MikroRNA-säätely rintasyövässä - ekspressiodatan bayesilainen analyysi

MicroRNAs are a class of small, non-coding RNAs, which regulate gene expression post-transcriptionally. They downregulate genes by targeting messenger RNA transcripts and causing their degradation and inhibition of translation. Research has revealed microRNAs to participate in diverse cellular functions, such as differentiation and apoptosis, and many pathological processes, including cancer. Identification of microRNA target genes is crucial in understanding their function in cell biology and disease. A wide range of methods have been proposed for computational prediction of microRNA targets. Early target prediction methods used sequence information, while recent tools have integrated expression measurements of target genes and microRNAs. A limited number of studies have integrated protein, gene and microRNA expression for target prediction. Breast cancer is the most common cancer in women and a significant cause of morbidity and mortality globally. Analyses of gene expression data have provided insight into the pathogenesis of breast cancer, and intrinsic subtypes correlating with prognosis have been identified. A range of microRNAs have been indicated to contribute to breast cancer pathogenesis. In this thesis, a recent Bayesian variable selection method was applied for uncovering putative microRNA targets in breast cancer. The proposed model integrated protein, gene and microRNA expression data. Results were compared with another popular prediction method. Analyses showed that the proposed method is applicable to microRNA target prediction. Limitations and refinements of the method and study are discussed, and the importance of an integrative approach is highlighted.MikroRNA:t ovat lyhyitä RNA-molekyylejä, jotka säätelevät geeniekspressiota sitoutumalla lähetti-RNA-molekyyleihin estäen siten niiden translaation proteiiniksi. Aiemmat tutkimukset ovat osoittaneet, että mikroRNA:t osallistuvat monipuolisesti solujen toiminnan säätelyyn, kuten erilaistumiseen ja apoptoosiin, ja ovat osallisena monien tautien, kuten syövän synnyssä. MikroRNA:n säätelemien kohdegeenien tunnistaminen on olennainen askel mikroRNA:n toiminnan ymmärtämisessä. Kohdegeenien ennustamiseen on kehitetty lukuisia laskennallisia menetelmiä. Varhaiset menetelmät perustuivat RNA-sekvenssien vertailuun. Uudemmat työkalut yhdistävät geeni- ja mikroRNA-ekspressiodataa kohdegeenien tunnistamiseksi. Proteiini-, geeni- ja mikroRNA-ekspressiota yhdistäviä kohdegeenien tunnistamiseen tähtääviä tutkimuksia on julkaistu toistaiseksi suhteellisen vähän. Rintasyöpä on naisten yleisin syöpä ja merkittävä sairastavuuden ja kuolleisuuden aiheuttaja maailmanlaajuisesti. Geeniekspressiodatan analysointi on lisännyt tietoa rintasyövän synnystä, ja geeniekspressioon perustuen on kyetty tunnistamaan rintasyövän alatyyppejä, jotka korreloivat syövän ennusteeseen. MikroRNA:n on todettu olevan osatekijä rintasyövän synnyssä. Tässä diplomityössä sovellettiin äskettäin julkaistua bayesilaista muuttujavalintamenetelmää mikroRNA-molekyylien kohdegeenien ennustamiseen rintasyövässä. Tähän tarkoitukseen käytettiin proteiini-, geeni- ja mikroRNA- ekspressiodataa. Tulokset osoittivat, että menetelmä soveltuu kohdegeenien ennustamiseen. Työssä esitetään vaihtoehtoja mallin jatkokehittämiseksi

Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies

Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies.</p

Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data

Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area. In this article we propose an odds-ratio (OR) statistic for identification of regulatory miRNAs. It is based on integrative analysis of matched miRNA and mRNA time-course microarray data. The OR-statistic was used for (i) identification of miRNAs with regulatory potential, (ii) identification of miRNA-target mRNA pairs and (iii) identification of time lags between changes in miRNA expression and those of its target mRNAs. We applied the OR-statistic to a cancer data set and identified a small set of miRNAs that were negatively correlated to mRNAs. A literature survey revealed that some of the miRNAs that were predicted to be regulatory, were indeed oncogenic or tumor suppressors. Finally, some of the predicted miRNA targets have been shown to be experimentally valid

Current tools for the identification of miRNA genes and their targets

The discovery of microRNAs (miRNAs), almost 10 years ago, changed dramatically our perspective on eukaryotic gene expression regulation. However, the broad and important functions of these regulators are only now becoming apparent. The expansion of our catalogue of miRNA genes and the identification of the genes they regulate owe much to the development of sophisticated computational tools that have helped either to focus or interpret experimental assays. In this article, we review the methods for miRNA gene finding and target identification that have been proposed in the last few years. We identify some problems that current approaches have not yet been able to overcome and we offer some perspectives on the next generation of computational methods

A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions

microRNAs (miRNAs) are short non-coding RNAs with regulatory functions in various biological processes including cell differentiation, development and oncogenic transformation. They can bind to mRNA transcripts of protein-coding genes and repress their translation or lead to mRNA degradation. Conversely, the transcription of miRNAs is regulated by proteins including transcription factors, co-factors, and messenger molecules in signaling pathways, yielding a bidirectional regulatory network of gene and miRNA expression. We describe here a least angle regression approach for uncovering the functional interplay of gene and miRNA regulation based on paired gene and miRNA expression profiles. First, we show that gene expression profiles can indeed be reconstructed from the expression profiles of miRNAs predicted to be regulating the specific gene. Second, we propose a two-step model where in the first step, sequence information is used to constrain the possible set of regulating miRNAs and in the second step, this constraint is relaxed to find regulating miRNAs that do not rely on perfect seed binding. Finally, a bidirectional network comprised of miRNAs regulating genes and genes regulating miRNAs is built from our previous regulatory predictions. After applying the method to a human cancer cell line data set, an analysis of the underlying network reveals miRNAs known to be associated with cancer when dysregulated are predictors of genes with functions in apoptosis. Among the predicted and newly identified targets that lack a classical miRNA seed binding site of a specific oncomir, miR-19b-1, we found an over-representation of genes with functions in apoptosis, which is in accordance with the previous finding that this miRNA is the key oncogenic factor in the mir-17-92 cluster. In addition, we found genes involved in DNA recombination and repair that underline its importance in maintaining the integrity of the cell

Survey of Computational Algorithms for MicroRNA Target Prediction

MicroRNAs (miRNAs) are 19 to 25 nucleotides non-coding RNAs known to possess important post-transcriptional regulatory functions. Identifying targeting genes that miRNAs regulate are important for understanding their specific biological functions. Usually, miRNAs down-regulate target genes through binding to the complementary sites in the 3' untranslated region (UTR) of the targets. In part, due to the large number of miRNAs and potential targets, an experimental based prediction design would be extremely laborious and economically unfavorable. However, since the bindings of the animal miRNAs are not a perfect one-to-one match with the complementary sites of their targets, it is difficult to predict targets of animal miRNAs by accessing their alignment to the 3' UTRs of potential targets. Consequently, sophisticated computational approaches for miRNA target prediction are being considered as essential methods in miRNA research

Inference of gene regulation from expression datasets

The development of high throughput techniques and the accumulation of large scale gene expression data provide researchers great opportunities to more efficiently solve important but complex biological problems, such as reconstruction of gene regulatory networks and identification of miRNA-target interactions. In the past decade, many algorithms have been developed to address these problems. However, prediction and simulation of gene expression data have not yet received as much attention. In this study, we present a model based on stepwise multiple linear regression (SMLR) that can be applied for prediction and simulation of gene expression, as well as reconstruction of gene regulatory networks by analysis of time-series gene expression data, and we present its application in analysis of paired miRNA-mRNA expression data.Ph.D., Biomedical Engineering -- Drexel University, 201

A BAYESIAN GRAPHICAL MODELING APPROACH TO MICRORNA REGULATORY NETWORK INFERENCE

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in animals and plants. Genes regulated by miRNAs are called targets. Typically, methods for target prediction are based solely on sequence data and on the structure information. In this paper we propose a Bayesian graphical modeling approach that infers the miRNA regulatory network by integrating expression levels of miRNAs with their potential mRNA targets and, via the prior probability model, with their sequence/structure information. We use a directed graphical model with a particular structure adapted to our data based on biological considerations. We then achieve network inference using stochastic search methods for variable selection that allow us to explore the huge model space via MCMC. A time-dependent coefficients model is also implemented. We consider experimental data from a study on a very well-known developmental toxicant causing neural tube defects, hyperthermia. Some of the pairs of target gene and miRNA we identify seem very plausible and warrant future investigation. Our proposed method is general and can be easily applied to other types of network inference by integrating multiple data sources.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS360 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org