MicroRNA-9 controls dendritic development by targeting REST

MicroRNAs (miRNAs) are conserved noncoding RNAs that function as posttranscriptional regulators of gene expression. miR-9 is one of the most abundant miRNAs in the brain. Although the function of miR-9 has been well characterized in neural progenitors, its role in dendritic and synaptic development remains largely unknown. In order to target miR-9 in vivo, we developed a transgenic miRNA sponge mouse line allowing conditional inactivation of the miR-9 family in a spatio-temporal-controlled manner. Using this novel approach, we found that miR-9 controls dendritic growth and synaptic transmission in vivo. Furthermore, we demonstrate that miR-9-mediated downregulation of the transcriptional repressor REST is essential for proper dendritic growth.Fil: Giusti, Sebastian Alejandro. Max Planck Institute of Psychiatry; AlemaniaFil: Vogl, Annette M.. Max Planck Institute of Psychiatry; AlemaniaFil: Brockmann, Marina M.. Max Planck Institute of Psychiatry; AlemaniaFil: Vercelli, Claudia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; ArgentinaFil: Rein, Martin L.. Max Planck Institute of Psychiatry; AlemaniaFil: Trümbach, Dietrich. Helmholtz Zentrum München; AlemaniaFil: Wurst, Wolfgang. Helmholtz Zentrum München; AlemaniaFil: Cazalla, Demian. University of Utah; Estados UnidosFil: Stein, Valentin. Universitaet Bonn; AlemaniaFil: Deussing, Jan M.. Max Planck Institute of Psychiatry; AlemaniaFil: Refojo, Damian. Max Planck Institute of Psychiatry; Alemani

High precision in microRNA prediction: a novel genome-wide approach with convolutional deep residual networks

MicroRNAs (miRNAs) are small non-coding RNAs that have a key role in the regulation of gene expression. The importance of miRNAs is widely acknowledged by the community nowadays and computational methods are needed for the precise prediction of novel candidates to miRNA. This task can be done by searching homologous with sequence alignment tools, but results are restricted to sequences that are very similar to the known miRNA precursors (pre-miRNAs). Besides, a very important property of pre-miRNAs, their secondary structure, is not taken into account by these methods. To fill this gap, many machine learning approaches were proposed in the last years. However, the methods are generally tested in very controlled conditions. If these methods were used under real conditions, the false positives increase and the precisions fall quite below those published. This work provides a novel approach for dealing with the computational prediction of pre-miRNAs: a convolutional deep residual neural network (mirDNN). This model was tested with several genomes of animals and plants, the full-genomes, achieving a precision up to 5 times larger than other approaches at the same recall rates. Furthermore, a novel validation methodology was used to ensure that the performance reported in this study can be effectively achieved when using mirDNN in novel species. To provide fast an easy access to mirDNN, a web demo is available at http://sinc.unl.edu.ar/web-demo/mirdnn/. The demo can process FASTA files with multiple sequences to calculate the prediction scores and generates the nucleotide importance plots.Fil: Yones, Cristian Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Raad, Jonathan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery

Computer Systems, Imagery and Medi

Graph based fusion of high-dimensional gene- and microRNA expression data

One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs, correlations between miRNA and mRNA expression data as well as target prediction information were used to build a bipartite graph representing the relations between miRNAs and mRNAs. Feature selection is a critical part when fitting prediction models to high- dimensional data. Most methods treat features, in this case genes or miRNAs, as independent, an assumption that does not hold true when dealing with combined gene and miRNA expression data. To improve prediction accuracy, a description of the correlation structure in the data is needed. In this work the bipartite graph was used to guide the feature selection and therewith improve prediction results and find a stable prognostic signature of miRNAs and genes. The method is evaluated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse, an important event in prostate cancer treatment, was used as clinical endpoint. Biochemical relapse coins the renewed rise of the blood level of a prostate marker (PSA) after surgical removal of the prostate. The relapse is a hint for metastases and usually the point in clinical practise to decide for further treatment. A boosting approach was used to predict the biochemical relapse. It could be shown that the bipartite graph in combination with miRNA and mRNA expression data could improve prediction performance. Furthermore the ap- proach improved the stability of the feature selection and therewith yielded more consistent marker sets. Of course, the marker sets produced by this new method contain mRNAs as well as miRNAs. The new approach was compared to two state-of-the-art methods suited for high-dimensional data and showed better prediction performance in both cases

Using gene and microRNA expression in the human airway for lung cancer diagnosis

Lung cancer surpasses all other causes of cancer-related deaths worldwide. Gene-expression microarrays have shown that differences in the cytologically normal bronchial airway can distinguish between patients with and without lung cancer. In research reported here, we have used microRNA expression in bronchial epithelium and gene expression in nasal epithelium to advance biological understanding of the lung-cancer "field of injury" and develop new biomarkers for lung cancer diagnosis. MicroRNAs are known to mediate the airway response to tobacco smoke exposure but their role in the lung-cancer-associated field of injury was previously unknown. Microarrays can measure microRNA expression; however, they are probe-based and limited to detecting annotated microRNAs. MicroRNA sequencing, on the other hand, allows the identification of novel microRNAs that may play important biological roles. We have used microRNA sequencing to discover novel microRNAs in the bronchial epithelium. One of the predicted microRNAs, now known as miR-4423, is associated with lung cancer and airway development. This finding demonstrates for the first time a microRNA expression change associated with the lung-cancer field of injury and microRNA mediation of gene expression changes within that field. The National Lung Screening Trial showed that screening high-risk smokers using CT scans decreases lung-cancer-associated mortality. Nodules were detected in over 20% of participants; however, the overwhelming majority of screening-detected nodules were non-malignant. We therefore need biomarkers to determine which screening-detected nodules are benign and do not require further invasive testing. Given that the lung-cancer-associated field of injury extends to the bronchial epithelium, our group hypothesized that the field of injury may extend farther up in the airway. Using gene expression microarrays, we have identified a nasal epithelium gene-expression signature associated with lung cancer. Using samples from the bronchial epithelium and the nasal epithelium, we have established that there is a common lung-cancer-associated gene-expression signature throughout the airway. In addition, we have developed a nasal epithelium gene-expression biomarker for lung cancer together with a clinico-genomic classifier that includes both clinical factors and gene expression. Our data suggests that gene expression profiling in nasal epithelium might serve as a non-invasive approach for lung cancer diagnosis and screenin

A Network of microRNAs Acts to Promote Cell Cycle Exit and Differentiation of Human Pancreatic Endocrine Cells.

Pancreatic endocrine cell differentiation is orchestrated by the action of transcription factors that operate in a gene regulatory network to activate endocrine lineage genes and repress lineage-inappropriate genes. MicroRNAs (miRNAs) are important modulators of gene expression, yet their role in endocrine cell differentiation has not been systematically explored. Here we characterize miRNA-regulatory networks active in human endocrine cell differentiation by combining small RNA sequencing, miRNA over-expression, and network modeling approaches. Our analysis identified Let-7g, Let-7a, miR-200a, miR-127, and miR-375 as endocrine-enriched miRNAs that drive endocrine cell differentiation-associated gene expression changes. These miRNAs are predicted to target different transcription factors, which converge on genes involved in cell cycle regulation. When expressed in human embryonic stem cell-derived pancreatic progenitors, these miRNAs induce cell cycle exit and promote endocrine cell differentiation. Our study delineates the role of miRNAs in human endocrine cell differentiation and identifies miRNAs that could facilitate endocrine cell reprogramming

Inference of miRNA targets using evolutionary conservation and pathway analysis

BACKGROUND: MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially. RESULTS: We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development. CONCLUSION: We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and assigns a posterior probability to each putative target site. The results presented here indicate that our general method achieves very good performance in predicting miRNA target sites, providing at the same time insights into the evolution of target sites for individual miRNAs. Moreover, by combining our predictions with pathway analysis, we propose functions of specific miRNAs in nervous system development, inter-cellular communication and cell growth. The complete target site predictions as well as the miRNA/pathway associations are accessible on the ElMMo web server

The Fate of miRNA* Strand through Evolutionary Analysis: Implication for Degradation As Merely Carrier Strand or Potential Regulatory Molecule?

BACKGROUND: During typical microRNA (miRNA) biogenesis, one strand of a approximately 22 nt RNA duplex is preferentially selected for entry into a silencing complex, whereas the other strand, known as the passenger strand or miRNA* strand, is degraded. Recently, some miRNA* sequences were reported as guide miRNAs with abundant expression. Here, we intended to discover evolutionary implication of the fate of miRNA* strand by analyzing miRNA/miRNA* sequences across vertebrates. PRINCIPAL FINDINGS: Mature miRNAs based on gene families were well conserved especially for their seed sequences across vertebrates, while their passenger strands always showed various divergence patterns. The divergence mainly resulted from divergence of different animal species, homologous miRNA genes and multicopy miRNA hairpin precursors. Some miRNA* sequences were phylogenetically conserved in seed and anchor sequences similar to mature miRNAs, while others revealed high levels of nucleotide divergence despite some of their partners were highly conserved. Most of those miRNA precursors that could generate abundant miRNAs from both strands always were well conserved in sequences of miR-#-5p and miR-#-3p, especially for their seed sequences. CONCLUSIONS: The final fate of miRNA* strand, either degraded as merely carrier strand or expressed abundantly as potential functional guide miRNA, may be destined across evolution. Well-conserved miRNA* strands, particularly conservation in seed sequences, maybe afford potential opportunities for contributing to regulation network. The study will broaden our understanding of potential functional miRNA* species

Gene expression trees in lymphoid development

<p>Abstract</p> <p>Background</p> <p>The regulatory processes that govern cell proliferation and differentiation are central to developmental biology. Particularly well studied in this respect is the lymphoid system due to its importance for basic biology and for clinical applications. Gene expression measured in lymphoid cells in several distinguishable developmental stages helps in the elucidation of underlying molecular processes, which change gradually over time and lock cells in either the B cell, T cell or Natural Killer cell lineages. Large-scale analysis of these <it>gene expression trees </it>requires computational support for tasks ranging from visualization, querying, and finding clusters of similar genes, to answering detailed questions about the functional roles of individual genes.</p> <p>Results</p> <p>We present the first statistical framework designed to analyze gene expression data as it is collected in the course of lymphoid development through clusters of co-expressed genes and additional heterogeneous data. We introduce dependence trees for continuous variates, which model the inherent dependencies during the differentiation process naturally as gene expression trees. Several trees are combined in a mixture model to allow inference of potentially overlapping clusters of co-expressed genes. Additionally, we predict microRNA targets.</p> <p>Conclusion</p> <p>Computational results for several data sets from the lymphoid system demonstrate the relevance of our framework. We recover well-known biological facts and identify promising novel regulatory elements of genes and their functional assignments. The implementation of our method (licensed under the GPL) is available at <url>http://algorithmics.molgen.mpg.de/Supplements/ExpLym/</url>.</p

The GDR : a novel approach to detect large-scale genomic sequence patterns

Utvikling av ny sekvenseringsteknologi de to siste tiårene har tillatt dypere dykk ned i de biomolekylære aspektene ved menneskets oppskrift. Hel-genom data fra flere hundre tusen mennesker er allerede tilgjengelig, men hvordan den økende mengden informasjon kan settes sammen til meningsfull funksjonell tolkning er komplisert og krever nye metoder. MikroRNA - mRNA interaksjoner utgjør et enormt genreguleringsnettverk som er vanskelig å predikere, selv for dagens beste maskinlæringsalgoritmer(1). Disse ikke-kodende elementene er involvert i omtrent alle cellulære prosesser i mennesket, primært via delvis komplementær baseparing mellom mikroRNA og mRNA, men det er mye vi ikke forstår av dette nettverkets betydning i vår biologi (2-4). Nye metoder er nødvendige for å kunne utforske genetisk variasjon i dette nettverket, som kan gi nye innblikk i hvordan genene våre reguleres. Her presenteres «The Group Diversity Ratio» (GDR) som en ny målenhet til å møte denne utfordringen. GDR kan kvantifisere evolusjonær struktur av variasjon i store mengder genomisk sekvensdata, med et resultat som kan statistisk valideres. Metoden baserer seg på å måle gruppe-struktur i et distanse-basert fylogenetisk tre av sekvensdata, for forhåndsdefinerte grupper av «blader» i treet. Gruppene representerer en egenskap som kan relateres til sekvensdataen, og det undersøkes til hvilken grad det finnes en sammenheng mellom de to. Metoden kan primært brukes til å raskt skaffe overblikk over store mengder genomisk sekvensdata, som kan gi verdifulle innblikk til videre etterforskning. For å teste metoden ble GDR brukt til å identifisere variasjon assosiert med etniske populasjoner i 3’UTR data fra «The 1000 Genomes Project» (1KGP). 1KGP var det første store prosjektet som adresserte den etniske skjevheten som nå finnes i genom-databaser, og som utgjør en god grunn til å utforske etnisk genetisk variasjon (5). I tillegg til identifikasjon av mer enn 1000 3’UTR sekvenser som inneholder signifikant etnisitet-spesifikk variasjon, viser dette studiet GDR-metodens høye potensial til å undersøke genetisk variasjon i stor skala.The emergence of new sequencing technologies over the past two decades has enabled us to dive deeper into the biomolecular aspect of the human recipe. Entire genomes from several hundred thousand people are already accessible, but how to interpretate the connections between the blueprints and the phenotypes are complicated, even for the best developed machine learning algorithms. Prediction of the microRNA-mRNA targeting network is a classic example, which is involved with gene regulation of all living cell processes. These non-coding features make up complex networks of interactions, where microRNAs primarily target 3’UTRs through partial complementary base-pairing. Thus, the challenge to investigate patterns in such large-scaled genomic sequence data requires new approaches. The Group Diversity Ratio (GDR) metric is presented here as a novel approach to aid in this challenge. The GDR quantifies genome-wide structure in large-scale sequence data with a statistically testable result. Patterns are measured for a group feature that may be related to variation in sequence samples, based on phylogenetic distance estimations. It opens opportunities to quickly gain insights into genomic regions of interests and used to guide further research. To demonstrate the use of the GDR metric, ethnicity-associated variation patterns in more than 1000 human 3’UTRs was identified with the GDR. The study set was from 1000 Genomes project, which was the first major effort to address the problem of ethnic bias in genetic studies and contained more than 2500 whole-genome sequences from 26 ethnic lineages. In addition to detecting significantly distinct 3’UTR elements for ethnic populations, the key finding of this study was the high potentials of the GDR to facilitate more high-throughput characterization of genomic sequence data.M-BIA