Discovering dysfunction of multiple microRNAs cooperation in disease by a conserved microRNA co-expression network

Enhancement of Bottle Gourd Oil Activity via Optimized Self-Dispersing Lipid Formulations (SDLFs) to Mitigate Isoproterenol-Evoked Cardiac Toxicity in Rats via Modulating BMP, MMP2, and miRNA-21 and miRNA-23a Genes' Expression

Bottle gourd (BG) oil (family Cucurbitaceae) has several pharmacological activities including a reduction of the hazard of cardiovascular and atherosclerosis conditions. This work aimed to develop and optimize self-dispersing lipid formulations (SDLFs) of BG oil by applying a full 32 factorial design. The formulation variables (oil concentration and surfactant mixture ratio) showed an obvious impact on the characters of the prepared BG-SDLFs including droplet size (DS), polydispersity index (PDI), emulsification time (ET), and transmission percentage (Tr%). The optimum BG-SDLF composed of 30% oil and Tween 80/Cremophor® RH40 (1:1) showed good emulsification characteristics and a better drug release profile compared with BG oil. In vivo study in isoproterenol-injected rats showed that BG oil and the optimized BG-SDLF improved cardiac function, by elevating the miRNA-23a gene expression level and decreasing miRNA-21 gene expression. They also caused the inhibition of the plasma B-type natriuretic peptide (BNP), N-terminal proatrial natriuretic peptide (NT-pro-BNP), cystatin c, galectin-3, lipoprotein-associated phospholipase A2 (Lp-PLA2), matrix metallopeptidase 2 (MMP2), cardiac troponin I (cTnI), and cardiac troponin T (cTnT). Our study demonstrated that BG oil and the optimized BG-SDLF provided a cardioprotection against isoproterenol-induced cardiac toxicity with better results in groups treated with the optimized BG-SDLF.

MiRNA and LncRNA as Potential Biomarkers in Triple-Negative Breast Cancer: A Review

Noncoding RNAs (ncRNAs) include a diverse range of RNA species, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). MiRNAs, ncRNAs of approximately 19-25 nucleotides in length, are involved in gene expression regulation either via degradation or silencing of the messenger RNAs (mRNAs) and have roles in multiple biological processes, including cell proliferation, differentiation, migration, angiogenesis, and apoptosis. LncRNAs, which are longer than 200 nucleotides, comprise one of the largest and most heterogeneous RNA families. LncRNAs can activate or repress gene expression through various mechanisms, acting alone or in combination with miRNAs and other molecules as part of various pathways. Until recently, most research has focused on individual lncRNA and miRNA functions as regulators, and there is limited available data on ncRNA interactions relating to the tumor growth, metastasis, and therapy of cancer, acting either on mRNA alone or as competing endogenous RNA (ceRNA) networks. Triple-negative breast cancer (TNBC) represents approximately 10%-20% of all breast cancers (BCs) and is highly heterogenous and more aggressive than other types of BC, for which current targeted treatment options include hormonotherapy, PARP inhibitors, and immunotherapy; however, no targeted therapies for TNBC are available, partly because of a lack of predictive biomarkers. With advances in proteomics, new evidence has emerged demonstrating the implications of dysregulation of ncRNAs in TNBC etiology. Here, we review the roles of lncRNAs and miRNAs implicated in TNBC, including their interactions and regulatory networks. Our synthesis provides insight into the mechanisms involved in TNBC progression and has potential to aid the discovery of new diagnostic and therapeutic strategies.

Profiling of tRNA Halves and YRNA Fragments in Serum and Tissue From Oral Squamous Cell Carcinoma Patients Identify Key Role of 5' tRNA-Val-CAC-2-1 Half

Oral squamous cell carcinoma (OSCC) is the most common type of head and neck cancer and, as indicated by The Oral Cancer Foundation, kills at an alarming rate of roughly one person per hour. With this study, we aimed at better understanding disease mechanisms and identifying minimally invasive disease biomarkers by profiling novel small non-coding RNAs (specifically, tRNA halves and YRNA fragments) in both serum and tumor tissue from humans. Small RNA-Sequencing identified multiple 5' tRNA halves and 5' YRNA fragments that displayed significant differential expression levels in circulation and/or tumor tissue, as compared to control counterparts. In addition, by implementing a modification of weighted gene coexpression network analysis, we identified an upregulated genetic module comprised of 5' tRNA halves and miRNAs (miRNAs were described in previous study using the same samples) with significant association with the cancer trait. By consequently implementing miRNA-overtargeting network analysis, the biological function of the module (and by "guilt by association," the function of the 5' tRNA-Val-CAC-2-1 half) was found to involve the transcriptional targeting of specific genes involved in the negative regulation of the G1/S transition of the mitotic cell cycle. These findings suggest that 5' tRNA-Val-CAC-2-1 half (reduced in serum of OSCC patients and elevated in the tumor tissue) could potentially serve as an OSCC circulating biomarker and/or target for novel anticancer therapies. To our knowledge, this is the first time that the specific molecular function of a 5'-tRNA half is specifically pinpointed in OSCC.

Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder

The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature.

p73-Governed miRNA Networks: Translating Bioinformatics Approaches to Therapeutic Solutions for Cancer Metastasis

The transcription factor p73 synthesizes a large number of isoforms and presents high structural and functional homology with p53, a well-known tumor suppressor and a famous "Holy Grail" of anticancer targeting. p73 has attracted increasing attention mainly because (a) unlike p53, p73 is rarely mutated in cancer, (b) some p73 isoforms can inhibit all hallmarks of cancer, and (c) it has the ability to mimic oncosuppressive functions of p53, even in p53-mutated cells. These attributes render p73 and its downstream pathways appealing for therapeutic targeting, especially in mutant p53-driven cancers. p73 functions are, at least partly, mediated by microRNAs (miRNAs), which constitute nodal components of p73-governed networks. p73 not only regulates transcription of crucial miRNA genes, but is also predicted to affect miRNA populations in a transcription-independent manner by developing protein-protein interactions with components of the miRNA processing machinery. This combined effect of p73, both in miRNA transcription and maturation, appears to be isoform-dependent and can result in a systemic switch of cell miRNomes toward either an anti-oncogenic or oncogenic outcome. In this review, we combine literature search with bioinformatics approaches to reconstruct the p73-governed miRNA network and discuss how these crosstalks may be exploited to develop next-generation therapeutics.

Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes

OBJECTIVE

Short-term intensive insulin therapy (IIT) early in the course of type 2 diabetes acutely improves beta-cell function with long-lasting effects on glycemic control. However, conventional measures cannot determine which patients are better suited for IIT, and little is known about the molecular mechanisms determining response. Therefore, this study aimed to develop a model that could accurately predict the response to IIT and provide insight into molecular mechanisms driving such response in humans.

METHODS

Twenty-four patients with early type 2 diabetes were assessed at baseline and four weeks after IIT, consisting of basal detemir and premeal insulin aspart. Twelve individuals had a beneficial beta-cell response to IIT (responders) and 12 did not (nonresponders). Beta-cell function was assessed by multiple methods, including Insulin Secretion-Sensitivity Index-2. MicroRNAs (miRNAs) were profiled in plasma samples before and after IIT. The response to IIT was modeled using a machine learning algorithm and potential miRNA-mediated regulatory mechanisms assessed by differential expression, correlation, and functional network analyses (FNA).

RESULTS

Baseline levels of circulating miR-145-5p, miR-29c-3p, and HbA1c accurately (91.7%) predicted the response to IIT (OR = 121 [95% CI: 6.7, 2188.3]). Mechanistically, a previously described regulatory loop between miR-145-5p and miR-483-3p/5p, which controls TP53-mediated apoptosis, appears to also occur in our study population of humans with early type 2 diabetes. In addition, significant (fold change > 2, P < 0.05) longitudinal changes due to IIT in the circulating levels of miR-138-5p, miR-192-5p, miR-195-5p, miR-320b, and let-7a-5p further characterized the responder group and significantly correlated (|r| > 0.4, P < 0.05) with the changes in measures of beta-cell function and insulin sensitivity. FNA identified a network of coordinately/cooperatively regulated miRNA-targeted genes that potentially drives the IIT response through negative regulation of apoptotic processes that underlie beta cell dysfunction and concomitant positive regulation of proliferation.

CONCLUSIONS

Responses to IIT in people with early type 2 diabetes are associated with characteristic miRNA signatures. This study represents a first step to identify potential responders to IIT (a current limitation in the field) and provides important insight into the pathophysiologic determinants of the reversibility of beta-cell dysfunction. ClinicalTrial.gov identifier: NCT01270789.

A Filtering Method for Identification of Significant Target mRNAs of Coexpressed and Differentially Expressed MicroRNA Clusters

MicroRNA (miRNA) binding is primarily based on sequence, but structure-specific binding is also possible. Various prediction algorithms have been developed for predicting miRNA target genes; the results, however, have relatively high levels of false positives, and the degree of overlap between predicted targets from different methods is poor or null. We devised a new method for identifying significant miRNA target genes from an extensive list of predicted miRNA target gene relationships using hypergeometric distributions. We evaluated our method in statistical and semantic aspects using a common miRNA cluster from six solid tumors. Our method provides statistically and semantically significant miRNA target genes. Complementing target prediction algorithms with our proposed method may have a significant synergistic effect in finding and evaluating functional annotation and enrichment analysis for miRNA.

Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites

MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA–mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows’ diets with 5% linseed oil (LSO) (n = 6 cows) or 5% safflower oil (SFO) (n = 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor–beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (p = 0.03) correlated with the turquoise module in LSO treatment while protein yield (p = 0.003) and milk yield (p = 7 × 10⁻⁰⁴) correlated with the turquoise model, protein and milk yields and lactose percent (p < 0.05) correlated with the blue module and fat percent (p = 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (p < 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA–mRNA pairs: miR-183/RHBDD2 (p = 0.003), miR-484/EIF1AD (p = 0.011) and miR-130a/SBSPON (p = 0.004) with lowest p-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (p < 0.05) with 28, 31 and 5 miRNA–mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA–mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows’ diets with 5% LSO or 5% SFO.

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network

Motivation

Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level.

Results

It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

Identification of functional modules induced by bare-metal stents and paclitaxel-eluting stents in coronary heart disease

Bare-metal stents (BMS) and paclitaxel-eluting stent (PES) are frequently used in medicine for the treatment of coronary heart disease, with millions of patients treated worldwide. The protein-protein interactions (PPI) were adopted to construct the networks. The M-module algorithm was used to identify multiple differential modules. Gene Ontology enrichment and pathway enrichment analysis were performed to analyze characteristics of modules. With the PPI and microarray data, two differential co-expressed networks were constructed, module 1 indicating PES and module 2 indicating BMS, with the same genes but different edges. At a module connectivity dynamic score P-value cut-off of <0.05, module 1 was identified with 142 nodes and 460 edges and in the module 2, 73 nodes and 222 edges were identified. Significant biological processes and pathways were found different in the two modules. Through the two differential modules, we revealed the potential molecular changes induced by PES and BMS providing new insights into the underlying mechanisms in human left internal mammary arteries after inserted with a stent.

In silico identification of microRNAs predicted to regulate N-myristoyltransferase and Methionine Aminopeptidase 2 functions in cancer and infectious diseases

Protein myristoylation is a key protein modification carried out by N-Myristoyltransferase (NMT) after Methionine aminopeptidase 2 (MetAP2) removes methionine from the amino-terminus of the target protein. Protein myristoylation by NMT augments several signaling pathways involved in a myriad of cellular processes, including developmental pathways and pathways that when dysregulated lead to cancer or immune dysfunction. The emerging evidence pointing to NMT-mediated myristoylation as a major cellular regulator underscores the importance of understanding the framework of this type of signaling event. Various studies have investigated the role that myristoylation plays in signaling dysfunction by examining differential gene or protein expression between normal and diseased states, such as cancers or following HIV-1 infection, however no study exists that addresses the role of microRNAs (miRNAs) in the regulation of myristoylation. By performing a large scale bioinformatics and functional analysis of the miRNAs that target key genes involved in myristoylation (NMT1, NMT2, MetAP2), we have narrowed down a list of promising candidates for further analysis. Our condensed panel of miRNAs identifies 35 miRNAs linked to cancer, 21 miRNAs linked to developmental and immune signaling pathways, and 14 miRNAs linked to infectious disease (primarily HIV). The miRNAs panel that was analyzed revealed several NMT-targeting mRNAs (messenger RNA) that are implicated in diseases associated with NMT signaling alteration, providing a link between the realms of miRNA and myristoylation signaling. These findings verify miRNA as an additional facet of myristoylation signaling that must be considered to gain a full perspective. This study provides the groundwork for future studies concerning NMT-transcript-binding miRNAs, and will potentially lead to the development of new diagnostic/prognostic biomarkers and therapeutic targets for several important diseases.

Omega-3 Polyunsaturated Fatty Acids Time-Dependently Reduce Cell Viability and Oncogenic MicroRNA-21 Expression in Estrogen Receptor-Positive Breast Cancer Cells (MCF-7)

The omega-3 polyunsaturated fatty acid (n-3 PUFA), α-linolenic acid (ALA), and its metabolites, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), independently reduce the growth of breast cancer cells in vitro, but the mechanisms, which may involve microRNA (miRNA), are still unclear. The expression of the oncomiR, miR-21, is reduced by DHA treatment, but the effects of ALA on miR-21, alone or combined with EPA and DHA under physiologically relevant concentrations, have not been investigated. The effects of ALA alone and +/-EPA and DHA at the blood molar ratios seen in either humans (1.0:1.0:2.5, ALA:EPA:DHA) or mice (1.0:0.4:3.1, ALA:EPA:DHA) post flaxseed oil consumption (containing ALA) were assessed in vitro in MCF-7 breast cancer cells. Cell viability and the expression of miR-21 and its molecular target, phosphatase and tension homolog (PTEN, gene and protein), at different time points, were examined. At 1, 3, 48 and 96 h ALA alone and 24 h animal ratio treatments significantly reduced MCF-7 cell viability, while 1 and 3 h ALA alone and human and animal ratio treatments all significantly reduced miR-21 expression, and 24 h animal ratio treatment reduced miR-21 expression; these effects were not associated with changes in PTEN gene or protein expressions. We showed for the first time that ALA alone or combined with EPA and DHA at levels seen in human and animal blood post-ALA consumption can significantly reduce cell viability and modulate miR-21 expression in a time- and concentration-dependent manner, with the animal ratio containing higher DHA having a greater effect. The time dependency of miR-21 effects suggests the significance of considering time as a variable in miRNA studies, particularly of miR-21.

A serum miRNA profile of human longevity: findings from the Baltimore Longitudinal Study of Aging (BLSA)

In C. elegans, miRNAs are genetic biomarkers of aging. Similarly, multiple miRNAs are differentially expressed between younger and older persons, suggesting that miRNA-regulated biological mechanisms affecting aging are evolutionarily conserved. Previous human studies have not considered participants' lifespans, a key factor in identifying biomarkers of aging. Using PCR arrays, we measured miRNA levels from serum samples obtained longitudinally at ages 50, 55, and 60 from 16 non-Hispanic males who had documented lifespans from 58 to 92. Numerous miRNAs showed significant changes in expression levels. At age 50, 24 miRNAs were significantly upregulated, and 73 were significantly downregulated in the long-lived subgroup (76-92 years) as compared with the short-lived subgroup (58-75 years). In long-lived participants, the most upregulated was miR-373-5p, while the most downregulated was miR-15b-5p. Longitudinally, significant Pearson correlations were observed between lifespan and expression of nine miRNAs (p value<0.05). Six of these nine miRNAs (miR-211-5p, 374a-5p, 340-3p, 376c-3p, 5095, 1225-3p) were also significantly up- or downregulated when comparing long-lived and short-lived participants. Twenty-four validated targets of these miRNAs encoded aging-associated proteins, including PARP1, IGF1R, and IGF2R. We propose that the expression profiles of the six miRNAs (miR-211-5p, 374a-5p, 340-3p, 376c-3p, 5095, and 1225-3p) may be useful biomarkers of aging.

Co-Expression Network and Pathway Analyses Reveal Important Modules of miRNAs Regulating Milk Yield and Component Traits

Co-expression network analyses provide insights into the molecular interactions underlying complex traits and diseases. In this study, co-expression network analysis was performed to detect expression patterns (modules or clusters) of microRNAs (miRNAs) during lactation, and to identify miRNA regulatory mechanisms for milk yield and component traits (fat, protein, somatic cell count (SCC), lactose, and milk urea nitrogen (MUN)) via miRNA target gene enrichment analysis. miRNA expression (713 miRNAs), and milk yield and components (Fat%, Protein%, lactose, SCC, MUN) data of nine cows at each of six different time points (day 30 (D30), D70, D130, D170, D230 and D290) of an entire lactation curve were used. Four modules or clusters (GREEN, BLUE, RED and TURQUOISE) of miRNAs were identified as important for milk yield and component traits. The GREEN and BLUE modules were significantly correlated (|r| > 0.5) with milk yield and lactose, respectively. The RED and TURQUOISE modules were significantly correlated (|r| > 0.5) with both SCC and lactose. In the GREEN module, three abundantly expressed miRNAs (miR-148a, miR-186 and miR-200a) were most significantly correlated to milk yield, and are probably the most important miRNAs for this trait. DDR1 and DDHX1 are hub genes for miRNA regulatory networks controlling milk yield, while HHEX is an important transcription regulator for these networks. miR-18a, miR-221/222 cluster, and transcription factors HOXA7, and NOTCH 3 and 4, are important for the regulation of lactose. miR-142, miR-146a, and miR-EIA17-14144 (a novel miRNA), and transcription factors in the SMAD family and MYB, are important for the regulation of SCC. Important signaling pathways enriched for target genes of miRNAs of significant modules, included protein kinase A and PTEN signaling for milk yield, eNOS and Noth signaling for lactose, and TGF β, HIPPO, Wnt/β-catenin and cell cycle signaling for SCC. Relevant enriched gene ontology (GO)-terms related to milk and mammary gland traits included cell differentiation, G-protein coupled receptor activity, and intracellular signaling transduction. Overall, this study uncovered regulatory networks in which miRNAs interacted with each other to regulate lactation traits.

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite

BACKGROUND

MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics.

METHODS AND RESULTS

We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs.

CONCLUSIONS

With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.

Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods

MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR–target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge. A large amount of data generated by high-throughput methods from various sources are available to investigate MTIs. The development of data-driven tools to harness these multi-dimensional data has resulted in significant progress over the past decade. In parallel, large-scale cancer genomic projects are allowing new insights into the commonalities and disparities of miR–target regulation across cancers. In the first half of this review, we explore methods for identification of pairwise MTIs, and in the second half, we explore computational tools for discovery of miR-regulatory modules in a cancer-specific and pan-cancer context. We highlight strengths and limitations of each of these tools as a practical guide for the computational biologists.

miRNA-dependent target regulation: functional characterization of single-nucleotide polymorphisms identified in genome-wide association studies of Alzheimer's disease

Background

A growing body of evidence suggests that microRNAs (miRNAs) are involved in Alzheimer’s disease (AD) and that some disease-associated genetic variants are located within miRNA binding sites. In the present study, we sought to characterize functional polymorphisms in miRNA target sites within the loci defined in earlier genome-wide association studies (GWAS). The main objectives of this study were to (1) facilitate the identification of the gene or genes responsible for the GWAS signal within a locus of interest and (2) determine how functional polymorphisms might be involved in the AD process (e.g., by affecting miRNA-mediated variations in gene expression).

Methods

Stringent in silico analyses were developed to select potential polymorphisms susceptible to impairment of miRNA-mediated repression, and subsequent functional assays were performed in HeLa and HEK293 cells.

Results

Two polymorphisms were identified and further analyzed in vitro. The AD-associated rs7143400-T allele (located in 3′ untranslated region [3′-UTR] of FERMT2) cotransfected with miR-4504 resulted in lower protein levels relative to the rs7143400-G allele cotransfected with the same miRNA. The AD-associated rs9909-C allele in the 3′-UTR of NUP160 abolished the miR-1185-1-3p-regulated expression observed for the rs9909-G allele.

Conclusions

When considered in conjunction with the findings of previous association studies, our results suggest that decreased expression of FERMT2 might be a risk factor in the etiopathology of AD, whereas increased expression of NUP160 might protect against the disease. Our data therefore provide new insights into AD by highlighting two new proteins putatively involved in the disease process.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-016-0186-x) contains supplementary material, which is available to authorized users.

Dissecting the chromatin interactome of microRNA genes

Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II-associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA-target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR-MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

MicroRNAs in bovine adipogenesis: genomic context, expression and function

Background

MicroRNAs (miRNAs) are small non-coding RNAs found to regulate several biological processes including adipogenesis. Understanding adipose tissue regulation is critical for beef cattle as fat is an important determinant of beef quality and nutrient value. This study analyzed the association between genomic context characteristics of miRNAs with their expression and function in bovine adipose tissue. Twenty-four subcutaneous adipose tissue biopsies were obtained from eight British-continental crossbred steers at 3 different time points. Total RNA was extracted and miRNAs were profiled using a miRNA microarray with expression further validated by qRT-PCR.

Results

A total of 224 miRNAs were detected of which 155 were expressed in all steers (n = 8), and defined as the core miRNAs of bovine subcutaneous adipose tissue. Core adipose miRNAs varied in terms of genomic location (59.5% intergenic, 38.7% intronic, 1.2% exonic, and 0.6% mirtron), organization (55.5% non-clustered and 44.5% clustered), and conservation (49% highly conserved, 14% conserved and 37% poorly conserved). Clustered miRNAs and highly conserved miRNAs were more highly expressed (p < 0.05) and had more predicted targets than non-clustered or less conserved miRNAs (p < 0.001). A total of 34 miRNAs were coordinately expressed, being part of six identified relevant networks. Two intronic miRNAs (miR-33a and miR-1281) were confirmed to have coordinated expression with their host genes, transcriptional factor SREBF2 and EP300 (a transcriptional co-activator of transcriptional factor C/EBPα), respectively which are involved in lipid metabolism, suggesting these miRNAs may also play a role in regulation of bovine lipid metabolism/adipogenesis. Furthermore, a total of 17 bovine specific miRNAs were predicted to be involved in the regulation of energy balance in adipose tissue.

Conclusions

These findings improve our understanding on the behavior of miRNAs in the regulation of bovine adipogenesis and fat metabolism as it reveals that miRNA expression patterns and functions are associated with miRNA genomic location, organization and conservation.

Identification of miR-185 as a regulator of de novo cholesterol biosynthesis and low density lipoprotein uptake

Dysregulation of cholesterol homeostasis is associated with various metabolic diseases, including atherosclerosis and type 2 diabetes. The sterol response element binding protein (SREBP)-2 transcription factor induces the expression of genes involved in de novo cholesterol biosynthesis and low density lipoprotein (LDL) uptake, thus it plays a crucial role in maintaining cholesterol homeostasis. Here, we found that overexpressing microRNA (miR)-185 in HepG2 cells repressed SREBP-2 expression and protein level. miR-185-directed inhibition caused decreased SREBP-2-dependent gene expression, LDL uptake, and HMG-CoA reductase activity. In addition, we found that miR-185 expression was tightly regulated by SREBP-1c, through its binding to a single sterol response element in the miR-185 promoter. Moreover, we found that miR-185 expression levels were elevated in mice fed a high-fat diet, and this increase correlated with an increase in total cholesterol level and a decrease in SREBP-2 expression and protein. Finally, we found that individuals with high cholesterol had a 5-fold increase in serum miR-185 expression compared with control individuals. Thus, miR-185 controls cholesterol homeostasis through regulating SREBP-2 expression and activity. In turn, SREBP-1c regulates miR-185 expression through a complex cholesterol-responsive feedback loop. Thus, a novel axis regulating cholesterol homeostasis exists that exploits miR-185-dependent regulation of SREBP-2 and requires SREBP-1c for function.

MicroRNA-10b promotes the migration of mouse bone marrow-derived mesenchymal stem cells and downregulates the expression of E-cadherin

The ability of mesenchymal stem cells (MSCs) to migrate is an important determinant of the efficiency of MSC transplant therapy. MicroRNA-10b (miR-10b) has been positively involved in the migration of a number of tumor cells lineages. To date, it remains unknown whether miR-10b affects the migration of MSCs. In the current study, the effect of miR-10b on the migration of mouse bone marrow-derived MSCs (bmMSCs) was investigated. Third-passage bmMSCs were transfected with miR-10b mimic and negative control precursor miRNA using Lipofectamine™ 2000. miR-10b and E-cadherin expression and bmMSC migration were determined. The present results showed that primary bmMSCs exhibit a spindled or triangular morphology and that third‑passage bmMSCs present a typical fibroblast-like morphology, exhibiting CD90-positive and CD45-negative expression. Compared with the transfection of negative control miRNA, transfection of miR-10b mimic markedly upregulated miR-10b expression in bmMSCs, increased their migration and downregulated E-cadherin expression. The current observations indicate that the upregulation of miR-10b increases bmMSC migration ability, which may be involved in the downregulation of E-cadherin.

Joint analysis of expression profiles from multiple cancers improves the identification of microRNA-gene interactions

MOTIVATION

MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA-gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA-gene interactions without combining all the data into one single dataset.

RESULTS

We developed a novel statistical method to jointly analyze expression profiles from multiple cancers to identify miRNA-gene interactions that are both common across cancers and specific to certain cancers. The benefit of this joint analysis approach is demonstrated by both simulation studies and real data analysis of The Cancer Genome Atlas datasets. Compared with simple aggregate analysis or single sample analysis, our method can effectively use the shared information among different but related cancers to improve the identification of miRNA-gene interactions. Another useful property of our method is that it can estimate similarity among cancers through their shared miRNA-gene interactions.

AVAILABILITY AND IMPLEMENTATION

The program, MCMG, implemented in R is available at http://bioinformatics.med.yale.edu/group/.

Maximal information component analysis: a novel non-linear network analysis method

Background: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.

Results: We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.

Conclusions: In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.

MicroRNA-centric measurement improves functional enrichment analysis of co-expressed and differentially expressed microRNA clusters

Background

Functional annotations are available only for a very small fraction of microRNAs (miRNAs) and very few miRNA target genes are experimentally validated. Therefore, functional analysis of miRNA clusters has typically relied on computational target gene prediction followed by Gene Ontology and/or pathway analysis. These previous methods share the limitation that they do not consider the many-to-many-to-many tri-partite network topology between miRNAs, target genes, and functional annotations. Moreover, the highly false-positive nature of sequence-based target prediction algorithms causes propagation of annotation errors throughout the tri-partite network.

Results

A new conceptual framework is proposed for functional analysis of miRNA clusters, which extends the conventional target gene-centric approaches to a more generalized tri-partite space. Under this framework, we construct miRNA-, target link-, and target gene-centric computational measures incorporating the whole tri-partite network topology. Each of these methods and all their possible combinations are evaluated on publicly available miRNA clusters and with a wide range of variations for miRNA-target gene relations. We find that the miRNA-centric measures outperform others in terms of the average specificity and functional homogeneity of the GO terms significantly enriched for each miRNA cluster.

Conclusions

We propose novel miRNA-centric functional enrichment measures in a conceptual framework that connects the spaces of miRNAs, genes, and GO terms in a unified way. Our comprehensive evaluation result demonstrates that functional enrichment analysis of co-expressed and differentially expressed miRNA clusters can substantially benefit from the proposed miRNA-centric approaches.

Molecular network analysis of human microRNA targetome: from cancers to Alzheimer's disease

MicroRNAs (miRNAs), a class of endogenous small noncoding RNAs, mediate posttranscriptional regulation of protein-coding genes by binding chiefly to the 3’ untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation. A single miRNA concurrently downregulates hundreds of target mRNAs designated “targetome”, and thereby fine-tunes gene expression involved in diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. Recently, we characterized the molecular network of the whole human miRNA targetome by using bioinformatics tools for analyzing molecular interactions on the comprehensive knowledgebase. We found that the miRNA targetome regulated by an individual miRNA generally constitutes the biological network of functionally-associated molecules in human cells, closely linked to pathological events involved in cancers and neurodegenerative diseases. We also identified a collaborative regulation of gene expression by transcription factors and miRNAs in cancer-associated miRNA targetome networks. This review focuses on the workflow of molecular network analysis of miRNA targetome in silico. We applied the workflow to two representative datasets, composed of miRNA expression profiling of adult T cell leukemia (ATL) and Alzheimer’s disease (AD), retrieved from Gene Expression Omnibus (GEO) repository. The results supported the view that miRNAs act as a central regulator of both oncogenesis and neurodegeneration.

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.