Regulatory Interaction Networks Research Articles

Genes under weaker stabilizing selection increase network evolvability and rapid regulatory adaptation to an environmental shift.

Regulatory networks play a central role in the modulation of gene expression, the control of cellular differentiation, and the emergence of complex phenotypes. Regulatory networks could constrain or facilitate evolutionary adaptation in gene expression levels. Here, we model the adaptation of regulatory networks and gene expression levels to a shift in the environment that alters the optimal expression level of a single gene. Our analyses show signatures of natural selection on regulatory networks that both constrain and facilitate rapid evolution of gene expression level towards new optima. The analyses are interpreted from the standpoint of neutral expectations and illustrate the challenge to making inferences about network adaptation. Furthermore, we examine the consequence of variable stabilizing selection across genes on the strength and direction of interactions in regulatory networks and in their subsequent adaptation. We observe that directional selection on a highly constrained gene previously under strong stabilizing selection was more efficient when the gene was embedded within a network of partners under relaxed stabilizing selection pressure. The observation leads to the expectation that evolutionarily resilient regulatory networks will contain optimal ratios of genes whose expression is under weak and strong stabilizing selection. Altogether, our results suggest that the variable strengths of stabilizing selection across genes within regulatory networks might itself contribute to the long-term adaptation of complex phenotypes.

Modular reorganization of the global network of gene regulatory interactions during perinatal human brain development.

BackgroundDuring early development of the nervous system, gene expression patterns are known to vary widely depending on the specific developmental trajectories of different structures. Observable changes in gene expression profiles throughout development are determined by an underlying network of precise regulatory interactions between individual genes. Elucidating the organizing principles that shape this gene regulatory network is one of the central goals of developmental biology. Whether the developmental programme is the result of a dynamic driven by a fixed architecture of regulatory interactions, or alternatively, the result of waves of regulatory reorganization is not known.ResultsHere we contrast these two alternative models by examining existing expression data derived from the developing human brain in prenatal and postnatal stages. We reveal a sharp change in gene expression profiles at birth across brain areas. This sharp division between foetal and postnatal profiles is not the result of pronounced changes in level of expression of existing gene networks. Instead we demonstrate that the perinatal transition is marked by the widespread regulatory rearrangement within and across existing gene clusters, leading to the emergence of new functional groups. This rearrangement is itself organized into discrete blocks of genes, each targeted by a distinct set of transcriptional regulators and associated to specific biological functions.ConclusionsOur results provide evidence of an acute modular reorganization of the regulatory architecture of the brain transcriptome occurring at birth, reflecting the reassembly of new functional associations required for the normal transition from prenatal to postnatal brain development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12861-016-0111-3) contains supplementary material, which is available to authorized users.

Implications of Developmental Gene Regulatory Networks Inside and Outside Developmental Biology.

The insight that the genomic control of developmental process is encoded in the form of gene regulatory networks has profound impacts on many areas of modern bioscience. Most importantly, it affects developmental biology itself, as it means that a causal understanding of development requires knowledge of the architecture of regulatory network interactions. Furthermore, it follows that functional changes in developmental gene regulatory networks have to be considered as a primary mechanism for evolutionary process. We here discuss some of the recent advances in gene regulatory network biology and how they have affected our current understanding of development, evolution, and regulatory genomics.

Nonlinear and temporal multiscale dynamics of gene regulatory networks: A qualitative simulator

Advanced experimental technologies have made the disclosure of networks of intricate regulatory interactions between genes and gene products feasible and revealed their extreme complexity. Thus, understanding which particular dynamical behaviors derive from specific gene regulatory structures poses a challenging question, at both scientific and application level, that necessarily requires computational tools to be answered. Herein, we discuss the algorithmic aspects and the implementation of a mathematical method, grounded on singular perturbation analysis, for the study of the dynamics of regulated gene networks. This results in a gene regulatory network simulator of the full range of possible dynamics of a specific class of ordinary differential equations adequate to model gene regulatory networks. The considered class of equations represent phenomenological models of the long interaction chains in a network: genes are the main players and the interactions between them are modeled by steep threshold-dependent response functions. The simulator we propose operates in the presence of incomplete knowledge of parameter values. It assumes that threshold-dependent regulation is modeled by continuous steep sigmoid functions, and each transcription factor only regulates one gene at each of its thresholds. Under these assumptions, the simulator derives sound predictions of the nonlinear and temporal multiscale dynamics of a gene regulatory network from an initial state and parameter space, symbolically described by inequalities between parameters. Beside its predictive soundness, it outperforms other qualitative simulators as for characterization of trajectories and possible calculation of the probability of occurrence of each behavior when parameters are assigned stochastic values.

Forward Individualized Medicine from Personal Genomes to Interactomes.

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Total Binding Affinity Profiles of Regulatory Regions Predict Transcription Factor Binding and Gene Expression in Human Cells.

Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data.

Evidence and potential clinical significance of changes in gene network interactions in ovarian cancer

The molecular basis of cancer is not merely the consequence of structural and/or regulatory mutations in genes, but additionally to disruptions in networks of regulatory interactions existing among these genes and other components of the genome. Disruptions in network relationships may manifest as the loss, gain or reversal of functionally significant interactive gene relationships in cancer cells. In this study, we first employ an unsupervised (Pearson correlation) approach to quantitatively estimate the overall change in network relationships between precursor (control) ovarian surface epithelial cells and ovarian cancer epithelial cells. We find that ovarian cancer cells display a significant overall reduction in correlated gene network interactions relative to normal precursor cells reflective of an overall loss of regulatory control. We next focus on gene relationships that qualitatively change between normal and cancer samples. We find that biological processes significantly over represented among differentially expressed genes are substantially different from those associated with genes involved in qualitatively disrupted network interactions. Our findings provide novel insights into the processes underlying ovarian cancer and identify a potential new class of genes for targeted therapy.

MALAT1-miR-124-RBG2 axis is involved in growth and invasion of HR-HPV-positive cervical cancer cells.

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT 1) is a large, infrequently spliced non-coding RNA aberrantly expressed in cervical cancer. But the molecular mechanisms of its oncogenic role are still not quite clear. The present study explored whether there is a competing endogenous RNAs (ceRNAs) mechanism involved in the oncogenic effect of MALAT1. MALAT1 expression was firstly verified in high-risk human papillomavirus (HR-HPV)-positive tumor tissues and cell lines. Its regulation over miR-124 and the downstream target of miR-124 in regulation of growth, invasion, and apoptosis of the cancer cells are also studied. Findings of this study confirmed higher MALAT1 expression in HR-HPV (+) cervical cancer. Knockdown of endogenous MALAT1 significantly reduced cell growth rate and invasion and increased cell apoptosis of Hela and siHa cells. Besides, knockdown of MALAT1 increased the expression of miRNA-124, while ectopic expression of miR-124 decreased MALAT1 expression. In addition, we also verified a direct interaction between miR-124 and 3'UTR of GRB2. MALAT1 can indirectly modulate GRB2 expression via competing miR-124. Knockdown of GRB2 reduced cell invasion and increased cell apoptosis. In conclusion, MALAT1 can promote HR-HPV (+) cancer cell growth and invasion at least partially through the MALAT1-miR-124-RBG2 axis. This finding might provide some useful evidence about the lncRNA interaction regulatory network in tumorigenesis cervical cancer.

Transcription Factor Activity Mapping of a Tissue-Specific In Vivo Gene Regulatory Network

A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Thus, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of 19 fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs. This GRN shows only modest overlap with physical interactions, indicating that many regulatory interactions are indirect. We applied nested effects modeling to uncover information flow between TFs in the intestine that converges on a small set of physical TF-promoter interactions. We found numerous cell nonautonomous regulatory interactions, illustrating tissue-to-tissue communication. Altogether, our study illuminates the complexity of gene regulation in the context of a living animal.

Character trees from transcriptome data: Origin and individuation of morphological characters and the so-called "species signal".

We elaborate a framework for investigating the evolutionary history of morphological characters. We argue that morphological character trees generated by phylogenetic analysis of transcriptomes provide a useful tool for identifying causal gene expression differences underlying the development and evolution of morphological characters. They also enable rigorous testing of different models of morphological character evolution and origination, including the hypothesis that characters originate via divergence of repeated ancestral characters. Finally, morphological character trees provide evidence that character transcriptomes undergo concerted evolution. We argue that concerted evolution of transcriptomes can explain the so-called "species signal" found in several recent comparative transcriptome studies. The species signal is the phenomenon that transcriptomes cluster by species rather than character type, even though the characters are older than the respective species. We suggest the species signal is a natural consequence of concerted gene expression evolution resulting from mutations that alter gene regulatory network interactions shared by the characters under comparison. Thus, character trees generated from transcriptomes allow us to investigate the variational independence, or individuation, of morphological characters at the level of genetic programs.

Integrative network-based approach identifies key genetic elements in breast invasive carcinoma.

BackgroundBreast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.ResultsOur results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.ConclusionThe integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

Yeast protein-protein interaction network model based on biological experimental data

Duplication and divergence have been widely recognized as the two dominant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction (PPI) networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topological properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experimental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.

Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development.

Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.

Tightly regulated expression of Autographa californica multicapsid nucleopolyhedrovirus immediate early genes emerges from their interactions and possible collective behaviors.

To infect their hosts, DNA viruses must successfully initiate the expression of viral genes that control subsequent viral gene expression and manipulate the host environment. Viral genes that are immediately expressed upon infection play critical roles in the early infection process. In this study, we investigated the expression and regulation of five canonical regulatory immediate-early (IE) genes of Autographa californica multicapsid nucleopolyhedrovirus: ie0, ie1, ie2, me53, and pe38. A systematic transient gene-expression analysis revealed that these IE genes are generally transactivators, suggesting the existence of a highly interactive regulatory network. A genetic analysis using gene knockout viruses demonstrated that the expression of these IE genes was tolerant to the single deletions of activator IE genes in the early stage of infection. A network graph analysis on the regulatory relationships observed in the transient expression analysis suggested that the robustness of IE gene expression is due to the organization of the IE gene regulatory network and how each IE gene is activated. However, some regulatory relationships detected by the genetic analysis were contradictory to those observed in the transient expression analysis, especially for IE0-mediated regulation. Statistical modeling, combined with genetic analysis using knockout alleles for ie0 and ie1, showed that the repressor function of ie0 was due to the interaction between ie0 and ie1, not ie0 itself. Taken together, these systematic approaches provided insight into the topology and nature of the IE gene regulatory network.

Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways.

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

Elucidation of regulatory interaction networks underlying human prostate adenocarcinoma

The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to hormone refractory prostate cancer that is difficult to treat and needs newer interventions that are more effective. The objective of this study was to determine functionally-relevant biological networks, to appreciate the potential crosstalk between signaling members, and to identify biomarker signatures in prostate cancer. We used microarray analyses to identify key genes that were upregulated or down regulated at least five-fold in human prostate cancer and constructed canonical interaction networks that are important in prostate cancer through metabolomics analyses. Our prostate cancer network architecture revealed several key biomarkers including ERK1/2, JNK, p38, MEK, PI3 K, NFκB, AP-1, 14-3-3, VEGF, PDGF, Rb, WNT8 A, WNT10 A, CD44, ESR2, FSH and LH. Furthermore, the top ten transcription factors identified by TFBS-association signature analysis in the regulatory elements of co-regulated biomarkers were delineated, which may crosstalk with upstream or downstream genes elicited in our network architecture. Taken together, our results demonstrate that the regulatory interaction networks in prostate cancer provide a universal view of crosstalk between important biomarkers, i.e., key players in the pathogenesis of this disease. This will facilitate more rapid screening of functional biomarkers in early/intermediate drug discovery.

Small RNA sequencing identifies miRNA roles in ovule and fibre development.

MicroRNAs (miRNAs) have been found to be differentially expressed during cotton fibre development. However, which specific miRNAs and how they are involved in fibre development is unclear. Here, using deep sequencing, 65 conserved miRNA families were identified and 32 families were differentially expressed between leaf and ovule. At least 40 miRNAs were either leaf or ovule specific, whereas 62 miRNAs were shared in both leaf and ovule. qRT-PCR confirmed these miRNAs were differentially expressed during fibre early development. A total of 820 genes were potentially targeted by the identified miRNAs, whose functions are involved in a series of biological processes including fibre development, metabolism and signal transduction. Many predicted miRNA-target pairs were subsequently validated by degradome sequencing analysis. GO and KEGG analyses showed that the identified miRNAs and their targets were classified to 1027 GO terms including 568 biological processes, 324 molecular functions and 135 cellular components and were enriched to 78 KEGG pathways. At least seven unique miRNAs participate in trichome regulatory interactionnetwork. Eleven trans-acting siRNA (tasiRNA) candidate genes were also identified in cotton. One has never been found in other plant species and two of them were derived fromMYB and ARF, both of which play important roles in cotton fibre development. Sixteen genes were predicted to be tasiRNA targets, including sucrose synthase and MYB2. Together, this study discovered new miRNAs in cotton and offered evidences that miRNAs play important roles in cotton ovule/fibre development. The identification of tasiRNA genes and their targets broadens our understanding of the complicated regulatory mechanism of miRNAs in cotton.

SATRAT: Staphylococcus aureus transcript regulatory network analysis tool.

Staphylococcus aureus is a commensal organism that primarily colonizes the nose of healthy individuals. S. aureus causes a spectrum of infections that range from skin and soft-tissue infections to fatal invasive diseases. S. aureus uses a large number of virulence factors that are regulated in a coordinated fashion. The complex regulatory mechanisms have been investigated in numerous high-throughput experiments. Access to this data is critical to studying this pathogen. Previously, we developed a compilation of microarray experimental data to enable researchers to search, browse, compare, and contrast transcript profiles. We have substantially updated this database and have built a novel exploratory tool—SATRAT—the S. aureus transcript regulatory network analysis tool, based on the updated database. This tool is capable of performing deep searches using a query and generating an interactive regulatory network based on associations among the regulators of any query gene. We believe this integrated regulatory network analysis tool would help researchers explore the missing links and identify novel pathways that regulate virulence in S. aureus. Also, the data model and the network generation code used to build this resource is open sourced, enabling researchers to build similar resources for other bacterial systems.

K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores.

BackgroundNetwork biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals.ResultsThe domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains.ConclusionMutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.Electronic supplementary materialThe online version of this article (doi:10.1186/s13336-015-0016-6) contains supplementary material, which is available to authorized users.

Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability

Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.