GeneSet2miRNA: finding the signature of cooperative miRNA activities in the gene lists

A microRNA-microRNA crosstalk network inferred from genome-wide single nucleotide polymorphism variants in natural populations of Arabidopsis thaliana

To adapt to variable natural conditions, plants have evolved several strategies to respond to different environmental stresses. MicroRNA (miRNA)-mediated gene regulation is one of such strategies. Variants, e.g., single nucleotide polymorphisms (SNPs) within the mature miRNAs or their target sites may cause the alteration of regulatory networks and serious phenotype changes. In this study, we proposed a novel approach to construct a miRNA-miRNA crosstalk network in Arabidopsis thaliana based on the notion that two cooperative miRNAs toward common targets are under a strong pressure to be inherited together across ecotypes. By performing a genome-wide scan of the SNPs within the mature miRNAs and their target sites, we defined a "regulation fate profile" to describe a miRNA-target regulation being static (kept) or dynamic (gained or lost) across 1,135 ecotypes compared with the reference genome of Col-0. The cooperative miRNA pairs were identified by estimating the similarity of their regulation fate profiles toward the common targets. The reliability of the cooperative miRNA pairs was supported by solid expressional correlation, high PPImiRFS scores, and similar stress responses. Different combinations of static and dynamic miRNA-target regulations account for the cooperative miRNA pairs acting on various biological characteristics of miRNA conservation, expression, homology, and stress response. Interestingly, the targets that are co-regulated dynamically by both cooperative miRNAs are more likely to be responsive to stress. Hence, stress-related genes probably bear selective pressures in a certain group of ecotypes, in which miRNA regulations on the stress genes reprogram. Finally, three case studies showed that reprogramming miRNA-miRNA crosstalk toward the targets in specific ecotypes was associated with these ecotypes' climatic variables and geographical locations. Our study highlights the potential of miRNA-miRNA crosstalk as a genetic basis underlying environmental adaptation in natural populations.

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity among miRNAs is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA individually, but when act together, induce strong repression. miRCoop uses kernel-based statistical interaction tests, together with miRNA and mRNA target information. We apply our approach to patient data of two different cancer types. In kidney cancer, we identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that identified triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. In applying the method on tumor data with the primary liver site, we find 3105 potential triplet interactions. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

Gene-specific MicroRNA antagonism protects against HIV Tat and TGF-β-mediated suppression of CFTR mRNA and function

BACKGROUND

MicroRNAs play an important role in health and disease. TGF-β signaling, upregulated by HIV Tat, and in chronic airway diseases and smokers upregulates miR-145-5p to suppress cystic fibrosis transmembrane conductance regulator (CFTR). CFTR suppression in chronic airway diseases like Cystic Fibrosis, COPD and smokers has been associated with suppressed MCC and recurrent lung infections and inflammation. This can explain the emergence of recurrent lung infections and inflammation in people living with HIV.

METHODS

Tat-induced aberrant microRNAome was identified by miRNA expression analysis. microRNA mimics and antagomirs were used to validate the identified miRNAs involved in Tat mediated CFTR mRNA suppression. CRISPR-based editing of the miRNA target sites in CFTR 3'UTR was used to determine rescue of CFTR mRNA and function in airway epithelial cell lines and in primary human bronchial epithelial cells exposed to TGF-β and Tat.

FINDINGS

HIV Tat upregulates miR-145-5p and miR-509-3p. The two miRNAs demonstrate co-operative effects in suppressing CFTR. CRISPR-based editing of the miRNA target site preserves CFTR mRNA and function in airway epithelial cells INTERPRETATION: Given the important roles of TGF-β signaling and the multitude of genes regulated by miRNAs, we demonstrate that CRISPR-based gene-specific microRNA antagonism approach can preserve CFTR mRNA and function in the context of HIV Tat and TGF-β signaling without suppressing expression of other genes regulated by miR-145-5p.

A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer's disease

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer's disease is the most common form of dementia. The pathogenesis of Alzheimer's disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer's disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confirmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer's disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer's disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases.

Computationally Modeling ncRNA-ncRNA Crosstalk

Our understanding of complex gene regulatory networks have been improved by the discovery of ncRNA-ncRNA crosstalk in normal and disease-specific physiological conditions. Previous studies have proposed numerous approaches for constructing ncRNA-ncRNA networks via ncRNA-mRNA regulation, functional information, or phenomics alone, or by combining heterogeneous data. Furthermore, it has been shown that ncRNA-ncRNA crosstalk can be rewired in different tissues or specific diseases. Therefore, it is necessary to integrate transcriptome data to construct context-specific ncRNA-ncRNA networks. In this chapter, we elucidated the commonly used ncRNA-ncRNA network modeling methods, and highlighted the need to integrate heterogeneous multi-mics data. Finally, we suggest future directions for studies of ncRNAs crosstalk. This comprehensive description and discussion elucidated in this chapter will provide constructive insights into ncRNA-ncRNA crosstalk.

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.

miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set

Background

The knowledge of miRNAs regulating the expression of sets of mRNAs has led to novel insights into numerous and diverse cellular mechanisms. While a single miRNA may regulate many genes, one gene can be regulated by multiple miRNAs, presenting a complex relationship to model for accurate predictions.

Results

Here, we introduce miREM, a program that couples an expectation-maximization (EM) algorithm to the common approach of hypergeometric probability (HP), which improves the prediction and prioritization of miRNAs from gene-sets of interest. miREM has been made available through a web-server (https://bioinfo-csi.nus.edu.sg/mirem2/) that can be accessed through an intuitive graphical user interface. The program incorporates a large compendium of human/mouse miRNA-target prediction databases to enhance prediction. Users may upload their genes of interest in various formats as an input and select whether to consider non-conserved miRNAs, amongst filtering options. Results are reported in a rich graphical interface that allows users to: (i) prioritize predicted miRNAs through a scatterplot of HP p-values and EM scores; (ii) visualize the predicted miRNAs and corresponding genes through a heatmap; and (iii) identify and filter homologous or duplicated predictions by clustering them according to their seed sequences.

Conclusion

We tested miREM using RNAseq datasets from two single “spiked” knock-in miRNA experiments and two double knock-out miRNA experiments. miREM predicted these manipulated miRNAs as having high EM scores from the gene set signatures (i.e. top predictions for single knock-in and double knock-out miRNA experiments). Finally, we have demonstrated that miREM predictions are either similar or better than results provided by existing programs.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2292-1) contains supplementary material, which is available to authorized users.

miR-206 Inhibits Stemness and Metastasis of Breast Cancer by Targeting MKL1/IL11 Pathway

Purpose: Effective targeting of cancer stem cells is necessary and important for eradicating cancer and reducing metastasis-related mortality. Understanding of cancer stemness-related signaling pathways at the molecular level will help control cancer and stop metastasis in the clinic.Experimental Design: By analyzing miRNA profiles and functions in cancer development, we aimed to identify regulators of breast tumor stemness and metastasis in human xenograft models in vivo and examined their effects on self-renewal and invasion of breast cancer cells in vitro To discover the direct targets and essential signaling pathways responsible for miRNA functions in breast cancer progression, we performed microarray analysis and target gene prediction in combination with functional studies on candidate genes (overexpression rescues and pheno-copying knockdowns).Results: In this study, we report that hsa-miR-206 suppresses breast tumor stemness and metastasis by inhibiting both self-renewal and invasion. We identified that among the candidate targets, twinfilin (TWF1) rescues the miR-206 phenotype in invasion by enhancing the actin cytoskeleton dynamics and the activity of the mesenchymal lineage transcription factors, megakaryoblastic leukemia (translocation) 1 (MKL1), and serum response factor (SRF). MKL1 and SRF were further demonstrated to promote the expression of IL11, which is essential for miR-206's function in inhibiting both invasion and stemness of breast cancer.Conclusions: The identification of the miR-206/TWF1/MKL1-SRF/IL11 signaling pathway sheds lights on the understanding of breast cancer initiation and progression, unveils new therapeutic targets, and facilitates innovative drug development to control cancer and block metastasis. Clin Cancer Res; 23(4); 1091-103. ©2016 AACR.

Differentiation and loss of malignant character of spontaneous pulmonary metastases in patient-derived breast cancer models

Patient-derived human-in-mouse xenograft models of breast cancer (PDX models) that exhibit spontaneous lung metastases offer a potentially powerful model of cancer metastasis. In this study, we evaluated the malignant character of lung micrometastases that emerge in such models after orthotopic implantation of human breast tumor cells into the mouse mammary fat pad. Interestingly, relative to the parental primary breast tumors, the lung metastasis (met)-derived mammary tumors exhibited a slower growth rate and a reduced metastatic potential with a more differentiated epithelial status. Epigenetic correlates were determined by gene array analyses. Lung met-derived tumors displayed differential expression of negative regulators of cell proliferation and metabolism and positive regulators of mammary epithelial differentiation. Clinically, this signature correlated with breast tumor subtypes. We identified hsa-miR-138 (miR-138) as a novel regulator of invasion and epithelial-mesenchymal transition in breast cancer cells, acting by directly targeting the polycomb epigenetic regulator EZH2. Mechanistic investigations showed that GATA3 transcriptionally controlled miR-138 levels in lung metastases. Notably, the miR-138 activity signature served as a novel independent prognostic marker for patient survival beyond traditional pathologic variables, intrinsic subtypes, or a proliferation gene signature. Our results highlight the loss of malignant character in some lung micrometastatic lesions and the epigenetic regulation of this phenotype.

miRror2.0: a platform for assessing the joint action of microRNAs in cell regulation

microRNAs (miRNAs) are short, noncoding RNAs that negatively regulate the levels of mRNA post-transcriptionally. Recent experiments revealed thousands of mRNA-miRNA pairs in which multiple miRNAs may bind the same transcript. These results raised the notion of miRNAs teamwork for a wide range of cellular context. miRror2.0 utilizes the miRNA-target predictions from over a dozen programs and resources and unifies them under a common statistical basis. The platform, called miRror2.0, considers the combinatorial regulation by miRNAs in different tissues, cell lines and under a broad range of conditions. A flexible setting permits the selection of the preferred combination of miRNA-target prediction resources as well as the statistical parameters for the analysis. miRror2.0 covers six major model organisms including human and mouse. Importantly, the system is capable of analyzing hundreds of genes that were subjected to miRNAs' regulation. Activating miRror2.0 by introducing thousands of genes from miRNA overexpression experiments successfully identified the objective miRNAs. The output from miRror2.0 is a list of genes that is optimally regulated by a defined set of miRNAs. A symmetric application of miRror2.0 starts with a set of miRNAs, and the system then seeks the preferred set of genes that are regulated by that miRNA composition. The results from miRror2.0 are empowered by an iterative procedure called PSI-miRror. PSI-miRror tests the robustness of miRror2.0 prediction. It allows a refinement of the initial list of genes in view of the miRNAs that optimally regulate this list. We present miRror2.0 as a valuable resource for supporting cellular experimentalists that seek recovery of combinatorial regulation by miRNAs from noisy experimental data. miRror2.0 is available at http://www.mirrorsuite.cs.huji.ac.il .

MicroRNA target identification: lessons from hypoxamiRs

SIGNIFICANCE

MicroRNAs (miRNAs) are small noncoding RNAs that have emerged as key regulators of many physiological and pathological processes, including those relevant to hypoxia such as cancer, neurological dysfunctions, myocardial infarction, and lung diseases.

RECENT ADVANCES

During the last 5 years, miRNAs have been shown to play a role in the regulation of the cellular response to hypoxia. The identification of several bona fide targets of these hypoxamiRs has underlined their pleiotropic functions and the complexity of the molecular rules directing miRNA::target transcript pairing.

CRITICAL ISSUES

This review outlines the main in silico and experimental approaches used to identify the targetome of hypoxamiRs and presents new recent relevant methodologies for future studies.

FUTURE DIRECTIONS

Since hypoxia plays key roles in many pathophysiological conditions, the precise characterization of regulatory hypoxamiRs networks will be instrumental both at a fundamental level and for their future potential therapeutic applications.

C2Analyzer: Co-target-co-function analyzer

MicroRNAs (miRNAs) interact with their target mRNAs and regulate biological processes at post-transcriptional level. While one miRNA can target many mRNAs, a single mRNA can also be targeted by a set of miRNAs. The targeted mRNAs may be involved in different biological processes that are described by gene ontology (GO) terms. The major challenges involved in analyzing these multitude regulations include identification of the combinatorial regulation of miRNAs as well as determination of the co-functionally-enriched miRNA pairs. The C2Analyzer: Co-target–Co-function Analyzer, is a Perl-based, versatile and user-friendly web tool with online instructions. Based on the hypergeometric analysis, this novel tool can determine whether given pairs of miRNAs are co-functionally enriched. For a given set of GO term(s), it can also identify the set of miRNAs whose targets are enriched in the given GO term(s). Moreover, C2Analyzer can also identify the co-targeting miRNA pairs, their targets and GO processes, which they are involved in. The miRNA–miRNA co-functional relationship can also be saved as a .txt file, which can be used to further visualize the co-functional network by using other software like Cytoscape. C2Analyzer is freely available at www.bioinformatics.org/c2analyzer.

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.

Functional interpretation of microRNA-mRNA association in biological systems using R

The prediction of microRNA targets is a challenging task that has given rise to several prediction algorithms. Databases of predicted targets can be used in a microRNA target enrichment analysis, enhancing our capacity to extract functional information from gene lists. However, the available tools in this field analyze gene sets one by one limiting their use in a meta-analysis. Here, we present an R system for miRNA enrichment analysis that is suitable for systems biology. These collection of R scripts and embedded data allow using predicted targets of public databases or a custom integration of them. As a proof-of-principle, we have successfully performed the challenging analysis of 2158 tumoral samples at a time. The obtained results have been summarized in a network where each cancer disease is linked to enriched miRNAs and overrepresented functions. These network connections have proven to be an invaluable resource for the study of biological and pathological causes and effects of the expression of miRNAs.

Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures

BACKGROUND

MicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways. A useful step for understanding their functional role is characterizing their influence on the protein context of the targets. Using miRNA context-specific influence as a functional signature is promising to identify functional associations between miRNAs and other gene signatures, and thus advance our understanding of miRNA mode of action.

RESULTS

In the current study we utilized the power of regularized regression models to construct functional associations between gene signatures. Genes that are influenced by miRNAs directly(computational miRNA target prediction) or indirectly (protein partners of direct targets) are defined as functional miRNA gene signature. The combined direct and indirect miRNA influence is defined as context-specific effects of miRNAs, and is used to identify regulatory effects of miRNAs on curated gene signatures. Elastic-net regression was used to build functional associations between context-specific effect of miRNAs and other gene signatures (disease, pathway signatures) by identifying miRNAs whose targets are enriched in gene lists. As a proof of concept, elastic-net regression was applied on lists of genes downregulated upon pre-miRNA transfection, and successfully identified the treated miRNA. This model was then extended to construct functional relationships between miRNAs and disease and pathway gene lists. Integrating context-specific effects of miRNAs on a protein network reveals more significant miRNA enrichment in prostate gene signatures compared to miRNA direct targets. The model identified novel list of miRNAs that are associated with prostate clinical variables.

CONCLUSIONS

Elastic-net regression is used as a model to construct functional associations between miRNA signatures and other gene signatures. Defining miRNA context-specific functional gene signature by integrating the downstream effect of miRNAs demonstrates better performance compared to the miRNA signature alone (direct targets). miRNA functional signatures can greatly facilitate miRNA research to uncover new functional associations between miRNAs and diseases, drugs or pathways.

MicroRNA-30c inhibits human breast tumour chemotherapy resistance by regulating TWF1 and IL-11

Chemotherapy resistance frequently drives tumour progression. However, the underlying molecular mechanisms are poorly characterized. Epithelial-to-mesenchymal transition has been shown to correlate with therapy resistance, but the functional link and signalling pathways remain to be elucidated. Here we report that microRNA-30c, a human breast tumour prognostic marker, has a pivotal role in chemoresistance by a direct targeting of the actin-binding protein twinfilin 1, which promotes epithelial-to-mesenchymal transition. An interleukin-6 family member, interleukin-11 is identified as a secondary target of twinfilin 1 in the microRNA-30c signalling pathway. Expression of microRNA-30c inversely correlates with interleukin-11 expression in primary breast tumours and low interleukin-11 correlates with relapse-free survival in breast cancer patients. Our study demonstrates that microRNA-30c is transcriptionally regulated by GATA3 in breast tumours. Identification of a novel microRNA-mediated pathway that regulates chemoresistance in breast cancer will facilitate the development of novel therapeutic strategies.

Identification of human microRNA-like sequences embedded within the protein-encoding genes of the human immunodeficiency virus

BACKGROUND

MicroRNAs (miRNAs) are highly conserved, short (18-22 nts), non-coding RNA molecules that regulate gene expression by binding to the 3' untranslated regions (3'UTRs) of mRNAs. While numerous cellular microRNAs have been associated with the progression of various diseases including cancer, miRNAs associated with retroviruses have not been well characterized. Herein we report identification of microRNA-like sequences in coding regions of several HIV-1 genomes.

RESULTS

Based on our earlier proteomics and bioinformatics studies, we have identified 8 cellular miRNAs that are predicted to bind to the mRNAs of multiple proteins that are dysregulated during HIV-infection of CD4+ T-cells in vitro. In silico analysis of the full length and mature sequences of these 8 miRNAs and comparisons with all the genomic and subgenomic sequences of HIV-1 strains in global databases revealed that the first 18/18 sequences of the mature hsa-miR-195 sequence (including the short seed sequence), matched perfectly (100%), or with one nucleotide mismatch, within the envelope (env) genes of five HIV-1 genomes from Africa. In addition, we have identified 4 other miRNA-like sequences (hsa-miR-30d, hsa-miR-30e, hsa-miR-374a and hsa-miR-424) within the env and the gag-pol encoding regions of several HIV-1 strains, albeit with reduced homology. Mapping of the miRNA-homologues of env within HIV-1 genomes localized these sequence to the functionally significant variable regions of the env glycoprotein gp120 designated V1, V2, V4 and V5.

CONCLUSIONS

We conclude that microRNA-like sequences are embedded within the protein-encoding regions of several HIV-1 genomes. Given that the V1 to V5 regions of HIV-1 envelopes contain specific, well-characterized domains that are critical for immune responses, virus neutralization and disease progression, we propose that the newly discovered miRNA-like sequences within the HIV-1 genomes may have evolved to self-regulate survival of the virus in the host by evading innate immune responses and therefore influencing persistence, replication and/or pathogenicity.

Protein network-based Lasso regression model for the construction of disease-miRNA functional interactions

Background

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

Results

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

Conclusions

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

Working together: combinatorial regulation by microRNAs

MicroRNAs (miRNAs) negatively regulate gene expression level of mRNA post-transcriptionally. Deep sequencing and large-scale screening methods have yielded about 1,500 miRNA sequences in human. Each miRNA contains a seed sequence that is required, but not sufficient, for the correct matching with its targets. Recent technological advances make it possible to capture the miRNAs with their cognate mRNAs at the RISC complex. These experiments have revealed thousands of validated mRNA-miRNA pairing events. In the context of human stem cells, 90% of the identified transcripts appear to be paired with at least two different miRNAs.In this chapter, we present a comprehensive outline for a combinatorial regulation mode by miRNAs. Initially, we summarize the computational and experimental evidence that support a combined effect of multiple miRNAs. Then, we describe miRror2.0, a platform specifically convened to consider the likelihood of miRNAs cooperativity in view of the targets, tissues and cell lines. We show that results from miRror2.0 can be further refined by an iterative procedure, calls Psi-miRror that gauges the robustness of the regulation. We illustrate the combinatorial regulation projected onto graphs of human pathways and show that these pathways are amenable to disruption by a small set of miRNAs. Finally, we propose that miRNA combinatorial regulation is an attractive regulatory strategy not only at the level of single target, but also at the level of pathways and cellular homeostasis. The joint operation of miRNAs is a powerful means to overcome the low specificity inherent in each individual miRNA.

MicroRNA-30c targets cytoskeleton genes involved in breast cancer cell invasion

Metastasis remains a significant challenge in treating cancer. A better understanding of the molecular mechanisms underlying metastasis is needed to develop more effective treatments. Here, we show that human breast tumor biomarker miR-30c regulates invasion by targeting the cytoskeleton network genes encoding twinfilin 1 (TWF1) and vimentin (VIM). Both VIM and TWF1 have been shown to regulate epithelial-to-mesenchymal transition. Similar to TWF1, VIM also regulates F-actin formation, a key component of cellular transition to a more invasive mesenchymal phenotype. To further characterize the role of the TWF1 pathway in breast cancer, we found that IL-11 is an important target of TWF1 that regulates breast cancer cell invasion and STAT3 phosphorylation. The miR-30c-VIM/TWF1 signaling cascade is also associated with clinical outcome in breast cancer patients.

Mining protein lists from proteomics studies: applications for drug discovery

IMPORTANCE OF THE FIELD

In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts.

AREAS COVERED IN THIS REVIEW

We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared.

WHAT THE READER WILL GAIN

The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored.

TAKE HOME MESSAGE

Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs.

"Seed-Milarity" confers to hsa-miR-210 and hsa-miR-147b similar functional activity

Specificity of interaction between a microRNA (miRNA) and its targets crucially depends on the seed region located in its 5′-end. It is often implicitly considered that two miRNAs sharing the same biological activity should display similarity beyond the strict six nucleotide region that forms the seed, in order to form specific complexes with the same mRNA targets. We have found that expression of hsa-miR-147b and hsa-miR-210, though triggered by different stimuli (i.e. lipopolysaccharides and hypoxia, respectively), induce very similar cellular effects in term of proliferation, migration and apoptosis. Hsa-miR-147b only shares a “minimal” 6-nucleotides seed sequence with hsa-miR-210, but is identical with hsa-miR-147a over 20 nucleotides, except for one base located in the seed region. Phenotypic changes induced after heterologous expression of miR-147a strikingly differ from those induced by miR-147b or miR-210. In particular, miR-147a behaves as a potent inhibitor of cell proliferation and migration. These data fit well with the gene expression profiles observed for miR-147b and miR-210, which are very similar, and the gene expression profile of miR-147a, which is distinct from the two others. Bioinformatics analysis of all human miRNA sequences indicates multiple cases of miRNAs from distinct families exhibiting the same kind of similarity that would need to be further characterized in terms of putative functional redundancy. Besides, it implies that functional impact of some miRNAs can be masked by robust expression of miRNAs belonging to distinct families.

Inferring the regulatory network behind a gene expression experiment

Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.

Experimental verification of a predicted intronic microRNA in human NGFR gene with a potential pro-apoptotic function

Neurotrophins (NTs) are a family of secreted growth factor proteins primarily involved in the regulation of survival and appropriate development of neural cells, functioning by binding to their specific (TrkA, TtkB, and TrkC) and/or common NGFR receptor. NGFR is the common receptor of NTs, binding with low-affinity to all members of the family. Among different functions assigned to NGFR, it is also involved in apoptosis induction and tumorigenesis processes. Interestingly, some of the functions of NGFR appear to be ligand-independent, suggesting a probable involvement of non-coding RNA residing within the sequence of the gene. Here, we are reporting the existence of a conserved putative microRNA, named Hsa-mir-6165 [EBI accession#: FR873488]. Transfection of a DNA segment corresponding to the pre-mir-6165 sequence in Hela cell line caused the generation of mature exogenous mir-6165 (a ∼200,000 fold overexpression). Furthermore, using specific primers, we succeeded to detect the endogenous expression of mir-6165 in several glioma cell lines and glioma primary tumors known to express NGFR. Similar to the pro-apoptotic role of NGFR in some cell types, overexpression of pre-mir-6165 in U87 cell line resulted in an elevated rate of apoptosis. Moreover, coordinated with the increased level of mir-6165 in the transfected U87 cell line, two of its predicted target genes (Pkd1 and DAGLA) were significantly down-regulated. The latter findings suggest that some of the previously attributed functions of NGFR could be explained indirectly by co-transcription of mir-6165 in the cells.

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

MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links.

Dynamic mRNA and miRNA profiling of CHO-K1 suspension cell cultures

In spite of the importance of Chinese hamster ovary (CHO) cells for recombinant protein production, very little is known about the molecular and gene regulatory mechanisms that control cellular phenotypes such as enhanced growth under serum-free conditions or high productivity. Most microarray analyses to this purpose are performed with samples taken during the exponential growth phase. However, the cellular transcriptome is dynamic, changing in response to external and internal stimuli and thus reflecting the current functional capacity of cells as well as their ability to adapt to a changing environment. Therefore, during batch or fed-batch cultivations it can be expected that the transcription pattern of genes will change and that such changes may give indications on the cellular state in terms of viability, growth, and productivity. In the current study we monitored the change in expression patterns of mRNAs and microRNAs (miRNA) during lag, exponential, and stationary phases in CHO-K1 suspension cell cultures. In total, over 1400 mRNAs and more than 100 miRNAs were differentially regulated (p<0.05) relative to the batch culture at the starting point. Functional clustering revealed groups of genes with similar expression patterns, which were subjected to functional pathway analysis. In addition, as miRNAs generally act as negative post-transcriptional regulators of mRNAs, we looked for changes in their expression that were inverse to those of their predicted target mRNAs.

BioProfiling.de: analytical web portal for high-throughput cell biology

BioProfiling.de provides a comprehensive analytical toolkit for the interpretation gene/protein lists. As input, BioProfiling.de accepts a gene/protein list. As output, in one submission, the gene list is analyzed by a collection of tools which employs advanced enrichment or network-based statistical frameworks. The gene list is profiled with respect to the most information available regarding gene function, protein interactions, pathway relationships, in silico predicted microRNA to gene associations, as well as, information collected by text mining. BioProfiling.de provides a user friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. The web portal is freely available at http://www.BioProfiling.de/gene_list.

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.

MIPS: curated databases and comprehensive secondary data resources in 2010

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

MiRonTop: mining microRNAs targets across large scale gene expression studies

SUMMARY

Current challenges in microRNA (miRNA) research are to improve the identification of in vivo mRNA targets and clarify the complex interplay existing between a specific miRNA and multiple biological networks. MiRonTop is an online java web tool that integrates DNA microarrays or high-throughput sequencing data to identify the potential implication of miRNAs on a specific biological system. It allows a rapid characterization of the most pertinent mRNA targets according to several existing miRNA target prediction approaches. It also provides useful representations of the enrichment scores according to the position of the target site along the 3'-UTR, where the contribution of the sites located in the vicinity of the stop codon and of the polyA tail can be clearly highlighted. It provides different graphs of miRNA enrichment associated with up- or down-regulated transcripts and different summary tables about selections of mRNA targets and their functional annotations by Gene Ontology.

AVAILABILITY

http://www.microarray.fr:8080/miRonTop/index.

CCancer: a bird's eye view on gene lists reported in cancer-related studies

CCancer is an automatically collected database of gene lists, which were reported mostly by experimental studies in various biological and clinical contexts. At the moment, the database covers 3369 gene lists extracted from 2644 papers published in ∼80 peer-reviewed journals. As input, CCancer accepts a gene list. An enrichment analyses is implemented to generate, as output, a highly informative survey over recently published studies that report gene lists, which significantly intersect with the query gene list. A report on gene pairs from the input list which were frequently reported together by other biological studies is also provided. CCancer is freely available at http://mips.helmholtz-muenchen.de/proj/ccancer.

R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases

R spider is a web-based tool for the analysis of a gene list using the systematic knowledge of core pathways and reactions in human biology accumulated in the Reactome and KEGG databases. R spider implements a network-based statistical framework, which provides a global understanding of gene relations in the supplied gene list, and fully exploits the Reactome and KEGG knowledge bases. R spider provides a user-friendly dialog-driven web interface for several model organisms and supports most available gene identifiers. R spider is freely available at http://mips.helmholtz-muenchen.de/proj/rspider.

miR-31 functions as a negative regulator of lymphatic vascular lineage-specific differentiation in vitro and vascular development in vivo

The lymphatic vascular system maintains tissue fluid homeostasis, helps mediate afferent immune responses, and promotes cancer metastasis. To address the role microRNAs (miRNAs) play in the development and function of the lymphatic vascular system, we defined the in vitro miRNA expression profiles of primary human lymphatic endothelial cells (LECs) and blood vascular endothelial cells (BVECs) and identified four BVEC signature and two LEC signature miRNAs. Their vascular lineage-specific expression patterns were confirmed in vivo by quantitative real-time PCR and in situ hybridization. Functional characterization of the BVEC signature miRNA miR-31 identified a novel BVEC-specific posttranscriptional regulatory mechanism that inhibits the expression of lymphatic lineage-specific transcripts in vitro. We demonstrate that suppression of lymphatic differentiation is partially mediated via direct repression of PROX1, a transcription factor that functions as a master regulator of lymphatic lineage-specific differentiation. Finally, in vivo studies of Xenopus and zebrafish demonstrated that gain of miR-31 function impaired venous sprouting and lymphatic vascular development, thus highlighting the importance of miR-31 as a negative regulator of lymphatic development. Collectively, our findings identify miR-31 is a potent regulator of vascular lineage-specific differentiation and development in vertebrates.

Computational methods for the identification of microRNA targets

MicroRNAs are pivotal regulators of development and cellular homeostasis. They act as post-transcriptional regulators, which control the stability and translation efficiency of their target mRNAs. The prediction of microRNA targets and detection of microRNA-mRNA regulatory modules (MRMs) are crucial components for understanding of microRNA functions. Numerous computational methods for microRNA target prediction have been developed. Computationally-predicted targets have been recently used in the integrative analysis of microRNA and mRNA expression analysis to identify microRNA targets and MRMs. In this article we review these recent developments in the integrative analysis methods. We also discuss the remaining challenges and our insights on future directions.