The interplay between transcription factors and microRNAs in genome-scale regulatory networks

A comprehensive analysis of GATA-1-regulated miRNAs reveals miR-23a to be a positive modulator of erythropoiesis

miRNAs play important roles in many biological processes, including erythropoiesis. Although several miRNAs regulate erythroid differentiation, how the key erythroid regulator, GATA-1, directly orchestrates differentiation through miRNA pathways remains unclear. In this study, we identified miR-23a as a key regulator of erythropoiesis, which was upregulated both during erythroid differentiation and in GATA-1 gain-of-function experiments, as determined by miRNA expression profile analysis. In primary human CD34+ hematopoietic progenitor cells, miR-23a increased in a GATA-1-dependent manner during erythroid differentiation. Gain- or loss-of-function analysis of miR-23a in mice or zebrafish demonstrated that it was essential for normal morphology in terminally differentiated erythroid cells. Furthermore, a protein tyrosine phosphatase, SHP2, was identified as a downstream target of miR-23a that mediated its regulation of erythropoiesis. Taken together, our data identify a key GATA-1–miRNA axis in erythroid differentiation.

An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury

Background

The regenerative response of Schwann cells after peripheral nerve injury is a critical process directly related to the pathophysiology of a number of neurodegenerative diseases. This SC injury response is dependent on an intricate gene regulatory program coordinated by a number of transcription factors and microRNAs, but the interactions among them remain largely unknown. Uncovering the transcriptional and post-transcriptional regulatory networks governing the Schwann cell injury response is a key step towards a better understanding of Schwann cell biology and may help develop novel therapies for related diseases. Performing such comprehensive network analysis requires systematic bioinformatics methods to integrate multiple genomic datasets.

Results

In this study we present a computational pipeline to infer transcription factor and microRNA regulatory networks. Our approach combined mRNA and microRNA expression profiling data, ChIP-Seq data of transcription factors, and computational transcription factor and microRNA target prediction. Using mRNA and microRNA expression data collected in a Schwann cell injury model, we constructed a regulatory network and studied regulatory pathways involved in Schwann cell response to injury. Furthermore, we analyzed network motifs and obtained insights on cooperative regulation of transcription factors and microRNAs in Schwann cell injury recovery.

Conclusions

This work demonstrates a systematic method for gene regulatory network inference that may be used to gain new information on gene regulation by transcription factors and microRNAs.

A compendium of Caenorhabditis elegans RNA binding proteins predicts extensive regulation at multiple levels

Gene expression is regulated at multiple levels, including transcription and translation, as well as mRNA and protein stability. Although systems-level functions of transcription factors and microRNAs are rapidly being characterized, few studies have focused on the posttranscriptional gene regulation by RNA binding proteins (RBPs). RBPs are important to many aspects of gene regulation. Thus, it is essential to know which genes encode RBPs, which RBPs regulate which gene(s), and how RBP genes are themselves regulated. Here we provide a comprehensive compendium of RBPs from the nematode Caenorhabditis elegans (wRBP1.0). We predict that as many as 887 (4.4%) of C. elegans genes may encode RBPs ~250 of which likely function in a gene-specific manner. In addition, we find that RBPs, and most notably gene-specific RBPs, are themselves enriched for binding and modification by regulatory proteins, indicating the potential for extensive regulation of RBPs at many different levels. wRBP1.0 will provide a significant contribution toward the comprehensive delineation of posttranscriptional regulatory networks and will provide a resource for further studies regulation by RBPs.

Vitamin D receptor signaling mechanisms: integrated actions of a well-defined transcription factor

The main physiological actions of the biologically most active metabolite of vitamin D, 1α,25-dihydroxyvitamin D(3) (1α,25(OH)(2)D(3)), are calcium and phosphorus uptake and transport and thereby controlling bone formation. Other emergent areas of 1α,25(OH)(2)D(3) action are in the control of immune functions, cellular growth and differentiation. All genomic actions of 1α,25(OH)(2)D(3) are mediated by the transcription factor vitamin D receptor (VDR) that has been the subject of intense study since the 1980's. Thus, vitamin D signaling primarily implies the molecular actions of the VDR. In this review, we present different perspectives on the VDR that incorporate its role as transcription factor and member of the nuclear receptor superfamily, its dynamic changes in genome-wide locations and DNA binding modes, its interaction with chromatin components and its primary protein-coding and non-protein coding target genes and finally how these aspects are united in regulatory networks. By comparing the actions of the VDR, a relatively well-understood and characterized protein, with those of other transcription factors, we aim to build a realistic positioning of vitamin D signaling in the context of other intracellular signaling systems.

Important miRs of pathways in different tumor types

We computationally determined miRs that are significantly connected to molecular pathways by utilizing gene expression profiles in different cancer types such as glioblastomas, ovarian and breast cancers. Specifically, we assumed that the knowledge of physical interactions between miRs and genes indicated subsets of important miRs (IM) that significantly contributed to the regression of pathway-specific enrichment scores. Despite the different nature of the considered cancer types, we found strongly overlapping sets of IMs. Furthermore, IMs that were important for many pathways were enriched with literature-curated cancer and differentially expressed miRs. Such sets of IMs also coincided well with clusters of miRs that were experimentally indicated in numerous other cancer types. In particular, we focused on an overlapping set of 99 overall important miRs (OIM) that were found in glioblastomas, ovarian and breast cancers simultaneously. Notably, we observed that interactions between OIMs and leading edge genes of differentially expressed pathways were characterized by considerable changes in their expression correlations. Such gains/losses of miR and gene expression correlation indicated miR/gene pairs that may play a causal role in the underlying cancers.

We assume that a network of physical interactions between miRs and genes allows us to determine miRs that influence the expression of whole pathways in different tumor types. Specifically, we represented each pathway by an enrichment score and an array of miRs counting the number of genes in the pathway a given miR can bind. Despite the different nature of the considered tumor types, we obtained a large set of overlapping miRs using a machine-learning algorithm. Such associated miRs were enriched with literature-curated cancer and differentially expressed miRs and also coincided well with clusters of miRs that were experimentally indicated in numerous other cancer types. Focusing on such sets of miRs we observed that interactions with genes in differentially expressed pathways were characterized by massive gains/losses of expression correlations. Such drastic changes of miR and gene expression correlation indicate miR/gene pairs that may play a causal role in the underlying cancers.

miRNA-transcription factor interactions: a combinatorial regulation of gene expression

Developmental processes require a precise spatio-temporal regulation of gene expression wherein a diverse set of transcription factors control the signalling pathways. MicroRNAs (miRNAs), a class of small non-coding RNA molecules have recently drawn attention for their prominent role in development and disease. These tiny sequences are essential for regulation of processes, including cell signalling, cell development, cell death, cell proliferation, patterning and differentiation. The consequence of gene regulation by miRNAs is similar to that by transcription factors (TFs). A regulatory cascade essential for appropriate execution of several biological events is triggered through a combinatorial action of miRNAs and TFs. These two important regulators share similar regulatory logics and bring about a cooperative action in the gene regulatory network, dependent on the binding sites present on the target gene. The review addresses the biogenesis and nomenclature of miRNAs, outlines the mechanism of action and regulation of their expression, and focuses on the combinatorial action of miRNAs and TFs for the expression of genes in various regulatory cascades.

Functional characteristics of a double negative feedback loop mediated by microRNAs

MicroRNAs (miRNAs) are a class of small, noncoding RNAs that play crucial roles in almost all cellular processes. As key post-transcriptional regulators of gene expression, miRNAs mainly induce mRNA degradation or translational repression. Recently computational and experimental studies have identified an abundance of motifs involving miRNAs and transcriptional factors (TFs). Here, we study the functional characteristics of one such motif, a two-node miRNA-mediated double negative feedback loop (MDNFL) in which a TF suppresses an miRNA and the TF itself is negatively regulated by the miRNA. Several examples of this motif are described from the literature. We propose a general computational model for the MDNFL based on biochemical regulations and explore its dynamics by using bifurcation analysis. Our results show that the MDNFL can behave as a bistable switch. This functional feature is in agreement with experimental observations of the widespread appearance of miRNAs in fate decisions such as differentiation during development. Importantly, it is found that under the interplay of a TF and an miRNA, the MDNFL model can behave as switches for wide ranges of parameters even without cooperative binding of the TF. In addition, we also investigate how extrinsic noise affects dynamic behavior of the MDNFL. Interestingly, it is found that when the MDNFL is in the bistable region, by choosing the appropriate extrinsic noise source, the MDNFL system can switch from one steady state to the other and meanwhile the production of either miRNA or protein is amplified significantly. From an engineering perspective, this noise-based switch and amplifier for gene expression is very easy to control. It is hoped that the results presented here would provide a new insight on how gene expression is regulated by miRNAs and further guidance for experiments.

MicroRNAs in the regulation of TLR and RIG-I pathways

The innate immune system recognizes invading pathogens through germline-encoded pattern recognition receptors (PRRs), which elicit innate antimicrobial and inflammatory responses and initiate adaptive immunity to control or eliminate infection. Toll-like receptors (TLRs) and retinoic acid-inducible gene I (RIG-I) are the key innate immune PRRs and are tightly regulated by elaborate mechanisms to ensure a beneficial outcome in response to foreign invaders. Although much of the focus in the literature has been on the study of protein regulators of inflammation, microRNAs (miRNAs) have emerged as important controllers of certain features of the inflammatory process. Several miRNAs are induced by TLR and RIG-I activation in myeloid cells and act as feedback regulators of TLR and RIG-I signaling. In this review, we comprehensively discuss the recent understanding of how miRNA networks respond to TLR and RIG-I signaling and their role in the initiation and termination of inflammatory responses. Increasing evidence also indicates that both virus-encoded miRNAs and cellular miRNAs have important functions in viral replication and host anti-viral immunity.

A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules

Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs.

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling.

Methods

By utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.

Results

Globally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.

Conclusion

In this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks

Background

Transcriptional networks of higher eukaryotes are difficult to obtain. Available experimental data from conventional approaches are sporadic, while those generated with modern high-throughput technologies are biased. Computational predictions are generally perceived as being flooded with high rates of false positives. New concepts about the structure of regulatory regions and the function of master regulator sites may provide a way out of this dilemma.

Methods

We combined promoter scanning with positional weight matrices with a 4-genome conservativity analysis to predict high-affinity, highly conserved transcription factor (TF) binding sites and to infer TF-target gene relations. They were expanded to paralogous TFs and filtered for tissue-specific expression patterns to obtain a reference transcriptional network (RTN) as well as tissue-specific transcriptional networks (TTNs).

Results

When validated with experimental data sets, the predictions done showed the expected trends of true positive and true negative predictions, resulting in satisfying sensitivity and specificity characteristics. This also proved that confining the network reconstruction to the 1% top-ranking TF-target predictions gives rise to networks with expected degree distributions. Their expansion to paralogous TFs enriches them by tissue-specific regulators, providing a reasonable basis to reconstruct tissue-specific transcriptional networks.

Conclusions

The concept of master regulator or seed sites provides a reasonable starting point to select predicted TF-target relations, which, together with a paralogous expansion, allow for reconstruction of tissue-specific transcriptional networks.

IL-10-induced microRNA-187 negatively regulates TNF-α, IL-6, and IL-12p40 production in TLR4-stimulated monocytes

IL-10 is a potent anti-inflammatory molecule that, in phagocytes, negatively targets cytokine expression at transcriptional and posttranscriptional levels. Posttranscriptional checkpoints also represent the specific target of a recently discovered, evolutionary conserved class of small silencing RNAs known as "microRNAs" (miRNAs), which display the peculiar function of negatively regulating mRNA processing, stability, and translation. In this study, we report that activation of primary human monocytes up-regulates the expression of miR-187 both in vitro and in vivo. Accordingly, we identify miR-187 as an IL-10-dependent miRNA playing a role in IL-10-mediated suppression of TNF-α, IL-6, and the p40 subunit of IL-12 (IL-12p40) produced by primary human monocytes following activation of Toll-like receptor 4 (TLR4). Ectopic expression of miR-187 consistently and selectively reduces TNFα, IL-6, and IL-12p40 produced by LPS-activated monocytes. Conversely, the production of LPS-induced TNF-α, IL-6, and IL-12p40 is increased significantly when miR-187 expression is silenced. Our data demonstrate that miR-187 directly targets TNF-α mRNA stability and translation and indirectly decreases IL-6 and IL-12p40 expression via down-modulation of IκBζ, a master regulator of the transcription of these latter two cytokines. These results uncover an miRNA-mediated pathway controlling cytokine expression and demonstrate a central role of miR-187 in the physiological regulation of IL-10-driven anti-inflammatory responses.

Deep sequencing reveals predominant expression of miR-21 amongst the small non-coding RNAs in retinal microvascular endothelial cells

The retinal vascular endothelium is essential for angiogenesis and is involved in maintaining barrier selectivity and vascular tone. The aim of this study was to identify and quantify microRNAs and other small regulatory non-coding RNAs (ncRNAs) which may regulate these crucial functions. Primary bovine retinal microvascular endothelial cells (RMECs) provide a well-characterized in vitro system for studying angiogenesis. RNA extracted from RMECs was used to prepare a small RNA library for deep sequencing (Illumina Genome Analyzer). A total of 6.8 million reads were mapped to 250 known microRNAs in miRBase (release 16). In many cases, the most frequent isomiR differed from the sequence reported in miRBase. In addition, five novel microRNAs, 13 novel bovine orthologs of known human microRNAs and multiple new members of the miR-2284/2285 family were detected. Several ∼30 nucleotide sno-miRNAs were identified, with the most highly expressed being derived from snoRNA U78. Highly expressed microRNAs previously associated with endothelial cells included miR-126 and miR-378, but the most highly expressed was miR-21, comprising more than one-third of all mapped reads. Inhibition of miR-21 with an LNA inhibitor significantly reduced proliferation, migration, and tube-forming capacity of RMECs. The independence from prior sequence knowledge provided by deep sequencing facilitates analysis of novel microRNAs and other small RNAs. This approach also enables quantitative evaluation of microRNA expression, which has highlighted the predominance of a small number of microRNAs in RMECs. Knockdown of miR-21 suggests a role for this microRNA in regulation of angiogenesis in the retinal microvasculature. J. Cell. Biochem. 113: 2098–2111, 2012. © 2012 Wiley Periodicals, Inc.

Stability of Boolean multilevel networks

The study of the interplay between the structure and dynamics of complex multilevel systems is a pressing challenge nowadays. In this paper, we use a semiannealed approximation to study the stability properties of random Boolean networks in multiplex (multilayered) graphs. Our main finding is that the multilevel structure provides a mechanism for the stabilization of the dynamics of the whole system even when individual layers work on the chaotic regime, therefore identifying new ways of feedback between the structure and the dynamics of these systems. Our results point out the need for a conceptual transition from the physics of single-layered networks to the physics of multiplex networks. Finally, the fact that the coupling modifies the phase diagram and the critical conditions of the isolated layers suggests that interdependency can be used as a control mechanism.

Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs

Background

Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods.

Results

In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3’UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes.

Conclusions

We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.

SyStemCell: a database populated with multiple levels of experimental data from stem cell differentiation research

Elucidation of the mechanisms of stem cell differentiation is of great scientific interest. Increasing evidence suggests that stem cell differentiation involves changes at multiple levels of biological regulation, which together orchestrate the complex differentiation process; many related studies have been performed to investigate the various levels of regulation. The resulting valuable data, however, remain scattered. Most of the current stem cell-relevant databases focus on a single level of regulation (mRNA expression) from limited stem cell types; thus, a unifying resource would be of great value to compile the multiple levels of research data available. Here we present a database for this purpose, SyStemCell, deposited with multi-level experimental data from stem cell research. The database currently covers seven levels of stem cell differentiation-associated regulatory mechanisms, including DNA CpG 5-hydroxymethylcytosine/methylation, histone modification, transcript products, microRNA-based regulation, protein products, phosphorylation proteins and transcription factor regulation, all of which have been curated from 285 peer-reviewed publications selected from PubMed. The database contains 43,434 genes, recorded as 942,221 gene entries, for four organisms (Homo sapiens, Mus musculus, Rattus norvegicus, and Macaca mulatta) and various stem cell sources (e.g., embryonic stem cells, neural stem cells and induced pluripotent stem cells). Data in SyStemCell can be queried by Entrez gene ID, symbol, alias, or browsed by specific stem cell type at each level of genetic regulation. An online analysis tool is integrated to assist researchers to mine potential relationships among different regulations, and the potential usage of the database is demonstrated by three case studies. SyStemCell is the first database to bridge multi-level experimental information of stem cell studies, which can become an important reference resource for stem cell researchers. The database is available at http://lifecenter.sgst.cn/SyStemCell/.

Involvement of microRNA families in cancer

Collecting representative sets of cancer microRNAs (miRs) from the literature we show that their corresponding families are enriched in sets of highly interacting miR families. Targeting cancer genes on a statistically significant level, such cancer miR families strongly intervene with signaling pathways that harbor numerous cancer genes. Clustering miR family-specific profiles of pathway intervention, we found that different miR families share similar interaction patterns. Resembling corresponding patterns of cancer miRs families, such interaction patterns may indicate a miR family’s potential role in cancer. As we find that the number of targeted cancer genes is a naïve proxy for a cancer miR family, we design a simple method to predict candidate miR families based on gene-specific interaction profiles. Assessing the impact of miR families to distinguish between (non-)cancer genes, we predict a set of 84 potential candidate families, including 75% of initially collected cancer miR families. Further confirming their relevance, predicted cancer miR families are significantly indicated in increasing, non-random numbers of tumor types.

The negative feedback-loop between the oncomir Mir-24-1 and menin modulates the Men1 tumorigenesis by mimicking the "Knudson's second hit"

Multiple endocrine neoplasia type 1 (MEN1) syndrome is a rare hereditary cancer disorder characterized by tumors of the parathyroids, of the neuroendocrine cells, of the gastro-entero-pancreatic tract, of the anterior pituitary, and by non-endocrine neoplasms and lesions. MEN1 gene, a tumor suppressor gene, encodes menin protein. Loss of heterozygosity at 11q13 is typical of MEN1 tumors, in agreement with the Knudson’s two-hit hypothesis. In silico analysis with Target Scan, Miranda and Pictar-Vert softwares for the prediction of miRNA targets indicated miR-24-1 as capable to bind to the 3′UTR of MEN1 mRNA. We investigated this possibility by analysis of miR-24-1 expression profiles in parathyroid adenomatous tissues from MEN1 gene mutation carriers, in their sporadic non-MEN1 counterparts, and in normal parathyroid tissue. Interestingly, the MEN1 tumorigenesis seems to be under the control of a “negative feedback loop” between miR-24-1 and menin protein, that mimics the second hit of Knudson’s hypothesis and that could buffer the effect of the stochastic factors that contribute to the onset and progression of this disease. Our data show an alternative way to MEN1 tumorigenesis and, probably, to the “two-hit dogma”. The functional significance of this regulatory mechanism in MEN1 tumorigenesis is also the basis for opening future developments of RNA antagomir(s)-based strategies in the in vivo control of tumorigenesis in MEN1 carriers.

Identifying transcription factors and microRNAs as key regulators of pathways using Bayesian inference on known pathway structures

Background

Transcription factors and microRNAs act in concert to regulate gene expression in eukaryotes. Numerous computational methods based on sequence information are available for the prediction of target genes of transcription factors and microRNAs. Although these methods provide a static snapshot of how genes may be regulated, they are not effective for the identification of condition-specific regulators.

Results

We propose a new method that combines: a) transcription factors and microRNAs that are predicted to target genes in pathways, with b) microarray expression profiles of microRNAs and mRNAs, in conjunction with c) the known structure of molecular pathways. These elements are integrated into a Bayesian network derived from each pathway that, through probability inference, allows for the prediction of the key regulators in the pathway. We demonstrate 1) the steps to discretize the expression data for the computation of conditional probabilities in a Bayesian network, 2) the procedure to construct a Bayesian network using the structure of a known pathway and the transcription factors and microRNAs predicted to target genes in that pathway, and 3) the inference results as potential regulators of three signaling pathways using microarray expression profiles of microRNA and mRNA in estrogen receptor positive and estrogen receptor negative tumors.

Conclusions

We displayed the ability of our framework to integrate multiple sets of microRNA and mRNA expression data, from two phenotypes, with curated molecular pathway structures by creating Bayesian networks. Moreover, by performing inference on the network using known evidence, e.g., status of differentially expressed genes, or by entering hypotheses to be tested, we obtain a list of potential regulators of the pathways. This, in turn, will help increase our understanding about the regulatory mechanisms relevant to the two phenotypes.

FSTVAL: a new web tool to validate bulk flanking sequence tags

BACKGROUND

Information about a transgene locus is one of the major concerns in transgenic research because expression of the transgene or a gene interrupted by the integration event could be affected. Thus, the flanking sequences obtained from transgenic plants need to be analyzed in terms of genomic context, such as genic and intergenic regions. This process may consist of several steps: 1) elimination of a vector sequence from the flanking sequence, 2) finding the locations in the target genome, and 3) statistics of the integration sites. These steps could be automated for flanking sequences from several dozens of transgenic plants generated in an ordinary targeted gene expression strategy. It would be indispensable in a genome-wide mutagenesis screen using T-DNA or transposons because these projects often generate several thousands of transgenic lines and just as many loci of the transgene among the transgenic plants.

RESULTS

We present an open access web tool, flanking sequence tags validator (FSTVAL), to manage bulk flanking sequence tags (FSTs). FSTVAL automatically evaluates the FSTs and finds the best mapping positions of the FST against a known genome sequence. The statistics, in terms of genic and intergenic regions, are presented as a table, a distribution map, and a frequency graph along the chromosomes. Currently, 17 plant genome sequences, including Arabidopsis thaliana, Oryza sativa, and Glycine max, are available as reference genomes. We evaluated the utility and accuracy of the tool with 5,144 rice FSTs. The whole process, from uploading the sequences to generating tables of insertions, required a few minutes, with less than 4 clicks in the web environment.

CONCLUSIONS

Run for 1 year and tested over 1,000 times, we have confirmed FSTVAL efficiently handles bulk FSTs. FSTVAL is freely available without login at http://bioinfo.mju.ac.kr/fstval/.

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer

BACKGROUND

Both transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. At present, the combinatorial regulatory network composed of transcriptional regulations and post-transcriptional regulations is often constructed through a forward engineering strategy that is based solely on searching of transcriptional factor binding sites or miRNA seed regions in the putative target sequences. If the reverse engineering strategy is integrated with the forward engineering strategy, a more accurate and more specific combinatorial regulatory network will be obtained.

RESULTS

In this work, utilizing both sequence-matching information and parallel expression datasets of miRNAs and mRNAs, we integrated forward engineering with reverse engineering strategies and as a result built a hypothetical combinatorial gene regulatory network in human cancer. The credibility of the regulatory relationships in the network was validated by random permutation procedures and supported by authoritative experimental evidence-based databases. The global and local architecture properties of the combinatorial regulatory network were explored, and the most important tumor-regulating miRNAs and TFs were highlighted from a topological point of view.

CONCLUSIONS

By integrating the forward engineering and reverse engineering strategies, we manage to sketch a genome-scale combinatorial gene regulatory network in human cancer, which includes transcriptional regulations and miRNA regulations, allowing systematic study of cancer gene regulation. Our work establishes a pipeline that can be extended to reveal conditional combinatorial regulatory landscapes correlating to specific cellular contexts.

Joint Bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data

MOTIVATION

There have been many successful experimental and bioinformatics efforts to elucidate transcription factor (TF)-target networks in several organisms. For many organisms, these annotations are complemented by miRNA-target networks of good quality. Attempts that use these networks in combination with gene expression data to draw conclusions on TF or miRNA activity are, however, still relatively sparse.

RESULTS

In this study, we propose Bayesian inference of regulation of transcriptional activity (BIRTA) as a novel approach to infer both, TF and miRNA activities, from combined miRNA and mRNA expression data in a condition specific way. That means our model explains mRNA and miRNA expression for a specific experimental condition by the activities of certain miRNAs and TFs, hence allowing for differentiating between switches from active to inactive (negative switch) and inactive to active (positive switch) forms. Extensive simulations of our model reveal its good prediction performance in comparison to other approaches. Furthermore, the utility of BIRTA is demonstrated at the example of Escherichia coli data comparing aerobic and anaerobic growth conditions, and by human expression data from pancreas and ovarian cancer.

AVAILABILITY AND IMPLEMENTATION

The method is implemented in the R package birta, which is freely available for Bio-conductor (>=2.10) on http://www.bioconductor.org/packages/release/bioc/html/birta.html.

Rigorous assessment of gene set enrichment tests

MOTIVATION

Several statistical tests are available to detect the enrichment of differential expression in gene sets. Such tests were originally proposed for analyzing gene sets associated with biological processes. The objective evaluation of tests on real measurements has not been possible as it is difficult to decide a priori, which processes will be affected in given experiments.

RESULTS

We present a first large study to rigorously assess and compare the performance of gene set enrichment tests on real expression measurements. Gene sets are defined based on the targets of given regulators such as transcription factors (TFs) and microRNAs (miRNAs). In contrast to processes, TFs and miRNAs are amenable to direct perturbations, e.g. regulator over-expression or deletion. We assess the ability of 14 different statistical tests to predict the perturbations from expression measurements in Escherichia coli, Saccharomyces cerevisiae and human. We also analyze how performance depends on the quality and comprehensiveness of the regulator targets via a permutation approach. We find that ANOVA and Wilcoxons test consistently perform better than for instance Kolmogorov-Smirnov and hypergeometric tests. For scenarios where the optimal test is not known, we suggest to combine all evaluated tests into an unweighted consensus, which also performs well in our assessment. Our results provide a guide for the selection of existing tests as well as a basis for the development and assessment of novel tests.

Mechanisms generating bistability and oscillations in microRNA-mediated motifs

The importance of post-transcriptional regulation by microRNAs (miRNAs) has recently been recognized in almost all cellular processes. When participating in cellular processes, miRNAs mainly mediate mRNA degradation or translational repression. Recently computational and experimental studies have identified an abundance of motifs involving miRNAs and transcriptional factors (TFs). The simplest motif is a two-node miRNA-mediated feedback loop (MFL) in which a TF regulates an miRNA and the TF itself is negatively regulated by the miRNA. In this paper we present a general computational model for the MFL based on biochemical regulations and explore its dynamics by using bifurcation analysis. Our results show that the MFL can behave either as switches or as oscillators, depending on the TF as a repressor or an activator. These functional features are consistent with the widespread appearance of miRNAs in fate decisions such as proliferation, differentiation, and apoptosis during development. We found that under the interplay of a TF and an miRNA, the MFL model can behave as switches for wide ranges of parameters even without cooperative binding of the TF. In addition, oscillations induced by the miRNA in the MFL model require neither an additional positive feedback loop, nor self-activation of the gene, nor cooperative binding of the TF, nor saturated degradation. Therefore, the MFL may provide a general network structure to induce bistability or oscillations. It is hoped that the results presented here will provide a new view on how gene expression is regulated by miRNAs and further guidance for experiments. Moreover, the insight gained from this study is also expected to provide a basis for the investigation of more complex networks assembled by simple building blocks.

A Novel YY1-miR-1 regulatory circuit in skeletal myogenesis revealed by genome-wide prediction of YY1-miRNA network

microRNAs (miRNAs) are non-coding RNAs that regulate gene expression post-transcriptionally, and mounting evidence supports the prevalence and functional significance of their interplay with transcription factors (TFs). Here we describe the identification of a regulatory circuit between muscle miRNAs (miR-1, miR-133 and miR-206) and Yin Yang 1 (YY1), an epigenetic repressor of skeletal myogenesis in mouse. Genome-wide identification of potential down-stream targets of YY1 by combining computational prediction with expression profiling data reveals a large number of putative miRNA targets of YY1 during skeletal myoblasts differentiation into myotubes with muscle miRs ranking on top of the list. The subsequent experimental results demonstrate that YY1 indeed represses muscle miRs expression in myoblasts and the repression is mediated through multiple enhancers and recruitment of Polycomb complex to several YY1 binding sites. YY1 regulating miR-1 is functionally important for both C2C12 myogenic differentiation and injury-induced muscle regeneration. Furthermore, we demonstrate that miR-1 in turn targets YY1, thus forming a negative feedback loop. Together, these results identify a novel regulatory circuit required for skeletal myogenesis and reinforce the idea that regulatory circuitries involving miRNAs and TFs are prevalent mechanisms.

MicroRNA control of bone formation and homeostasis

MicroRNAs (miRNAs) repress cellular protein levels to provide a sophisticated parameter of gene regulation that coordinates a broad spectrum of biological processes. Bone organogenesis is a complex process involving the differentiation and crosstalk of multiple cell types for formation and remodeling of the skeleton. Inhibition of mRNA translation by miRNAs has emerged as an important regulator of developmental osteogenic signaling pathways, osteoblast growth and differentiation, osteoclast-mediated bone resorption activity and bone homeostasis in the adult skeleton. miRNAs control multiple layers of gene regulation for bone development and postnatal functions, from the initial response of stem/progenitor cells to the structural and metabolic activity of the mature tissue. This Review brings into focus an emerging concept of bone-regulating miRNAs, the evidence for which has been gathered largely from in vivo mouse models and in vitro studies in human and mouse skeletal cell populations. Characterization of miRNAs that operate through tissue-specific transcription factors in osteoblast and osteoclast lineage cells, as well as intricate feedforward and reverse loops, has provided novel insights into the supervision of signaling pathways and regulatory networks controlling normal bone formation and turnover. The current knowledge of miRNAs characteristic of human pathologic disorders of the skeleton is presented with a future goal towards translational studies.

Biological robustness and the role of microRNAs: a network perspective

Over the past decade, microRNA molecules have emerged as critical regulators in the expression and function of animal genomes. This review discusses the relationship between microRNA-mediated regulation and the robustness of biochemical networks that contain microRNAs. Most biochemical networks are robust; they are relatively insensitive to the precise values of reaction constants and concentrations of molecules acting within the network. MicroRNAs involved in network robustness may appear to be nonessential under favourable uniform conditions used in conventional laboratory experiments. However, the function of these molecules can be revealed under environmental and genetic perturbations. Recent advances have revealed unexpected features of microRNA organization in networks that help explain their promotion of robustness.

Transcription factor binding sites are highly enriched within microRNA precursor sequences

Background

Transcription factors are thought to regulate the transcription of microRNA genes in a manner similar to that of protein-coding genes; that is, by binding to conventional transcription factor binding site DNA sequences located in or near promoter regions that lie upstream of the microRNA genes. However, in the course of analyzing the genomics of human microRNA genes, we noticed that annotated transcription factor binding sites commonly lie within 70- to 110-nt long microRNA small hairpin precursor sequences.

Results

We report that about 45% of all human small hairpin microRNA (pre-miR) sequences contain at least one predicted transcription factor binding site motif that is conserved across human, mouse and rat, and this rises to over 75% if one excludes primate-specific pre-miRs. The association is robust and has extremely strong statistical significance; it affects both intergenic and intronic pre-miRs and both isolated and clustered microRNA genes. We also confirmed and extended this finding using a separate analysis that examined all human pre-miR sequences regardless of conservation across species.

Conclusions

The transcription factor binding sites localized within small hairpin microRNA precursor sequences may possibly regulate their transcription. Transcription factors may also possibly bind directly to nascent primary microRNA gene transcripts or small hairpin microRNA precursors and regulate their processing.

Reviewers

This article was reviewed by Guillaume Bourque (nominated by Jerzy Jurka), Dmitri Pervouchine (nominated by Mikhail Gelfand), and Yuriy Gusev.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

BACKGROUND

To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.

RESULTS

To identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease.

CONCLUSIONS

MIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

A curated database of miRNA mediated feed-forward loops involving MYC as master regulator

BACKGROUND

The MYC transcription factors are known to be involved in the biology of many human cancer types. But little is known about the Myc/microRNAs cooperation in the regulation of genes at the transcriptional and post-transcriptional level.

METHODOLOGY/PRINCIPAL FINDINGS

Employing independent databases with experimentally validated data, we identified several mixed microRNA/Transcription Factor Feed-Forward Loops regulated by Myc and characterized completely by experimentally supported regulatory interactions, in human. We then studied the statistical and functional properties of these circuits and discussed in more detail a few interesting examples involving E2F1, PTEN, RB1 and VEGF.

CONCLUSIONS/SIGNIFICANCE

We have assembled and characterized a catalogue of human mixed Transcription Factor/microRNA Feed-Forward Loops, having Myc as master regulator and completely defined by experimentally verified regulatory interactions.

The role of incoherent microRNA-mediated feedforward loops in noise buffering

MicroRNAs are endogenous non-coding RNAs which negatively regulate the expression of protein-coding genes in plants and animals. They are known to play an important role in several biological processes and, together with transcription factors, form a complex and highly interconnected regulatory network. Looking at the structure of this network, it is possible to recognize a few overrepresented motifs which are expected to perform important elementary regulatory functions. Among them, a special role is played by the microRNA-mediated feedforward loop in which a master transcription factor regulates a microRNA and, together with it, a set of target genes. In this paper we show analytically and through simulations that the incoherent version of this motif can couple the fine-tuning of a target protein level with an efficient noise control, thus conferring precision and stability to the overall gene expression program, especially in the presence of fluctuations in upstream regulators. Among the other results, a nontrivial prediction of our model is that the optimal attenuation of fluctuations coincides with a modest repression of the target expression. This feature is coherent with the expected fine-tuning function and in agreement with experimental observations of the actual impact of a wide class of microRNAs on the protein output of their targets. Finally, we describe the impact on noise-buffering efficiency of the cross-talk between microRNA targets that can naturally arise if the microRNA-mediated circuit is not considered as isolated, but embedded in a larger network of regulations.

Non-coding RNAs: Meet thy masters

New DNA sequencing technologies have provided novel insights into eukaryotic genomes, epigenomes, and the transcriptome, including the identification of new non-coding RNA (ncRNA) classes such as promoter-associated RNAs and long RNAs. Moreover, it is now clear that up to 90% of eukaryotic genomes are transcribed, generating an extraordinary range of RNAs with no coding capacity. Taken together, these new discoveries are modifying the status quo in genomic science by demonstrating that the eukaryotic gene pool is divided into two distinct categories of transcripts: protein-coding and non-coding. The function of the majority of ncRNAs produced by the transcriptome is largely unknown; however, it is probable that many are associated with epigenetic mechanisms. The purpose of this review is to describe the most recent discoveries in the ncRNA field that implicate these molecules as key players in the epigenome.

Prediction of Associations between microRNAs and Gene Expression in Glioma Biology

Despite progress in the determination of miR interactions, their regulatory role in cancer is only beginning to be unraveled. Utilizing gene expression data from 27 glioblastoma samples we found that the mere knowledge of physical interactions between specific mRNAs and miRs can be used to determine associated regulatory interactions, allowing us to identify 626 associated interactions, involving 128 miRs that putatively modulate the expression of 246 mRNAs. Experimentally determining the expression of miRs, we found an over-representation of over(under)-expressed miRs with various predicted mRNA target sequences. Such significantly associated miRs that putatively bind over-expressed genes strongly tend to have binding sites nearby the 3′UTR of the corresponding mRNAs, suggesting that the presence of the miRs near the translation stop site may be a factor in their regulatory ability. Our analysis predicted a significant association between miR-128 and the protein kinase WEE1, which we subsequently validated experimentally by showing that the over-expression of the naturally under-expressed miR-128 in glioma cells resulted in the inhibition of WEE1 in glioblastoma cells.

miRNAs give worms the time of their lives: small RNAs and temporal control in Caenorhabditis elegans

Alteration in the timing of particular developmental events can lead to major morphological changes that have profound effects on the life history of an organism. Insights into developmental timing mechanisms have been revealed in the model organism Caenorhabditis elegans, in which a regulatory network of heterochronic genes times events during larval development, ensuring that stage-specific programs occur in the appropriate sequence and on schedule. Developmental timing studies in C. elegans led to the landmark discovery of miRNAs and continue to enhance our understanding of the regulation and activity of these small regulatory molecules. Current views of the heterochronic gene pathway are summarized here, with a focus on the ways in which miRNAs contribute to temporal control and how miRNAs themselves are regulated. Finally, the conservation of heterochronic genes and their functions in timing, as well as their related roles in stem cells and cancer, are highlighted.

CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse

BACKGROUND

Transcription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them.

DESCRIPTION

Here we describe a dynamic web-accessible database, CircuitsDB, supporting a genome-wide transcriptional and post-transcriptional regulatory network integration, for the human and mouse genomes, based on a bioinformatic sequence-analysis approach. In particular, CircuitsDB is currently focused on the study of mixed miRNA/TF Feed-Forward regulatory Loops (FFLs), i.e. elementary circuits in which a master TF regulates an miRNA and together with it a set of Joint Target protein-coding genes. The database was constructed using an ab-initio oligo analysis procedure for the identification of the transcriptional and post-transcriptional interactions. Several external sources of information were then pooled together to obtain the functional annotation of the proposed interactions. Results for human and mouse genomes are presented in an integrated web tool, that allows users to explore the circuits, investigate their sequence and functional properties and thus suggest possible biological experiments.

CONCLUSIONS

We present CircuitsDB, a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: http://biocluster.di.unito.it/circuits/

Gene-centered regulatory networks

Differential gene expression plays a critical role in the development and physiology of multicellular organisms. At a 'systems level' (e.g. at the level of a tissue, organ or whole organism), this process can be studied using gene regulatory network (GRN) models that capture physical and regulatory interactions between genes and their regulators. In the past years, significant progress has been made toward the mapping of GRNs using a variety of experimental and computational approaches. Here, we will discuss gene-centered approaches that we employed to characterize GRNs and describe insights that we have obtained into the global design principles of gene regulation in complex metazoan systems.