Analysis of regulatory network topology reveals functionally distinct classes of microRNAs.
ABSTRACT: MicroRNAs (miRNAs) negatively regulate the expression of target genes at the post-transcriptional level. Little is known about the crosstalk between miRNAs and transcription factors (TFs). Here we provide data suggesting that the interaction patterns between TFs and miRNAs can influence the biological functions of miRNAs. From this global survey, we find that a regulated feedback loop, in which two TFs regulate each other and one miRNA regulates both of the factors, is the most significantly overrepresented network motif. Mathematical modeling shows that the miRNA in this motif stabilizes the feedback loop to resist environmental perturbation, providing one mechanism to explain the robustness of developmental programs that is contributed by miRNAs. Furthermore, on the basis of a network motif profile analysis, we demonstrate the existence of two classes of miRNAs with distinct network topological properties. The first class of miRNAs is regulated by a large number of TFs, whereas the second is regulated by only a few TFs. The differential expression level of the two classes of miRNAs in embryonic developmental stages versus adult tissues suggests that the two classes may have fundamentally different biological functions. Our results demonstrate that the TFs and miRNAs extensively interact with each other and the biological functions of miRNAs may be wired in the regulatory network topology.
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. They play an important role in several biological processes such as cell development and differentiation. Similar to transcription factors (TFs), miRNAs regulate gene expression in a combinatorial fashion, i.e., an individual miRNA can regulate multiple genes, and an individual gene can be regulated by multiple miRNAs. The functions of TFs in biological regulatory networks have been well explored. And, recently, a few studies have explored miRNA functions in the context of gene regulation networks. However, how TFs and miRNAs function together in the gene regulatory network has not yet been examined. In this paper, we propose a new computational method to discover the gene regulatory modules that consist of miRNAs, TFs, and genes regulated by them. We analyzed the regulatory associations among the sets of predicted miRNAs and sets of TFs on the sets of genes regulated by them in the human genome. We found 182 gene regulatory modules of combinatorial regulation by miRNAs and TFs (miR-TF modules). By validating these modules with the Gene Ontology (GO) and the literature, it was found that our method allows us to detect functionally-correlated gene regulatory modules involved in specific biological processes. Moreover, our miR-TF modules provide a global view of coordinated regulation of target genes by miRNAs and TFs.
Project description:Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN formed by TFs, miRNAs, and target genes. To increase the reliability of the proposed network and examine the regulation activity of TFs and miRNAs, we further incorporated the mRNA and miRNA expression profiles in seven cancer types using The Cancer Genome Atlas data. We observed that regulation rewiring was prevalent during tumorigenesis and found that the rewired regulatory feedback loops formed by TFs and miRNAs were highly associated with cancer. Interestingly, we identified one regulatory feedback loop between STAT1 and miR-155-5p that is consistently activated in all seven cancer types with its function to regulate tumor-related biological processes. Our results provide insights on the losing equilibrium of the regulatory feedback loop between STAT1 and miR-155-5p influencing tumorigenesis.
Project description:The NF-?B family of transcription factors regulate the expression of genes encoding proteins and microRNAs (miRNA, miR) precursors that may either positively or negatively regulate a variety of biological processes such as cell cycle progression, cell survival, and cell differentiation. The NF-?B-miRNA transcriptional regulatory network has been implicated in the regulation of proinflammatory, immune, and stress-like responses. Gene regulation by miRNAs has emerged as an additional epigenetic mechanism at the post-transcriptional level. The expression of miRNAs can be regulated by specific transcription factors (TFs), including the NF-?B TF family, and vice versa. The interplay between TFs and miRNAs creates positive or negative feedback loops and also regulatory networks, which can control cell fate. In the current review, we discuss the impact of NF-?B-miRNA interplay and feedback loops and networks impacting on inflammation in cancer. We provide several paradigms of specific NF-?B-miRNA networks that can regulate inflammation linked to cancer. For example, the NF-?B-miR-146 and NF-?B-miR-155 networks fine-tune the activity, intensity, and duration of inflammation, while the NF-?B-miR-21 and NF-?B-miR-181b-1 amplifying loops link inflammation to cancer; and p53- or NF-?B-regulated miRNAs interconnect these pathways and may shift the balance to cancer development or tumor suppression. The availability of genomic data may be useful to verify and find novel interactions, and provide a catalogue of 162 miRNAs targeting and 40 miRNAs possibly regulated by NF-?B. We propose that studying active TF-miRNA transcriptional regulatory networks such as NF-?B-miRNA networks in specific cancer types can contribute to our further understanding of the regulatory interplay between inflammation and cancer, and also perhaps lead to the development of pharmacologically novel therapeutic approaches to combat cancer.
Project description:BACKGROUND: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. RESULTS: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. CONCLUSIONS: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
Project description:miRNAs are proved to have causal roles in tumorgenesis involving various types of human cancers, but the mechanism is not clear. We aimed to explore the effect of miRNAs on the development of ovarian cancer and the underlying mechanism.The miRNA expression profile GSE31801 was downloaded from GEO (Gene Expression Omnibus) database. Firstly, the differentially expressed miRNAs were screened. Target genes of the miRNAs were collected from TargetScan, PicTar, miRanda, and DIANA-microT database, then the miRNA-miRNA co-regulating network was constructed using miRNA pairs with common regulated target genes. Next, the functional modules in the network were studied, the miRNA pairs regulated at least one modules were enriched to form the miRNA functional synergistic network (MFSN).Risk miRNA were selected in MFSN according to the topological structure. Transcript factors (TFs) in MFSN were identified, followed by the miRNA-transcript factor networks construction. Totally, 42 up- and 61 down-regulated differentially expressed miRNAs were identified, of which 68 formed 2292 miRNA pairs in the miRNA-miRNA co-regulating network. GO: 0007268 (synaptic transmission) and GO: 0019226 (transmission of nerve impulse) were the two common functions of miRNAs in MFSN, and hsa-miR-579 (36), hsa-miR-942 (31), hsa-miR-105 (31), hsa-miR-150 (34), and hsa-miR-27a* (32) were selected as the hub nodes in MFSN.In all, 17 TFs, including CREM, ERG, and CREB1 were screened as the cancer related TFs in MFSN. Other TFs, such as BIN1, FOXN3, FOXK1, FOXP2, and ESRRG with high degrees may be inhibited in ovarian cancer. MFSN gave us a new shed light on the mechanism studies in ovarian cancer.
Project description:BACKGROUND: Transcription factors (TFs) and miRNAs are essential for the regulation of gene expression; however, the global view of human gene regulatory networks remains poorly understood. For example, how is the expression of so many genes regulated by limited cohorts of regulators and how are genes differentially expressed in different tissues despite the genetic code being the same in all tissues? RESULTS: We analyzed the network properties of housekeeping and tissue-specific genes in gene regulatory networks from seven human tissues. Our results show that different classes of genes behave quite differently in these networks. Tissue-specific miRNAs show a higher average target number compared with non-tissue specific miRNAs, which indicates that tissue-specific miRNAs tend to regulate different sets of targets. Tissue-specific TFs exhibit higher in-degree, out-degree, cluster coefficient and betweenness values, indicating that they occupy central positions in the regulatory network and that they transfer genetic information from upstream genes to downstream genes more quickly than other TFs. Housekeeping TFs tend to have higher cluster coefficients compared with other genes that are neither housekeeping nor tissue specific, indicating that housekeeping TFs tend to regulate their targets synergistically. Several topological properties of disease-associated miRNAs and genes were found to be significantly different from those of non-disease-associated miRNAs and genes. CONCLUSIONS: Tissue-specific miRNAs, TFs and disease genes have particular topological properties within the transcriptional regulatory networks of the seven human tissues examined. The tendency of tissue-specific miRNAs to regulate different sets of genes shows that a particular tissue-specific miRNA and its target gene set may form a regulatory module to execute particular functions in the process of tissue differentiation. The regulatory patterns of tissue-specific TFs reflect their vital role in regulatory networks and their importance to biological functions in their respective tissues. The topological differences between disease and non-disease genes may aid the discovery of new disease genes or drug targets. Determining the network properties of these regulatory factors will help define the basic principles of human gene regulation and the molecular mechanisms of disease.
Project description:BACKGROUND: microRNAs (miRNAs) are important cellular components. The understanding of their evolution is of critical importance for the understanding of their function. Although some specific evolutionary rules of miRNAs have been revealed, the rules of miRNA evolution in cellular networks remain largely unexplored. According to knowledge from protein-coding genes, the investigations of gene evolution in the context of biological networks often generate valuable observations that cannot be obtained by traditional approaches. RESULTS: Here, we conducted the first systems-level analysis of miRNA evolution in a human transcription factor (TF)-miRNA regulatory network that describes the regulatory relations among TFs, miRNAs, and target genes. We found that the architectural structure of the network provides constraints and functional innovations for miRNA evolution and that miRNAs showed different and even opposite evolutionary patterns from TFs and other protein-coding genes. For example, miRNAs preferentially coevolved with their activators but not with their inhibitors. During transcription, rapidly evolving TFs frequently activated but rarely repressed miRNAs. In addition, conserved miRNAs tended to regulate rapidly evolving targets, and upstream miRNAs evolved more rapidly than downstream miRNAs. CONCLUSIONS: In this study, we performed the first systems level analysis of miRNA evolution. The findings suggest that miRNAs have a unique evolution process and thus may have unique functions and roles in various biological processes and diseases. Additionally, the network presented here is the first TF-miRNA regulatory network, which will be a valuable platform of systems biology.
Project description:Accumulating evidence suggests a role for microRNAs (miRNAs) in regulating various processes of mammalian postnatal development and aging. To investigate the changes in blood-based miRNA expression from preterm infants to adulthood, we compared 365 miRNA expression profiles in a screening set of preterm infants and adults. Approximately one-third of the miRNAs were constantly expressed from postnatal development to adulthood, another one-third were differentially expressed between preterm infants and adults, and the remaining one-third were not detectable in these two groups. Based on their expression in infants and adults, the miRNAs were categorized into five classes, and six of the seven miRNAs chosen from each class except one with age-constant expression were confirmed in a validation set containing infants, children, and adults. Comparing the chromosomal locations of the different miRNA classes revealed two hot spots: the miRNA cluster on 14q32.31 exhibited age-constant expression, and the one on 9q22.21 exhibited up-regulation in adults. Furthermore, six miRNAs detectable in adults were down-regulated in older adults, and four chosen for individual quantification were verified in the validation set. Analysis of the network functions revealed that differentially regulated miRNAs between infants and adults and miRNAs that decreased during aging shared two network functions: inflammatory disease and inflammatory response. Four expression patterns existed in the 11 miRNAs from infancy to adulthood, with a significant transition in ages 9-20 years. Our results provide an overview on the regulation pattern of blood miRNAs throughout life and the possible biological functions performed by different classes of miRNAs.
Project description:BACKGROUND: Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods. RESULTS: In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3'UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes. CONCLUSIONS: We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.
Project description:MicroRNAs (miRNAs), a type of short (21-23 nucleotides), non-coding RNA molecule, mediate repressive gene regulation through RNA silencing at the post-transcriptional level, and play an important role in defense and response to abiotic and biotic stresses. In the present study, Affymetrix® miRNA Array, real-time quantitative PCR (qPCR) for miRNAs and their targets, and miRNA promoter analysis were used to validate the gene expression patterns of miRNAs in Populus trichocarpa plantlets induced with the poplar stem canker pathogen, Botryosphaeria dothidea. Twelve miRNAs (miR156, miR159, miR160, miR164, miR166, miR168, miR172, miR319, miR398, miR408, miR1448, and miR1450) were upregulated in the stem bark of P. trichocarpa, but no downregulated miRNAs were found. Based on analysis of the miRNAs and their targets, a potential co-regulatory network was developed to describe post-transcriptional regulation in the pathological development of poplar stem canker. There was highly complex cross-talk between diverse miRNA pathway responses to biotic and abiotic stresses. The results suggest that miR156 is probably an integral component of the miRNA response to all environmental stresses in plants. Cis-regulatory elements were binding sites for the transcription factors (TFs) on DNA. Promoter analysis revealed that TC-rich repeats and a W1-box motif were both tightly related disease response motifs in Populus. Promoter analysis and target analysis of miRNAs also revealed that some TFs regulate their activation/repression. Furthermore, a feedback regulatory network in the pathological development of poplar stem canker is provided. The results confirm that miRNA pathways regulate gene expression during the pathological development of plant disease, and provide new insights into understanding the onset and development of poplar stem canker.