MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at www.mirz.unibas.ch. The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapiens.
Recently, microRNAs (miRNAs) have taken centre stage in the field of human molecular oncology. Several studies have shown that miRNA profiling analyses offer new possibilities in cancer classification, diagnosis and prognosis. However, the function of miRNAs that are dysregulated in tumours remains largely a mystery. Global analysis of miRNA-target gene expression has helped illuminate the role of miRNAs in developmental gene expression programs, but such an approach has not been reported in cancer transcriptomics.
In this study, we globally analysed the expression patterns of miRNA target genes in prostate cancer by using several public microarray datasets. Intriguingly, we found that, in contrast to global mRNA transcript levels, putative miRNA targets showed a reduced abundance in prostate tumours relative to benign prostate tissue. Additionally, the down-regulation of these miRNA targets positively correlated with the number of types of miRNA target-sites in the 3' untranslated regions of these targets. Further investigation revealed that the globally low expression was mainly driven by the targets of 36 specific miRNAs that were reported to be up-regulated in prostate cancer by a miRNA expression profiling study. We also found that the transcript levels of miRNA targets were lower in androgen-independent prostate cancer than in androgen-dependent prostate cancer. Moreover, when the global analysis was extended to four other cancers, significant differences in transcript levels between miRNA targets and total mRNA backgrounds were found.
Global gene expression analysis, along with further investigation, suggests that miRNA targets have a significantly reduced transcript abundance in prostate cancer, when compared with the combined pool of all mRNAs. The abnormal expression pattern of miRNA targets in human cancer could be a common feature of the human cancer transcriptome. Our study may help to shed new light on the functional roles of miRNAs in cancer transcriptomics.
MicroRNAs (miRNAs) are a class of endogenous, small and highly conserved noncoding RNAs that control gene expression either by degradation of target mRNAs or by inhibition of protein translation. They play important roles in cancer progression. A single miRNA can provoke a chain reaction and further affect protein interaction network (PIN). Therefore, we developed a novel integrative approach to identify the functional roles and the regulated PIN of oncomirs.
We integrated the expression profiles of miRNA and mRNA with the human PIN to reveal miRNA-regulated PIN in specific biological conditions. The potential functions of miRNAs were determined by functional enrichment analysis and the activities of miRNA-regulated PINs were evaluated by the co-expression of protein-protein interactions (PPIs). The function of a specific miRNA, miR-148a, was further examined by clinical data analysis and cell-based experiments. We uncovered several miRNA-regulated networks which were enriched with functions related to cancer progression. One miRNA, miR-148a, was identified and its function is to decrease tumor proliferation and metastasis through its regulated PIN. Furthermore, we found that miR-148a could reduce the invasiveness, migratory and adhesive activities of gastric tumor cells. Most importantly, elevated miR-148a level in gastric cancer tissues was strongly correlated with distant metastasis, organ and peritoneal invasion and reduced survival rate.
This study provides a novel method to identify active oncomirs and their potential functions in gastric cancer progression. The present data suggest that miR-148a could be a potential prognostic biomarker of gastric cancer and function as a tumor suppressor through repressing the activity of its regulated PIN.
MicroRNAs (miRNAs) are key post-transcriptional regulators that inhibit gene expression by promoting mRNA decay and/or suppressing translation. However, the relative contributions of these two mechanisms to gene repression remain controversial. Early studies favor a translational repression-centric scenario, whereas recent large-scale studies suggest a dominant role of mRNA decay in miRNA regulation. Here we generated proteomics data for nine colorectal cancer cell lines and integrated them with matched miRNA and mRNA expression data to infer and characterize miRNA-mediated regulation. Consistent with previous reports, we found that 8mer site, site positioning within 3′UTR, local AU-rich context, and additional 3′ pairing could all help boost miRNA-mediated mRNA decay. However, these sequence features were generally not correlated with increased translational repression, except for local AU-rich context. Thus the contribution of translational repression might be underestimated in recent studies in which the analyses were based primarily on the response of genes with canonical 7–8 mer sites in 3′UTRs. Indeed, we found that translational repression was involved in more than half, and played a major role in one-third of all predicted miRNA-target interactions. It was even the predominant contributor to miR-138 mediated regulation, which was further supported by the observation that differential expression of miR-138 in two genetically matched cell lines corresponded to altered protein but not mRNA abundance of most target genes. In addition, our study also provided interesting insights into colon cancer biology such as the possible contributions of miR-138 and miR-141/miR-200c in inducing specific phenotypes of SW480 and RKO cell lines, respectively.
MicroRNA (miRNA) is an endogenous non-protein coding small RNA molecule that negatively regulates gene expression by the degradation of messenger RNA (mRNA) or the suppression of mRNA translation. miRNA plays important roles in physiologic processes such as cellular development, differentiation, proliferation, apoptosis, and stem cell self-renewal. Studies show that deregulation of miRNA expression is closely associated with tumorigenicity, invasion, and metastasis. The functionality of aberrant miRNAs in cancer could act either as oncogenes or tumor suppressors during tumor initiation and progression. Similar to protein-coding gene regulation, dysregulation of miRNAs may be related to changes in miRNA gene copy numbers, epigenetic modulation, polymorphisms, or biogenesis modifications. Elucidation of the miRNA expression profiles (miRNomes) of many types of cancers is starting to decode the regulatory network of miRNA-mRNA interactions from a systems biology perspective. Experimental evidence demonstrates that modulation of specific miRNA alterations in cancer cells using miRNA replacement or anti-miRNA technologies can restore miRNA activities and repair gene regulatory networks affecting apoptotic signaling pathways or drug sensitivity, and improve the outcome of treatment. Numerous animal studies for miRNA-based therapy offer the hope of targeting miRNAs as an alternative cancer treatment. Developing the small molecules to interfere with miRNAs could be of great pharmaceutical interest in the future.
Consistent with the role of microRNAs (miRNAs) in down-regulating gene expression by reducing translation and/or stability of target mRNAs1, the levels of specific miRNAs are important for correct embryonic development and have been linked to several forms of cancer2-4. However, the regulatory mechanisms by which primary miRNAs (pri-miRNAs) are processed first to precursor miRNAs (pre-miRNAs) and then to mature miRNAs by the multiprotein Drosha and Dicer complexes5-8, respectively, remain largely unknown. The KH-type splicing regulatory protein (KSRP) interacts with single strand AU-rich elements (ARE)-containing mRNAs and is a key mediator of mRNA decay9,10. Here, we show that KSRP also serves as a component of both Drosha and Dicer complexes and regulates the biogenesis of a subset of miRNAs. KSRP binds with high affinity to the terminal loop (TL) of the target miRNA precursors and promotes their maturation. This mechanism is required for specific changes in target mRNA expression that affects specific biological programs, including proliferation, apoptosis and differentiation. These findings reveal an unexpected mechanism that links KSRP to the machinery regulating maturation of a cohort of miRNAs, that, in addition to its role in promoting mRNA decay, independently serves to integrate specific regulatory programs of protein expression.
Cancer initiation and progression involve microRNAs that can function like tumor suppressors and oncogenes. The functional significance of most miRNAs is currently unknown. To determine systematically which microRNAs are essential for glioma growth, we screened a precursor microRNA library in three human glioblastoma and one astroglial cell line model systems. The most prominent and consistent cell proliferation–reducing hits were validated in secondary screening with an additional apoptosis endpoint. The functional screening data were integrated in the miRNA expression data to find underexpressed true functional tumor suppressor miRNAs. In addition, we used miRNA-target gene predictions and combined siRNA functional screening data to find the most probable miRNA-target gene pairs with a similar functional effect on proliferation. Nine novel functional miRNAs (hsa-miR-129, -136, -145, -155, -181b, -342-5p, -342-3p, -376a/b) in GBM cell lines were validated for their importance in glioma cell growth, and similar effects for six target genes (ROCK1, RHOA, MET, CSF1R, EIF2AK1, FGF7) of these miRNAs were shown functionally. The clinical significance of the functional hits was validated in miRNA expression data from the TCGA glioblastoma multiforme (GBM) tumor cohort. Five tumor suppressor miRNAs (hsa-miR-136, -145, -342, -129, -376a) showed significant underexpression in clinical GBM tumor samples from the TCGA GBM cohort further supporting the role of these miRNAs in vivo. Most importantly, higher hsa-miR-145 expression in GBM tumors yielded significantly better survival (p<0.005) in a subset of patients thus validating it as a genuine tumor suppressor miRNA. This systematic functional profiling provides important new knowledge about functionally relevant miRNAs in GBM biology and may offer new targets for treating glioma.
A genome wide analysis of factors affecting repression of mRNAs by microRNAs reveals roles for 3'UTR length, the number of target sites on the mRNA and the distance between pairs of binding sites.
MicroRNAs (miRNAs) are small noncoding RNAs that bind mRNA target transcripts and repress gene expression. They have been implicated in multiple diseases, such as cancer, but the mechanisms of this involvement are not well understood. Given the complexity and degree of interactions between miRNAs and target genes, understanding how miRNAs achieve their specificity is important to understanding miRNA function and identifying their role in disease.
Here we report factors that influence miRNA regulation by considering the effects of both single and multiple miRNAs targeting human genes. In the case of single miRNA targeting, we developed a metric that integrates miRNA and mRNA expression data to calculate how changes in miRNA expression affect target mRNA expression. Using the metric, our global analysis shows that the repression of a given miRNA on a target mRNA is modulated by 3' untranslated region length, the number of target sites, and the distance between a pair of binding sites. Additionally, we show that some miRNAs preferentially repress transcripts with longer CTG repeats, suggesting a possible role for miRNAs in repeat expansion disorders such as myotonic dystrophy. We also examine the large class of genes targeted by multiple miRNAs and show that specific types of genes are progressively more enriched as the number of targeting miRNAs increases. Expression microarray data further show that these highly targeted genes are downregulated relative to genes targeted by few miRNAs, which suggests that highly targeted genes are tightly regulated and that their dysregulation may lead to disease. In support of this idea, cancer genes are strongly enriched among highly targeted genes.
Our data show that the rules governing miRNA targeting are complex, but that understanding the mechanisms that drive such control can uncover miRNAs' role in disease. Our study suggests that the number and arrangement of miRNA recognition sites can influence the degree and specificity of miRNA-mediated gene repression.
MicroRNAs (miRNAs) are a class of endogenous non-coding small RNAs that are evolutionarily conserved and widely distributed among species. Their major function is to negatively regulate mRNA target genes, and miRNA expression has been found to be deregulated in all human cancers, where miRNAs play critical roles in tumorigenesis, functioning either as tumor suppressors or as oncogenes. This review provides a current overview of the connection between miRNAs and cancer by covering the recent advances in miRNA involvement in human cancer including initiation, growth, invasion, and metastasis. We will also highlight the literature where application of miRNAs has created the foundation for the development of potential future miRNA therapy.
Cancer; microRNAs; non-coding RNAs
Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes.
We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits.
Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.
miRNA-mRNA interaction networks; Hypergraph-based model; Higher-order gene modules; Evolutionary learning; Cancer genomics data analysis
MicroRNAs (miRNAs) are key regulators of gene expression. In this study, we explored whether altered miRNA expression has a prominent role in defining the inflammatory breast cancer (IBC) phenotype.
We used quantitative PCR technology to evaluate the expression of 384 miRNAs in 20 IBC and 50 non-IBC samples. To gain understanding on the biological functions deregulated by aberrant miRNA expression, we looked for direct miRNA targets by performing pair-wise correlation coefficient analysis on expression levels of 10 962 messenger RNAs (mRNAs) and by comparing these results with predicted miRNA targets from TargetScan5.1.
We identified 13 miRNAs for which expression levels were able to correctly predict the nature of the sample analysed (IBC vs non-IBC). For these miRNAs, we detected a total of 17 295 correlated miRNA–mRNA pairs, of which 7012 and 10 283 pairs showed negative and positive correlations, respectively. For four miRNAs (miR-29a, miR-30b, miR-342-3p and miR-520a-5p), correlated genes were concordant with predicted targets. A gene set enrichment analysis on these genes demonstrated significant enrichment in biological processes related to cell proliferation and signal transduction.
This study represents, to the best of our knowledge, the first integrated analysis of miRNA and mRNA expression in IBC. We identified a set of 13 miRNAs of which expression differed between IBC and non-IBC, making these miRNAs candidate markers for the IBC subtype.
microRNA; inflammatory breast cancer; dicer; ago2; mRNA
MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.
We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.
A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.
In few years our understanding of microRNA (miRNA) biogenesis, molecular mechanisms by which miRNAs regulate gene expression, and the functional roles of miRNAs has been expanded. Interestingly, numerous miRNAs are expressed in a spatially and temporally controlled manner in the nervous system, suggesting that their posttrascriptional regulation may be particularly relevant in neural development and function. MiRNA studies in neurobiology showed their involvement in synaptic plasticity and brain diseases. In this review ,correlations between miRNA-mediated gene silencing and Alzheimer's, Parkinson's, and other neurodegenerative diseases will be discussed. Molecular and cellular neurobiological studies of the miRNAs in neurodegeneration represent the exploration of a new Frontier of miRNAs biology and the potential development of new diagnostic tests and genetic therapies for neurodegenerative diseases.
microRNAs (miRNAs) are small RNAs shown to contribute to a number of cellular processes including cell growth, differentiation, and apoptosis. MiRNAs regulate gene expression of their targets post-transcriptionally by binding to messenger RNA (mRNA), causing translational inhibition or mRNA degradation. Dysregulation of miRNA expression can promote cancer formation and progression. Research has largely focused on the function and expression of single miRNAs. However, complex physiological processes require the interaction, regulation and coordination of many molecules including miRNAs and proteins. Highly connected molecules often serve important roles in the cell. A protein–protein interaction network of established miRNA targets confirmed these proteins to be highly connected and essential to the cell, affecting tumorigenesis, cell growth/proliferation, cellular death, cell assembly, and maintenance pathways. This analysis showed that miRNAs contribute to the overall health of the prostate, and their aberrant expression destabilized homeostatic balance. This integrative network approach can reveal important miRNAs and proteins in prostate cancer that will be useful to identify specific disease biomarkers, which may be used as targets for therapeutics or drugs in themselves.
MicroRNAs (miRNAs) are a class of 20–24 nt non-coding RNAs that regulate gene expression primarily through post-transcriptional repression or mRNA degradation in a sequence-specific manner. The roles of miRNAs are just beginning to be understood, but the study of miRNA function has been limited by poor understanding of the general principles of gene regulation by miRNAs. Here we used CNE cells from a human nasopharyngeal carcinoma cell line as a cellular system to investigate miRNA-directed regulation of VEGF and other angiogenic factors under hypoxia, and to explore the principles of gene regulation by miRNAs. Through computational analysis, 96 miRNAs were predicted as putative regulators of VEGF. But when we analyzed the miRNA expression profile of CNE and four other VEGF-expressing cell lines, we found that only some of these miRNAs could be involved in VEGF regulation, and that VEGF may be regulated by different miRNAs that were differentially chosen from 96 putative regulatory miRNAs of VEGF in different cells. Some of these miRNAs also co-regulate other angiogenic factors (differential regulation and co-regulation principle). We also found that VEGF was regulated by multiple miRNAs using different combinations, including both coordinate and competitive interactions. The coordinate principle states that miRNAs with independent binding sites in a gene can produce coordinate action to increase the repressive effect of miRNAs on this gene. By contrast, the competitive principle states when multiple miRNAs compete with each other for a common binding site, or when a functional miRNA competes with a false positive miRNA for the same binding site, the repressive effects of miRNAs may be decreased. Through the competitive principle, false positive miRNAs, which cannot directly repress gene expression, can sometimes play a role in miRNA-mediated gene regulation. The competitive principle, differential regulation, multi-miRNA binding sites, and false positive miRNAs might be useful strategies in the avoidance of unwanted cross-action among genes targeted by miRNAs with multiple targets.
MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal.
During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells.
Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.
MicroRNAs (miRNAs) have been recognized as significantly involved in prostate cancer (PCa). Since androgen receptor (AR) plays a central role in PCa carcinogenesis and progression, it is imperative to systematically elucidate the causal association between AR and miRNAs, focusing on the molecular mechanisms by which miRNAs mediate AR signalling. In this study, we performed a series of time-course microarrays to observe the dynamic genome-wide expressions of mRNAs and miRNAs in parallel in hormone-sensitive prostate cancer LNCaP cells stimulated by androgen. Accordingly, we introduced Response Score to identify AR target miRNAs, as well as Modulation Score to identify miRNA target mRNAs. Based on theoretical identification and experimental validation, novel mechanisms addressing cell viability in PCa were unravelled for 3 miRNAs newly recognized as AR targets. (1) miR-19a is directly up-regulated by AR, and represses SUZ12, RAB13, SC4MOL, PSAP and ABCA1, respectively. (2) miR-27a is directly up-regulated by AR, and represses ABCA1 and PDS5B. (3) miR-133b is directly up-regulated by AR, and represses CDC2L5, PTPRK, RB1CC1, and CPNE3, respectively. Moreover, we found miR-133b is essential to PCa cell survival. Our study gives certain clues on miRNAs mediated AR signalling to cell viability by influencing critical pathways, especially by breaking through androgen’s growth restriction effect on normal prostate tissue.
MicroRNAs (miRNAs) have rapidly emerged as biologically important mediators of posttranscriptional and epigenetic regulation in both plants and animals. miRNAs function through a variety of mechanisms including mRNA degradation and translational repression; additionally, miRNAs may guide gene expression by serving as transcription factors. miRNAs are highly expressed in human brain. Tissue and cell type-specific enrichments of certain miRNAs within the nervous system argue for a biological significance during neurodevelopmental stages. On the other hand, a large number of studies have reported links between alterations of miRNA homeostasis and pathologic conditions such as cancer, heart diseases, and neurodegeneration. Thus, profiles of distinct or aberrant miRNA signatures have most recently surged as one of the most fascinating interests in current biology. Here, the most recent insights into the involvement of miRNAs in the biology of the nervous system and the occurrence of neuropathological disorders are reviewed and discussed.
Despite a large amount of microRNAs (miRNAs) have been validated to play crucial roles in human biology and disease, there is little systematic insight into the nature and scale of the potential synergistic interactions executed by miRNAs themselves. Here we established an integrated parameter synergy score to determine miRNA synergy, by combining the two mechanisms for miRNA-miRNA interactions, miRNA-mediated gene co-regulation and functional association between target gene products, into one single parameter. Receiver operating characteristic (ROC) analysis indicated that synergy score accurately identified the gene ontology-defined miRNA synergy (AUC = 0.9415, p<0.001). Only a very small portion of the random miRNA-miRNA combinations generated potent synergy, implying poor expectancy of widespread synergy. However, targeting more key genes made two miRNAs more likely to act synergistically. Compared to other miRNAs, miR-21 was a highly exceptional case due to frequent appearance in the top synergistic miRNA pairs. This result highlighted its essential role in coordinating or strengthening physiological and pathological functions of other miRNAs. The synergistic effect of miR-21 and miR-1 were functionally validated for their significant influences on myocardial apoptosis, cardiac hypertrophy and fibrosis. The novel approach established in this study enables easy and effective identification of condition-restricted potent miRNA synergy simply by concentrating the available protein interactomics and miRNA-target interaction data into a single parameter synergy score. Our results may be important for understanding synergistic gene regulation by miRNAs and may have significant implications for miRNA combination therapy of cardiovascular disease.
Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. MiRNAs were shown to play an important role in development and disease, and accurately determining the networks regulated by these miRNAs in a specific condition is of great interest. Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions.
Results: We developed the Protein Interaction-based MicroRNA Modules (PIMiM), a regression-based probabilistic method that integrates sequence, expression and interaction data to identify modules of mRNAs controlled by small sets of miRNAs. We formulate an optimization problem and develop a learning framework to determine the module regulation and membership. Applying PIMiM to cancer data, we show that by adding protein interaction data and modeling cooperative regulation of mRNAs by a small number of miRNAs, PIMiM can accurately identify both miRNA and their targets improving on previous methods. We next used PIMiM to jointly analyze a number of different types of cancers and identified both common and cancer-type-specific miRNA regulators.
Supplementary data are available at Bioinformatics online.
MicroRNAs (miRNAs), a growing class of small RNAs with crucial regulatory roles at the post-transcriptional level, are usually found to be clustered on chromosomes. However, with the exception of a few individual cases, so far little is known about the functional consequence of this conserved clustering of miRNA loci. In animal genomes such clusters often contain non-homologous miRNA genes. One hypothesis to explain this heterogeneity suggests that clustered miRNAs are functionally related by virtue of co-targeting downstream pathways.
Integrating of miRNA cluster information with protein protein interaction (PPI) network data, our research supports the hypothesis of the functional coordination of clustered miRNAs and links it to the topological features of miRNAs' targets in PPI network. Specifically, our results demonstrate that clustered miRNAs jointly regulate proteins in close proximity of the PPI network. The possibility that two proteins yield to this coordinated regulation is negatively correlated with their distance in PPI network. Guided by the knowledge of this preference, we found several network communities enriched with target genes of miRNA clusters. In addition, our results demonstrate that the variance of this propensity can also partly be explained by protein's connectivity and miRNA's conservation.
In summary, this work supports the hypothesis of intra-cluster coordination and investigates the extent of this coordination.
The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.
microRNA; feature selection; bootstrap error; support vector machine
MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, miRFam, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes.
An existing miRNA family system prepared by miRBase was downloaded online. We first employed n-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%.
Based on experimental results, we argue that miRFam is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information.
The source code of miRFam, written in C++, is freely and publicly available at: http://admis.fudan.edu.cn/projects/miRFam.htm.
microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs.
We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future.
This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.
Microbiota are known to modulate host gene expression, yet the underlying molecular mechanisms remain elusive. MicroRNAs (miRNAs) are importantly implicated in many cellular functions by post-transcriptionally regulating gene expression via binding to the 3′-untranslated regions (3′-UTRs) of the target mRNAs. However, a role for miRNAs in microbiota-host interactions remains unknown. Here we investigated if miRNAs are involved in microbiota-mediated regulation of host gene expression. Germ-free mice were colonized with the microbiota from pathogen-free mice. Comparative profiling of miRNA expression using miRNA arrays revealed one and eight miRNAs that were differently expressed in the ileum and the colon, respectively, of colonized mice relative to germ-free mice. A computational approach was then employed to predict genes that were potentially targeted by the dysregulated miRNAs during colonization. Overlapping the miRNA potential targets with the microbiota-induced dysregulated genes detected by a DNA microarray performed in parallel revealed several host genes that were regulated by miRNAs in response to colonization. Among them, Abcc3 was identified as a highly potential miRNA target during colonization. Using the murine macrophage RAW 264.7 cell line, we demonstrated that mmu-miR-665, which was dysregulated during colonization, down-regulated Abcc3 expression by directly targeting the Abcc3 3′-UTR. In conclusion, our study demonstrates that microbiota modulate host microRNA expression, which could in turn regulate host gene expression.