No description available.

Much research has been directed toward understanding the roles of essential components in the cell, such as proteins, microRNAs, and genes. This dissertation focuses on two interesting problems in bioinformatics research: microRNA-target prediction and the identification of conserved protein complexes across species. We define the two problems and develop novel approaches for solving them. MicroRNAs are short non-coding RNAs that mediate gene expression. The goal is to predict microRNA targets. Existing methods rely on sequence features to predict targets. These features are neither sufficient nor necessary to identify functional target sites and ignore the cellular conditions in which microRNA and mRNA interact. We developed MicroTarget to predict microRNA-mRNA interactions using heterogeneous data sources. MicroTarget uses expression data to learn candidate target set for each microRNA. Then, sequence data is used to provide evidence of direct interactions and ranking the predicted targets. The predicted targets overlap with many of the experimentally validated ones. The results indicate that using expression data helps in predicting microRNA targets accurately. Protein complexes conserved across species specify processes that are core to cell machinery. Methods that have been devised to identify conserved complexes are severely limited by noise in PPI data. Behind PPIs, there are domains interacting physically to perform the necessary functions. Therefore, employing domains and domain interactions gives a better view of the protein interactions and functions. We developed novel strategy for local network alignment, DONA. DONA maps proteins into their domains and uses DDIs to improve the network alignment. We developed novel strategy for constructing an alignment graph and then uses this graph to discover the conserved sub-networks. DONA shows better performance in terms of the overlap with known protein complexes with higher precision and recall rates than existing methods. The result shows better semantic similarity computed with respect to both the biological process and the molecular function of the aligned sub-networks. / Ph. D.

MicroRNAs represent a special class of small (~21–25 nucleotides) non-coding RNA molecules which exert powerful post-transcriptional control over gene expression in eukaryotes. Indeed microRNAs likely represent the most abundant class of regulators in animal gene regulatory networks. This study describes the recovery and network analyses of a suite of homologous microRNA targets recovered through two different prediction methods for whole gene regions across twelve Drosophila species. Phylogenetic criteria under an accepted tree topology were used as a reference frame to 1) make inference into microRNA-target predictions, 2) study mathematical properties of microRNA-gene regulatory networks, 3) and conduct novel phylogenetic analyses using character data derived from weighted edges of the microRNA-target networks. This study investigates the evidences of natural selection and phylogenetic signatures inherent within the microRNA regulatory networks and quantifies time and mutation necessary to rewire a microRNA regulatory network. Selective factors that appear to operate upon seed aptamers include cooperativity (redundancy) of interactions and transcript length. Topological analyses of microRNA regulatory networks recovered significant enrichment for a motif possessing a redundant link in all twelve species sampled. This would suggest that optimization of the whole interactome topology itself has been historically subject to natural selection where resilience to attack have offered selective advantage. It seems that only a modest number of microRNA–mRNA interactions exhibit conservation over Drosophila cladogenesis. The decrease in conserved microRNA-target interactions with increasing phylogenetic distance exhibited a cure typical of a saturation phenomena. Scale free properties of a network intersection of microRNA target predictions methods were found to transect taxonomic hierarchy.

A unique computational approach was undertaken to identify targets of miR-17-3p that impart an oncogenic potential to the cells of the prostate. Utilizing this approach, we identified insulin growth factor receptor 1 (IGF1R) as a potential target of miR-17-3p. IGF1R imparts an oncogenic approach to the cells by helping cells escape apoptosis, become hypertrophic and increase the production of extracellular proteases that allow cells to detach from neighbors. The regulation of insulin growth factor receptor 1 by human microRNA-17-3p was evaluated using a western blot analysis of prostate cancer cell lines. Protein levels were compared in a cell line that expressed a non-targeting control RNA and a cell line that expressed microRNA-17-3p. The cell line that expressed the non-targeting control had significantly higher levels of IGF1R protein than the cell line expressing more of the active microRNA. Based on this experiment, it appears that microRNA-17-3p might regulate the insulin growth factor receptor 1.

The specification and modulation of cell-type specific dendritic morphologies plays a pivotal role in nervous system development, connectivity, structural plasticity, and function. Regulation of gene expression is controlled by a wide variety of cellular and molecular mechanisms, of which two major types are transcription factors (TFs) and microRNAs (miRNAs). In Drosophila, dendritic complexity of dendritic arborization (da) sensory neurons of the peripheral nervous system are known to be regulated by two transcription factors Cut and Knot, although much remains unknown about the molecular mechanisms and regulatory networks via which they regulate the final arbor shape through spatio-temporal modulation of dendritic development and dynamics. Here we use bioinformatics analysis of transcriptomic data to identify putative genomic targets of these TFs with a particular emphasis on those that effect neuronal cytoskeletal architecture. We use transcriptomic, as well as data from various genomic and protein interaction databases, to build a weighted functional gene regulatory network for Knot, to identify the biological pathways and downstream genes that this TF regulates. To corroborate bioinformatics network predictions, knot putative targets, which classify into neuronal and cytoskeletal functional groups, have been experimentally validated by in vivo genetic perturbations to elucidate their role in Knot-mediated Class IV (CIV) dendritogenesis. MicroRNAs (miRNAs) have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel R based tool, IntramiR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithm to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using D.melanogaster as a model organism for bioinformatics analyses and functional validation, and identified targets for 83 intragenic miRNAs. Predicted targets were validated, using in vivo genetic perturbation. Moreover, we are constructing interaction maps of intragenic miRNAs focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Philosophiae Doctor - PhD / The discovery of microRNAs (miRNAs) is one of the most exciting scientific breakthroughs in the last decade. miRNAs are short RNA molecules that do not encode proteins but instead, regulate gene expression. Over the past several years, thousands of miRNAs have been identified in various insect genomes through cloning and sequencing, and even by computational prediction. However, information concerning possible roles of miRNAs in mosquitoes is limited. Within this context, we report here the first systematic analysis of these tiny RNAs and their target mRNAs in one of the principal African malaria vectors, Anopheles funestus s.s. Firstly, to extend the known repertoire of miRNAs expressed in this insect, the small RNAs from the four developmental stages (egg, larvae, pupae and the adult females), were sequenced using next generation sequencing technology. A total of 98 miRNAs were identified, which included 65 known Anopheles miRNAs, 25 miRNAs conserved in other insects and 8 novel miRNAs that had not been reported in any species. We further characterized new variants for miR-2 and miR-927 and stem-loop precursors for miR-286 and miR-2944. The analysis showed that many miRNAs have stage-specific expression, and co-transcribed and co-regulated during development. Secondly, for a better understanding of the molecular details of the miRNAs function, we identified the target genes for the Anopheles miRNAs using a novel approach that identifies overlap genes among three target prediction tools followed by filtering genes based on functional enrichment of GO terms and KEGG pathways. We found that most of the miRNAs are metabolic regulators. Moreover, the results suggest implication of some miRNAs not only in the development but also in insect-parasite interaction. Finally, we developed the InsecTar database (http://insectar.sanbi.ac.za) for miRNA targets in the three mosquito species; Anopheles gambiae, Aedes aegypti, and Culex quinquefasciatus, which incorporates prediction and the functional analysis of these target genes. The proposed database will undoubtedly assist to explore the roles of these regulatory molecules in insects. This type of analysis is a key step towards improving our understanding of the complexity and regulationmode of miRNAs in mosquitoes. Moreover, this study opens the door for exploration of miRNA in regulation of critical physiological functions specific to vector arthropods which may lead to novel approaches to combat mosquito-borne infectious diseases.

Les microARN (miARN) sont de petits ARN non-codants qui répriment la traduction de leurs gènes cibles par hybridation à leur ARN messager (ARNm). L'identification de cibles biologiquement actives de miARN est cruciale afin de mieux comprendre leurs rôles. Ce problème est cependant difficile parce que leurs sites ne sont définis que par sept nucléotides. Dans cette thèse je montre qu'il est possible de modéliser certains aspects des miARN afin d'identifier leurs cibles biologiquement actives à travers deux modélisations d'un aspect des miARN. La première modélisation s'intéresse aux aspects de la régulation des miARN par l'identification de boucles de régulation entre des miARN et des facteurs de transcription (FT). Cette modélisation a permis, notamment, d'identifier plus de 700 boucles de régulation miARN/FT, conservées entre l'humain et la souris. Les résultats de cette modélisation ont permis, en particulier, d'identifier deux boucles d'auto-régulation entre LMO2 et les miARN miR-223 et miR-363. Des expériences de transplantation de cellules souches hématopoïétiques et de progéniteurs hématopoïétiques ont ensuite permis d'assigner à ces deux miARN un rôle dans la détermination du destin cellulaire hématopoïétique. La deuxième modélisation s'intéresse directement aux interactions des miARN avec les ARNm afin de déterminer les cibles des miARN. Ces travaux ont permis la mise au point d'une méthode simple de prédiction de cibles de miARN dont les performances sont meilleures que les outils courant. Cette modélisation a aussi permis de mettre en lumière certaines conséquences insoupçonnées de l'effet des miARN, telle que la spécificité des cibles de miARN au contexte cellulaire et l'effet de saturation de certains ARNm par les miARN. Cette méthode peut également être utilisée pour identifier des ARNm dont la surexpression fait augmenter un autre ARNm par l'entremise de miARN partagés et dont les effets sur les ARNm non ciblés seraient minimaux. / microRNAs (miRNAs) are small non coding RNAs that repress the translation of their target genes by pairing to their messenger RNA (mRNA). The identification of miRNAs' biologically active targets is a difficult problem because their binding sites are defined by only seven nucleotides. In this thesis, I show that it is possible to model specific aspects of miRNAs to identify their biologically active targets through two modeling of each one aspect of miRNAs. The first modeling considers the miRNAs regulations through the identification of regulatory loops between miRNAs and transcription factors (TFs). Through this modeling, we identified over 700 miRNA/TF regulatory loops conserved between human and mouse. With the results of this modeling, we were able to identify, in particular, two regulatory loops between LMO2 and the miRNAs miR-223 and miR-363. Using hematopoietic stem cells and progenitor cells transplantation experiment we showed that miR-223 and miR-363 are involved in hematopoietic cell fate determination. The second modeling focuses directly on the interaction between miARN and messenger RNA (mRNA) to determine the miRNA targets. With this work, we developed a simple method for predicting miRNA targets that outperforms the current state of the art tool. This modeling also highlighted some unsuspected consequences of miRNA effects such as the cell context specificity and the saturation of mRNA targets by miRNA. This method can also be used to identify mRNAs whose overexpression increases the expression level of another mRNA through their shared miRNA and whose global effects on other genes are minimal.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target pr ediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, netwo rk context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Computational microRNA (miRNA) target identification in animal genomes is a challenging problem due to the imperfect pairing of the miRNA with the target site. Techniques based on sequence alone are prone to produce many false positive interactions. Therefore, integrative techniques have been developed to utilize additional genomic, structural features, and evolu- tionary conservation information for reducing the high false positive rate. We propose that the context of a putative miRNA target in a protein-protein interaction (PPI) network can be used as an additional filter in a computational miRNA target prediction algorithm. We compute several graph theoretic measures on human PPI network as indicators of network context. We assess the performance of individual and combined contextual measures in increasing the precision of a popular miRNA target prediction tool, TargetScan, using low throughput and high throughput datasets of experimentally verified human miRNA targets. We used clas- sification algorithms for that assessment. Since there exists only miRNA targets as training samples, this problem becomes a One Class Classification (OCC) problem. We devised a novel OCC method, DiVo, based on simple distance metrics and voting. Comparative analysis with the state of the art methods show that, DiVo attains better classification performance. Our eventual results indicate that topological properties of target gene products in PPI networks are valuable sources of information for filtering out false positive miRNA target genes. We show that, for targets of a number of miRNAs, network context correlates better with being a target compared to a sequence based score provided by the prediction tool.

Les microARN (miARN) sont de petits ARN non-codants qui répriment la traduction de leurs gènes cibles par hybridation à leur ARN messager (ARNm). L'identification de cibles biologiquement actives de miARN est cruciale afin de mieux comprendre leurs rôles. Ce problème est cependant difficile parce que leurs sites ne sont définis que par sept nucléotides. Dans cette thèse je montre qu'il est possible de modéliser certains aspects des miARN afin d'identifier leurs cibles biologiquement actives à travers deux modélisations d'un aspect des miARN. La première modélisation s'intéresse aux aspects de la régulation des miARN par l'identification de boucles de régulation entre des miARN et des facteurs de transcription (FT). Cette modélisation a permis, notamment, d'identifier plus de 700 boucles de régulation miARN/FT, conservées entre l'humain et la souris. Les résultats de cette modélisation ont permis, en particulier, d'identifier deux boucles d'auto-régulation entre LMO2 et les miARN miR-223 et miR-363. Des expériences de transplantation de cellules souches hématopoïétiques et de progéniteurs hématopoïétiques ont ensuite permis d'assigner à ces deux miARN un rôle dans la détermination du destin cellulaire hématopoïétique. La deuxième modélisation s'intéresse directement aux interactions des miARN avec les ARNm afin de déterminer les cibles des miARN. Ces travaux ont permis la mise au point d'une méthode simple de prédiction de cibles de miARN dont les performances sont meilleures que les outils courant. Cette modélisation a aussi permis de mettre en lumière certaines conséquences insoupçonnées de l'effet des miARN, telle que la spécificité des cibles de miARN au contexte cellulaire et l'effet de saturation de certains ARNm par les miARN. Cette méthode peut également être utilisée pour identifier des ARNm dont la surexpression fait augmenter un autre ARNm par l'entremise de miARN partagés et dont les effets sur les ARNm non ciblés seraient minimaux. / microRNAs (miRNAs) are small non coding RNAs that repress the translation of their target genes by pairing to their messenger RNA (mRNA). The identification of miRNAs' biologically active targets is a difficult problem because their binding sites are defined by only seven nucleotides. In this thesis, I show that it is possible to model specific aspects of miRNAs to identify their biologically active targets through two modeling of each one aspect of miRNAs. The first modeling considers the miRNAs regulations through the identification of regulatory loops between miRNAs and transcription factors (TFs). Through this modeling, we identified over 700 miRNA/TF regulatory loops conserved between human and mouse. With the results of this modeling, we were able to identify, in particular, two regulatory loops between LMO2 and the miRNAs miR-223 and miR-363. Using hematopoietic stem cells and progenitor cells transplantation experiment we showed that miR-223 and miR-363 are involved in hematopoietic cell fate determination. The second modeling focuses directly on the interaction between miARN and messenger RNA (mRNA) to determine the miRNA targets. With this work, we developed a simple method for predicting miRNA targets that outperforms the current state of the art tool. This modeling also highlighted some unsuspected consequences of miRNA effects such as the cell context specificity and the saturation of mRNA targets by miRNA. This method can also be used to identify mRNAs whose overexpression increases the expression level of another mRNA through their shared miRNA and whose global effects on other genes are minimal.