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.

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.

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.