Lack of Enantioselectivity in the SULT1A3-catalyzed Sulfoconjugation of Normetanephrine Enantiomers: An In Vitro and Computational Study.

(1R)-Normetanephrine is the natural stereoisomeric substrate for sulfotransferase 1A3 (SULT1A3)-catalyzed sulfonation. Nothing appears known on the enantioselectivity of the reaction despite its potential significance in the metabolism of adrenergic amines and in clinical biochemistry. We confronted the kinetic parameters of the sulfoconjugation of synthetic (1R)-normetanephrine and (1S)-normetanephrine by recombinant human SULT1A3 to a docking model of each normetanephrine enantiomer with SULT1A3 and the 3'-phosphoadenosine-5'-phosphosulfate cofactor on the basis of molecular modeling and molecular dynamics simulations of the stability of the complexes. The K(M) , V(max) , and k(cat) values for the sulfonation of (1R)-normetanephrine, (1S)-normetanephrine, and racemic normetanephrine were similar. In silico models were consistent with these findings as they showed that the binding modes of the two enantiomers were almost identical. In conclusion, SULT1A3 is not substrate-enantioselective toward normetanephrine, an unexpected finding explainable by a mutual adaptability between the ligands and SULT1A3 through an "induced-fit model" in the catalytic pocket. Chirality, 00:000-000, 2012.© 2012 Wiley Periodicals, Inc.

Structured RNAs in the ENCODE selected regions of the human genome.

Functional RNA structures play an important role both in the context of noncoding RNA transcripts as well as regulatory elements in mRNAs. Here we present a computational study to detect functional RNA structures within the ENCODE regions of the human genome. Since structural RNAs in general lack characteristic signals in primary sequence, comparative approaches evaluating evolutionary conservation of structures are most promising. We have used three recently introduced programs based on either phylogenetic-stochastic context-free grammar (EvoFold) or energy directed folding (RNAz and AlifoldZ), yielding several thousand candidate structures (corresponding to approximately 2.7% of the ENCODE regions). EvoFold has its highest sensitivity in highly conserved and relatively AU-rich regions, while RNAz favors slightly GC-rich regions, resulting in a relatively small overlap between methods. Comparison with the GENCODE annotation points to functional RNAs in all genomic contexts, with a slightly increased density in 3'-UTRs. While we estimate a significant false discovery rate of approximately 50%-70% many of the predictions can be further substantiated by additional criteria: 248 loci are predicted by both RNAz and EvoFold, and an additional 239 RNAz or EvoFold predictions are supported by the (more stringent) AlifoldZ algorithm. Five hundred seventy RNAz structure predictions fall into regions that show signs of selection pressure also on the sequence level (i.e., conserved elements). More than 700 predictions overlap with noncoding transcripts detected by oligonucleotide tiling arrays. One hundred seventy-five selected candidates were tested by RT-PCR in six tissues, and expression could be verified in 43 cases (24.6%).

Cell-based computational modeling of vascular morphogenesis using Tissue Simulation Toolkit.

Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.

Systems biology of plants : from intraspecific genome variation to computational morphodynamics

Recently, the introduction of second generation sequencing and further advance-ments in confocal microscopy have enabled system-level studies for the functional characterization of genes. The degree of complexity intrinsic to these approaches needs the development of bioinformatics methodologies and computational models for extracting meaningful biological knowledge from the enormous amount of experi¬mental data which is continuously generated. This PhD thesis presents several novel bioinformatics methods and computational models to address specific biological questions in Plant Biology by using the plant Arabidopsis thaliana as a model system. First, a spatio-temporal qualitative analysis of quantitative transcript and protein profiles is applied to show the role of the BREVIS RADIX (BRX) protein in the auxin- cytokinin crosstalk for root meristem growth. Core of this PhD work is the functional characterization of the interplay between the BRX protein and the plant hormone auxin in the root meristem by using a computational model based on experimental evidence. Hyphotesis generated by the modelled to the discovery of a differential endocytosis pattern in the root meristem that splits the auxin transcriptional response via the plasma membrane to nucleus partitioning of BRX. This positional information system creates an auxin transcriptional pattern that deviates from the canonical auxin response and is necessary to sustain the expression of a subset of BRX-dependent auxin-responsive genes to drive root meristem growth. In the second part of this PhD thesis, we characterized the genome-wide impact of large scale deletions on four divergent Arabidopsis natural strains, through the integration of Ultra-High Throughput Sequencing data with data from genomic hybridizations on tiling arrays. Analysis of the identified deletions revealed a considerable portion of protein coding genes affected and supported a history of genomic rearrangements shaped by evolution. In the last part of the thesis, we showed that VIP3 gene in Arabidopsis has an evo-lutionary conserved role in the 3' to 5' mRNA degradation machinery, by applying a novel approach for the analysis of mRNA-Seq data from random-primed mRNA. Altogether, this PhD research contains major advancements in the study of natural genomic variation in plants and in the application of computational morphodynamics models for the functional characterization of biological pathways essential for the plant. - Récemment, l'introduction du séquençage de seconde génération et les avancées dans la microscopie confocale ont permis des études à l'échelle des différents systèmes cellulaires pour la caractérisation fonctionnelle de gènes. Le degrés de complexité intrinsèque à ces approches ont requis le développement de méthodologies bioinformatiques et de modèles mathématiques afin d'extraire de la masse de données expérimentale générée, des information biologiques significatives. Ce doctorat présente à la fois des méthodes bioinformatiques originales et des modèles mathématiques pour répondre à certaines questions spécifiques de Biologie Végétale en utilisant la plante Arabidopsis thaliana comme modèle. Premièrement, une analyse qualitative spatio-temporelle de profiles quantitatifs de transcripts et de protéines est utilisée pour montrer le rôle de la protéine BREVIS RADIX (BRX) dans le dialogue entre l'auxine et les cytokinines, des phytohormones, dans la croissance du méristème racinaire. Le noyau de ce travail de thèse est la caractérisation fonctionnelle de l'interaction entre la protéine BRX et la phytohormone auxine dans le méristème de la racine en utilisant des modèles informatiques basés sur des preuves expérimentales. Les hypothèses produites par le modèle ont mené à la découverte d'un schéma différentiel d'endocytose dans le méristème racinaire qui divise la réponse transcriptionnelle à l'auxine par le partitionnement de BRX de la membrane plasmique au noyau de la cellule. Cette information positionnelle crée une réponse transcriptionnelle à l'auxine qui dévie de la réponse canonique à l'auxine et est nécessaire pour soutenir l'expression d'un sous ensemble de gènes répondant à l'auxine et dépendant de BRX pour conduire la croissance du méristème. Dans la seconde partie de cette thèse de doctorat, nous avons caractérisé l'impact sur l'ensemble du génome des délétions à grande échelle sur quatre souches divergentes naturelles d'Arabidopsis, à travers l'intégration du séquençage à ultra-haut-débit avec l'hybridation génomique sur puces ADN. L'analyse des délétions identifiées a révélé qu'une proportion considérable de gènes codant était affectée, supportant l'idée d'un historique de réarrangement génomique modelé durant l'évolution. Dans la dernière partie de cette thèse, nous avons montré que le gène VÏP3 dans Arabidopsis a conservé un rôle évolutif dans la machinerie de dégradation des ARNm dans le sens 3' à 5', en appliquant une nouvelle approche pour l'analyse des données de séquençage d'ARNm issue de transcripts amplifiés aléatoirement. Dans son ensemble, cette recherche de doctorat contient des avancées majeures dans l'étude des variations génomiques naturelles des plantes et dans l'application de modèles morphodynamiques informatiques pour la caractérisation de réseaux biologiques essentiels à la plante. - Le développement des plantes est écrit dans leurs codes génétiques. Pour comprendre comment les plantes sont capables de s'adapter aux changements environnementaux, il est essentiel d'étudier comment leurs gènes gouvernent leur formation. Plus nous essayons de comprendre le fonctionnement d'une plante, plus nous réalisons la complexité des mécanismes biologiques, à tel point que l'utilisation d'outils et de modèles mathématiques devient indispensable. Dans ce travail, avec l'utilisation de la plante modèle Arabidopsis thalicinci nous avons résolu des problèmes biologiques spécifiques à travers le développement et l'application de méthodes informatiques concrètes. Dans un premier temps, nous avons investigué comment le gène BREVIS RADIX (BRX) régule le développement de la racine en contrôlant la réponse à deux hormones : l'auxine et la cytokinine. Nous avons employé une analyse statistique sur des mesures quantitatives de transcripts et de produits de gènes afin de démontrer que BRX joue un rôle antagonisant dans le dialogue entre ces deux hormones. Lorsque ce-dialogue moléculaire est perturbé, la racine primaire voit sa longueur dramatiquement réduite. Pour comprendre comment BRX répond à l'auxine, nous avons développé un modèle informatique basé sur des résultats expérimentaux. Les simulations successives ont mené à la découverte d'un signal positionnel qui contrôle la réponse de la racine à l'auxine par la régulation du mouvement intracellulaire de BRX. Dans la seconde partie de cette thèse, nous avons analysé le génome entier de quatre souches naturelles d'Arabidopsis et nous avons trouvé qu'une grande partie de leurs gènes étaient manquant par rapport à la souche de référence. Ce résultat indique que l'historique des modifications génomiques conduites par l'évolution détermine une disponibilité différentielle des gènes fonctionnels dans ces plantes. Dans la dernière partie de ce travail, nous avons analysé les données du transcriptome de la plante où le gène VIP3 était non fonctionnel. Ceci nous a permis de découvrir le rôle double de VIP3 dans la régulation de l'initiation de la transcription et dans la dégradation des transcripts. Ce rôle double n'avait jusqu'alors été démontrée que chez l'homme. Ce travail de doctorat supporte le développement et l'application de méthodologies informatiques comme outils inestimables pour résoudre la complexité des problèmes biologiques dans la recherche végétale. L'intégration de la biologie végétale et l'informatique est devenue de plus en plus importante pour l'avancée de nos connaissances sur le fonctionnement et le développement des plantes.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Computational fluid dynamics of the right ventricular outflow tract and of the pulmonary artery: a bench model of flow dynamics.

OBJECTIVES: The reconstruction of the right ventricular outflow tract (RVOT) with valved conduits remains a challenge. The reoperation rate at 5 years can be as high as 25% and depends on age, type of conduit, conduit diameter and principal heart malformation. The aim of this study is to provide a bench model with computer fluid dynamics to analyse the haemodynamics of the RVOT, pulmonary artery, its bifurcation, and left and right pulmonary arteries that in the future may serve as a tool for analysis and prediction of outcome following RVOT reconstruction. METHODS: Pressure, flow and diameter at the RVOT, pulmonary artery, bifurcation of the pulmonary artery, and left and right pulmonary arteries were measured in five normal pigs with a mean weight of 24.6 ± 0.89 kg. Data obtained were used for a 3D computer fluid-dynamics simulation of flow conditions, focusing on the pressure, flow and shear stress profile of the pulmonary trunk to the level of the left and right pulmonary arteries. RESULTS: Three inlet steady flow profiles were obtained at 0.2, 0.29 and 0.36 m/s that correspond to the flow rates of 1.5, 2.0 and 2.5 l/min flow at the RVOT. The flow velocity profile was constant at the RVOT down to the bifurcation and decreased at the left and right pulmonary arteries. In all three inlet velocity profiles, low sheer stress and low-velocity areas were detected along the left wall of the pulmonary artery, at the pulmonary artery bifurcation and at the ostia of both pulmonary arteries. CONCLUSIONS: This computed fluid real-time model provides us with a realistic picture of fluid dynamics in the pulmonary tract area. Deep shear stress areas correspond to a turbulent flow profile that is a predictive factor for the development of vessel wall arteriosclerosis. We believe that this bench model may be a useful tool for further evaluation of RVOT pathology following surgical reconstructions.

Molecular modeling study of the binding and signaling properties of the TCRpMHC complex

The activation of the specific immune response against tumor cells is based on the recognition by the CD8+ Cytotoxic Τ Lymphocytes (CTL), of antigenic peptides (p) presented at the surface of the cell by the class I major histocompatibility complex (MHC). The ability of the so-called T-Cell Receptors (TCR) to discriminate between self and non-self peptides constitutes the most important specific control mechanism against infected cells. The TCR/pMHC interaction has been the subject of much attention in cancer therapy since the design of the adoptive transfer approach, in which Τ lymphocytes presenting an interesting response against tumor cells are extracted from the patient, expanded in vitro, and reinfused after immunodepletion, possibly leading to cancer regression. In the last decade, major progress has been achieved by the introduction of engineered lypmhocytes. In the meantime, the understanding of the molecular aspects of the TCRpMHC interaction has become essential to guide in vitro and in vivo studies. In 1996, the determination of the first structure of a TCRpMHC complex by X-ray crystallography revealed the molecular basis of the interaction. Since then, molecular modeling techniques have taken advantage of crystal structures to study the conformational space of the complex, and understand the specificity of the recognition of the pMHC by the TCR. In the meantime, experimental techniques used to determine the sequences of TCR that bind to a pMHC complex have been used intensively, leading to the collection of large repertoires of TCR sequences that are specific for a given pMHC. There is a growing need for computational approaches capable of predicting the molecular interactions that occur upon TCR/pMHC binding without relying on the time consuming resolution of a crystal structure. This work presents new approaches to analyze the molecular principles that govern the recognition of the pMHC by the TCR and the subsequent activation of the T-cell. We first introduce TCRep 3D, a new method to model and study the structural properties of TCR repertoires, based on homology and ab initio modeling. We discuss the methodology in details, and demonstrate that it outperforms state of the art modeling methods in predicting relevant TCR conformations. Two successful applications of TCRep 3D that supported experimental studies on TCR repertoires are presented. Second, we present a rigid body study of TCRpMHC complexes that gives a fair insight on the TCR approach towards pMHC. We show that the binding mode of the TCR is correctly described by long-distance interactions. Finally, the last section is dedicated to a detailed analysis of an experimental hydrogen exchange study, which suggests that some regions of the constant domain of the TCR are subject to conformational changes upon binding to the pMHC. We propose a hypothesis of the structural signaling of TCR molecules leading to the activation of the T-cell. It is based on the analysis of correlated motions in the TCRpMHC structure. - L'activation de la réponse immunitaire spécifique dirigée contre les cellules tumorales est basée sur la reconnaissance par les Lymphocytes Τ Cytotoxiques (CTL), d'un peptide antigénique (p) présenté à la suface de la cellule par le complexe majeur d'histocompatibilité de classe I (MHC). La capacité des récepteurs des lymphocytes (TCR) à distinguer les peptides endogènes des peptides étrangers constitue le mécanisme de contrôle le plus important dirigé contre les cellules infectées. L'interaction entre le TCR et le pMHC est le sujet de beaucoup d'attention dans la thérapie du cancer, depuis la conception de la méthode de transfer adoptif: les lymphocytes capables d'une réponse importante contre les cellules tumorales sont extraits du patient, amplifiés in vitro, et réintroduits après immunosuppression. Il peut en résulter une régression du cancer. Ces dix dernières années, d'importants progrès ont été réalisés grâce à l'introduction de lymphocytes modifiés par génie génétique. En parallèle, la compréhension du TCRpMHC au niveau moléculaire est donc devenue essentielle pour soutenir les études in vitro et in vivo. En 1996, l'obtention de la première structure du complexe TCRpMHC à l'aide de la cristallographie par rayons X a révélé les bases moléculaires de l'interaction. Depuis lors, les techniques de modélisation moléculaire ont exploité les structures expérimentales pour comprendre la spécificité de la reconnaissance du pMHC par le TCR. Dans le même temps, de nouvelles techniques expérimentales permettant de déterminer la séquence de TCR spécifiques envers un pMHC donné, ont été largement exploitées. Ainsi, d'importants répertoires de TCR sont devenus disponibles, et il est plus que jamais nécessaire de développer des approches informatiques capables de prédire les interactions moléculaires qui ont lieu lors de la liaison du TCR au pMHC, et ce sans dépendre systématiquement de la résolution d'une structure cristalline. Ce mémoire présente une nouvelle approche pour analyser les principes moléculaires régissant la reconnaissance du pMHC par le TCR, et l'activation du lymphocyte qui en résulte. Dans un premier temps, nous présentons TCRep 3D, une nouvelle méthode basée sur les modélisations par homologie et ab initio, pour l'étude de propriétés structurales des répertoires de TCR. Le procédé est discuté en détails et comparé à des approches standard. Nous démontrons ainsi que TCRep 3D est le plus performant pour prédire des conformations pertinentes du TCR. Deux applications à des études expérimentales des répertoires TCR sont ensuite présentées. Dans la seconde partie de ce travail nous présentons une étude de complexes TCRpMHC qui donne un aperçu intéressant du mécanisme d'approche du pMHC par le TCR. Finalement, la dernière section se concentre sur l'analyse détaillée d'une étude expérimentale basée sur les échanges deuterium/hydrogène, dont les résultats révèlent que certaines régions clés du domaine constant du TCR sont sujettes à un changement conformationnel lors de la liaison au pMHC. Nous proposons une hypothèse pour la signalisation structurelle des TCR, menant à l'activation du lymphocyte. Celle-ci est basée sur l'analyse des mouvements corrélés observés dans la structure du TCRpMHC.

Computational problems in perfect phylogeny haplotyping: typing without calling the allele.

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes.

Comparative performance of supertree algorithms in large data sets using the soapberry family (Sapindaceae) as a case study.

For the last 2 decades, supertree reconstruction has been an active field of research and has seen the development of a large number of major algorithms. Because of the growing popularity of the supertree methods, it has become necessary to evaluate the performance of these algorithms to determine which are the best options (especially with regard to the supermatrix approach that is widely used). In this study, seven of the most commonly used supertree methods are investigated by using a large empirical data set (in terms of number of taxa and molecular markers) from the worldwide flowering plant family Sapindaceae. Supertree methods were evaluated using several criteria: similarity of the supertrees with the input trees, similarity between the supertrees and the total evidence tree, level of resolution of the supertree and computational time required by the algorithm. Additional analyses were also conducted on a reduced data set to test if the performance levels were affected by the heuristic searches rather than the algorithms themselves. Based on our results, two main groups of supertree methods were identified: on one hand, the matrix representation with parsimony (MRP), MinFlip, and MinCut methods performed well according to our criteria, whereas the average consensus, split fit, and most similar supertree methods showed a poorer performance or at least did not behave the same way as the total evidence tree. Results for the super distance matrix, that is, the most recent approach tested here, were promising with at least one derived method performing as well as MRP, MinFlip, and MinCut. The output of each method was only slightly improved when applied to the reduced data set, suggesting a correct behavior of the heuristic searches and a relatively low sensitivity of the algorithms to data set sizes and missing data. Results also showed that the MRP analyses could reach a high level of quality even when using a simple heuristic search strategy, with the exception of MRP with Purvis coding scheme and reversible parsimony. The future of supertrees lies in the implementation of a standardized heuristic search for all methods and the increase in computing power to handle large data sets. The latter would prove to be particularly useful for promising approaches such as the maximum quartet fit method that yet requires substantial computing power.

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.

Integrative molecular bioinformatics study of human adrenocortical tumors : microRNA, tissue-specific target prediction, and pathway analysis.

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.

A combined computational and functional approach identifies new residues involved in pH-dependent gating of ASIC1a.

Acid-sensing ion channels (ASICs) are key receptors for extracellular protons. These neuronal nonvoltage-gated Na(+) channels are involved in learning, the expression of fear, neurodegeneration after ischemia, and pain sensation. We have applied a systematic approach to identify potential pH sensors in ASIC1a and to elucidate the mechanisms by which pH variations govern ASIC gating. We first calculated the pK(a) value of all extracellular His, Glu, and Asp residues using a Poisson-Boltzmann continuum approach, based on the ASIC three-dimensional structure, to identify candidate pH-sensing residues. The role of these residues was then assessed by site-directed mutagenesis and chemical modification, combined with functional analysis. The localization of putative pH-sensing residues suggests that pH changes control ASIC gating by protonation/deprotonation of many residues per subunit in different channel domains. Analysis of the function of residues in the palm domain close to the central vertical axis of the channel allowed for prediction of conformational changes of this region during gating. Our study provides a basis for the intrinsic ASIC pH dependence and describes an approach that can also be applied to the investigation of the mechanisms of the pH dependence of other proteins.

Computational problems in perfect phylogeny haplotyping: Typing without calling the allele

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics and is a variant of the PP haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is '' full '' genotypes, here, we assume less informative input and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by the tree they map onto and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes and present a linear algorithm for identifying those genotypes.