. The human pathogen Mycobacterium tuberculosis responds to stress encountered during infection by arresting multiplication and implementing critical metabolic changes that lead to or sustain the nonreplicative state. Much of this differentiation program is recapitulated when M. tuberculosis cultures are subjected to gradual oxygen depletion in vitro. Here we report that hypoxic induction of critical central metabolism genes in the glyoxylate shunt (icl1) and in the methylcitrate cycle (gltA1) involves both global and local regulators. The global regulators are accessory sigma factors σ(B) for icl1 and σ(E) for gltA1. The local regulators are the products of two paralogous genes mapping at positions adjacent to the corresponding effector gene or operon. We call these genes lrpI and lrpG (for local regulatory protein of icl1 and gltA1). We also found that (i) each sigma factor controls the corresponding local regulator, (ii) both global and local regulators are required for effector gene induction, and (iii) the occurrence of sigma factor control of effector gene induction is independent of its control over the corresponding local regulator. Together, these data indicate that induction of icl1 and gltA1 utilizes parallel feed-forward loops with an AND input function. Both feed-forward loops are affected by σ(E), since this sigma factor is part of the gltA1 loop and controls sigB in the icl1 loop. Feed-forward loops may critically contribute to the cellular developmental program associated with M. tuberculosis dormancy.

Integrative analysis of genomic, functional and protein interaction data predicts long-range enhancer-target gene interactions

Multicellular organismal development is controlled by a complex network of transcription factors, promoters and enhancers. Although reliable computational and experimental methods exist for enhancer detection, prediction of their target genes remains a major challenge. On the basis of available literature and ChIP-seq and ChIP-chip data for enhanceosome factor p300 and the transcriptional regulator Gli3, we found that genomic proximity and conserved synteny predict target genes with a relatively low recall of 12–27% within 2 Mb intervals centered at the enhancers. Here, we show that functional similarities between enhancer binding proteins and their transcriptional targets and proximity in the protein–protein interactome improve prediction of target genes. We used all four features to train random forest classifiers that predict target genes with a recall of 58% in 2 Mb intervals that may contain dozens of genes, representing a better than two-fold improvement over the performance of prediction based on single features alone. Genome-wide ChIP data is still relatively poorly understood, and it remains difficult to assign biological significance to binding events. Our study represents a first step in integrating various genomic features in order to elucidate the genomic network of long-range regulatory interactions.

Simultaneous Genome-Wide Inference of Physical, Genetic, Regulatory, and Functional Pathway Components

Biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data. This results in a comprehensive compendium of whole-genome networks for yeast, derived from ∼3,500 experimental conditions and describing 30 interaction types, which range from general (e.g. physical or regulatory) to specific (e.g. phosphorylation or transcriptional regulation). We used these networks to investigate molecular pathways in carbon metabolism and cellular transport, proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications. Additionally, 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated. We analyzed the systems-level network features within all interactomes, verifying the presence of small-world properties and enrichment for recurring network motifs. This compendium of physical, synthetic, regulatory, and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function.princeton.edu/bioweaver/.

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

BackgroundAlthough Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.ResultsWe used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.ConclusionsWe predicted a set of five novel TF - target gene interactions in E. coli. One of them, the regulation of lipA by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.

Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks

Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here, we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an Escherichia coli metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models. Supplementary Material is available online at www.liebertonline.com.

Genome-wide expression profiling and functional network analysis upon neuroectodermal conversion of human mesenchymal stem cells suggest HIF-1 and miR-124a as important regulators

Tissue-specific stem cells, such as bone-marrow-derived human mesenchymal stem cells (hMSCs), are thought to be lineage restricted and therefore, could only be differentiated into cell types of the tissue of origin. Several recent studies however have suggested that these types of stem cells might be able to break barriers of germ layer commitment and differentiate in vitro into cells with neuroectodermal properties. We reported earlier about efficient conversion of adult hMSCs into a neural stem cell (NSC)-like population (hmNSCs, for human marrow-derived NSC-like cells) with all major properties of NSCs including functional neuronal differentiation capacity. Here we compared the transcriptomes from hMSCs and hmNSCs using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses to shed light on regulatory protein networks involved in this neuroectodermal conversion process. We found differential regulation of extracellular matrix protein transcripts, up-regulation of distinct neuroectodermal and NSCs marker genes and local chromosomal transcriptional up-regulation at chromosome 4q13.3. In comparison to hMSCs and primary adult hippocampal NSCs, the transcriptome of hmNSCs displayed minor overlap with both other cell populations. Advanced bioinformatics of regulated genes upon neuroectodermal conversion identified transcription factor networks with HIF-1 and microRNA miR-124a as potential major regulators. Together, transgerminal neuroectodermal conversion of hMSCs into NSC-like cells is accompanied by extensive changes of their global gene expression profile, which might be controlled in part by transcription factor networks related to HIF-1 and miR-124a.

Cellular Physiology of Bacterial Ribosome Assembly

The bacterial ribosome is composed of 3 large RNA molecules and 53 small proteins. The biogenesis and assembly of ribosomes accounts for approximately one third of the energy budget for a rapidly dividing bacterial cell, and is thus a central aspect of cellular physiology. Not surprisingly, the high level of synthesis of the ribosomal components is subject to tight regulation to ensure coordinated production of each component in the required amount. While some general principles of feedback autoregulation have been identified, understanding of the network of regulatory interactions that coordinates ribosome assembly is very incomplete.We have developed a range of isotope pulse assays using 15N‐labeling to monitor ribosome assembly in rapidly growing E. coli. These experiments permit precise quantitation of free ribosomal protein levels, the levels of proteins in intermediate assembly particles, and the rate at which these particles turnover. Using chemical probes, protein overexpression, and analysis of deletion strains, we are developing a quantitative framework for understanding the process of ribosome biogenesis in bacteria.