MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes

Probing the limits to microRNA-mediated control of gene expression

According to the `ceRNA hypothesis', microRNAs (miRNAs) may act as mediators of an effective positive interaction between long coding or non-coding RNA molecules, carrying significant potential implications for a variety of biological processes. Here, inspired by recent work providing a quantitative description of small regulatory elements as information-conveying channels, we characterize the effectiveness of miRNA-mediated regulation in terms of the optimal information flow achievable between modulator (transcription factors) and target nodes (long RNAs). Our findings show that, while a sufficiently large degree of target derepression is needed to activate miRNA-mediated transmission, (a) in case of differential mechanisms of complex processing and/or transcriptional capabilities, regulation by a post-transcriptional miRNA-channel can outperform that achieved through direct transcriptional control; moreover, (b) in the presence of large populations of weakly interacting miRNA molecules the extra noise coming from titration disappears, allowing the miRNA-channel to process information as effectively as the direct channel. These observations establish the limits of miRNA-mediated post-transcriptional cross-talk and suggest that, besides providing a degree of noise buffering, this type of control may be effectively employed in cells both as a failsafe mechanism and as a preferential fine tuner of gene expression, pointing to the specific situations in which each of these functionalities is maximized.Comment: 16 page

Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications

A Multi-Layered Study on Harmonic Oscillations in Mammalian Genomics and Proteomics

Cellular, organ, and whole animal physiology show temporal variation predominantly featuring 24-h (circadian) periodicity. Time-course mRNA gene expression profiling in mouse liver showed two subsets of genes oscillating at the second (12-h) and third (8-h) harmonic of the prime (24-h) frequency. The aim of our study was to identify specific genomic, proteomic, and functional properties of ultradian and circadian subsets. We found hallmarks of the three oscillating gene subsets, including different (i) functional annotation, (ii) proteomic and electrochemical features, and (iii) transcription factor binding motifs in upstream regions of 8-h and 12-h oscillating genes that seemingly allow the link of the ultradian gene sets to a known circadian network. Our multifaceted bioinformatics analysis of circadian and ultradian genes suggests that the different rhythmicity of gene expression impacts physiological outcomes and may be related to transcriptional, translational and post-translational dynamics, as well as to phylogenetic and evolutionary components

PuTmiR: A database for extracting neighboring transcription factors of human microRNAs

<p>Abstract</p> <p>Background</p> <p>Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome. Recent studies suggest that these putative TFs are possible regulators of those miRNAs.</p> <p>Description</p> <p>The interface is built around two datasets that consist of the exhaustive lists of putative TFs binding respectively in the 10 kb upstream region (USR) and downstream region (DSR) of human miRNAs. A web server, named as PuTmiR, is designed. It provides an option for extracting the putative TFs for human miRNAs, as per the requirement of a user, based on genomic locality, i.e., any upstream or downstream region of interest less than 10 kb. The degree distributions of the number of putative TFs and miRNAs against each other for the 10 kb USR and DSR are analyzed from the data and they explore some interesting results. We also report about the finding of a significant regulatory activity of the YY1 protein over a set of oncomiRNAs related to the colon cancer.</p> <p>Conclusion</p> <p>The interface provided by the PuTmiR web server provides an important resource for analyzing the direct and indirect regulation of human miRNAs. While it is already an established fact that miRNAs are regulated by TFs binding to their USR, this database might possibly help to study whether an miRNA can also be regulated by the TFs binding to their DSR.</p

Activity of microRNAs and transcription factors in Gene Regulatory Networks

In biological research, diverse high-throughput techniques enable the investigation of whole systems at the molecular level. The development of new methods and algorithms is necessary to analyze and interpret measurements of gene and protein expression and of interactions between genes and proteins. One of the challenges is the integrated analysis of gene expression and the associated regulation mechanisms. The two most important types of regulators, transcription factors (TFs) and microRNAs (miRNAs), often cooperate in complex networks at the transcriptional and post-transcriptional level and, thus, enable a combinatorial and highly complex regulation of cellular processes. For instance, TFs activate and inhibit the expression of other genes including other TFs whereas miRNAs can post-transcriptionally induce the degradation of transcribed RNA and impair the translation of mRNA into proteins. The identification of gene regulatory networks (GRNs) is mandatory in order to understand the underlying control mechanisms. The expression of regulators is itself regulated, i.e. activating or inhibiting regulators in varying conditions and perturbations. Thus, measurements of gene expression following targeted perturbations (knockouts or overexpressions) of these regulators are of particular importance. The prediction of the activity states of the regulators and the prediction of the target genes are first important steps towards the construction of GRNs. This thesis deals with these first bioinformatics steps to construct GRNs. Targets of TFs and miRNAs are determined as comprehensively and accurately as possible. The activity state of regulators is predicted for specific high-throughput data and specific contexts using appropriate statistical approaches. Moreover, (parts of) GRNs are inferred, which lead to explanations of given measurements. The thesis describes new approaches for these tasks together with accompanying evaluations and validations. This immediately defines the three main goals of the current thesis: 1. The development of a comprehensive database of regulator-target relation. Regulators and targets are retrieved from public repositories, extracted from the literature via text mining and collected into the miRSel database. In addition, relations can be predicted using various published methods. In order to determine the activity states of regulators (see 2.) and to infer GRNs (3.) comprehensive and accurate regulator-target relations are required. It could be shown that text mining enables the reliable extraction of miRNA, gene, and protein names as well as their relations from scientific free texts. Overall, the miRSel contains about three times more relations for the model organisms human, mouse, and rat as compared to state-of-the-art databases (e.g. TarBase, one of the currently most used resources for miRNA-target relations). 2. The prediction of activity states of regulators based on improved target sets. In order to investigate mechanisms of gene regulation, the experimental contexts have to be determined in which the respective regulators become active. A regulator is predicted as active based on appropriate statistical tests applied to the expression values of its set of target genes. For this task various gene set enrichment (GSE) methods have been proposed. Unfortunately, before an actual experiment it is unknown which genes are affected. The missing standard-of-truth so far has prevented the systematic assessment and evaluation of GSE tests. In contrast, the trigger of gene expression changes is of course known for experiments where a particular regulator has been directly perturbed (i.e. by knockout, transfection, or overexpression). Based on such datasets, we have systematically evaluated 12 current GSE tests. In our analysis ANOVA and the Wilcoxon test performed best. 3. The prediction of regulation cascades. Using gene expression measurements and given regulator-target relations (e.g. from the miRSel database) GRNs are derived. GSE tests are applied to determine TFs and miRNAs that change their activity as cellular response to an overexpressed miRNA. Gene regulatory networks can constructed iteratively. Our models show how miRNAs trigger gene expression changes: either directly or indirectly via cascades of miRNA-TF, miRNA-kinase-TF as well as TF-TF relations. In this thesis we focus on measurements which have been obtained after overexpression of miRNAs. Surprisingly, a number of cancer relevant miRNAs influence a common core of TFs which are involved in processes such as proliferation and apoptosis

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Description</p> <p>Here we describe a dynamic web-accessible database, <monospace>CircuitsDB</monospace>, 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, <monospace>CircuitsDB</monospace> 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.</p> <p>Conclusions</p> <p>We present <monospace>CircuitsDB</monospace>, a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: <url>http://biocluster.di.unito.it/circuits/</url></p