Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks.
ABSTRACT: Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (?20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise.
Project description:Feed?forward loops (FFLs) are three?gene modules that exert significant effects on a series of biological processes and carcinogenesis development. MicroRNA?associated FFLs (miR?FFLs) represent a new era in disease research. However, analysis of the miR?FFL network motifs has yet to be systematically performed, and their potential role in cardiac hypertrophy and acute myocardial infarction (AMI) requires investigation. The present study used a computational method to establish a comprehensive miR?FFL network for cardiac hypertrophy and AMI, by integrating high?throughput data from different sources and performing multi?aspect analysis of the network features. Several heart disease?associated miR?FFL motifs were identified that were specific or common to the two diseases investigated. Functional analysis further revealed that miR?FFL motifs provided specific drug targets for the clinical treatment of cardiac hypertrophy and AMI. Associations between specific drugs associated with heart disease and dysregulated FFLs were also identified. The present study highlighted the components of FFL motifs in cardiac hypertrophy and AMI, and revealed their possibility as heart disease biomarkers and novel treatment targets.
Project description:Long non-coding RNAs (lncRNAs), transcription factors and microRNAs can form lncRNA-mediated feed-forward loops (L-FFLs), which are functional network motifs that regulate a wide range of biological processes, such as development and carcinogenesis. However, L-FFL network motifs have not been systematically identified, and their roles in human cancers are largely unknown. In this study, we computationally integrated data from multiple sources to construct a global L-FFL network for six types of human cancer and characterized the topological features of the network. Our approach revealed several dysregulated L-FFL motifs common across different cancers or specific to particular cancers. We also found that L-FFL motifs can take part in other types of regulatory networks, such as mRNA-mediated FFLs and ceRNA networks, and form the more complex networks in human cancers. In addition, survival analyses further indicated that L-FFL motifs could potentially serve as prognostic biomarkers. Collectively, this study elucidated the roles of L-FFL motifs in human cancers, which could be beneficial for understanding cancer pathogenesis and treatment.
Project description:Gestational diabetes mellitus (GDM) is a condition associated with the onset of abnormal glucose tolerance during pregnancy. Long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and genes can form lncRNA-mediated feedforward loops (lnc-FFLs), which are functional network motifs that regulate a wide range of biological processes and diseases. However, lnc-FFL network motifs have not been systematically investigated in GDM, and their role in the disease remains largely unknown. In the present study, a global lnc-FFL network was constructed and analyzed. Glycometabolism- and hormone-related lnc-FFL networks were extracted from the global network. An integrated algorithm was designed to identify dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM. The patterns of dysregulated lnc-FFLs in GDM were complex. Moreover, there were strong associations between dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM. Core modules were extracted from the dysregulated lnc-FFL networks in GDM and showed specific and essential functions. In addition, dysregulated lnc-FFLs could combine with ceRNAs and form more complex modules, which could play novel roles in GDM. Notably, we discovered that the dysregulated lnc-FFLs were enriched in the thyroid hormone signaling pathway. Some drug-repurposing candidates, such as hormonal drugs, could be identified based on lnc-FFLs in GDM. In summary, the present study highlighted the effect of dysregulated glycometabolism- and hormone-related lnc-FFLs in GDM and revealed their potential for the discovery of novel biomarkers and therapeutic targets for GDM.
Project description:The combinatorial cross-regulation of transcription factors (TFs) plays an important role in cellular identity and function; however, the dynamic regulation of TFs during glioma progression remains largely unknown. Here, we used the genome-wide expression of TFs to construct an extensive human TF network comprising interactions among 513 TFs and to analyse the dynamics of the TF-TF network during glioma progression. We found that the TF regulatory networks share a common architecture and that the topological structures are conserved. Strikingly, despite the conservation of the network architecture, TF regulatory networks are highly grade specific, and TF circuitry motifs are dynamically rewired during glioma progression. In addition, the most frequently observed structure in the grade-specific TF networks was the feedforward loop (FFL). We described applications that show how investigating the behaviour of FFLs in glioblastoma can reveal FFLs (such as RARG-NR1I2-CDX2) that are associated with prognosis. We constructed comprehensive TF-TF networks and systematically analysed the circuitry, dynamics, and topological principles of the networks during glioma progression, which will further enhance our understanding of the functions of TFs in glioma.
Project description:Engineered systems are often built of recurring circuit modules that carry out key functions. Transcription networks that regulate the responses of living cells were recently found to obey similar principles: they contain several biochemical wiring patterns, termed network motifs, which recur throughout the network. One of these motifs is the feed-forward loop (FFL). The FFL, a three-gene pattern, is composed of two input transcription factors, one of which regulates the other, both jointly regulating a target gene. The FFL has eight possible structural types, because each of the three interactions in the FFL can be activating or repressing. Here, we theoretically analyze the functions of these eight structural types. We find that four of the FFL types, termed incoherent FFLs, act as sign-sensitive accelerators: they speed up the response time of the target gene expression following stimulus steps in one direction (e.g., off to on) but not in the other direction (on to off). The other four types, coherent FFLs, act as sign-sensitive delays. We find that some FFL types appear in transcription network databases much more frequently than others. In some cases, the rare FFL types have reduced functionality (responding to only one of their two input stimuli), which may partially explain why they are selected against. Additional features, such as pulse generation and cooperativity, are discussed. This study defines the function of one of the most significant recurring circuit elements in transcription networks.
Project description:Functionally similar pathways are often seen in biological systems, forming feed-forward controls. The robustness in network motifs such as feed-forward loops (FFLs) has been reported previously. In this work, we studied noise propagation in a development network that has multiple interlinked FFLs. A FFL has the potential of asymmetric noise-filtering (i.e., it works at either the "ON" or the "OFF" state in the target gene). With multiple, interlinked FFLs, we show that the propagated noises are largely filtered regardless of the states in the input genes. The noise-filtering property of an interlinked FFL can be largely derived from that of the individual FFLs, and with interlinked FFLs, it is possible to filter noises in both "ON" and "OFF" states in the output. We demonstrated the noise filtering effect in the developmental regulatory network of Caenorhabditis elegans that controls the timing of distal tip cell (DTC) migration. The roles of positive feedback loops involving blmp-1 and the degradation regulation of DRE-1 also studied. Our analyses allow for better inference from network structures to noise-filtering properties, and provide insights into the mechanisms behind the precise DTC migration controls in space and time.
Project description:Genetic circuits that regulate distinct cellular processes can differ in their wiring pattern of interactions (architecture) and susceptibility to stochastic fluctuations (noise). Whether the link between circuit architecture and noise is of biological importance remains, however, poorly understood. To investigate this problem, we performed a computational study of gene expression noise for all possible circuit architectures of feed-forward loop (FFL) motifs. Results revealed that FFL architectures fall into two categories depending on whether their ON (stimulated) or OFF (unstimulated) steady states exhibit noise. To explore the biological importance of this difference in noise behavior, we analyzed 858 documented FFLs in Escherichia coli that were divided into 39 functional categories. The majority of FFLs were found to regulate two subsets of functional categories. Interestingly, these two functional categories associated with FFLs of opposite noise behaviors. This opposite noise preference revealed two noise-based strategies to cope with environmental constraints where cellular responses are either initiated or terminated stochastically to allow probabilistic sampling of alternative states. FFLs may thus be selected for their architecture-dependent noise behavior, revealing a biological role for noise that is encoded in gene circuit architectures.
Project description:Epigenetic modification can affect many important biological processes, such as cell proliferation and apoptosis. It can alter chromatin conformation and contribute to gene regulation. To investigate how chromatin states associated with network motifs, we assembled chromatin state-modified regulatory networks by combining 269 ChIP-seq data and chromatin states in four cell types. We found that many chromatin states were significantly associated with network motifs, especially for feedforward loops (FFLs). These distinct chromatin state compositions contribute to different expression levels and translational control of targets in FFLs. Strikingly, the chromatin state-modified FFLs were highly cell-specific and, to a large extent, determined cell-selective functions, such as the embryonic stem cell-specific bivalent modification-related FFL with an important role in poising developmentally important genes for expression. Besides, comparisons of chromatin state-modified FFLs between cancerous/stem and primary cell lines revealed specific type of chromatin state alterations that may act together with motif structural changes cooperatively contribute to cell-to-cell functional differences. Combination of these alterations could be helpful in prioritizing candidate genes. Together, this work highlights that a dynamic epigenetic dimension can help network motifs to control cell-specific functions.
Project description:In this work, we describe a computational framework for the genome-wide identification and characterization of mixed transcriptional/post-transcriptional regulatory circuits in humans. We concentrated in particular on feed-forward loops (FFL), in which a master transcription factor regulates a microRNA, and together with it, a set of joint target protein coding genes. The circuits were assembled with a two step procedure. We first constructed separately the transcriptional and post-transcriptional components of the human regulatory network by looking for conserved over-represented motifs in human and mouse promoters, and 3'-UTRs. Then, we combined the two subnetworks looking for mixed feed-forward regulatory interactions, finding a total of 638 putative (merged) FFLs. In order to investigate their biological relevance, we filtered these circuits using three selection criteria: (I) GeneOntology enrichment among the joint targets of the FFL, (II) independent computational evidence for the regulatory interactions of the FFL, extracted from external databases, and (III) relevance of the FFL in cancer. Most of the selected FFLs seem to be involved in various aspects of organism development and differentiation. We finally discuss a few of the most interesting cases in detail.
Project description: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.