• Alzheimer’s disease
• NEAT1
• competitive endogenous RNA
• convolutional neural networks
• lncRNA-miRNA interaction
• transformer encoder

• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Deep Learning
• Humans
• Computational Biology/methods

• PCL2023A09 R&D Program of Pengcheng Laboratory
• SRPG22-001 Pengcheng Laboratory, Research and Development Program of Guangzhou Laboratory
• R&D Program of Pengcheng Laboratory

• Convolutional neural networks
• Deep learning
• MicroRNAs
• Target prediction
• Transformer encoder
• miRNA targets

• MicroRNAs/genetics
• MicroRNAs/metabolism
• Deep Learning
• Neural Networks, Computer
• Software
• RNA, Messenger/genetics

• SRPG22-001 R&D Program of Guangzhou Laboratory
• R&D Program of Guangzhou Laboratory

Epithelial-to-mesenchymal transition (EMT) is a transcriptionally governed process by which cancer cells establish a front-rear polarity axis that facilitates motility and invasion. Dynamic assembly of focal adhesions and other actin-based cytoskeletal structures on the leading edge of motile cells requires precise spatial and temporal control of protein trafficking. Yet, the way in which EMT-activating transcriptional programs interface with vesicular trafficking networks that effect cell polarity change remains unclear. Here, by utilizing multiple approaches to assess vesicular transport dynamics through endocytic recycling and retrograde trafficking pathways in lung adenocarcinoma cells at distinct positions on the EMT spectrum, we find that the EMT-activating transcription factor ZEB1 accelerates endocytosis and intracellular trafficking of plasma membrane-bound proteins. ZEB1 drives turnover of the MET receptor tyrosine kinase by hastening receptor endocytosis and transport to the lysosomal compartment for degradation. ZEB1 relieves a plus-end-directed microtubule-dependent kinesin motor protein (KIF13A) and a clathrin-associated adaptor protein complex subunit (AP1S2) from microRNA-dependent silencing, thereby accelerating cargo transport through the endocytic recycling and retrograde vesicular pathways, respectively. Depletion of KIF13A or AP1S2 mitigates ZEB1-dependent focal adhesion dynamics, front-rear axis polarization, and cancer cell motility. Thus, ZEB1-dependent transcriptional networks govern vesicular trafficking dynamics to effect cell polarity change.

The way in which metastatic tumour cells control endocytic vesicular trafficking networks to establish a front-rear polarity axis that facilitates motility remains unclear. Here, the authors show that the EMT activator ZEB1 influences vesicular trafficking dynamics to execute cell polarity change.

Rod and cone photoreceptors differ in their shape, photopigment expression, synaptic connection patterns, light sensitivity, and distribution across the retina. Although rods greatly outnumber cones, human vision is mostly dependent on cone photoreceptors since cones are essential for our sharp visual acuity and color discrimination. In humans and other primates, the fovea centralis (fovea), a specialized region of the central retina, contains the highest density of cones. Despite the vast importance of the fovea for human vision, the molecular mechanisms guiding the development of this region are largely unknown. MicroRNAs (miRNAs) are small post-transcriptional regulators known to orchestrate developmental transitions and cell fate specification in the retina. Here, we have characterized the transcriptional landscape of the developing rhesus monkey retina. Our data indicates that non-human primate fovea development is significantly accelerated compared to the equivalent retinal region at the other side of the optic nerve head, as described previously. Notably, we also identify several miRNAs differentially expressed in the presumptive fovea, including miR-15b-5p, miR-342-5p, miR-30b-5p, miR-103-3p, miR-93-5p as well as the miRNA cluster miR-183/-96/-182. Interestingly, miR-342-5p is enriched in the nasal primate retina and in the peripheral developing mouse retina, while miR-15b is enriched in the temporal primate retina and increases over time in the mouse retina in a central-to-periphery gradient. Together our data constitutes the first characterization of the developing rhesus monkey retinal miRNome and provides novel datasets to attain a more comprehensive understanding of foveal development.

• fovea
• miR-15b
• miR-342-5p
• microRNAs
• retinal development
• rhesus monkey

We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median 19.4%) improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes.

Hodgkin Lymphoma (HL) is a type of aggressive malignancy in lymphoma that has high incidence in young adults and elderly patients. Identification of reliable diagnostic markers and efficient therapeutic targets are especially important for the diagnosis and treatment of HL. Although many HL-related molecules have been identified, our understanding on the molecular mechanisms underlying the disease is still far from complete due to its complex and heterogeneous characteristics. In such situation, exploring the molecular mechanisms underlying HL via systems biology approaches provides a promising option. In this study, we try to elucidate the molecular mechanisms related to the disease and identify potential pharmaceutical targets from a network-based perspective.

We constructed a series of network models. Based on the analysis of these networks, we attempted to identify the biomarkers and elucidate the molecular mechanisms underlying HL. Initially, we built three different but related protein networks, i.e., background network, HL-basic network and HL-specific network. By analyzing these three networks, we investigated the connection characteristic of the HL-related proteins. Subsequently, we explored the miRNA regulation on HL-specific network and analyzed three kinds of simple regulation patterns, i.e., co-regulation of protein pairs, as well as the direct and indirect regulation of triple proteins. Finally, we constructed a simplified protein network combined with the regulation of miRNAs on proteins to better understand the relation between HL-related proteins and miRNAs.

We find that the HL-related proteins are more likely to connect with each other compared to other proteins. Moreover, the HL-specific network can be further divided into five sub-networks and 49 proteins as the backbone of HL-specific network make up and connect these 5 sub-networks. Thus, they may be closely associated with HL. In addition, we find that the co-regulation of protein pairs is the main regulatory pattern of miRNAs on the protein network in the HL-specific network. According to the regulation of miRNA on protein network, we have identified 5 core miRNAs as the potential biomarkers for diagnostic of HL. Finally, several protein pathways have been identified to closely associated with HL, which provides deep insights into underlying mechanism of HL.

The online version of this article (10.1186/s12859-019-3041-9) contains supplementary material, which is available to authorized users.

• Hodgkin Lymphoma
• Network analysis
• Protein interaction network
• miRNA regulation

• Competition
• Mathematical modeling
• Sponging
• ceRNA
• miRNA

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.

• WGCNA
• competing endogenous RNA
• differentially expressed RNAs
• long non-coding RNA
• regulatory network
• thyroid carcinoma

The underlying molecular mechanisms of how dysregulated microRNAs (miRNAs) cause neurodegeneration after traumatic brain injury (TBI) remain elusive. Here we analyzed the biological roles of approximately 600 genes - we previously found these dysregulated in dying and surviving rat hippocampal neurons - that are targeted by ten TBI-altered miRNAs. Bioinformatic analysis suggests that neurodegeneration results from a global miRNA-mediated suppression of genes essential for maintaining proteostasis; many are hub genes - involved in RNA processing, cytoskeletal metabolism, intracellular trafficking, cell cycle progression, repair/maintenance, bioenergetics and cell-cell signaling - whose disrupted expression is linked to human disease. Notably, dysregulation of these essential genes would significantly impair synaptic function and functional brain connectivity. In surviving neurons, upregulated miRNA target genes are co-regulated members of prosurvival pathways associated with cellular regeneration, neural plasticity, and development. This study captures the diversity of miRNA-regulated genes that may be essential for cell repair and survival responses after TBI.

Epithelial–mesenchymal transition (EMT) is a biological process of phenotypic transition of epithelial cells that can promote physiological development as well as tissue healing and repair. In recent years, cancer researchers have noted that EMT is closely related to the occurrence and development of tumors. When tumor cells undergo EMT, they can develop enhanced migration and local tissue invasion abilities, which can lead to metastatic growth. Nevertheless, two researches in NATURE deny its necessity in specific tumors and that is discussed in this review. The degree of EMT and the detection of EMT-associated marker molecules can also be used to judge the risk of metastasis and to evaluate patients’ prognosis. MicroRNAs (miRNAs) are noncoding small RNAs, which can inhibit gene expression and protein translation through specific binding with the 3′ untranslated region of mRNA. In this review, we summarize the miRNAs that are reported to influence EMT through transcription factors such as ZEB, SNAIL, and TWIST, as well as some natural products that regulate EMT in tumors. Moreover, mutual inhibition occurs between some transcription factors and miRNAs, and these effects appear to occur in a complex regulatory network. Thus, understanding the role of miRNAs in EMT and tumor growth may lead to new treatments for malignancies. Natural products can also be combined with conventional chemotherapy to enhance curative effects.

• SNAIL
• ZEB
• epithelial–mesenchymal transition
• miRNA
• natural products
• tumor

Development of myopia is associated with large-scale changes in ocular tissue gene expression. Although differential expression of coding genes underlying development of myopia has been a subject of intense investigation, the role of non-coding genes such as microRNAs in the development of myopia is largely unknown. In this study, we explored myopia-associated miRNA expression profiles in the retina and sclera of C57Bl/6J mice with experimentally induced myopia using microarray technology. We found a total of 53 differentially expressed miRNAs in the retina and no differences in miRNA expression in the sclera of C57BL/6J mice after 10 days of visual form deprivation, which induced -6.93 ± 2.44 D (p < 0.000001, n = 12) of myopia. We also identified their putative mRNA targets among mRNAs found to be differentially expressed in myopic retina and potential signaling pathways involved in the development of form-deprivation myopia using miRNA-mRNA interaction network analysis. Analysis of myopia-associated signaling pathways revealed that myopic response to visual form deprivation in the retina is regulated by a small number of highly integrated signaling pathways. Our findings highlighted that changes in microRNA expression are involved in the regulation of refractive eye development and predicted how they may be involved in the development of myopia by regulating retinal gene expression.

• Animals
• Disease Models, Animal
• Gene Expression Profiling
• Gene Regulatory Networks
• Mice
• Mice, Inbred C57BL
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Myopia/etiology
• Myopia/genetics
• Myopia/metabolism
• Photic Stimulation
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Retina/metabolism
• Sclera/metabolism
• Sensory Deprivation
• Signal Transduction/genetics

• R01 EY023839 NEI NIH HHS
• R01 EY023287 NEI NIH HHS
• P30 EY019007 NEI NIH HHS
• R01 EY014685 NEI NIH HHS
• R01 EY016228 NEI NIH HHS
• National Eye Institute

The molecular mechanisms that regulate B-cell development and tolerance remain incompletely understood. In this study, we identify a critical role for the miR-17∼92 microRNA cluster in regulating B-cell central tolerance and demonstrate that these miRNAs control early B-cell development in a cell-intrinsic manner. While the cluster member miR-19 suppresses the expression of Pten and plays a key role in regulating B-cell tolerance, miR-17 controls early B-cell development through other molecular pathways. These findings demonstrate differential control of two closely linked B-cell developmental stages by different members of a single microRNA cluster through distinct molecular pathways.

MicroRNA cluster 17∼92 plays a critical role in B-cell differentiation. Here the authors show that miR-19 regulates B-cell tolerance via suppressing the expression of PTEN, whereas miR- 17 is essential for early B-cell development independently of Pten, Phlpp2, or Bim.

microRNAs and microRNA-independent RNA-binding proteins are 2 classes of post-transcriptional regulators that have been shown to cooperate in gene-expression regulation. We compared the genome-wide target sets of microRNAs and RBPs identified by recent CLIP-Seq technologies, finding that RBPs have distinct target sets and favor gene interaction network hubs. To identify microRNAs and RBPs with a similar functional context, we developed simiRa, a tool that compares enriched functional categories such as pathways and GO terms. We applied simiRa to the known functional cooperation between Pumilio family proteins and miR-221/222 in the regulation of tumor supressor gene p27 and show that the cooperation is reflected by similar enriched categories but not by target genes. SimiRa also predicts possible cooperation of microRNAs and RBPs beyond direct interaction on the target mRNA for the nuclear RBP TAF15. To further facilitate research into cooperation of microRNAs and RBPs, we made simiRa available as a web tool that displays the functional neighborhood and similarity of microRNAs and RBPs: http://vsicb-simira.helmholtz-muenchen.de.

• RNA-binding proteins
• coregulation
• functional similarity
• microRNAs
• web application.

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.

• elastic net regression
• head and neck squamous cell carcinoma
• local enrichment analysis
• mRNA expression
• mi-/mRNA regulatory network
• miRNA expression

• Condition-specific co-regulation of PPIs
• Protein–protein interaction
• TF
• miRNA

• Algorithms
• Breast Neoplasms/genetics
• Female
• Gene Expression Profiling/methods
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks
• Humans
• Lung Neoplasms/genetics
• MicroRNAs/genetics
• MicroRNAs/physiology
• Ovarian Neoplasms/genetics
• Protein Interaction Domains and Motifs/physiology
• Transcription Factors/genetics
• Transcription Factors/physiology

Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data.

Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression.

Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.

• Animals
• Cell Differentiation/drug effects
• Cell Line
• Gene Expression Profiling/methods
• Gene Expression Regulation/drug effects
• Gene Regulatory Networks/drug effects
• Insulin-Like Growth Factor I/pharmacology
• Mice
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Multigene Family/drug effects
• Muscle Fibers, Skeletal/cytology
• Muscle Fibers, Skeletal/drug effects
• Muscle Fibers, Skeletal/metabolism
• RNA, Messenger/genetics
• Tumor Necrosis Factor-alpha/pharmacology

• cyclic AMP (cAMP)
• development
• lung
• microRNA (miRNA)
• pulmonary surfactant
• transcription factor
• transforming growth factor beta (TGF-beta)

• concordant dysregulation
• lung cancer
• miR-5p/-3p arms
• protein array
• protein complex

• Cell Proliferation
• Cell Transformation, Neoplastic/genetics
• Cell Transformation, Neoplastic/metabolism
• Computational Biology/methods
• Forkhead Box Protein M1
• Forkhead Transcription Factors/metabolism
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks
• Humans
• Lung Neoplasms/genetics
• Lung Neoplasms/metabolism
• Lung Neoplasms/pathology
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Signal Transduction

• R03 CA167695 NCI NIH HHS
• P50CA095103 NCI NIH HHS
• P30CA68485 NCI NIH HHS
• R01 CA177786 NCI NIH HHS
• P50 CA095103 NCI NIH HHS
• R03CA167695 NCI NIH HHS
• P50CA090949 NCI NIH HHS
• R01LM011177 NLM NIH HHS
• P50 CA090949 NCI NIH HHS
• P30 CA068485 NCI NIH HHS
• R01 LM011177 NLM NIH HHS

The majority of human protein-coding genes are predicted to be targets of miRNA-mediated post-transcriptional regulation. The widespread influence of miRNAs is illustrated by their essential roles in all biological processes. Regulated miRNA expression is essential for maintaining cellular differentiation; therefore alterations in miRNA expression patterns are associated with several diseases, including various cancers. High-throughput sequencing technologies revealed low level expressing miRNA isoforms, termed isomiRs. IsomiRs may differ in sequence, length, target preference and expression patterns from their parental miRNA and can arise from differences in miRNA biosynthesis, RNA editing, or SNPs inherent to the miRNA gene. The association between isomiR expression and disease progression is largely unknown. Misregulated miRNA expression is thought to contribute to the formation and/or progression of cancer. However, due to the diversity of targeted transcripts, miRNAs can function as both tumor-suppressor genes and oncogenes as defined by cellular context. Despite this, miRNA profiling studies concluded that the differential expression of particular miRNAs in diseased tissue could aid the diagnosis and treatment of some cancers.

• endometrial cancer
• microrna
• database

• Databases, Genetic
• Endometrial Neoplasms/genetics
• Female
• Humans
• MicroRNAs/genetics

The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets.

Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs.

An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line.

A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.

The online version of this article (doi:10.1186/s13073-015-0135-5) contains supplementary material, which is available to authorized users.

• Animals
• Base Sequence
• Bayes Theorem
• Computational Biology
• Evolution, Molecular
• Gene Regulatory Networks/genetics
• Humans
• Mice
• MicroRNAs/genetics
• Models, Genetic
• Molecular Sequence Data
• Phylogeny
• Sequence Alignment
• Zebrafish

Altered cell metabolism is inherently connected with pathological conditions including cancer and viral infections. Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiological agent of Kaposi's sarcoma (KS). KS tumour cells display features of lymphatic endothelial differentiation and in their vast majority are latently infected with KSHV, while a small number are lytically infected, producing virions. Latently infected cells express only a subset of viral genes, mainly located within the latency-associated region, among them 12 microRNAs. Notably, the metabolic properties of KSHV-infected cells closely resemble the metabolic hallmarks of cancer cells. However, how and why KSHV alters host cell metabolism remains poorly understood. Here, we investigated the effect of KSHV infection on the metabolic profile of primary dermal microvascular lymphatic endothelial cells (LEC) and the functional relevance of this effect. We found that the KSHV microRNAs within the oncogenic cluster collaborate to decrease mitochondria biogenesis and to induce aerobic glycolysis in infected cells. KSHV microRNAs expression decreases oxygen consumption, increase lactate secretion and glucose uptake, stabilize HIF1α and decreases mitochondria copy number. Importantly this metabolic shift is important for latency maintenance and provides a growth advantage. Mechanistically we show that KSHV alters host cell energy metabolism through microRNA-mediated down regulation of EGLN2 and HSPA9. Our data suggest that the KSHV microRNAs induce a metabolic transformation by concurrent regulation of two independent pathways; transcriptional reprograming via HIF1 activation and reduction of mitochondria biogenesis through down regulation of the mitochondrial import machinery. These findings implicate viral microRNAs in the regulation of the cellular metabolism and highlight new potential avenues to inhibit viral latency.

Kaposi's sarcoma (KS) is the most common cancer in HIV-infected untreated individuals. Kaposi's sarcoma-associated herpesvirus (KSHV) is the infectious cause of this neoplasm. The discovery of KSHV and its oncogenic enigmas has enlightened many fields of tumor biology and viral oncogenesis. The metabolic properties of KS significantly differ from those of normal cells and resemble cancer cells in general, but the mechanisms employed by KSHV to alter host cell metabolism are poorly understood. Our work demonstrates that KSHV microRNAs can alter cell metabolism through coherent control of independent pathways, a key feature of microRNA-mediated control of cellular functions. This provides a fresh perspective for how microRNA-encoding pathogens shape a cell's metabolism to create an optimal environment for their survival and/or replication. Indeed, we show that, in the case of KSHV, viral microRNA-driven regulation of metabolism is important for viral latency. These findings will evoke new and exciting approaches to prevent KSHV from establishing latency and later on KS.

• Aerobiosis
• Blotting, Western
• Bone Neoplasms/metabolism
• Bone Neoplasms/pathology
• Bone Neoplasms/virology
• Cell Proliferation
• Endothelial Cells/metabolism
• Endothelial Cells/pathology
• Endothelial Cells/virology
• Endothelium, Vascular/metabolism
• Endothelium, Vascular/pathology
• Endothelium, Vascular/virology
• Energy Metabolism
• Gene Expression Regulation, Viral
• Glucose/metabolism
• Herpesvirus 8, Human/physiology
• Humans
• Lactic Acid/metabolism
• MicroRNAs/genetics
• Mitochondria/metabolism
• Mitochondria/pathology
• Mitochondria/virology
• Osteosarcoma/metabolism
• Osteosarcoma/pathology
• Osteosarcoma/virology
• Oxygen Consumption
• RNA, Messenger/genetics
• Real-Time Polymerase Chain Reaction
• Reverse Transcriptase Polymerase Chain Reaction
• Sarcoma, Kaposi/metabolism
• Sarcoma, Kaposi/pathology
• Sarcoma, Kaposi/virology
• Tumor Cells, Cultured
• Virion/metabolism
• Virus Latency

• C7893/A13100 Cancer Research UK
• 13100 Cancer Research UK
• MC_QA137913 Medical Research Council
• C7893/A12796 Cancer Research UK
• MC_U137973817 Medical Research Council
• G1000801G Medical Research Council

Growing evidence suggests that breast cancer cell plasticity arises due to a partial reactivation of epithelial-mesenchymal transition (EMT) programs in order to give cells pluripotency, leading to a stemness-like phenotype. A complete EMT would be a dead end program that would render cells unable to fully metastasize to distant organs. Evoking the EMT-mesenchymal-to-epithelial transition (MET) cascade promotes successful colonization of distal target tissues. It is unlikely that direct reprogramming or trans-differentiation without passing through a pluripotent stage would be the preferred mechanism during tumor progression. This review focuses on key EMT transcriptional regulators, EMT-transcription factors involved in EMT (TFs) and the miRNA pathway, which are deregulated in breast cancer, and discusses their implications in cancer cell plasticity. Cross-regulation between EMT-TFs and miRNAs, where miRNAs act as co-repressors or co-activators, appears to be a pivotal mechanism for breast cancer cells to acquire a stem cell-like state, which is implicated both in breast metastases and tumor recurrence. As a master regulator of miRNA biogenesis, the ribonuclease type III endonuclease Dicer plays a central role in EMT-TFs/miRNAs regulating networks. All these EMT-MET key regulators represent valuable new prognostic and predictive markers for breast cancer as well as promising new targets for drug-resistant breast cancers.

• Embryonic transcription factors
• Epithelial to mesenchymal transition
• Breast cancer
• MicroRNAs
• Dicer
• Feedback loop

• Apoptosis/genetics
• Cell Proliferation
• Computational Biology/methods
• Epigenesis, Genetic
• Epithelial-Mesenchymal Transition/genetics
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks
• Humans
• Male
• MicroRNAs/genetics
• Prostatic Neoplasms/genetics
• Prostatic Neoplasms/metabolism
• RNA Interference
• RNA Processing, Post-Transcriptional
• RNA, Messenger/genetics
• Signal Transduction
• Transcription Factors/genetics
• Transcription Factors/metabolism
• Transcriptome

Protein-protein interaction (PPI) is one of the most important functional components of a living cell. Recently, researchers have been interested in investigating the correlation between PPI and microRNA, which has been found to be a regulator at the post-transcriptional level. Studies on miRNA-regulated PPI networks will not only facilitate an understanding of the fine tuning role that miRNAs play in PPI networks, but will also provide potential candidates for tumor diagnosis. This review describes basic studies on the miRNA-regulated PPI network in the way of bioinformatics which includes constructing a miRNA-target protein network, describing the features of miRNA-regulated PPI networks and overviewing previous findings based on analysing miRNA-regulated PPI network features.

• Biomarkers, Tumor/metabolism
• Entropy
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic/genetics
• Gene Expression Regulation, Neoplastic/physiology
• Histone Deacetylase 1/metabolism
• Humans
• Male
• MicroRNAs/metabolism
• Multiprotein Complexes/metabolism
• Prostatic Neoplasms/metabolism
• Signal Transduction/genetics
• Signal Transduction/physiology
• Smad4 Protein/metabolism

• P41 GM103504 NIGMS NIH HHS

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.

Contact:zivbj@cs.cmu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

The molecular mechanisms underlying alcohol dependence involve different neurochemical systems and are brain region-dependent. Chronic Intermittent Ethanol (CIE) procedure, combined with a Two-Bottle Choice voluntary drinking paradigm, represents one of the best available animal models for alcohol dependence and relapse drinking. MicroRNAs, master regulators of the cellular transcriptome and proteome, can regulate their targets in a cooperative, combinatorial fashion, ensuring fine tuning and control over a large number of cellular functions. We analyzed cortex and midbrain microRNA expression levels using an integrative approach to combine and relate data to previous protein profiling from the same CIE-subjected samples, and examined the significance of the data in terms of relative contribution to alcohol consumption and dependence. MicroRNA levels were significantly altered in CIE-exposed dependent mice compared with their non-dependent controls. More importantly, our integrative analysis identified modules of coexpressed microRNAs that were highly correlated with CIE effects and predicted target genes encoding differentially expressed proteins. Coexpressed CIE-relevant proteins, in turn, were often negatively correlated with specific microRNA modules. Our results provide evidence that microRNA-orchestrated translational imbalances are driving the behavioral transition from alcohol consumption to dependence. This study represents the first attempt to combine ex vivo microRNA and protein expression on a global scale from the same mammalian brain samples. The integrative systems approach used here will improve our understanding of brain adaptive changes in response to drug abuse and suggests the potential therapeutic use of microRNAs as tools to prevent or compensate multiple neuroadaptations underlying addictive behavior.

• Adaptation, Physiological/drug effects
• Adaptation, Physiological/genetics
• Alcoholism/genetics
• Alcoholism/metabolism
• Alcoholism/pathology
• Animals
• Brain/drug effects
• Brain/metabolism
• Brain/pathology
• Ethanol/pharmacology
• Gene Expression Profiling
• Gene Regulatory Networks/drug effects
• Male
• Mice
• Mice, Inbred C57BL
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Neurons/drug effects
• Neurons/metabolism
• Proteome/analysis
• Proteome/metabolism
• Systems Integration
• Transcriptome

• AA020683 NIAAA NIH HHS
• U01 AA016648 NIAAA NIH HHS
• AA016648 NIAAA NIH HHS
• AA019382 NIAAA NIH HHS
• RC2 AA019382 NIAAA NIH HHS
• AA0107838 NIAAA NIH HHS
• P01 AA020683 NIAAA NIH HHS
• U01 AA020926 NIAAA NIH HHS
• R01 AA012404 NIAAA NIH HHS

• Epstein-Barr virus
• epithelial-mesenchymal transition
• metastasis
• microRNA
• nasopharyngeal carcinoma

• diseases
• microRNA
• modules
• regulation

• Carcinoma, Renal Cell/genetics
• Diabetes Mellitus, Type 2/genetics
• Gene Expression Profiling
• Gene Expression Regulation
• Gene Regulatory Networks
• Humans
• Interleukin-13/metabolism
• Interleukin-5/metabolism
• Kidney Neoplasms/genetics
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Pancreatic Neoplasms/genetics
• Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics
• Pulmonary Disease, Chronic Obstructive/genetics
• RNA, Messenger
• Rhinitis, Allergic, Seasonal/genetics
• Th2 Cells/immunology
• Th2 Cells/metabolism

Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes.

• functional genomics
• functional screens
• microRNA target
• protein-protein interaction
• proteomics
• systems biology and network biology

Modern high-throughput methods allow the investigation of biological functions across multiple ‘omics’ levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.

• 4,6-diamidino-2-phenylidole
• 4-methylthio-2-oxobutyric
• 6-OHDA
• 6-hydroxydopamine
• AD
• Anti
• B-cell lymphoma-3
• BKLF
• Bcl-3
• C-terminal binding protein
• C-terminal binding proteins (CtBPs)
• CCD
• CGNs
• Caspase-3
• Con
• CtBP
• DAPI
• DGCR8
• DiGeorge critical region 8
• EP
• G protein-coupled receptor kinase-interacting protein-z-short
• GITz-short
• Inv
• KO
• LTox
• MEFs
• MPP(+)
• MTOB
• Neuronal apoptosis
• PARP
• PBS
• PCR
• RNA silencing suppressor
• RSS
• SNP
• STS
• Tox B
• Toxin B
• WT
• actinomycin D
• basic Krüppel-like factor
• cerebellar granule neurons
• charge-coupled device
• control
• dCtBP
• drosophila CtBP
• endoporter
• inverse
• knockout
• lethal toxin
• mESCs
• methyl-4-phenylpyridinium
• miRNA
• micro RNA
• morpholino-antisense oligonucleotides
• mouse embryonic stem cells
• murine embryonic fibroblasts
• phosphate buffer saline
• poly-(ADP-ribose) polymerase
• polymerase chain reaction
• rCasp3
• recombinant caspase-3
• sodium nitroprusside
• staurosporine
• wild type

There is a growing body of evidence associating microRNAs (miRNAs) with human diseases. MiRNAs are new key players in the disease paradigm demonstrating roles in several human diseases. The functional association between miRNAs and diseases remains largely unclear and far from complete. With the advent of high-throughput functional genomics techniques that infer genes and biological pathways dysregulted in diseases, it is now possible to infer functional association between diseases and biological molecules by integrating disparate biological information.

Here, we first used Lasso regression model to identify miRNAs associated with disease signature as a proof of concept. Then we proposed an integrated approach that uses disease-gene associations from microarray experiments and text mining, and miRNA-gene association from computational predictions and protein networks to build functional associations network between miRNAs and diseases. The findings of the proposed model were validated against gold standard datasets using ROC analysis and results were promising (AUC=0.81). Our protein network-based approach discovered 19 new functional associations between prostate cancer and miRNAs. The new 19 associations were validated using miRNA expression data and clinical profiles and showed to act as diagnostic and prognostic prostate biomarkers. The proposed integrated approach allowed us to reconstruct functional associations between miRNAs and human diseases and uncovered functional roles of newly discovered miRNAs.

Lasso regression was used to find associations between diseases and miRNAs using their gene signature. Defining miRNA gene signature by integrating the downstream effect of miRNAs demonstrated better performance than the miRNA signature alone. Integrating biological networks and multiple data to define miRNA and disease gene signature demonstrated high performance to uncover new functional associations between miRNAs and diseases.

• Biological Evolution
• Genetic Association Studies
• Genetic Predisposition to Disease
• Genome, Human
• Humans
• Polymorphism, Genetic
• Pseudogenes/genetics

MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.