De Novo Assembly of the Pneumocystis jirovecii Genome from a Single Bronchoalveolar Lavage Fluid Specimen from a Patient.

ABSTRACT Pneumocystis jirovecii is a fungus that causes severe pneumonia in immunocompromised patients. However, its study is hindered by the lack of an in vitro culture method. We report here the genome of P. jirovecii that was obtained from a single bronchoalveolar lavage fluid specimen from a patient. The major challenge was the in silico sorting of the reads from a mixture representing the different organisms of the lung microbiome. This genome lacks virulence factors and most amino acid biosynthesis enzymes and presents reduced GC content and size. Together with epidemiological observations, these features suggest that P. jirovecii is an obligate parasite specialized in the colonization of human lungs, which causes disease only in immune-deficient individuals. This genome sequence will boost research on this deadly pathogen. IMPORTANCE Pneumocystis pneumonia is a major cause of mortality in patients with impaired immune systems. The availability of the P. jirovecii genome sequence allows new analyses to be performed which open avenues to solve critical issues for this deadly human disease. The most important ones are (i) identification of nutritional supplements for development of culture in vitro, which is still lacking 100 years after discovery of the pathogen; (ii) identification of new targets for development of new drugs, given the paucity of present treatments and emerging resistance; and (iii) identification of targets for development of vaccines.

Developmental and Environmental Regulation of Aquaporin Gene Expression across Populus Species: Divergence or Redundancy?

Aquaporins (AQPs) are membrane channels belonging to the major intrinsic proteins family and are known for their ability to facilitate water movement. While in Populus trichocarpa, AQP proteins form a large family encompassing fifty-five genes, most of the experimental work focused on a few genes or subfamilies. The current work was undertaken to develop a comprehensive picture of the whole AQP gene family in Populus species by delineating gene expression domain and distinguishing responsiveness to developmental and environmental cues. Since duplication events amplified the poplar AQP family, we addressed the question of expression redundancy between gene duplicates. On these purposes, we carried a meta-analysis of all publicly available Affymetrix experiments. Our in-silico strategy controlled for previously identified biases in cross-species transcriptomics, a necessary step for any comparative transcriptomics based on multispecies design chips. Three poplar AQPs were not supported by any expression data, even in a large collection of situations (abiotic and biotic constraints, temporal oscillations and mutants). The expression of 11 AQPs was never or poorly regulated whatever the wideness of their expression domain and their expression level. Our work highlighted that PtTIP1;4 was the most responsive gene of the AQP family. A high functional divergence between gene duplicates was detected across species and in response to tested cues, except for the root-expressed PtTIP2;3/PtTIP2;4 pair exhibiting 80% convergent responses. Our meta-analysis assessed key features of aquaporin expression which had remained hidden in single experiments, such as expression wideness, response specificity and genotype and environment interactions. By consolidating expression profiles using independent experimental series, we showed that the large expansion of AQP family in poplar was accompanied with a strong divergence of gene expression, even if some cases of functional redundancy could be suspected.

Plasma membrane H+-ATPase regulation is required for auxin gradient formation preceding phototropic growth.

Phototropism is a growth response allowing plants to align their photosynthetic organs toward incoming light and thereby to optimize photosynthetic activity. Formation of a lateral gradient of the phytohormone auxin is a key step to trigger asymmetric growth of the shoot leading to phototropic reorientation. To identify important regulators of auxin gradient formation, we developed an auxin flux model that enabled us to test in silico the impact of different morphological and biophysical parameters on gradient formation, including the contribution of the extracellular space (cell wall) or apoplast. Our model indicates that cell size, cell distributions, and apoplast thickness are all important factors affecting gradient formation. Among all tested variables, regulation of apoplastic pH was the most important to enable the formation of a lateral auxin gradient. To test this prediction, we interfered with the activity of plasma membrane H(+)-ATPases that are required to control apoplastic pH. Our results show that H(+)-ATPases are indeed important for the establishment of a lateral auxin gradient and phototropism. Moreover, we show that during phototropism, H(+)-ATPase activity is regulated by the phototropin photoreceptors, providing a mechanism by which light influences apoplastic pH.

Use of the FACTS solvation model for protein-ligand docking calculations. Application to EADock.

Protein-ligand docking has made important progress during the last decade and has become a powerful tool for drug development, opening the way to virtual high throughput screening and in silico structure-based ligand design. Despite the flattering picture that has been drawn, recent publications have shown that the docking problem is far from being solved, and that more developments are still needed to achieve high successful prediction rates and accuracy. Introducing an accurate description of the solvation effect upon binding is thought to be essential to achieve this goal. In particular, EADock uses the Generalized Born Molecular Volume 2 (GBMV2) solvent model, which has been shown to reproduce accurately the desolvation energies calculated by solving the Poisson equation. Here, the implementation of the Fast Analytical Continuum Treatment of Solvation (FACTS) as an implicit solvation model in small molecules docking calculations has been assessed using the EADock docking program. Our results strongly support the use of FACTS for docking. The success rates of EADock/FACTS and EADock/GBMV2 are similar, i.e. around 75% for local docking and 65% for blind docking. However, these results come at a much lower computational cost: FACTS is 10 times faster than GBMV2 in calculating the total electrostatic energy, and allows a speed up of EADock by a factor of 4. This study also supports the EADock development strategy relying on the CHARMM package for energy calculations, which enables straightforward implementation and testing of the latest developments in the field of Molecular Modeling.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Rpe65 microarrays, from genes to phosphorylated proteins

Purpose:To describe a novel in silico method to gather and analyze data from high-throughput heterogeneous experimental procedures, i.e. gene and protein expression arrays. Methods:Each microarray is assigned to a database which handles common data (names, symbols, antibody codes, probe IDs, etc.). Links between informations are automatically generated from knowledge obtained in freely accessible databases (NCBI, Swissprot, etc). Requests can be made from any point of entry and the displayed result is fully customizable. Results:The initial database has been loaded with two sets of data: a first set of data originating from an Affymetrix-based retinal profiling performed in an RPE65 knock-out mouse model of Leber's congenital amaurosis. A second set of data generated from a Kinexus microarray experiment done on the retinas from the same mouse model has been added. Queries display wild type versus knock out expressions at several time points for both genes and proteins. Conclusions:This freely accessible database allows for easy consultation of data and facilitates data mining by integrating experimental data and biological pathways.

Regulation of 11β-hydroxysteroid dehydrogenase type 2 by microRNA.

The enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) is selectively expressed in aldosterone target tissues, conferring aldosterone selectivity for the mineralocorticoid receptor. A diminished activity causes salt-sensitive hypertension. The mechanism of the variable and distinct 11β-hydroxysteroid dehydrogenase type 2 gene (HSD11B2) expression in the cortical collecting duct is poorly understood. Here, we analyzed for the first time whether the 11β-HSD2 expression is modulated by microRNAs (miRNAs). In silico analysis revealed 53 and 27 miRNAs with potential binding sites on human or rat HSD11B2 3'-untranslated region. A reporter assay demonstrated 3'-untranslated region-dependent regulation of human and rodent HSD11B2. miRNAs were profiled from cortical collecting ducts and proximal convoluted tubules. Bioinformatic analyses showed a distinct clustering for cortical collecting ducts and proximal convoluted tubules with 53 of 375 miRNAs, where 13 were predicted to bind to the rat HSD11B2 3'-untranslated region. To gain insight into potentially relevant miRNAs in vivo, we investigated 2 models with differential 11β-HSD2 activity linked with salt-sensitive hypertension. (1) Comparing Sprague-Dawley with low and Wistar rats with high 11β-HSD2 activity revealed rno-miR-20a-5p, rno-miR-19b-3p, and rno-miR-190a-5p to be differentially expressed. (2) Uninephrectomy lowered 11β-HSD2 activity in the residual kidney with differentially expressed rno-miR-19b-3p, rno-miR-29b-3p, and rno-miR-26-5p. In conclusion, miRNA-dependent mechanisms seem to modulate 11β-HSD2 dosage in health and disease states.

ANIBAL, stable isotope-based quantitative proteomics by aniline and benzoic acid labeling of amino and carboxylic groups

Identification and relative quantification of hundreds to thousands of proteins within complex biological samples have become realistic with the emergence of stable isotope labeling in combination with high throughput mass spectrometry. However, all current chemical approaches target a single amino acid functionality (most often lysine or cysteine) despite the fact that addressing two or more amino acid side chains would drastically increase quantifiable information as shown by in silico analysis in this study. Although the combination of existing approaches, e.g. ICAT with isotope-coded protein labeling, is analytically feasible, it implies high costs, and the combined application of two different chemistries (kits) may not be straightforward. Therefore, we describe here the development and validation of a new stable isotope-based quantitative proteomics approach, termed aniline benzoic acid labeling (ANIBAL), using a twin chemistry approach targeting two frequent amino acid functionalities, the carboxylic and amino groups. Two simple and inexpensive reagents, aniline and benzoic acid, in their (12)C and (13)C form with convenient mass peak spacing (6 Da) and without chromatographic discrimination or modification in fragmentation behavior, are used to modify carboxylic and amino groups at the protein level, resulting in an identical peptide bond-linked benzoyl modification for both reactions. The ANIBAL chemistry is simple and straightforward and is the first method that uses a (13)C-reagent for a general stable isotope labeling approach of carboxylic groups. In silico as well as in vitro analyses clearly revealed the increase in available quantifiable information using such a twin approach. ANIBAL was validated by means of model peptides and proteins with regard to the quality of the chemistry as well as the ionization behavior of the derivatized peptides. A milk fraction was used for dynamic range assessment of protein quantification, and a bacterial lysate was used for the evaluation of relative protein quantification in a complex sample in two different biological states

Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

Genome-wide association study identifies eight loci associated with blood pressure.

Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5 million genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N ≤ 71,225 European ancestry, N ≤ 12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N = 29,136). We identified association between systolic or diastolic blood pressure and common variants in eight regions near the CYP17A1 (P = 7 × 10(-24)), CYP1A2 (P = 1 × 10(-23)), FGF5 (P = 1 × 10(-21)), SH2B3 (P = 3 × 10(-18)), MTHFR (P = 2 × 10(-13)), c10orf107 (P = 1 × 10(-9)), ZNF652 (P = 5 × 10(-9)) and PLCD3 (P = 1 × 10(-8)) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.

The GENCODE pseudogene resource.

BACKGROUND: Pseudogenes have long been considered as nonfunctional genomic sequences. However, recent evidence suggests that many of them might have some form of biological activity, and the possibility of functionality has increased interest in their accurate annotation and integration with functional genomics data. RESULTS: As part of the GENCODE annotation of the human genome, we present the first genome-wide pseudogene assignment for protein-coding genes, based on both large-scale manual annotation and in silico pipelines. A key aspect of this coupled approach is that it allows us to identify pseudogenes in an unbiased fashion as well as untangle complex events through manual evaluation. We integrate the pseudogene annotations with the extensive ENCODE functional genomics information. In particular, we determine the expression level, transcription-factor and RNA polymerase II binding, and chromatin marks associated with each pseudogene. Based on their distribution, we develop simple statistical models for each type of activity, which we validate with large-scale RT-PCR-Seq experiments. Finally, we compare our pseudogenes with conservation and variation data from primate alignments and the 1000 Genomes project, producing lists of pseudogenes potentially under selection. CONCLUSIONS: At one extreme, some pseudogenes possess conventional characteristics of functionality; these may represent genes that have recently died. On the other hand, we find interesting patterns of partial activity, which may suggest that dead genes are being resurrected as functioning non-coding RNAs. The activity data of each pseudogene are stored in an associated resource, psiDR, which will be useful for the initial identification of potentially functional pseudogenes.

Optimal CD8 T cell responsiveness is controlled within a defined TCR affinity window

Protective immune responses relyon TCR-mediated recognition of antigenspresented by MHC molecules. Tcells directed against tumor antigensare thought to express TCRs of loweraffinity/avidity than pathogen-specificT lymphocytes. An attractivestrategy to improve anti-tumor T cellresponses is to adoptively transferCD8+ T cells engineered with TCRsof optimized affinity. However, themechanisms that control optimal Tcell activation and responsiveness remainpoorly defined. We aim at characterizingTCR-pMHC binding parametersand downstream signalingevents that regulate T cell functionalityby using an in silico designedpanel of tumor antigen-specific TCRsof incremental affinity for pMHC(Kd100 M- 15 nM).We found that optimalT cell responses (cytokine secretionand target cell killing) occurredwithin a well-defined window ofTCR-pMHC binding affinity (5 M-1 M), while drastic functional declinewas detected in T cells expressingvery low and very high TCRaffinities,which was not caused by any increasein apoptosis. Whole-genomemicroarray analysis revealed that Tcells with optimal TCR affinitieshighly up-regulated transcription ofgenes typical of T cell activation (i.e.IFN-, NF-B and TNFR), while reducedexpression was detected in Tcells of very low or very high TCR affinity.Strikingly, hierarchical clusteringshowed that the latter two variantsclustered together with the un-stimulatedcontrol Tcells.Yet, despite commonclustering, several genes seemedto be differentially expressed, suggestingthat the mechanisms involvedin this "unresponsiveness state" maydiffer between those two variants. Finally,calcium influx assays also demonstratedattenuated responses in Tcells of very high TCR affinity. Ourresults indicate that optimal T cellfunction is tightly controlled within adefinedTCRaffinity window throughvery proximal TCR-mediated mechanisms,possibly at the TCR-pMHCbinding interface. Uncovering themechanisms regulating optimal/maximalT cell function is essential to understandand promote therapeutic designlike adoptive T cell therapy.

EADock : design of a new molecular docking algorithm and some of its applications

3 Summary 3. 1 English The pharmaceutical industry has been facing several challenges during the last years, and the optimization of their drug discovery pipeline is believed to be the only viable solution. High-throughput techniques do participate actively to this optimization, especially when complemented by computational approaches aiming at rationalizing the enormous amount of information that they can produce. In siiico techniques, such as virtual screening or rational drug design, are now routinely used to guide drug discovery. Both heavily rely on the prediction of the molecular interaction (docking) occurring between drug-like molecules and a therapeutically relevant target. Several softwares are available to this end, but despite the very promising picture drawn in most benchmarks, they still hold several hidden weaknesses. As pointed out in several recent reviews, the docking problem is far from being solved, and there is now a need for methods able to identify binding modes with a high accuracy, which is essential to reliably compute the binding free energy of the ligand. This quantity is directly linked to its affinity and can be related to its biological activity. Accurate docking algorithms are thus critical for both the discovery and the rational optimization of new drugs. In this thesis, a new docking software aiming at this goal is presented, EADock. It uses a hybrid evolutionary algorithm with two fitness functions, in combination with a sophisticated management of the diversity. EADock is interfaced with .the CHARMM package for energy calculations and coordinate handling. A validation was carried out on 37 crystallized protein-ligand complexes featuring 11 different proteins. The search space was defined as a sphere of 15 R around the center of mass of the ligand position in the crystal structure, and conversely to other benchmarks, our algorithms was fed with optimized ligand positions up to 10 A root mean square deviation 2MSD) from the crystal structure. This validation illustrates the efficiency of our sampling heuristic, as correct binding modes, defined by a RMSD to the crystal structure lower than 2 A, were identified and ranked first for 68% of the complexes. The success rate increases to 78% when considering the five best-ranked clusters, and 92% when all clusters present in the last generation are taken into account. Most failures in this benchmark could be explained by the presence of crystal contacts in the experimental structure. EADock has been used to understand molecular interactions involved in the regulation of the Na,K ATPase, and in the activation of the nuclear hormone peroxisome proliferatoractivated receptors a (PPARa). It also helped to understand the action of common pollutants (phthalates) on PPARy, and the impact of biotransformations of the anticancer drug Imatinib (Gleevec®) on its binding mode to the Bcr-Abl tyrosine kinase. Finally, a fragment-based rational drug design approach using EADock was developed, and led to the successful design of new peptidic ligands for the a5ß1 integrin, and for the human PPARa. In both cases, the designed peptides presented activities comparable to that of well-established ligands such as the anticancer drug Cilengitide and Wy14,643, respectively. 3.2 French Les récentes difficultés de l'industrie pharmaceutique ne semblent pouvoir se résoudre que par l'optimisation de leur processus de développement de médicaments. Cette dernière implique de plus en plus. de techniques dites "haut-débit", particulièrement efficaces lorsqu'elles sont couplées aux outils informatiques permettant de gérer la masse de données produite. Désormais, les approches in silico telles que le criblage virtuel ou la conception rationnelle de nouvelles molécules sont utilisées couramment. Toutes deux reposent sur la capacité à prédire les détails de l'interaction moléculaire entre une molécule ressemblant à un principe actif (PA) et une protéine cible ayant un intérêt thérapeutique. Les comparatifs de logiciels s'attaquant à cette prédiction sont flatteurs, mais plusieurs problèmes subsistent. La littérature récente tend à remettre en cause leur fiabilité, affirmant l'émergence .d'un besoin pour des approches plus précises du mode d'interaction. Cette précision est essentielle au calcul de l'énergie libre de liaison, qui est directement liée à l'affinité du PA potentiel pour la protéine cible, et indirectement liée à son activité biologique. Une prédiction précise est d'une importance toute particulière pour la découverte et l'optimisation de nouvelles molécules actives. Cette thèse présente un nouveau logiciel, EADock, mettant en avant une telle précision. Cet algorithme évolutionnaire hybride utilise deux pressions de sélections, combinées à une gestion de la diversité sophistiquée. EADock repose sur CHARMM pour les calculs d'énergie et la gestion des coordonnées atomiques. Sa validation a été effectuée sur 37 complexes protéine-ligand cristallisés, incluant 11 protéines différentes. L'espace de recherche a été étendu à une sphère de 151 de rayon autour du centre de masse du ligand cristallisé, et contrairement aux comparatifs habituels, l'algorithme est parti de solutions optimisées présentant un RMSD jusqu'à 10 R par rapport à la structure cristalline. Cette validation a permis de mettre en évidence l'efficacité de notre heuristique de recherche car des modes d'interactions présentant un RMSD inférieur à 2 R par rapport à la structure cristalline ont été classés premier pour 68% des complexes. Lorsque les cinq meilleures solutions sont prises en compte, le taux de succès grimpe à 78%, et 92% lorsque la totalité de la dernière génération est prise en compte. La plupart des erreurs de prédiction sont imputables à la présence de contacts cristallins. Depuis, EADock a été utilisé pour comprendre les mécanismes moléculaires impliqués dans la régulation de la Na,K ATPase et dans l'activation du peroxisome proliferatoractivated receptor a (PPARa). Il a également permis de décrire l'interaction de polluants couramment rencontrés sur PPARy, ainsi que l'influence de la métabolisation de l'Imatinib (PA anticancéreux) sur la fixation à la kinase Bcr-Abl. Une approche basée sur la prédiction des interactions de fragments moléculaires avec protéine cible est également proposée. Elle a permis la découverte de nouveaux ligands peptidiques de PPARa et de l'intégrine a5ß1. Dans les deux cas, l'activité de ces nouveaux peptides est comparable à celles de ligands bien établis, comme le Wy14,643 pour le premier, et le Cilengitide (PA anticancéreux) pour la seconde.

A genome-wide association study of anorexia nervosa.

Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome-wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2907 cases with AN from 14 countries (15 sites) and 14 860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery data sets. Seventy-six (72 independent) single nucleotide polymorphisms were taken forward for in silico (two data sets) or de novo (13 data sets) replication genotyping in 2677 independent AN cases and 8629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication data sets comprised 5551 AN cases and 21 080 controls. AN subtype analyses (1606 AN restricting; 1445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01 × 10(-7)) in SOX2OT and rs17030795 (P=5.84 × 10(-6)) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76 × 10(-)(6)) between CUL3 and FAM124B and rs1886797 (P=8.05 × 10(-)(6)) near SPATA13. Comparing discovery with replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P=4 × 10(-6)), strongly suggesting that true findings exist but our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.

Bacterial variations on the methionine salvage pathway.

BACKGROUND: The thiomethyl group of S-adenosylmethionine is often recycled as methionine from methylthioadenosine. The corresponding pathway has been unravelled in Bacillus subtilis. However methylthioadenosine is subjected to alternative degradative pathways depending on the organism. RESULTS: This work uses genome in silico analysis to propose methionine salvage pathways for Klebsiella pneumoniae, Leptospira interrogans, Thermoanaerobacter tengcongensis and Xylella fastidiosa. Experiments performed with mutants of B. subtilis and Pseudomonas aeruginosa substantiate the hypotheses proposed. The enzymes that catalyze the reactions are recruited from a variety of origins. The first, ubiquitous, enzyme of the pathway, MtnA (methylthioribose-1-phosphate isomerase), belongs to a family of proteins related to eukaryotic intiation factor 2B alpha. mtnB codes for a methylthioribulose-1-phosphate dehydratase. Two reactions follow, that of an enolase and that of a phosphatase. While in B. subtilis this is performed by two distinct polypeptides, in the other organisms analyzed here an enolase-phosphatase yields 1,2-dihydroxy-3-keto-5-methylthiopentene. In the presence of dioxygen an aci-reductone dioxygenase yields the immediate precursor of methionine, ketomethylthiobutyrate. Under some conditions this enzyme produces carbon monoxide in B. subtilis, suggesting a route for a new gaseous mediator in bacteria. Ketomethylthiobutyrate is finally transaminated by an aminotransferase that exists usually as a broad specificity enzyme (often able to transaminate aromatic aminoacid keto-acid precursors or histidinol-phosphate). CONCLUSION: A functional methionine salvage pathway was experimentally demonstrated, for the first time, in P. aeruginosa. Apparently, methionine salvage pathways are frequent in Bacteria (and in Eukarya), with recruitment of different polypeptides to perform the needed reactions (an ancestor of a translation initiation factor and RuBisCO, as an enolase, in some Firmicutes). Many are highly dependent on the presence of oxygen, suggesting that the ecological niche may play an important role for the existence and/or metabolic steps of the pathway, even in phylogenetically related bacteria. Further work is needed to uncover the corresponding steps when dioxygen is scarce or absent (this is important to explore the presence of the pathway in Archaea). The thermophile T. tengcongensis, that thrives in the absence of oxygen, appears to possess the pathway. It will be an interesting link to uncover the missing reactions in anaerobic environments.