Discovery and visualization of miRNA-mRNA functional modules within integrated data using bicluster analysis

Biclustering data analysis: a comprehensive survey

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

MiR-526b-3p enhances sensitivity of head and neck squamous cell carcinoma cells to radiotherapy via suppressing exosomal LAMP3-mediated autophagy

Lysosomal associated membrane protein 3 (LAMP3) has been reported to be a tumour promoter in multiple cancer types by modulating tumour cell autophagy. However, the potential mechanism of LAMP3 in radio-resistance of head and neck squamous cell carcinoma (HNSCC) remains unknown. Therefore, our current study aims to detect the impacts of LAMP3 on the resistance of HNSCC cells to radiotherapy and meanwhile explore its functional mechanism. Through RT-Qpcr examination, LAMP3 expression was identified to be expressed at a significantly high level in irradiation-resistant HNSCC cell lines compared with irradiation-sensitive HNSCC cell lines. Functional assays including CCK-8, colony formation and Transwell assays demonstrated that LAMP3 enhanced the radio-resistance through inducing autophagy to promote HNSCC cell growth. Furthermore, irradiation-resistant HNSCC cells could transfer exosomal LAMP3 to elevate LAMP3 expression in irradiation-sensitive HNSCC cells. Mechanistically, microRNA (miRNA) miR-526b-3p could inhibit LAMP3 expression so as to strengthen sensitivity of HNSCC cells to radiotherapy. In a word, exosomal LAMP3 expression promoted radioresistance of HNSCC cells via inducing autophagy, while this effect could be suppressed by miR-526b-3p in a targeted manner.

Intermittent Hypoxia Mediates Cancer Development and Progression Through HIF-1 and miRNA Regulation

INTRODUCTION

There is still a debate for the link between obstructive sleep apnoea (OSA) and cancer. The mechanisms underlying this causality are poorly understood. Several miRNAs are involved in cancer development and progression with expression being influenced by hypoxia. The aims of this work were (i) to compare miRNAs expression in controls versus patients affected by OSA without or with cancer (ONCO-OSA) and (ii) in colorectal cancer cells exposed to intermittent hypoxia (IH), to evaluate miRNAs impact on tumor progression in vitro.

METHODS

We detected miRNAs by qRT-PCR in patients' sera and in CaCo2 cells exposed to 2-32h of IH with or without acriflavine (ACF), a HIF-1 inhibitor. Viability and transwell invasion test were applied to investigate the proliferation and migration of CaCo2 exposed to IH and treated with miRNA inhibitors or acriflavine. HIF-1α activity was evaluated in CaCo2 cells after IH.

RESULTS

The levels of miR-21, miR-26a and miR-210 increased in OSA and ONCO-OSA patients compared to controls. MiR-23b increased in ONCO-OSA patients, and miR-27b and miR-145 increased in OSA but not ONCO-OSA patients. MiR-21, miR-26a, miR-23b and miR-210 increased in cells after IH. IH stimulated cell proliferation and migration. This effect was reduced after either miRNA inhibition or acriflavine treatment. MiRNA inhibition reduces HIF-1α gene expression. Conversely, acriflavine reduced the expression of these miRNAs.

CONCLUSIONS

We identified a signature of miRNAs, induced by the IH environment. They could be implicated in cancer development and progression through a regulatory loop involving HIF-1.

Identifying miRNA-Gene Common and Specific Regulatory Modules for Cancer Subtyping by a High-Order Graph Matching Model

Identifying regulatory modules between miRNAs and genes is crucial in cancer research. It promotes a comprehensive understanding of the molecular mechanisms of cancer. The genomic data collected from subjects usually relate to different cancer statuses, such as different TNM Classifications of Malignant Tumors (TNM) or histological subtypes. Simple integrated analyses generally identify the core of the tumorigenesis (common modules) but miss the subtype-specific regulatory mechanisms (specific modules). In contrast, separate analyses can only report the differences and ignore important common modules. Therefore, there is an urgent need to develop a novel method to jointly analyze miRNA and gene data of different cancer statuses to identify common and specific modules. To that end, we developed a High-Order Graph Matching model to identify Common and Specific modules (HOGMCS) between miRNA and gene data of different cancer statuses. We first demonstrate the superiority of HOGMCS through a comparison with four state-of-the-art techniques using a set of simulated data. Then, we apply HOGMCS on stomach adenocarcinoma data with four TNM stages and two histological types, and breast invasive carcinoma data with four PAM50 subtypes. The experimental results demonstrate that HOGMCS can accurately extract common and subtype-specific miRNA-gene regulatory modules, where many identified miRNA-gene interactions have been confirmed in several public databases.

Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.

Network-based cancer genomic data integration for pattern discovery

BACKGROUND

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.

RESULTS

In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered.

CONCLUSIONS

All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

A holistic miRNA-mRNA module discovery

The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. In this paper we will focus on miRNA-mRNA modules, where regulation jointly occurs in miRNA-mRNA pairs. Namely, we propose a holistic procedure to identify miRNA-mRNA modules within a population of candidate pairs. Since current methods still leave open issues, we adopt the strategy of postponing any decision on the value of the module ingredients exactly at the end, i.e. at the moment of biologically exploiting the results. This diverts chains of statistical tests into sequences of specially-devised-evolving metrics on the possible solutions. This strategy is rather expensive under a computational perspective, so needing implementations on HPC. The reward stands in the discovery of new modules, possibly hosting non differentially expressed miRNAs and mRNAs and pairs containing genes that currently are considered not targeted. In the paper we implement the procedure on a Multiple Myeloma dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues. These results, jointly with the normal manageability of the computations, suggest that the discovery procedure may be profitably extended to a wide spectrum of diseases where miRNA-mRNA interactions play a relevant role.

miR-877 inhibits the proliferation, migration, and invasion of osteosarcoma cells by targeting gamma-glutamylcyclotransferase

Gamma-glutamylcyclotransferase (GGCT) can promote the progression of osteosarcoma (OS). MicroRNAs also play significant roles in regulating the progression of OS. This study was designed to investigate whether miR-877 exerts its function in OS by targeting GGCT. The proliferation of OS cells (Saos-2 and U2OS) was detected by MTT and colony formation assays. The migration and invasion of OS cells were detected by transwell assays. The expressions of miRNAs and GGCT were detected by quantitative real-time PCR and Western blot. The luciferase reporter assay was performed to assess whether miR-877 could target GGCT. miR-877 was down-regulated both in OS tissues and OS cell lines (Saos-2 and U2OS). The overexpression of miR-877 inhibited the proliferation, migration, and invasion of OS cell lines, while the knockdown of miR-877 could negate effects. The expression of GGCT was increased in Saos-2 and U2OS cells. miR-877 could target GGCT, and the mRNA level of GGCT in Saos-2 and U2OS cells was decreased by the overexpression of miR-877. miR-877 overexpression inhibited the migration and invasion and suppressed the proliferation of Saos-2 and U2OS cells, and the overexpression of GGCT reversed this effects. The knockdown of miR-877 promoted the migration and invasion and facilitated the proliferation of Saos-2 and U2OS cells, and the silence of GGCT abolished this effects. Our findings suggested that miR-877 could inhibit the proliferation, migration, and invasion of OS cells by targeting GGCT.

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.

MMiRNA-Viewer2, a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network

Background

Although there are many studies on the characteristics of miRNA-mRNA interactions using miRNA and mRNA sequencing data, the complexity of the change of the correlation coefficients and expression values of the miRNA-mRNA pairs between tumor and normal samples is still not resolved, and this hinders the potential clinical applications. There is an urgent need to develop innovative methodologies and tools that can characterize and visualize functional consequences of cancer risk gene and miRNA pairs while analyzing the tumor and normal samples simultaneously.

Results

We developed an innovative bioinformatics tool for visualizing functional annotation of miRNA-mRNA pairs in a network, known as MMiRNA-Viewer². The tool takes mRNA and miRNA interaction pairs and visualizes mRNA and miRNA regulation network. Moreover, our MMiRNA-Viewer² web server integrates and displays the mRNA and miRNA gene annotation information, signaling cascade pathways and direct cancer association between miRNAs and mRNAs. Functional annotation and gene regulatory information can be directly retrieved from our web server, which can help users quickly identify significant interaction sub-network and report possible disease or cancer association. The tool can identify pivotal miRNAs or mRNAs that contribute to the complexity of cancer, while engaging modern next-generation sequencing technology to analyze the tumor and normal samples concurrently. We compared our tools with other visualization tools.

Conclusion

Our MMiRNA-Viewer² serves as a multitasking platform in which users can identify significant interaction clusters and retrieve functional and cancer-associated information for miRNA-mRNA pairs between tumor and normal samples. Our tool is applicable across a range of diseases and cancers and has advantages over existing tools.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Clinical significance of miRNAs in autoimmunity

MicroRNAs (miRNAs) are evolutionally conserved, single-stranded RNAs that regulate gene expression at the posttranscriptional level by disrupting translation. MiRNAs are key players in variety of biological processes that regulate the differentiation, development and activation of immune cells in both innate and adaptive immunity. The disruption and dysfunction of miRNAs can perturb the immune response, stimulate the release of inflammatory cytokines and initiate the production of autoantibodies, and contribute to the pathogenesis of autoimmune diseases, including systemic lupus erythmatosus (SLE), rheumatoid arthritis (RA), primary biliary cholangitis (PBC), and multiple sclerosis (MS). Accumulating studies demonstrate that miRNAs, which can be collected by noninvasive methods, have the potential to be developed as diagnostic and therapeutic biomarkers, the discovery and validation of which is essential for the improvement of disease diagnosis and clinical monitoring. Recently, with the development of detection tools, such as microarrays and NGS (Next Generation Sequencing), large amounts of miRNAs have been identified and suggest a critical role in the pathogenesis of autoimmune diseases. Several miRNAs associated diagnostic biomarkers have been developed and applied clinically, though the pharmaceutical industry is still facing challenges in commercialization and drug delivery. The development of miRNAs is less advanced for autoimmune diseases compared with cancer. However, drugs that target miRNAs have been introduced as candidates and adopted in clinical trials. This review comprehensively summarizes the differentially expressed miRNAs in several types of autoimmune diseases and discusses the role and the significance of miRNAs in clinical management. The study of miRNAs in autoimmunity promises to provide novel and broad diagnostic and therapeutic strategies for a clinical market that is still in its infancy.

MicroRNA-320a inhibits invasion and metastasis in osteosarcoma by targeting cytoplasmic polyadenylation element-binding protein 1

Osteosarcoma is a primary malignant bone tumor, which affects children, adolescents, and young adults commonly. MicroRNAs (miRNAs) have been proved to be dysregulated in different cancers, including osteosarcoma. Although miR‐320a has been implicated in many types of malignancies, little is known about the role of miR‐320a in osteosarcoma. In this study, we show that the overexpression of miR‐320a or knockdown of cytoplasmic polyadenylation element‐binding protein 1 (CPEB1) inhibited osteosarcoma cell migration and invasion. miR‐320a downregulates CPEB1 expression by directly targeting the CPEB1 3′‐UTR. Furthermore, CPEB1 reintroduction reversed the antiproliferation, antimigration, and antiinvasion roles of miR‐320a, indicating that miR‐320a might function as a tumor suppressor in osteosarcoma through CPEB1. In conclusion, our study demonstrates that miR‐320a plays a critical role in osteosarcoma progression and may provide a potential therapeutic target for osteosarcoma.

Understanding the Modus Operandi of MicroRNA Regulatory Clusters

MicroRNAs (miRNAs) are non-coding RNAs that regulate a wide range of biological pathways by post-transcriptionally modulating gene expression levels. Given that even a single miRNA may simultaneously control several genes enrolled in multiple biological functions, one would expect that these tiny RNAs have the ability to properly sort among distinctive cellular processes to drive protein production. To test this hypothesis, we scrutinized previously published microarray datasets and clustered protein-coding gene expression profiles according to the intensity of fold-change levels caused by the exogenous transfection of 10 miRNAs (miR-1, miR-7, miR-9, miR-124, miR-128a, miR-132, miR-133a, miR-142, miR-148b, miR-181a) in a human cell line. Through an in silico functional enrichment analysis, we discovered non-randomic regulatory patterns, proper of each cluster identified. We demonstrated that miRNAs are capable of equivalently modulate the expression signatures of target genes in regulatory clusters according to the biological function they are assigned to. Moreover, target prediction analysis applied to ten vertebrate species, suggest that such miRNA regulatory modus operandi is evolutionarily conserved within vertebrates. Overall, we discovered a complex regulatory cluster-module strategy driven by miRNAs, which relies on the controlled intensity of the repression over distinct targets under specific biological contexts. Our discovery helps to clarify the mechanisms underlying the functional activity of miRNAs and makes it easier to take the fastest and most accurate path in the search for the functions of miRNAs in any distinct biological process of interest.

Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms

Background

microRNA (miRNA) is a short RNA (~ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python.

Methods

We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T_CC relative to N_CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM).

Results

MWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms.

Conclusions

miRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0562-z) contains supplementary material, which is available to authorized users.

Integrating microRNA and mRNA expression in rapamycin-treated T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) has a relatively improved remission rate, but the poor outcomes are primarily due to resistance and relapse. Moreover, organs infiltration trends to occur during remission. Rapamycin was applied to treat malignancies for decades. In this investigation, we aimed to explore the molecular mechanisms and pathway changes during the T-ALL therapeutic process. T-ALL cell line Molt-4 cells were treated with rapamycin and performed microarray analysis to identify the deregulated miRNAs and mRNAs (log2 fold change>2 or <-2). To obtain regulatory miRNA/mRNA network, miRNA target prediction softwares and Cytoscape were used to plot and modularize the rapamycin treatment-related network. Surprisingly, the enriched pathways were not involved in mediating either cell death or apoptosis but were responsible for angiogenesis, cell survival, and anti-apoptosis, which is consistent with the Gene Ontology analysis and PPI network based on all deregulated mRNAs, indicating that these elements likely play a role in promoting Molt-4 cell survival or escaping from rapamycin. The expression of 3 miRNAs (miR-149-3p, miR-361-3p, and miR-944) and their putative targets, which play central roles in their module, were validated by qRT-PCR. These results provide novel insight into potentially relevant biological pathways for T-ALL cells escaping from chemotherapy or developing central nervous system infiltration.

A mathematical model as a tool to identify microRNAs with highest impact on transcriptome changes

Background

Rapid changes in the expression of many messenger RNA (mRNA) species follow exposure of cells to ionizing radiation. One of the hypothetical mechanisms of this response may include microRNA (miRNA) regulation, since the amounts of miRNAs in cells also vary upon irradiation. To address this possibility, we designed experiments using cancer-derived cell lines transfected with luciferase reporter gene containing sequences targeted by different miRNA species in its 3′- untranslated region. We focus on the early time-course response (1 h past irradiation) to eliminate secondary mRNA expression waves.

Results

Experiments revealed that the irradiation-induced changes in the mRNA expression depend on the miRNAs which interact with mRNA. To identify the strongest interactions, we propose a mathematical model which predicts the mRNA fold expression changes, caused by perturbation of microRNA-mRNA interactions. Model was applied to experimental data including various cell lines, irradiation doses and observation times, both ours and literature-based. Comparison of modelled and experimental mRNA expression levels given miRNA level changes allows estimating how many and which miRNAs play a significant role in transcriptome response to stress conditions in different cell types. As an example, in the human melanoma cell line the comparison suggests that, globally, a major part of the irradiation-induced changes of mRNA expression can be explained by perturbed miRNA-mRNA interactions. A subset of about 30 out of a few hundred miRNAs expressed in these cells appears to account for the changes. These miRNAs play crucial roles in regulatory mechanisms observed after irradiation. In addition, these miRNAs have a higher average content of GC and a higher number of targeted transcripts, and many have been reported to play a role in the development of cancer.

Conclusions

Our proposed mathematical modeling approach may be used to identify miRNAs which participate in responses of cells to ionizing radiation, and other stress factors such as extremes of temperature, exposure to toxins, and drugs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5464-0) contains supplementary material, which is available to authorized users.

mRNA-miRNA bipartite network reconstruction to predict prognostic module biomarkers in colorectal cancer stage differentiation

Biomarker detection is one of the most important and challenging problems in cancer studies. Recently, non-coding RNA based biomarkers such as miRNA expression levels have been used for early diagnosis of many cancer types. In this study, a systems biology approach was used to detect novel miRNA based biomarkers for CRC diagnosis in early stages. The mRNA expression data from three CRC stages (Low-grade Intraepithelial Neoplasia (LIN), High-grade Intraepithelial Neoplasia (HIN) and Adenocarcinoma) were used to reconstruct co-expression networks. The networks were clustered to extract co-expression modules and detected low preserved modules among CRC stages. Then, the experimentally validated mRNA-miRNA interaction data were applied to reconstruct three mRNA-miRNA bipartite networks. Twenty miRNAs with the highest degree (hub miRNAs) were selected in each bipartite network to reconstruct three bipartite subnetworks for further analysis. The analysis of these hub miRNAs in the bipartite subnetworks revealed 30 distinct important miRNAs as prognostic markers in CRC stages. There are two novel CRC related miRNAs (hsa-miR-190a-3p and hsa-miR-1277-5p) in these 30 hub miRNAs that have not been previously reported in CRC. Furthermore, a drug-gene interaction network was reconstructed to detect potential candidate drugs for CRC treatment. Our analysis shows that the hub miRNAs in the mRNA-miRNA bipartite network are very essential in CRC progression and should be investigated precisely in future studies. In addition, there are many important target genes in the results that may be critical in CRC progression and can be analyzed as therapeutic targets in future research.

MicroRNAs contribute to postnatal development of laminar differences and neuronal subtypes in the rat medial entorhinal cortex

The medial entorhinal cortex (MEC) is important in spatial navigation and memory formation and its layers have distinct neuronal subtypes, connectivity, spatial properties, and disease susceptibility. As little is known about the molecular basis for the development of these laminar differences, we analyzed microRNA (miRNA) and messenger RNA (mRNA) expression differences between rat MEC layer II and layers III–VI during postnatal development. We identified layer and age-specific regulation of gene expression by miRNAs, which included processes related to neuron specialization and locomotor behavior. Further analyses by retrograde labeling and expression profiling of layer II stellate neurons and in situ hybridization revealed that the miRNA most up-regulated in layer II, miR-143, was enriched in stellate neurons, whereas the miRNA most up-regulated in deep layers, miR-219-5p, was expressed in ependymal cells, oligodendrocytes and glia. Bioinformatics analyses of predicted mRNA targets with negatively correlated expression patterns to miR-143 found that miR-143 likely regulates the Lmo4 gene, which is known to influence hippocampal-based spatial learning.

Novel microRNA discovery using small RNA sequencing in post-mortem human brain

BACKGROUND

MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression mainly through translational repression of target mRNA molecules. More than 2700 human miRNAs have been identified and some are known to be associated with disease phenotypes and to display tissue-specific patterns of expression.

METHODS

We used high-throughput small RNA sequencing to discover novel miRNAs in 93 human post-mortem prefrontal cortex samples from individuals with Huntington's disease (n = 28) or Parkinson's disease (n = 29) and controls without neurological impairment (n = 36). A custom miRNA identification analysis pipeline was built, which utilizes miRDeep* miRNA identification and result filtering based on false positive rate estimates.

RESULTS

Ninety-nine novel miRNA candidates with a false positive rate of less than 5 % were identified. Thirty-four of the candidate miRNAs show sequence similarity with known mature miRNA sequences and may be novel members of known miRNA families, while the remaining 65 may constitute previously undiscovered families of miRNAs. Nineteen of the 99 candidate miRNAs were replicated using independent, publicly-available human brain RNA-sequencing samples, and seven were experimentally validated using qPCR.

CONCLUSIONS

We have used small RNA sequencing to identify 99 putative novel miRNAs that are present in human brain samples.

Design of Potential RNAi (miRNA and siRNA) Molecules for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Gene Silencing by Computational Method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF1ab replicase polyprotein may be used as a suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence-specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In the current study, four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

Identification of Spinal Cord MicroRNA and Gene Signatures in a Model of Chronic Stress-Induced Visceral Hyperalgesia in Rat

Introduction

Animal studies have shown that stress could induce epigenetic and transcriptomic alterations essential in determining the balance between adaptive or maladaptive responses to stress. We tested the hypothesis that chronic stress in rats deregulates coding and non-coding gene expression in the spinal cord, which may underline neuroinflammation and nociceptive changes previously observed in this model.

Methods

Male Wistar rats were exposed to daily stress or handled, for 10 days. At day 11, lumbar spinal segments were collected and processed for mRNA/miRNA isolation followed by expression profiling using Agilent SurePrint Rat Exon and Rat miRNA Microarray platforms. Differentially expressed gene lists were generated using the dChip program. Microarrays were analyzed using the Ingenuity Pathways Analysis (IPA) tool from Ingenuity Systems. Multiple methods were used for the analysis of miRNA-mRNA functional modules. Quantitative real time RT-PCR for Interleukin 6 signal transducer (gp130), the Signal Transducer And Activator Of Transcription 3 (STAT3), glial fibrillary acidic protein and mir-17-5p were performed to confirm levels of expression.

Results

Gene network analysis revealed that stress deregulated different inflammatory (IL-6, JAK/STAT, TNF) and metabolic (PI3K/AKT) signaling pathways. MicroRNA array analysis revealed a signature of 39 deregulated microRNAs in stressed rats. MicroRNA-gene network analysis showed that microRNAs are regulators of two gene networks relevant to inflammatory processes. Specifically, our analysis of miRNA-mRNA functional modules identified miR-17-5p as an important regulator in our model. We verified miR-17-5p increased expression in stress using qPCR and in situ hybridization. In addition, we observed changes in the expression of gp130 and STAT3 (involved in intracellular signaling cascades in response to gp130 activation), both predicted targets for miR-17-5p. A modulatory role of spinal mir17-5p in the modulation of visceral sensitivity was confirmed in vivo.

Conclusion

Using an integrative high throughput approach, our findings suggest a link between miR-17-5p increased expression and gp130/STAT3 activation providing new insight into the possible mechanisms mediating the effect of chronic stress on neuroinflammation in the spinal cord.

Identification of subtype specific miRNA-mRNA functional regulatory modules in matched miRNA-mRNA expression data: multiple myeloma as a case

Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.

MicroRNA modules prefer to bind weak and unconventional target sites

Motivation: MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy.

Results: We employed the recently generated high throughput experimental data to study miRNA modules. We predicted 181 miRNA modules and 306 potential miRNA modules. We observed that the target sites of these predicted modules were in general weaker compared with those not bound by miRNA modules. We also discovered that miRNAs in predicted modules preferred to bind unconventional target sites rather than canonical sites. Surprisingly, contrary to a previous study, we found that most adjacent miRNA target sites from the same miRNA modules were not within the range of 10–130 nucleotides. Interestingly, the distance of target sites bound by miRNAs in the same modules was shorter when miRNA modules bound unconventional instead of canonical sites. Our study shed new light on miRNA binding and miRNA target sites, which will likely advance our understanding of miRNA regulation.

Availability and implementation: The software miRModule can be freely downloaded at http://hulab.ucf.edu/research/projects/miRNA/miRModule.

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:haihu@cs.ucf.edu or xiaoman@mail.ucf.edu.

Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

Design of potential RNAi (miRNA and siRNA) molecules for Middle East respiratory syndrome coronavirus (MERS-CoV) gene silencing by computational method

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a virus that manifests itself in viral infection with fever, cough, shortness of breath, renal failure and severe acute pneumonia, which often result in a fatal outcome. MERS-CoV has been shown to spread between people who are in close contact. Transmission from infected patients to healthcare personnel has also been observed and is irredeemable with present technology. Genetic studies on MERS-CoV have shown that ORF 1ab encodes replicase polyproteins and play a foremost role in viral infection. Therefore, ORF 1ab replicase polyprotein may be used as suitable target for disease control. Viral activity can be controlled by RNA interference (RNAi) technology, a leading method for post transcriptional gene silencing in a sequence specific manner. However, there is a genetic inconsistency in different viral isolates; it is a great challenge to design potential RNAi (miRNA and siRNA) molecules which can silence the respective target genes rather than any other viral gene simultaneously. In current study four effective miRNA and five siRNA molecules for silencing of nine different strains of MERS-CoV were rationally designed and corroborated using computational methods, which might lead to knockdown the activity of virus. siRNA and miRNA molecules were predicted against ORF1ab gene of different strains of MERS-CoV as effective candidate using computational methods. Thus, this method may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MERS-CoV, at genomic level.

ToppMiR: ranking microRNAs and their mRNA targets based on biological functions and context

Identifying functionally significant microRNAs (miRs) and their correspondingly most important messenger RNA targets (mRNAs) in specific biological contexts is a critical task to improve our understanding of molecular mechanisms underlying organismal development, physiology and disease. However, current miR-mRNA target prediction platforms rank miR targets based on estimated strength of physical interactions and lack the ability to rank interactants as a function of their potential to impact a given biological system. To address this, we have developed ToppMiR (http://toppmir.cchmc.org), a web-based analytical workbench that allows miRs and mRNAs to be co-analyzed via biologically centered approaches in which gene function associated annotations are used to train a machine learning-based analysis engine. ToppMiR learns about biological contexts based on gene associated information from expression data or from a user-specified set of genes that relate to context-relevant knowledge or hypotheses. Within the biological framework established by the genes in the training set, its associated information content is then used to calculate a features association matrix composed of biological functions, protein interactions and other features. This scoring matrix is then used to jointly rank both the test/candidate miRs and mRNAs. Results of these analyses are provided as downloadable tables or network file formats usable in Cytoscape.

Scan for Motifs: a webserver for the analysis of post-transcriptional regulatory elements in the 3' untranslated regions (3' UTRs) of mRNAs

Background

Gene expression in vertebrate cells may be controlled post-transcriptionally through regulatory elements in mRNAs. These are usually located in the untranslated regions (UTRs) of mRNA sequences, particularly the 3′UTRs.

Results

Scan for Motifs (SFM) simplifies the process of identifying a wide range of regulatory elements on alignments of vertebrate 3′UTRs. SFM includes identification of both RNA Binding Protein (RBP) sites and targets of miRNAs. In addition to searching pre-computed alignments, the tool provides users the flexibility to search their own sequences or alignments. The regulatory elements may be filtered by expected value cutoffs and are cross-referenced back to their respective sources and literature. The output is an interactive graphical representation, highlighting potential regulatory elements and overlaps between them. The output also provides simple statistics and links to related resources for complementary analyses. The overall process is intuitive and fast. As SFM is a free web-application, the user does not need to install any software or databases.

Conclusions

Visualisation of the binding sites of different classes of effectors that bind to 3′UTRs will facilitate the study of regulatory elements in 3′ UTRs.