Représentation et recherche de motifs cycliques et structuraux d’ARN connus dans les structures secondaires

L'acide désoxyribonucléique (ADN) et l'acide ribonucléique (ARN) sont des polymères de nucléotides essentiels à la cellule. À l'inverse de l'ADN qui sert principalement à stocker l'information génétique, les ARN sont impliqués dans plusieurs processus métaboliques. Par exemple, ils transmettent l’information génétique codée dans l’ADN. Ils sont essentiels pour la maturation des autres ARN, la régulation de l’expression génétique, la prévention de la dégradation des chromosomes et le ciblage des protéines dans la cellule. La polyvalence fonctionnelle de l'ARN résulte de sa plus grande diversité structurale. Notre laboratoire a développé MC-Fold, un algorithme pour prédire la structure des ARN qu'on représente avec des graphes d'interactions inter-nucléotidiques. Les sommets de ces graphes représentent les nucléotides et les arêtes leurs interactions. Notre laboratoire a aussi observé qu'un petit ensemble de cycles d'interactions à lui seul définit la structure de n'importe quel motif d'ARN. La formation de ces cycles dépend de la séquence de nucléotides et MC-Fold détermine les cycles les plus probables étant donnée cette séquence. Mon projet de maîtrise a été, dans un premier temps, de définir une base de données des motifs structuraux et fonctionnels d'ARN, bdMotifs, en terme de ces cycles. Par la suite, j’ai implanté un algorithme, MC-Motifs, qui recherche ces motifs dans des graphes d'interactions et, entre autres, ceux générés par MC-Fold. Finalement, j’ai validé mon algorithme sur des ARN dont la structure est connue, tels que les ARN ribosomaux (ARNr) 5S, 16S et 23S, et l'ARN utilisé pour prédire la structure des riborégulateurs. Le mémoire est divisé en cinq chapitres. Le premier chapitre présente la structure chimique, les fonctions cellulaires de l'ARN et le repliement structural du polymère. Dans le deuxième chapitre, je décris la base de données bdMotifs. Dans le troisième chapitre, l’algorithme de recherche MC-Motifs est introduit. Le quatrième chapitre présente les résultats de la validation et des prédictions. Finalement, le dernier chapitre porte sur la discussion des résultats suivis d’une conclusion sur le travail.Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are polymers of nucleotides essential for the survival of the cell. Contrary to DNA, whose main role is to store genetic information, RNA is involved in multiple metabolic processes. For example, RNA is involved in the transfer of information from DNA to protein, the processing and modification of other RNAs, the regulation of gene expression, the end-maintenance of chromosomes, and the sorting of proteins within the cell. This functional versatility of RNA comes from its structural diversity. Our laboratory developed MC-Fold, an algorithm that predicts RNA structures by representing them with nucleotide interaction graphs. The nodes in these graphs represent the nucleotides, and the edges the interactions between them. Our laboratory also observed that a limited number of interaction cycles can define the structure of any RNA motif. The formation of these cycles is determined by the nucleotide sequence and MC-Fold determines the most likely cycles based on that sequence. In this Master Degree project, I first built a database of structural and functional RNA motifs, bdMotifs, based on their constituent cycles. Then, I implemented an algorithm, MC-Motifs, which detects motifs within interaction graphs generated either by MC-Fold or by any other method. Finally, I validated my algorithm on known RNA structures such as the 5S, 16S and 23S ribosomal RNA (rRNA) and predicted structure of riboswitches. The Master thesis is divided into five chapters. The first chapter presents the chemical structure of RNA, its cellular functions and the structural folding of the polymer. In the second chapter, the database bdMotifs is described. In the third chapter, the MC-Motifs algorithm is introduced. In the fourth chapter, I present the results of MC-Motifs. Finally, in the last chapter, I discuss theses results and I give a conclusion on the project

FASTA Herder: a web application to trim protein sequence sets

The ever increasing number of sequences in protein databases usually turns out large numbers of homologs in sequence similarity searches. While information from homology can be very useful for functional prediction based on amino acid conservation, many of these homologs usually have high levels of identity among themselves, which hinders multiple sequence alignment computation and, especially, visualization. More generally, high redundancy reduces the usability of a protein set in machine learning applications and biases statistical analyses. We developed an algorithm to identify redundant sequence homologs that can be culled producing a streamlined FASTA file. As a difference from other automatic approaches that only aggregate sequences with high identity, our method clusters near full-length homologs allowing for lower sequence identity thresholds. Our method was fully tested and implemented in a web application called FASTA Herder, publicly available at http://fh.ogic.ca

Au-delà des frontières, jusqu’où va le Canada ?

Ce volume n°72 publie une série d’articles présentés au 39ème Colloque International de l’AFEC — à l’occasion duquel fut fêté le 35ème anniversaire de l’Institution — qui s’est déroulé à Montpellier du 15 au 18 juin 2011 et s’est penché sur cette question du rayonnement du Canada : « Au-delà des frontières, jusqu’où va le Canada » ? Ce colloque était organisé par Régis Marchiaro, directeur du Centre d’Études Canadiennes des Universités de Montpellier (CECAM). Le 17 octobre 2012, nous avons été douloureusement frappés par la nouvelle de sa disparition inattendue. Ce numéro lui est dédié

Association of haemolysis markers, blood viscosity and microcirculation function with organ damage in sickle cell disease in <scp>sub‐Saharan</scp> Africa (the <scp>BIOCADRE</scp> study)

International audienceSummary Sickle cell anaemia (SCA) is a monogenic disease with a highly variable clinical course. We aimed to investigate associations between microvascular function, haemolysis markers, blood viscosity and various types of SCA‐related organ damage in a multicentric sub‐Saharan African cohort of patients with SCA. In a cross‐sectional study, we selected seven groups of adult patients with SS phenotype in Dakar and Bamako based on the following complications: leg ulcer, priapism, osteonecrosis, retinopathy, high tricuspid regurgitant jet velocity (TRV), macro‐albuminuria or none. Clinical assessment, echocardiography, peripheral arterial tonometry, laboratory tests and blood viscosity measurement were performed. We explored statistical associations between the biological parameters and the six studied complications. Among 235 patients, 58 had high TRV, 46 osteonecrosis, 43 priapism, 33 leg ulcers, 31 retinopathy and 22 macroalbuminuria, whereas 36 had none of these complications. Multiple correspondence analysis revealed no cluster of complications. Lactate dehydrogenase levels were associated with high TRV, and blood viscosity was associated with retinopathy and the absence of macroalbuminuria. Despite extensive phenotyping of patients, no specific pattern of SCA‐related complications was identified. New biomarkers are needed to predict SCA clinical expression to adapt patient management, especially in Africa, where healthcare resources are scarce

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

<div><p>Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.</p> </div

Experimental reliability of annotated PPIs.

<p>(<b>A</b>) All 101,131 PPIs in HIPPIE are scored according to their associated experimental evidence with a value that ranges from 0 to 1 and increases with the quality and amount of experimental evidence reported in PPI databases <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002860#pcbi.1002860-Schaefer1" target="_blank">[14]</a>. We were able to infer context to a fraction of interactions according to: GO terms biological process (BP) and cellular component (CC), MeSH terms (subcategories disease and tissue) and tissue or housekeeping expression. The numbers in the bars indicate the mean experimental score of the non-annotated fraction (above, black font) and of the annotated fraction (below, white font), respectively. All mean-score differences between annotated and not annotated interactions were significant (p<0.001; Mann-Whitney-test). (<b>B–C</b>) Box plots visualizing the distribution of experimental scores of PPIs associated with GO (left) and MeSH (right) term categories. (<b>B</b>) The scores for GO and MeSH terms decreased generally for less specific terms (the only exception was GO terms depth 2, which was associated with interactions of a lower mean confidence as compared to GO terms depth 1). (<b>C</b>) GO and MeSH terms were subdivided in quartiles according to the number of interactions annotated for each category. The scores decreased for terms associated to higher numbers of interactions.</p

Filtering and highlighting a PPI subnetwork related to phosphorylation in Alzheimer's disease.

<p>A PPI network was generated as explained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002860#pcbi-1002860-g003" target="_blank">Figure 3</a> starting with 8 genes relevant for Alzheimer's disease (AD) and phosphorylation. (<b>A</b>) The PPI network contains 727 interactions. (<b>B</b>) Filtering for interactions between partners that are housekeeping or expressed in the brain (“whole brain” and “prefrontal cortex”), relate to the GO term “cell death”, and with experimental scores above 0.5, results in a much more focused subnetwork involving 6 of the 8 genes used as input (octagonal nodes). Nodes corresponding to receptors and transcription factors are colored (blue and pink nodes, respectively). Edge directed path analysis from receptors to transcription factors resulted in the association of directionality to some of the edges (arrows). The path LRP6-GSK3B-MAPT-AATF is highlighted in green and described in the text.</p

A Lentiviral Vector Allowing Physiologically Regulated Membrane-anchored and Secreted Antibody Expression Depending on B-cell Maturation Status

International audienceThe development of lentiviral vectors (LVs) for expression of a specific antibody can be achieved through the transduction of mature B-cells. This approach would provide a versatile tool for active immunotherapy strategies for infectious diseases or cancer, as well as for protein engineering. Here, we created a lentiviral expression system mimicking the natural production of these two distinct immunoglobulin isoforms. We designed a LV (FAM2-LV) expressing an anti-HCV-E2 surface glycoprotein antibody (AR3A) as a membrane-anchored Ig form or a soluble Ig form, depending on the B-cell maturation status. FAM2-LV induced high-level and functional membrane expression of the transgenic antibody in a nonsecretory B-cell line. In contrast, a plasma cell (PC) line transduced with FAM2-LV preferentially produced the secreted transgenic antibody. Similar results were obtained with primary B-cells transduced ex vivo. Most importantly, FAM2-LV transduced primary B-cells efficiently differentiated into PCs, which secreted the neutralizing anti-HCV E2 antibody upon adoptive transfer into immunodeficient NSG (NOD/SCIDγc(-/-)) recipient mice. Altogether, these results demonstrate that the conditional FAM2-LV allows preferential expression of the membrane-anchored form of an antiviral neutralizing antibody in B-cells and permits secretion of a soluble antibody following B-cell maturation into PCs in vivo