miRNA-mRNA Regulatory Modules Research Articles

The miRNA-mRNA Regulatory Modules of Pinus massoniana Lamb. in Response to Drought Stress.

Masson pine (Pinus massoniana Lamb.) is a major fast-growing woody tree species and pioneer species for afforestation in barren sites in southern China. However, the regulatory mechanism of gene expression in P. massoniana under drought remains unclear. To uncover candidate microRNAs, their expression profiles, and microRNA-mRNA interactions, small RNA-seq was used to investigate the transcriptome from seedling roots under drought and rewatering in P. massoniana. A total of 421 plant microRNAs were identified. Pairwise differential expression analysis between treatment and control groups unveiled 134, 156, and 96 differential expressed microRNAs at three stages. These constitute 248 unique microRNAs, which were subsequently categorized into six clusters based on their expression profiles. Degradome sequencing revealed that these 248 differentially expressed microRNAs targeted 2069 genes. Gene Ontology enrichment analysis suggested that these target genes were related to translational and posttranslational regulation, cell wall modification, and reactive oxygen species scavenging. miRNAs such as miR482, miR398, miR11571, miR396, miR166, miRN88, and miRN74, along with their target genes annotated as F-box/kelch-repeat protein, 60S ribosomal protein, copper-zinc superoxide dismutase, luminal-binding protein, S-adenosylmethionine synthase, and Early Responsive to Dehydration Stress may play critical roles in drought response. This study provides insights into microRNA responsive to drought and rewatering in Masson pine and advances the understanding of drought tolerance mechanisms in Pinus.

Combined lncRNA and mRNA Expression Profiles Identified the lncRNA–miRNA–mRNA Modules Regulating the Cold Stress Response in Ammopiptanthus nanus

Long non-coding RNAs (lncRNAs) have been shown to play critical regulatory roles in plants. Ammopiptanthus nanus can survive under severe low-temperature stress, and lncRNAs may play crucial roles in the gene regulation network underlying the cold stress response in A. nanus. To investigate the roles of lncRNAs in the cold stress response of A. nanus, a combined lncRNA and mRNA expression profiling under cold stress was conducted. Up to 4890 novel lncRNAs were identified in A. nanus and 1322 of them were differentially expressed under cold stress, including 543 up-regulated and 779 down-regulated lncRNAs. A total of 421 lncRNAs were found to participate in the cold stress response by forming lncRNA-mRNA modules and regulating the genes encoding the stress-related transcription factors and enzymes in a cis-acting manner. We found that 31 lncRNAs acting as miRNA precursors and 8 lncRNAs acting as endogenous competitive targets of miRNAs participated in the cold stress response by forming lncRNA-miRNA-mRNA regulatory modules. In particular, a cold stress-responsive lncRNA, TCONS00065739, which was experimentally proven to be an endogenous competitive target of miR530, contributed to the cold stress adaptation by regulating TZP in A. nanus. These results provide new data for understanding the biological roles of lncRNAs in response to cold stress in plants.

Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning.

Simple SummaryUnderstanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA–mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific).MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA–mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA–mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA–mRNA regulatory modules separately. We tested the model’s ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA–mRNA signatures.

MiRModuleNet: Detecting miRNA-mRNA Regulatory Modules

Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

A review on methods for predicting miRNA-mRNA regulatory modules.

Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

Identification of miRNA-mRNA Regulatory Modules Involved in Lipid Metabolism and Seed Development in a Woody Oil Tree (Camellia oleifera).

Tea oil camellia (Camellia oleifera), an important woody oil tree, is a source of seed oil of high nutritional and medicinal value that is widely planted in southern China. However, there is no report on the identification of the miRNAs involved in lipid metabolism and seed development in the high- and low-oil cultivars of tea oil camellia. Thus, we explored the roles of miRNAs in the key periods of oil formation and accumulation in the seeds of tea oil camellia and identified miRNA–mRNA regulatory modules involved in lipid metabolism and seed development. Sixteen small RNA libraries for four development stages of seed oil biosynthesis in high- and low-oil cultivars were constructed. A total of 196 miRNAs, including 156 known miRNAs from 35 families, and 40 novel miRNAs were identified, and 55 significantly differentially expressed miRNAs were found, which included 34 upregulated miRNAs, and 21 downregulated miRNAs. An integrated analysis of the miRNA and mRNA transcriptome sequence data revealed that 10 miRNA–mRNA regulatory modules were related to lipid metabolism; for example, the regulatory modules of ath-miR858b–MYB82/MYB3/MYB44 repressed seed oil biosynthesis, and a regulation module of csi-miR166e-5p–S-ACP-DES6 was involved in the formation and accumulation of oleic acid. A total of 23 miRNA–mRNA regulatory modules were involved in the regulation of the seed size, such as the regulatory module of hpe-miR162a_L-2–ARF19, involved in early seed development. A total of 12 miRNA–mRNA regulatory modules regulating growth and development were identified, such as the regulatory modules of han-miR156a_L+1–SPL4/SBP2, promoting early seed development. The expression changes of six miRNAs and their target genes were validated using quantitative real-time PCR, and the targeting relationship of the cpa-miR393_R-1–AFB2 regulatory module was verified by luciferase assays. These data provide important theoretical values and a scientific basis for the genetic improvement of new cultivars of tea oil camellia in the future.

Uncovering miRNA-mRNA Regulatory Modules in Developing Xylem of Pinus massoniana via Small RNA and Degradome Sequencing.

Xylem is required for the growth and development of higher plants to provide water and mineral elements. The thickening of the xylem secondary cell wall (SCW) not only improves plant survival, but also provides raw materials for industrial production. Numerous studies have found that transcription factors and non-coding RNAs regulate the process of SCW thickening. Pinus massoniana is an important woody tree species in China and is widely used to produce materials for construction, furniture, and packaging. However, the target genes of microRNAs (miRNAs) in the developing xylem of P. massoniana are not known. In this study, a total of 25 conserved miRNAs and 173 novel miRNAs were identified via small RNA sequencing, and 58 differentially expressed miRNAs were identified between the developing xylem (PM_X) and protoplasts isolated from the developing xylem (PM_XP); 26 of these miRNAs were significantly up-regulated in PM_XP compared with PM_X, and 32 were significantly down-regulated. A total of 153 target genes of 20 conserved miRNAs and 712 target genes of 113 novel miRNAs were verified by degradome sequencing. There may be conserved miRNA-mRNA modules (miRNA-MYB, miRNA-ARF, and miRNA-LAC) involved in softwood and hardwood formation. The results of qRT-PCR-based parallel validation were in relatively high agreement. This study explored the potential regulatory network of miRNAs in the developing xylem of P. massoniana and provides new insights into wood formation in coniferous species.

An efficient framework to identify key miRNA-mRNA regulatory modules in cancer.

Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA-mRNA regulatory modules. We presented a two-step framework to model miRNA-mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database. Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments. Supplementary data are available at Bioinformatics online.

Responses of functional miRNA-mRNA regulatory modules to a high-fat diet in the liver of hybrid yellow catfish (Pelteobagrus fulvidraco × P. vachelli)

Fatty liver disease is common in cultured yellow catfish as a result of high fat contents in feeds. However, little is known about the mechanism by which the excessive deposition of liver fat causes fatty liver disease. Hybrid yellow catfish (Pelteobagrus fulvidraco♀ × P. vachelli♂) were fed a high-fat diet (HFD) or a normal-fat diet (NFD) for 60 days. Compared with the NFD group, the HFD group showed lower growth performance, higher hepatosomatic and viscerosomatic indexes, increased hepatic triglyceride and cholesterol contents, and more and larger lipid droplets in liver tissue. Whole transcriptome mRNA libraries and microRNA libraries from fish in the NFD and HFD groups were constructed by high-throughput sequencing. Twelve miRNAs were differentially expressed (DE) between the HFD and NFD groups. Seven negatively correlated DE miRNA–DE mRNA pairs were selected, and the expression patterns of both were confirmed using qRT-PCR. Hybrid yellow catfish showed mediated oxidative degradation of liver glucose and fatty acid peroxidation, regulation of antioxidant enzyme activity, and various immune and inflammatory responses to fat deposition and stress. These findings have important biological significance for protecting the liver against stress, as well as economic significance for establishing healthy aquaculture conditions.

Integrated Analysis of Seed microRNA and mRNA Transcriptome Reveals Important Functional Genes and microRNA-Targets in the Process of Walnut (Juglans regia) Seed Oil Accumulation.

Walnut (Juglans regia) is known as a promising woody oil crop with abundant polyunsaturated fatty acids in its kernel. However, the regulation mechanism of walnut oil accumulation and fatty acid metabolism is still poorly understood, which restricted the breeding and genetic improvement of high-quality oil-bearing walnuts. To reveal the molecular mechanism of walnut oil accumulation, considering the potential regulation of microRNA (miRNA) in seed development, in this study, the oil content of walnut kernel on the 80th, 100th and 120th day after flowering (DAF) was tested and the corresponding proportions are 11.51%, 40.40% and 53.20%. Between DAF of 80th~120th, the content of stearic acid and oleic acid tended to increase, but the proportion of other fatty acids tended to decrease. Meanwhile, comparative transcriptome and sRNA-seq analysis on three stages (80th, 100th and 120th DAF), found 204 conserved miRNAs and 554 novel miRNAs in walnut kernels, among which 104 key genes related to walnut oil accumulation were screened. The phospholipid:diacylglycerol acyltransferase metabolic pathway may contribute more to oil accumulation in walnut. 16 miRNA-mRNA regulatory modules related to walnut oil accumulation and fatty acid synthesis were constructed. 8 known miRNAs and 9 novel miRNAs regulate 28 genes involved in fatty acid (FA) metabolism and lipid synthesis. Among them, jre-miRn105, jre-miRn434, jre-miR477d and jre-miR156a.2 are key miRNAs that regulate walnut FA synthesis. Jre-miRn411 and jre-miR399a.1 are closely related to oil accumulation. These data provide new insights and lay the foundation for subsequent studies on walnut FA synthesis and oil accumulation.

RFCM3: Computational Method for Identification of miRNA-mRNA Regulatory Modules in Cervical Cancer.

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Functional genomics data provides opportunities to study the aberrant microRNA-messenger RNA (miRNA-mRNA) interaction. Identification of miRNA-mRNA regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects set of miRNA-mRNA modules by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later, using the knowledge of the miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The prognostic value of the genes in a module with respect to cervical cancer is also demonstrated. The approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering.

MicroRNAs (miRNAs) play a key role in gene expression and regulation in various organisms. They control a wide range of biological processes and are involved in several types of cancers by causing mRNA degradation or translational inhibition. However, the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With the accumulation of the expression data of miRNAs and mRNAs, many computational methods have been proposed to predict miRNA-mRNA regulatory relationship. However, most existing methods require the number of modules predefined that may be difficult to determine beforehand. Here, we propose a novel computational method to discover miRNA-mRNA regulatory modules by combining Phase-only correlation and improved rough-Fuzzy Clustering (MIMPFC). The proposed method is evaluated on three heterogeneous datasets, and the obtained results are further validated through relevant literatures, biological significance and functional enrichment analysis. The analysis results show that the identified modules are highly correlated with the biological conditions. A large part of the regulatory relationships found by MIMPFC has been confirmed in the experimentally verified databases. It demonstrates that the modules found by MIMPFC are biologically significant.

Surveying computational algorithms for identification of miRNA–mRNA regulatory modules

MicroRNAs (miRNAs) are small non-coding RNA that target gene expression at post-transcriptional level. To understand the molecular basis of miRNA regulation, it is essential to identify reliable miRNA target mRNAs and miRNA–mRNA functional networks. Several computational algorithms and tools are widely used for discovering miRNA–mRNA regulatory modules. We have comprehensively reviewed thirty-two tools and databases that were developed over the past decade. Our in-depth survey considers all significant features including underlying methodologies in each tool for basis of miRNA–mRNA functional module discovery. Current miRNA–mRNA functional module identification tools utilizes sequence based prediction, graph theory combined with Bayesian network, probabilistic and rule learning methods. Further we discuss ease of use, availability, and suitability of these methods, and provide an overview of recent computational algorithms used in miRNA–mRNA regulatory module identification.

MiRNA-mRNA network involved in folliculogenesis interactome: systems biology approach.

At later phases of folliculogenesis, the mammalian ovarian follicle contains layers of granulosa cells surrounding an antral cavity. To better understand the molecular basis of follicular growth and granulosa cell maturation, we study transcriptome profiling of granulosa cells from small (<5 mm) and large (>10 mm) bovine follicles using simultaneous method of Affymetrix microarrays (24,128 probe sets) and RNA-Seq data sets. This study proposes a computational method to discover the functional miRNA-mRNA regulatory modules, that is, groups of miRNAs and their target mRNAs that are believed to take part cooperatively in post-transcriptional gene regulation under specific conditions. The reconstructed network was named Integrated miRNA-mRNA Bipartite Network. 277 genes and 6 key modules were disclosed through clustering for mRNA master list. The 66 genes are among the genes that belong to at least two modules. All these genes, being involved in at least one of the phenomena, namely cell survival, proliferation, metastasis and apoptosis, have an overexpression pattern (P < 0.01). For miRNA master list, a total of 172 sequences were differentially expressed (P < 0.01) between dominant (large) and each of subordinate (small) follicles. Within the follicle, these miRNAs were predominantly expressed in mural granulosa cells. Finally, predicted and validated targets of these miRNAs enriched in dominant (large) follicles were identified, which are mapped to signaling pathways involved in follicular cell proliferation, steroidogenesis, PI3K/AKT/mTOR and Ras/Raf/MEK/ERK. The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures.

Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering

Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.

A novel approach to identify the miRNA-mRNA causal regulatory modules in Cancer.

MicroRNAs (miRNAs) play an essential role in many biological processes by regulating the target genes, especially in the initiation and development of cancers. Therefore, the identification of the miRNA-mRNA regulatory modules is important for understanding the regulatory mechanisms. Most computational methods only used statistical correlations in predicting miRNA-mRNA modules, and neglected the fact there are causal relationships between miRNAs and their target genes. In this paper, we propose a novel approach called CALM(the causal regulatory modules) to identify the miRNA-mRNA regulatory modules through integrating the causal interactions and statistical correlations between the miRNAs and their target genes. Our algorithm largely consists of three steps: it first forms the causal regulatory relationships of miRNAs and genes from gene expression profiles and detects the miRNA clusters according to the GO function information of their target genes, then expands each miRNA cluster by greedy adding(discarding) the target genes to maximize the modularity score. To show the performance of our method, we apply CALM on four datasets including EMT, breast, ovarian, thyroid cancer and validate our results. The experiment results show that our method can not only outperform the compared method, but also achieve ideal overall performance in terms of the functional enrichment.

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice.

BackgroundMicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated.MethodsIn this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs).ResultsWe identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes.ConclusionsIn this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0735-z) contains supplementary material, which is available to authorized users.

Identification of miRNA-mRNA regulatory modules by exploring collective group relationships.

BackgroundmicroRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships.ResultsWe propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries.ConclusionsAnalysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2300-z) contains supplementary material, which is available to authorized users.

Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships.

BackgroundIntensive research based on the inverse expression relationship has been undertaken to discover the miRNA-mRNA regulatory modules involved in the infection of Hepatitis C virus (HCV), the leading cause of chronic liver diseases. However, biological studies in other fields have found that inverse expression relationship is not the only regulatory relationship between miRNAs and their targets, and some miRNAs can positively regulate a mRNA by binding at the 5' UTR of the mRNA.ResultsThis work focuses on the detection of both inverse and positive regulatory relationships from a paired miRNA and mRNA expression data set of HCV patients through a 'change-to-change' method which can derive connected discriminatory rules. Our study uncovered many novel miRNA-mRNA regulatory modules. In particular, it was revealed that GFRA2 is positively regulated by miR-557, miR-765 and miR-17-3p that probably bind at different locations of the 5' UTR of this mRNA. The expression relationship between GFRA2 and any of these three miRNAs has not been studied before, although separate research for this gene and these miRNAs have all drawn conclusions linked to hepatocellular carcinoma. This suggests that the binding of mRNA GFRA2 with miR-557, miR-765, or miR-17-3p, or their combinations, is worthy of further investigation by experimentation. We also report another mRNA QKI which has a strong inverse expression relationship with miR-129 and miR-493-3p which may bind at the 3' UTR of QKI with a perfect sequence match. Furthermore, the interaction between hsa-miR-129-5p (previous ID: hsa-miR-129) and QKI is supported with CLIP-Seq data from starBase. Our method can be easily extended for the expression data analysis of other diseases.ConclusionOur rule discovery method is useful for integrating binding information and expression profile for identifying HCV miRNA-mRNA regulatory modules and can be applied to the study of the expression profiles of other complex human diseases.

Identifying dysfunctional miRNA-mRNA regulatory modules by inverse activation, cofunction, and high interconnection of target genes: A case study of glioblastoma

Accumulating evidence demonstrates that complex diseases may arise from cooperative effects of multiple dysfunctional miRNAs. Thus, identifying abnormal functions cooperatively regulated by multiple miRNAs is useful for understanding the pathogenesis of complex diseases. In this study, we proposed a multistep method to identify dysfunctional miRNA-mRNA regulatory modules (dMiMRMs) in a specific disease, in which a group of miRNAs cooperatively regulate a group of target genes involved in a specific function. We identified dysfunctional miRNAs, which were differentially expressed and inversely regulated most of their target genes, by integrating paired miRNA and mRNA expression profiles and miRNA target information. Then, we identified cooperative functional units, in each of which a pair of miRNAs cooperatively repressed function-enriched and highly interconnected target genes. Finally, the cooperative functional units were assembled into dMiMRMs. We applied our method to glioblastoma (GBM) and identified GBM-associated dMiMRMs at the population, subtype, and individual levels. We identified 5 common dMiMRMs using all GBM samples, 3 of which were associated with the prognosis in patients with GBM and were better predictors of prognosis than were miRNAs or mRNAs alone. By applying our approach to GBM subtypes, we found consistent dMiMRMs across GBM subtypes, and some subtype-specific dMiMRMs were observed. Furthermore, personalized dMiMRMs were identified, suggesting significant individual differences in different patients with GBM. Our method provides the capability to identify miRNA-mediated dysfunctional mechanisms underlying complex diseases.