MicroRNA regulation of human protein protein interaction network

Integration of circulating microRNAs and transcriptome signatures identifies early-pregnancy biomarkers of preeclampsia

BACKGROUND

MicroRNAs (miRNAs) have been implicated in the pathobiology of preeclampsia, a common hypertensive disorder of pregnancy. In a nested matched case-control cohort within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we previously identified peripheral blood mRNA signatures related to preeclampsia and vitamin D status (≤30 ng/mL) during gestation from 10 to 18 weeks, using differential expression analysis.

METHODS

Using quantitative PCR arrays, we conducted profiling of circulating miRNAs at 10-18 weeks of gestation in the same VDAART cohort to identify differentially expressed (DE) miRNAs associated with preeclampsia and vitamin D status. For the validation of the expression of circulating miRNA signatures in the placenta, the HTR-8/SVneo trophoblast cell line was used. Targets of circulating miRNA signatures in the preeclampsia mRNA signatures were identified by consensus ranking of miRNA-target prediction scores from four sources. The connected component of target signatures was identified by mapping to the protein-protein interaction (PPI) network and hub targets were determined. As experimental validation, we examined the gene and protein expression of IGF1R, one of the key hub genes, as a target of the DE miRNA, miR-182-5p, in response to a miR-182-5p mimic in HTR-8/SVneo cells.

RESULTS

Pregnant women with preeclampsia had 16 circulating DE miRNAs relative to normal pregnancy controls that were also DE under vitamin D insufficiency (9/16 = 56% upregulated, FDR < .05). Thirteen miRNAs (13/16 = 81.3%) were detected in HTR-8/SVneo cells. Overall, 16 DE miRNAs had 122 targets, of which 87 were unique. Network analysis demonstrated that the 32 targets of DE miRNA signatures created a connected subnetwork in the preeclampsia module with CXCL8, CXCL10, CD274, MMP9 and IGF1R having the highest connectivity and centrality degree. In an in vitro validation experiment, the introduction of an hsa-miR-182-5p mimic resulted in significant reduction of its target IGF1R gene and protein expression within HTR-8/SVneo cells.

CONCLUSIONS

The integration of the circulating DE miRNA and mRNA signatures associated preeclampsia added additional insights into the subclinical molecular signature of preeclampsia. Our systems and network biology approach revealed several biological pathways, including IGF-1, that may play a role in the early pathophysiology of preeclampsia. These pathways and signatures also denote potential biomarkers for the early stages of preeclampsia and suggest possible preventive measures.

The Application of MicroRNAs in Glaucoma Research: A Bibliometric and Visualized Analysis

Glaucoma is similar to a neurodegenerative disorder and leads to global irreversible loss of vision. Despite extensive research, the pathophysiological mechanisms of glaucoma remain unclear, and no complete cure has yet been identified for glaucoma. Recent studies have shown that microRNAs can serve as diagnostic biomarkers or therapeutic targets for glaucoma; however, there are few bibliometric studies that focus on using microRNAs in glaucoma research. Here, we have adopted a bibliometric analysis in the field of microRNAs in glaucoma research to manifest the current tendencies and research hotspots and to present a visual map of the past and emerging tendencies in this field. In this study, we retrieved publications in the Web of Science database that centered on this field between 2007 and 2022. Next, we used VOSviewer, CiteSpace, Scimago Graphica, and Microsoft Excel to present visual representations of a co-occurrence analysis, co-citation analysis, tendencies, hotspots, and the contributions of authors, institutions, journals, and countries/regions. The United States was the main contributor. Investigative Ophthalmology and Visual Science has published the most articles in this field. Over the past 15 years, there has been exponential growth in the number of publications and citations in this field across various countries, organizations, and authors. Thus, this study illustrates the current trends, hotspots, and emerging frontiers and provides new insight and guidance for searching for new diagnostic biomarkers and clinical trials for glaucoma in the future. Furthermore, international collaborations can also be used to broaden and deepen the field of microRNAs in glaucoma research.

Protein-mediated interactions in the dynamic regulation of acute inflammation

Protein-mediated interactions are the fundamental mechanism through which cells regulate health and disease. These interactions require physical contact between proteins and their respective targets of interest. These targets include not only other proteins but also nucleic acids and other important molecules as well. These proteins are often involved in multibody complexes that work dynamically to regulate cellular health and function. Various techniques have been adapted to study these important interactions, such as affinity-based assays, mass spectrometry, and fluorescent detection. The application of these techniques has led to a greater understanding of how protein interactions are responsible for both the instigation and resolution of acute inflammatory diseases. These pursuits aim to provide opportunities to target specific protein interactions to alleviate acute inflammation.

The Role of MicroRNA and Microbiota in Depression and Anxiety

Depression and anxiety are devastating disorders. Understanding the mechanisms that underlie the development of depression and anxiety can provide new hints on novel treatments and preventive strategies. Here, we summarize the latest findings reporting the novel roles of gut microbiota and microRNAs (miRNAs) in the pathophysiology of depression and anxiety. The crosstalk between gut microbiota and the brain has been reported to contribute to these pathologies. It is currently known that some miRNAs can regulate bacterial growth and gene transcription while also modulate the gut microbiota composition, suggesting the importance of miRNAs in gut and brain health. Treatment and prevention strategies for neuropsychiatric diseases, such as physical exercise, diet, and probiotics, can modulate the gut microbiota composition and miRNAs expressions. Nonetheless, there are critical questions to be addressed to understand further the mechanisms involved in the interaction between the gut microbiota and miRNAs in the brain. This review summarizes the recent findings of the potential roles of microbiota and miRNA on the neuropathology of depression and anxiety, and its potential as treatment strategies.

Underpinning miRNA-miRNA co-functional interaction patterns in the metabolism of Oryza sativa by genome-scale network analysis

MicroRNA (miRNA) is a class of non-coding small RNAs, which post-transcriptionally regulate a large number of genes and are now known to be important regulators in a wide variety of biological processes including metabolism. Thus, for better understanding these complex biological networks, and to derive their significance and inter-dependency, a systems biology approach enables us to explore and draw vital insights into these molecular network architectures. In this study, we aimed to understand the significance of synergistic miRNA-miRNA interactions in rice by constructing and analysing metabolic networks. The construction of the network involves target gene prediction of experimentally verified miRNAs of rice and then appending associated metabolic pathways to the network. A genome-scale miRNA-miRNA co-functional network (MFSN) is constructed based on co-regulatory interactions among the miRNAs and common target genes by applying transformational procedures. The analysis of the extracted MFSN modules identifies co-regulated target genes that are associated with corresponding interconnected metabolic pathways such as VALDEG-PWY (L-valine degradation I pathway was found to be targeted by multiple miRNA families, such as osa-miR812, osa-miR818, osa-miR821, and osa-miR5799 families while another pathway that was found to be associated with multiple miRNA families was PWY-6952 (glycerophosphodiester degradation pathway), PWY-6952 was found to be targeted by osa-miR812, osa-miR11344 and osa-miR5801 families. Such extensive study will help in systematically elucidating the regulatory networks in metabolism of rice, which in turn can be utilised to devise strategies for crop improvement and novel cultivar development.

MicroRNAs as Emerging Regulators of Signaling in the Tumor Microenvironment

A myriad of signaling molecules in a heuristic network of the tumor microenvironment (TME) pose a challenge and an opportunity for novel therapeutic target identification in human cancers. MicroRNAs (miRs), due to their ability to affect signaling pathways at various levels, take a prominent space in the quest of novel cancer therapeutics. The role of miRs in cancer initiation, progression, as well as in chemoresistance, is being increasingly investigated. The canonical function of miRs is to target mRNAs for post-transcriptional gene silencing, which has a great implication in first-order regulation of signaling pathways. However, several reports suggest that miRs also perform non-canonical functions, partly due to their characteristic non-coding small RNA nature. Examples emerge when they act as ligands for toll-like receptors or perform second-order functions, e.g., to regulate protein translation and interactions. This review is a compendium of recent advancements in understanding the role of miRs in cancer signaling and focuses on the role of miRs as novel regulators of the signaling pathway in the TME.

Intragenic MicroRNAs Autoregulate Their Host Genes in Both Direct and Indirect Ways-A Cross-Species Analysis

MicroRNAs (miRNAs) function as master switches for post-transcriptional gene expression. Their genes are either located in the extragenic space or within host genes, but these intragenic miRNA::host gene interactions are largely enigmatic. The aim of this study was to investigate the location and co-regulation of all to date available miRNA sequences and their host genes in an unbiased computational approach. The majority of miRNAs were located within intronic regions of protein-coding and non-coding genes. These intragenic miRNAs exhibited both increased target probability as well as higher target prediction scores as compared to a model of randomly permutated genes. This was associated with a higher number of miRNA recognition elements for the hosted miRNAs within their host genes. In addition, strong indirect autoregulation of host genes through modulation of functionally connected gene clusters by intragenic miRNAs was demonstrated. In addition to direct miRNA-to-host gene targeting, intragenic miRNAs also appeared to interact with functionally related genes, thus affecting their host gene function through an indirect autoregulatory mechanism. This strongly argues for the biological relevance of autoregulation not only for the host genes themselves but, more importantly, for the entire gene cluster interacting with the host gene.

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

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.

Identification of microRNA clusters cooperatively acting on epithelial to mesenchymal transition in triple negative breast cancer

MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe 'clustered MiRNA Master Regulator Analysis (ClustMMRA)', a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signaling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analyses. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative breast cancer (TNBC) phenotype through its control of proliferation and EMT.

Analyzing the regulation of miRNAs on protein-protein interaction network in Hodgkin lymphoma

Background

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.

Results

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.

Conclusions

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.

Electronic supplementary material

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

Three miRNAs cooperate with host genes involved in human cardiovascular disease

Background

Understanding the roles of miRNAs in cardiovascular disease remains a challenge. Genomic linkage indicates a functional relationship between intronic miRNAs and their host genes. However, few studies have shown functional association between intronic miRNAs and their host coding genes that are genetically associated with cardiovascular disease.

Methods

In this study, we investigated functional relationship between three protein-coding genes genetically associated with cardiovascular disease, i.e., CDH13, SLC12A3, and CKAP5, and their intronic miRNAs using a data-driven approach.

Results

We found that the three protein-coding genes functionally interact with targets of their intronic miRNAs, i.e., miR-3182, miR-6863, and miR-5582, in a tissue-specific pattern. The intronic miRNAs preferentially impact important genes for the three host genes in the network, indicating their roles in maintaining the integrity of the interactome where the host genes are involved. Targets of the intronic miRNAs display functional similarity to the host genes. We furthermore present sets of target genes for future investigation on the possible miRNA-target interactions that potentially contribute to cardiovascular diseases.

Conclusions

Our work provides new insight into the regulatory network of the cardiovascular-associated pathways and opens the possibility for future experimental research.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0232-4) contains supplementary material, which is available to authorized users.

miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

microRNA dysregulation in neurodegenerative diseases: A systematic review

While the root causes for individual neurodegenerative diseases are distinct, many shared pathological features and mechanisms contribute to neurodegeneration across diseases. Altered levels of microRNAs, small non-coding RNAs involved in post transcriptional regulation of gene expression, are reported for numerous neurodegenerative diseases. Yet, comparison between diseases to uncover commonly dysregulated microRNAs during neurodegeneration in general is lagging. We performed a systematic review of peer-reviewed publications describing differential microRNA expression in neurodegenerative diseases and related animal models. We compiled the results from studies covering the prevalent neurodegenerative diseases in the literature: Alzheimer's disease, amyotrophic lateral sclerosis, age-related macular degeneration, ataxia, dementia, myotonic dystrophy, epilepsy, glaucoma, Huntington's disease, multiple sclerosis, Parkinson's disease, and prion disorders. MicroRNAs which were dysregulated most often in these diseases and their models included miR-9-5p, miR-21-5p, the miR-29 family, miR-132-3p, miR-124-3p, miR-146a-5p, miR-155-5p, and miR-223-3p. Common pathways targeted by these predominant miRNAs were identified and revealed great functional overlap across diseases. We also identified a strong role for each microRNA in both the neural and immune components of diseases. microRNAs regulate broad networks of genes and identifying microRNAs commonly dysregulated across neurodegenerative diseases could cultivate novel hypotheses related to common molecular mechanisms underlying neurodegeneration.

Genome-Wide Identification of Putative MicroRNAs in Cassava (Manihot esculenta Crantz) and Their Functional Landscape in Cellular Regulation

MicroRNAs are small noncoding RNAs, involved in the regulation of many cellular processes in plants. Hundreds of miRNAs have been identified in cassava by various techniques, yet these identifications were constrained by a lack of miRNA templates and the narrow range of conditions in transcriptome study. In this research, we conducted genome-wide analysis identification, whereby miRNAs from cassava genome were thoroughly screened using bioinformatics approach independent of predefined templates and studied conditions. Our work provided a catalog of putative mature miRNAs and explored the landscape of miRNAome in cassava. These putative miRNAs were validated using statistical analysis as well as available cassava expression data. We showed that the crowded locations of cassava miRNAs are consistent with other plants and animals and hypothesized to have the same evolutionary origin. At least 10 conserved miRNAs were identified in cassava based on the comparative study of miRNA conservation. Finally, investigation of miRNAs and target gene relationships enabled us to envisage the complexities of cellular regulatory systems modulated at posttranscriptional level.

Investigating HIV-Human Interaction Networks to Unravel Pathogenic Mechanism for Drug Discovery: A Systems Biology Approach

BACKGROUND

Two big issues in the study of pathogens are determining how pathogens infect hosts and how the host defends itself against infection. Therefore, investigating host-pathogen interactions is important for understanding pathogenicity and host defensive mechanisms and treating infections.

METHODS

In this study, we used omics data, including time-course data from high-throughput sequencing, real-time polymerase chain reaction, and human microRNA (miRNA) and protein-protein interaction to construct an interspecies protein-protein and miRNA interaction (PPMI) network of human CD4+ T cells during HIV-1 infection through system modeling and identification.

RESULTS

By applying a functional annotation tool to the identified PPMI network at each stage of HIV infection, we found that repressions of three miRNAs, miR-140-5p, miR-320a, and miR-941, are involved in the development of autoimmune disorders, tumor proliferation, and the pathogenesis of T cells at the reverse transcription stage. Repressions of miR-331-3p and miR-320a are involved in HIV-1 replication, replicative spread, anti-apoptosis, cell proliferation, and dysregulation of cell cycle control at the integration/replication stage. Repression of miR-341-5p is involved in carcinogenesis at the late stage of HIV-1 infection.

CONCLUSION

By investigating the common core proteins and changes in specific proteins in the PPMI network between the stages of HIV-1 infection, we obtained pathogenic insights into the functional core modules and identified potential drug combinations for treating patients with HIV-1 infection, including thalidomide, oxaprozin, and metformin, at the reverse transcription stage; quercetin, nifedipine, and fenbendazole, at the integration/replication stage; and staurosporine, quercetin, prednisolone, and flufenamic acid, at the late stage.

Regulatory Mechanism of miR-543-3p on GLT-1 in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) features the degeneration and death of dopamine neurons in the substantia nigra pars compacta and the formation of Lewy bodies that contain α-synuclein. Among the numerous PD etiologies, glutamate excitotoxicity is a research hot spot, and glutamate transporters play key roles in this theory. It has been shown that the expression of the glutamate transporter is regulated by microRNAs. In this study, we found that the levels of expression and function of glutamate transporter type 1 (GLT-1) were significantly reduced and miR-543-3p was upregulated during the development of PD. Furthermore, our results indicated that GLT-1 plays an important role in the pathomechanism of PD. We found that miR-543-3p can suppress the expression and function of GLT-1 in MPP⁺-treated astrocytes and MPTP-treated mice. Inhibition of miR-543-3p can rescue the expression and function of GLT-1 and relieve dyskinesia in the PD model, which suggests that inhibition of miR-543-3p could serve as a potential therapeutic target for PD.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

Kinetic Modelling of Competition and Depletion of Shared miRNAs by Competing Endogenous RNAs

Non-coding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting, e.g., the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.

miRNA-miRNA crosstalk: from genomics to phenomics

The discovery of microRNA (miRNA)-miRNA crosstalk has greatly improved our understanding of complex gene regulatory networks in normal and disease-specific physiological conditions. Numerous approaches have been proposed for modeling miRNA-miRNA networks based on genomic sequences, miRNA-mRNA regulation, functional information and phenomics alone, or by integrating heterogeneous data. In addition, it is expected that miRNA-miRNA crosstalk can be reprogrammed in different tissues or specific diseases. Thus, transcriptome data have also been integrated to construct context-specific miRNA-miRNA networks. In this review, we summarize the state-of-the-art miRNA-miRNA network modeling methods, which range from genomics to phenomics, where we focus on the need to integrate heterogeneous types of omics data. Finally, we suggest future directions for studies of crosstalk of noncoding RNAs. This comprehensive summarization and discussion elucidated in this work provide constructive insights into miRNA-miRNA crosstalk.

MicroRNA Expression Profiling in Behçet's Disease

Background

Behçet's disease (BD) is a chronic inflammatory multisystem disease characterized by oral and genital ulcers, uveitis, and skin lesions. MicroRNAs (miRNAs) are key regulators of immune responses. Differential expression of miRNAs has been reported in several inflammatory autoimmune diseases; however, their role in BD is not fully elucidated. We aimed to identify miRNA expression signatures associated with BD and to investigate their potential implication in the disease pathogenesis.

Methods

miRNA microarray analysis was performed in blood cells of BD patients and healthy controls. miRNA expression profiles were analyzed using Affymetrix arrays with a comprehensive coverage of miRNA sequences. Pathway analyses were performed, and the global miRNA profiling was combined with transcriptoma data in BD. Deregulation of selected miRNAs was validated by real-time PCR.

Results

We identified specific miRNA signatures associated with BD patients with active disease. These miRNAs target pathways relevant in BD, such as TNF, IFN gamma, and VEGF-VEGFR signaling cascades. Network analysis revealed several miRNAs regulating highly connected genes within the BD transcriptoma.

Conclusions

The combined analysis of deregulated miRNAs and BD transcriptome sheds light on some epigenetic aspects of BD identifying specific miRNAs, which may represent promising candidates as biomarkers and/or for the design of novel therapeutic strategies in BD.

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.

Tiresias: Context-sensitive Approach to Decipher the Presence and Strength of MicroRNA Regulatory Interactions

MicroRNAs (miRNAs) are short non-coding RNAs that regulate expression of target messenger RNAs (mRNAs) post-transcriptionally. Understanding the precise regulatory role of miRNAs is of great interest since miRNAs have been shown to play an important role in development, diseases, and other biological processes. Early work on miRNA target prediction has focused on static sequence-driven miRNA-mRNA complementarity. However, recent research also utilizes expression-level data to study context-dependent regulation effects in a more dynamic, physiologically-relevant setting.

Methods: We propose a novel artificial neural network (ANN) based method, named Tiresias, to predict such targets in a context-dependent manner by combining sequence and expression data. In order to predict the interacting pairs among miRNAs and mRNAs and their regulatory weights, we develop a two-stage ANN and present how to train it appropriately. Tiresias is designed to study various regulation models, ranging from a simple linear model to a complex non-linear model. Tiresias has a single hyper-parameter to control the sparsity of miRNA-mRNA interactions, which we optimize using Bayesian optimization.

Results: Tiresias performs better than existing computational methods such as GenMiR++, Elastic Net, and PIMiM, achieving an F1 score of >0.8 for a certain level of regulation strength. For the TCGA breast invasive carcinoma dataset, Tiresias results in the rate of up to 82% in detecting the experimentally-validated interactions between miRNAs and mRNAs, even if we assume that true regulations may result in a low level of regulation strength.

Conclusion: Tiresias is a two-stage ANN, computational method that deciphers context-dependent microRNA regulatory interactions. Experiment results demonstrate that Tiresias outperforms existing solutions and can achieve a high F1 score. Source code of Tiresias is available at https://bitbucket.org/cellsandmachines/.

Evolutionary rate heterogeneity between multi- and single-interface hubs across human housekeeping and tissue-specific protein interaction network: Insights from proteins' and its partners' properties

Integrating gene expression into protein-protein interaction network (PPIN) leads to the construction of tissue-specific (TS) and housekeeping (HK) sub-networks, with distinctive TS- and HK-hubs. All such hub proteins are divided into multi-interface (MI) hubs and single-interface (SI) hubs, where MI hubs evolve slower than SI hubs. Here we explored the evolutionary rate difference between MI and SI proteins within TS- and HK-PPIN and observed that this difference is present only in TS, but not in HK-class. Next, we explored whether proteins' own properties or its partners' properties are more influential in such evolutionary discrepancy. Statistical analyses revealed that this evolutionary rate correlates negatively with protein's own properties like expression level, miRNA count, conformational diversity and functional properties and with its partners' properties like protein disorder and tissue expression similarity. Moreover, partial correlation and regression analysis revealed that both proteins' and its partners' properties have independent effects on protein evolutionary rate.

ceRNA crosstalk stabilizes protein expression and affects the correlation pattern of interacting proteins

Gene expression is a noisy process and several mechanisms, both transcriptional and post-transcriptional, can stabilize protein levels in cells. Much work has focused on the role of miRNAs, showing in particular that miRNA-mediated regulation can buffer expression noise for lowly expressed genes. Here, using in silico simulations and mathematical modeling, we demonstrate that miRNAs can exert a much broader influence on protein levels by orchestrating competition-induced crosstalk between mRNAs. Most notably, we find that miRNA-mediated cross-talk (i) can stabilize protein levels across the full range of gene expression rates, and (ii) modifies the correlation pattern of co-regulated interacting proteins, changing the sign of correlations from negative to positive. The latter feature may constitute a potentially robust signature of the existence of RNA crosstalk induced by endogenous competition for miRNAs in standard cellular conditions.

Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments

INTRODUCTION

We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.

METHODS

We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.

RESULTS

We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex.

DISCUSSION

Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex.

The schizophrenia risk gene MIR137 acts as a hippocampal gene network node orchestrating the expression of genes relevant to nervous system development and function

MicroRNAs (miRs) are small regulatory molecules, which orchestrate neuronal development and plasticity through modulation of complex gene networks. MicroRNA-137 (miR-137) is a brain-enriched RNA with a critical role in regulating brain development and in mediating synaptic plasticity. Importantly, mutations in this miR are associated with the pathoetiology of schizophrenia (SZ), and there is a widespread assumption that disruptions in miR-137 expression lead to aberrant expression of gene regulatory networks associated with SZ. To systematically identify the mRNA targets for this miR, we performed miR-137 gain- and loss-of-function experiments in primary rat hippocampal neurons and profiled differentially expressed mRNAs through next-generation sequencing. We identified 500 genes that were bidirectionally activated or repressed in their expression by the modulation of miR-137 levels. Gene ontology analysis using two independent software resources suggested functions for these miR-137-regulated genes in neurodevelopmental processes, neuronal maturation processes and cell maintenance, all of which known to be critical for proper brain circuitry formation. Since many of the putative miR-137 targets identified here also have been previously shown to be associated with SZ, we propose that this miR acts as a critical gene network hub contributing to the pathophysiology of this neurodevelopmental disorder.

SpidermiR: An R/Bioconductor Package for Integrative Analysis with miRNA Data

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA-gene-gene and miRNA-protein-protein interactions, and to analyze miRNA GRNs in order to identify miRNA-gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.

Prediction of microRNAs involved in immune system diseases through network based features

MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.

Multifaceted enrichment analysis of RNA-RNA crosstalk reveals cooperating micro-societies in human colorectal cancer

Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA–miRNA and miRNA–miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non-tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional backbone was fulfilled by tight micro-societies of miRNAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signaling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA–RNA crosstalk, which mechanistically modulates several signaling pathways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis.

A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions

Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independently regulate their downstream targets, collaborate with each other to regulate gene expression. However, their synergistic roles in protein-protein interactions (PPIs) remain mostly unknown. In this paper, we present a novel framework (called CoRePPI) for inferring TF and miRNA co-regulation of PPIs. Particularly, CoRePPI is aimed at discovering the co-regulation specific to a condition of interest, by using heterogeneous data, including miRNA and messenger RNA (mRNA) expression profiles, putative miRNA targets, TF targets and PPIs. CoRePPI firstly finds the network motifs indicating the co-regulation of PPIs by TFs and miRNAs in tumor and normal conditions separately. Then by identifying the differential motifs found in one condition but not in the other, it builds the networks consisting of TFs, miRNAs and their co-regulated PPIs specific to different conditions respectively. To validate CoRePPI, we apply it to the Pan-Cancer dataset which includes the expression profiles of 12 cancer types from TCGA. Through network topology analysis, we found that the tumor and normal CoRePPI networks are scale-free. Furthermore, the results of differential and intersected network analysis between the tumor and normal CoRePPI networks suggest that only a small fraction of the regulatory relationships between TFs and miRNAs are conserved in both conditions but they co-regulate different downstream PPIs in tumor and normal conditions; and in different conditions the majority of the regulatory relationships between TFs and miRNAs are different although they may regulate the same PPIs in their respective conditions. The CoRePPI sub-networks constructed for the three types of cancers (breast cancer, lung cancer and ovarian cancer) are all scale-free, and the intersection of these CoRePPI sub-networks can be utilized as the biomarker CoRePPI sub-network of the three types of cancers. The PPI enrichment analyses of the tumor and normal CoRePPI networks suggest that the co-regulating TFs and miRNAs are significantly associated with the specific biological processes, diseases and pathways. In addition, comparing with the two non-condition-specific approaches, the tumor CoRePPI network is found to have the most enriched cancer-related PPIs. Altogether, the results uncover the combined regulatory patterns of TFs and miRNAs on the PPIs, and may provide new insights for research in cancer-associated TFs and miRNAs.

MicroRNA-derived network analysis of differentially methylated genes in schizophrenia, implicating GABA receptor B1 [GABBR1] and protein kinase B [AKT1]

BACKGROUND

While hundreds of genes have been implicated already in the etiology of schizophrenia, the exact cause is not known or the disease is considered multigenic in origin. Recent discoveries of new types of RNAs and the gradual elimination of the "junk DNA" hypothesis refocused the attention on the noncoding part of the human genome. Here we re-analyzed a recent dataset of differentially methylated genes from schizophrenic patients and cross-tabulated them with cis regulatory and repetitive elements and microRNAs known to be involved in schizophrenia.

RESULTS

We found that the number of schizophrenia-related (SZ) microRNA targets follows a scale-free distribution with several microRNA hubs and that schizophrenia-related microRNAs with shared targets form a small-world network. The top ten microRNAs with the highest number of SZ gene targets regulate approximately 80 % of all microRNA-regulated genes whereas the top two microRNAs regulate 40-52 % of all such genes. We also found that genes that are regulated by the same microRNAs tend to have more protein-protein interactions than randomly selected schizophrenia genes. This highlights the role microRNAs possibly play in coordinating the abundance of interacting proteins, an important function that has not been sufficiently explored before. The analysis revealed that GABBR1 is regulated by both of the top two microRNAs and acts as a hub by interacting with many schizophrenia-related genes and sharing several types of transcription-binding sites with its interactors. We also found that differentially methylated repetitive elements are significantly more methylated in schizophrenia, pointing out their potential role in the disease.

CONCLUSIONS

We find that GABBR1 has a central importance in schizophrenia, even if no direct cause and effect have been shown for it for the time. In addition to being a hub in microRNA-derived regulatory pathways and protein-protein interactions, its centrality is also supported by the high number of cis regulatory elements and transcription factor-binding sites that regulate its transcription. These findings are in line with several genome-wide association studies that repeatedly find the major histocompatibility region (where GABBR1 is located) to have the highest number of single nucleotide polymorphisms in schizophrenics. Our model also offers an explanation for the downregulation of protein kinase B, another consistent finding in schizophrenic patients. Our observations support the notion that microRNAs fine-tune the amount of proteins acting in the same biological pathways in schizophrenia, giving further support to the emerging theory of competing endogenous RNAs.

The miR-146b-3p/PAX8/NIS Regulatory Circuit Modulates the Differentiation Phenotype and Function of Thyroid Cells during Carcinogenesis

The presence of differentiated thyroid cells in thyroid cancer is critical for the antitumor response to radioactive iodide treatment, and loss of the differentiated phenotype is a key hallmark of iodide-refractory metastatic disease. The role of microRNAs (miRNA) in fine-tuning gene expression has become a major regulatory mechanism by which developmental and pathologic processes occur. In this study, we performed next-generation sequencing and expression analysis of eight papillary thyroid carcinomas (PTC) to comprehensively characterize miRNAs involved in loss of differentiation. We found that only a small set of abundant miRNAs is differentially expressed between PTC tissue and normal tissue from the same patient. In addition, we integrated computational prediction of potential targets and mRNA sequencing and identified a master miRNA regulatory network involved in essential biologic processes such as thyroid differentiation. Both mature products of mir-146b (miR-146b-5p and -3p) were among the most abundantly expressed miRNAs in tumors. Specifically, we found that miR-146b-3p binds to the 3'-untranslated region of PAX8 and sodium/iodide symporter (NIS), leading to impaired protein translation and a subsequent reduction in iodide uptake. Furthermore, our findings show that miR-146b and PAX8 regulate each other and share common target genes, thus highlighting a novel regulatory circuit that governs the differentiated phenotype of PTC. In conclusion, our study has uncovered the existence of a miR-146b-3p/PAX8/NIS regulatory circuit that may be exploited therapeutically to modulate thyroid cell differentiation and iodide uptake for improved treatment of advanced thyroid cancer.

Identification of lung cancer miRNA-miRNA co-regulation networks through a progressive data refining approach

Co-regulations of miRNAs have been much less studied than the research on regulations between miRNAs and their target genes, although these two problems are equally important for understanding the entire mechanisms of complex post-transcriptional regulations. The difficulty to construct a miRNA-miRNA co-regulation network lies in how to determine reliable miRNA pairs from various resources of data related to the same disease such as expression levels, gene ontology (GO) databases, and protein-protein interactions. Here we take a novel integrative approach to the discovery of miRNA-miRNA co-regulation networks. This approach can progressively refine the various types of data and the computational analysis results. Applied to three lung cancer miRNA expression data sets of different subtypes, our method has identified a miRNA-miRNA co-regulation network and co-regulating functional modules common to lung cancer. An example of these functional modules consists of genes SMAD2, ACVR1B, ACVR2A and ACVR2B. This module is synergistically regulated by let-7a/b/c/f, is enriched in the same GO category, and has a close proximity in the protein interaction network. We also find that the co-regulation network is scale free and that lung cancer related miRNAs have more synergism in the network. According to our literature survey and database validation, many of these results are biologically meaningful for understanding the mechanism of the complex post-transcriptional regulations in lung cancer.

Molecular signatures for obesity and associated disorders identified through partial least square regression models

Background

Obesity is now a worldwide epidemic disease and poses a major risk for diet related diseases like type 2 diabetes, cardiovascular disease, stroke and fatty liver among others. In the present study we employed the murine model of diet-induced obesity to determine the early, tissue-specific, gene expression signatures that characterized progression to obesity and type 2 diabetes.

Results

We used the C57BL/6 J mouse which is known as a counterpart for diet-induced human diabetes and obesity model. Our initial experiments involved two groups of mice, one on normal diet (ND) and the other on high-fat and high-sucrose (HFHSD). The later were then further separated into subgroups that either received no additional treatment, or were treated with different doses of the Ayurvedic formulation KAL-1. At different time points (week3, week6, week9, week12, week15 and week18) eight different tissues were isolated from mice being fed on different diet compositions. These tissues were used to extract gene-expression data through microarray experiment. Simultaneously, we also measured different body parameters like body weight, blood Glucose level and cytokines profile (anti-inflammatory & pro-inflammatory) at each time point for all the groups.

Using partial least square discriminant analysis (PLS-DA) method we identified gene-expression signatures that predict physiological parameters like blood glucose levels, body weight and the balance of pro- versus anti-inflammatory cytokines. The resulting models successfully predicted diet-induced changes in body weight and blood glucose levels, although the predictive power for cytokines profiles was relatively poor. In the former two instances, however, we could exploit the models to further extract the early gene-expression signatures that accurately predict the onset of diabetes and obesity. These extracted genes allowed definition of the regulatory network involved in progression of disease.

Conclusion

We identified the early gene-expression signature for the onset of obesity and type 2 diabetes. Further analysis of this data suggests that some of these genes could be used as potential biomarkers for these two disease-states.

A systems biology approach identified different regulatory networks targeted by KSHV miR-K12-11 in B cells and endothelial cells

Background

Kaposi’s sarcoma associated herpes virus (KSHV) is associated with tumors of endothelial and lymphoid origin. During latent infection, KSHV expresses miR-K12-11, an ortholog of the human tumor gene hsa-miR-155. Both gene products are microRNAs (miRNAs), which are important post-transcriptional regulators that contribute to tissue specific gene expression. Advances in target identification technologies and molecular interaction databases have allowed a systems biology approach to unravel the gene regulatory networks (GRNs) triggered by miR-K12-11 in endothelial and lymphoid cells. Understanding the tissue specific function of miR-K12-11 will help to elucidate underlying mechanisms of KSHV pathogenesis.

Results

Ectopic expression of miR-K12-11 differentially affected gene expression in BJAB cells of lymphoid origin and TIVE cells of endothelial origin. Direct miRNA targeting accounted for a small fraction of the observed transcriptome changes: only 29 genes were identified as putative direct targets of miR-K12-11 in both cell types. However, a number of commonly affected biological pathways, such as carbohydrate metabolism and interferon response related signaling, were revealed by gene ontology analysis. Integration of transcriptome profiling, bioinformatic algorithms, and databases of protein-protein interactome from the ENCODE project identified different nodes of GRNs utilized by miR-K12-11 in a tissue-specific fashion. These effector genes, including cancer associated transcription factors and signaling proteins, amplified the regulatory potential of a single miRNA, from a small set of putative direct targets to a larger set of genes.

Conclusions

This is the first comparative analysis of miRNA-K12-11’s effects in endothelial and B cells, from tissues infected with KSHV in vivo. MiR-K12-11 was able to broadly modulate gene expression in both cell types. Using a systems biology approach, we inferred that miR-K12-11 establishes its GRN by both repressing master TFs and influencing signaling pathways, to counter the host anti-viral response and to promote proliferation and survival of infected cells. The targeted GRNs are more reproducible and informative than target gene identification, and our approach can be applied to other regulatory factors of interest.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-668) contains supplementary material, which is available to authorized users.

Functional evolution of cardiac microRNAs in heart development and functions

MicroRNAs (miRNAs) are a class of endogenous small noncoding RNAs that regulate gene expression either by degrading target mRNAs or by suppressing protein translation. miRNAs have been found to be involved in many biological processes, such as development, differentiation, and growth. However, the evolution of miRNA regulatory functions and networks has not been well studied. In this study, we conducted a cross-species analysis to study the evolution of cardiac miRNAs and their regulatory functions and networks. We found that conserved cardiac miRNA target genes have maintained highly conserved cardiac functions. Additionally, most of cardiac miRNA target genes in human with annotations of cardiac functions evolved from the corresponding homologous targets, which are also involved in heart development-related functions. On the basis of these results, we investigated the functional evolution of cardiac miRNAs and presented a functional evolutionary map. From this map, we identified the evolutionary time at which the cardiac miRNAs became involved in heart development or function and found that the biological processes of heart development evolved earlier than those of heart functions, for example, heart contraction/relaxation or cardiac hypertrophy. Our study of the evolution of the cardiac miRNA regulatory networks revealed the emergence of new regulatory functional branches during evolution. Furthermore, we discovered that early evolved cardiac miRNA target genes tend to participate in the early stages of heart development. This study sheds light on the evolution of developmental features of genes regulated by cardiac miRNAs.

Integrative identification of deregulated miRNA/TF-mediated gene regulatory loops and networks in prostate cancer

MicroRNAs (miRNAs) have attracted a great deal of attention in biology and medicine. It has been hypothesized that miRNAs interact with transcription factors (TFs) in a coordinated fashion to play key roles in regulating signaling and transcriptional pathways and in achieving robust gene regulation. Here, we propose a novel integrative computational method to infer certain types of deregulated miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional and signaling levels. To reliably predict miRNA-target interactions from mRNA/miRNA expression data, our method collectively utilizes sequence-based miRNA-target predictions obtained from several algorithms, known information about mRNA and miRNA targets of TFs available in existing databases, certain molecular structures identified to be statistically over-represented in gene regulatory networks, available molecular subtyping information, and state-of-the-art statistical techniques to appropriately constrain the underlying analysis. In this way, the method exploits almost every aspect of extractable information in the expression data. We apply our procedure on mRNA/miRNA expression data from prostate tumor and normal samples and detect numerous known and novel miRNA-mediated deregulated loops and networks in prostate cancer. We also demonstrate instances of the results in a number of distinct biological settings, which are known to play crucial roles in prostate and other types of cancer. Our findings show that the proposed computational method can be used to effectively achieve notable insights into the poorly understood molecular mechanisms of miRNA-mediated interactions and dissect their functional roles in cancer in an effort to pave the way for miRNA-based therapeutics in clinical settings.

Identification of microRNA as sepsis biomarker based on miRNAs regulatory network analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

Computational developments in microRNA-regulated protein-protein interactions

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.