Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Identification of the pathological alterations underlying respiratory failure in myotonic dystrophy transgenic mice

AbstractMyotonic dystrophy type 1 (DM1), also known as Steinert's disease, is an inherited autosomal dominant disease. DM1 is characterized by myotonia, muscular weakness and atrophy, but it has a multisystemic phenotype. The genetic basis of the disease is the abnormal expansion of CTG repeats in the 3' untranslated region of the DM protein kinase (DMPK) gene on chromosome 19. The size of the expansion correlates to the severity of the disease and the age of onset.Respiratory problems have long been recognized to be a major feature of the disease and are the main factor contributing to mortality ; however the mechanisms are only partly known. The aim of our study is to investigate whether respiratory failure results only from the involvement of the dystrophic process at the level of the respiratory muscles or comes also from abnormalities in the neuronal network that generates and controls the respiratory rhythm. The generation of valid transgenic mice displaying the human DM1 phenotype by the group of Dr. Gourdon provided us a useful tool to analyze the brain stem respiratory neurons, spinal phrenic motoneurons and phrenic nerves. We examined therefore these structures in transgenic mice carrying 350-500 CTGs and displaying a mild form of the disease (DM1 mice). The morphological and morphometric analysis of diaphragm muscle sections revealed a denervation of the end-plates (EPs), characterized by a decrease in size and shape complexity of EPs and a reduction in the density of acetylcholine receptors (AChRs). Also a strong and significant reduction in the number of phrenic unmyelinated fibers was detected, but not in the myelinated fibers. In addition, no pathological changes were detected in the cervical motoneurons and medullary respiratory centers (Panaite et al., 2008). These results suggest that the breathing rhythm is probably not affected in mice expressing a mild form of DM1, but rather the transmission of action potentials at the level of diaphragm NMJs is deficient.Because size of the mutation increases over generations, new transgenic mice were obtained from the mice with 350-500 CTGs, resulting from a large increase of CTG repeat in successive generations, these mice carry more than 1300 CTGs (DMSXL) and display a severe DM1 phenotype (Gomes-Pereira et al., 2007). Before we study the mechanism underlying the respiratory failure in DMSXL mice, we analyzed the peripheral nervous system (PNS) in these mice by electrophysiological, histological and morphometric methods. Our results provide strong evidence that DMSXL mice have motor neuropathy (Panaite et al., 2010, submitted). Therefore the DMSXL mice expressing severe DM1 features represent for us a good tool to investigate, in the future, the physiological, structural and molecular alterations underlying respiratory failure in DM1. Understanding the mechanism of respiratory deficiency will help to better target the therapy of these problems in DM1 patients. In addition our results may, in the future, orientate pharmaceutical and clinical research towards possible development of therapy against respiratory deficits associated with the DM1.RésuméLa dystrophic myotonique type 1 (DM1), aussi dénommée maladie de Steinert, est une maladie héréditaire autosomique dominante. Elle est caractérisée par une myotonie, une faiblesse musculaire avec atrophie et se manifeste aussi par un phénotype multisystémique. La base génétique de la maladie est une expansion anormale de répétitions CTG dans une région non traduite en 3' du gène de la DM protéine kinase (DMPK) sur le chromosome 19. La taille de l'expansion est corrélée avec la sévérité et l'âge d'apparition de DM1.Bien que les problèmes respiratoires soient reconnus depuis longtemps comme une complication de la maladie et soient le principal facteur contribuant à la mortalité, les mécanismes en sont partiellement connus. Le but de notre étude est d'examiner si l'insuffisance respiratoire de la DM1 est dû au processus dystrophique au niveau des muscles respiratoires ou si elle est entraînée aussi par des anomalies dans le réseau neuronal qui génère et contrôle le rythme respiratoire. La production par le groupe du Dr. Gourdon de souris transgéniques de DM1, manifestant le phénotype de DM1 humaine, nous a fourni un outil pour analyser les nerfs phréniques, les neurones des centres respiratoires du tronc cérébral et les motoneurones phréniques. Par conséquence, nous avons examiné ces structures chez des souris transgéniques portant 350-500 CTG et affichant une forme légère de la maladie (souris DM1). L'analyse morphologique et morphométrique des sections du diaphragme a révélé une dénervation des plaques motrices et une diminution de la taille et de la complexité de la membrane postsynaptîque, ainsi qu'une réduction de la densité des récepteurs à l'acétylcholine. Nous avons aussi détecté une réduction significative du nombre de fibres nerveuses non myélinisées mais pas des fibres myélinisées. Par ailleurs, aucun changement pathologique n'a été détecté pour les neurones moteurs médullaires cervicaux et centres respiratoires du tronc cérébral (Panaite et al., 2008). Ces résultats suggèrent que le iythme respiratoire n'est probablement pas affecté chez les souris manifestant une forme légère du DM1, mais plutôt que la transmission des potentiels d'action au niveau des plaques motrices du diaphragme est déficiente.Comme la taille du mutation augmente au fil des générations, de nouvelles souris transgéniques ont été générés par le groupe Gourdon; ces souris ont plus de 1300 CTG (DMSXL) et manifestent un phénotype sévère du DM1 (Gomes-Pereira et al., 2007). Avant d'étudier le mécanisme sous-jacent de l'insuffisance respiratoire chez les souris DMSXL, nous avons analysé le système nerveux périphérique chez ces souris par des méthodes électrophysiologiques, histologiques et morphométriques. Nos résultats fournissent des preuves solides que les souris DMSXL manifestent une neuropathie motrice (Panaite et al., 2010, soumis). Par conséquent, les souris DMSXL représentent pour nous un bon outil pour étudier, à l'avenir, les modifications physiologiques, morphologiques et moléculaires qui sous-tendent l'insuffisance respiratoire du DM1. La connaissance du mécanisme de déficience respiratoire en DM1 aidera à mieux cibler le traitement de ces problèmes aux patients. De plus, nos résultats pourront, à l'avenir, orienter la recherche pharmaceutique et clinique vers le développement de thérapie contre le déficit respiratoire associé à DM1.

Development and application of computational methodologies in qualitative modeling

La présente thèse s'intitule "Développent et Application des Méthodologies Computationnelles pour la Modélisation Qualitative". Elle comprend tous les différents projets que j'ai entrepris en tant que doctorante. Plutôt qu'une mise en oeuvre systématique d'un cadre défini a priori, cette thèse devrait être considérée comme une exploration des méthodes qui peuvent nous aider à déduire le plan de processus regulatoires et de signalisation. Cette exploration a été mue par des questions biologiques concrètes, plutôt que par des investigations théoriques. Bien que tous les projets aient inclus des systèmes divergents (réseaux régulateurs de gènes du cycle cellulaire, réseaux de signalisation de cellules pulmonaires) ainsi que des organismes (levure à fission, levure bourgeonnante, rat, humain), nos objectifs étaient complémentaires et cohérents. Le projet principal de la thèse est la modélisation du réseau de l'initiation de septation (SIN) du S.pombe. La cytokinèse dans la levure à fission est contrôlée par le SIN, un réseau signalant de protéines kinases qui utilise le corps à pôle-fuseau comme échafaudage. Afin de décrire le comportement qualitatif du système et prédire des comportements mutants inconnus, nous avons décidé d'adopter l'approche de la modélisation booléenne. Dans cette thèse, nous présentons la construction d'un modèle booléen étendu du SIN, comprenant la plupart des composantes et des régulateurs du SIN en tant que noeuds individuels et testable expérimentalement. Ce modèle utilise des niveaux d'activité du CDK comme noeuds de contrôle pour la simulation d'évènements du SIN à différents stades du cycle cellulaire. Ce modèle a été optimisé en utilisant des expériences d'un seul "knock-out" avec des effets phénotypiques connus comme set d'entraînement. Il a permis de prédire correctement un set d'évaluation de "knock-out" doubles. De plus, le modèle a fait des prédictions in silico qui ont été validées in vivo, permettant d'obtenir de nouvelles idées de la régulation et l'organisation hiérarchique du SIN. Un autre projet concernant le cycle cellulaire qui fait partie de cette thèse a été la construction d'un modèle qualitatif et minimal de la réciprocité des cyclines dans la S.cerevisiae. Les protéines Clb dans la levure bourgeonnante présentent une activation et une dégradation caractéristique et séquentielle durant le cycle cellulaire, qu'on appelle communément les vagues des Clbs. Cet évènement est coordonné avec la courbe d'activation inverse du Sic1, qui a un rôle inhibitoire dans le système. Pour l'identification des modèles qualitatifs minimaux qui peuvent expliquer ce phénomène, nous avons sélectionné des expériences bien définies et construit tous les modèles minimaux possibles qui, une fois simulés, reproduisent les résultats attendus. Les modèles ont été filtrés en utilisant des simulations ODE qualitatives et standardisées; seules celles qui reproduisaient le phénotype des vagues ont été gardées. L'ensemble des modèles minimaux peut être utilisé pour suggérer des relations regulatoires entre les molécules participant qui peuvent ensuite être testées expérimentalement. Enfin, durant mon doctorat, j'ai participé au SBV Improver Challenge. Le but était de déduire des réseaux spécifiques à des espèces (humain et rat) en utilisant des données de phosphoprotéines, d'expressions des gènes et des cytokines, ainsi qu'un réseau de référence, qui était mis à disposition comme donnée préalable. Notre solution pour ce concours a pris la troisième place. L'approche utilisée est expliquée en détail dans le dernier chapitre de la thèse. -- The present dissertation is entitled "Development and Application of Computational Methodologies in Qualitative Modeling". It encompasses the diverse projects that were undertaken during my time as a PhD student. Instead of a systematic implementation of a framework defined a priori, this thesis should be considered as an exploration of the methods that can help us infer the blueprint of regulatory and signaling processes. This exploration was driven by concrete biological questions, rather than theoretical investigation. Even though the projects involved divergent systems (gene regulatory networks of cell cycle, signaling networks in lung cells), as well as organisms (fission yeast, budding yeast, rat, human), our goals were complementary and coherent. The main project of the thesis is the modeling of the Septation Initiation Network (SIN) in S.pombe. Cytokinesis in fission yeast is controlled by the SIN, a protein kinase signaling network that uses the spindle pole body as scaffold. In order to describe the qualitative behavior of the system and predict unknown mutant behaviors we decided to adopt a Boolean modeling approach. In this thesis, we report the construction of an extended, Boolean model of the SIN, comprising most SIN components and regulators as individual, experimentally testable nodes. The model uses CDK activity levels as control nodes for the simulation of SIN related events in different stages of the cell cycle. The model was optimized using single knock-out experiments of known phenotypic effect as a training set, and was able to correctly predict a double knock-out test set. Moreover, the model has made in silico predictions that have been validated in vivo, providing new insights into the regulation and hierarchical organization of the SIN. Another cell cycle related project that is part of this thesis was to create a qualitative, minimal model of cyclin interplay in S.cerevisiae. CLB proteins in budding yeast present a characteristic, sequential activation and decay during the cell cycle, commonly referred to as Clb waves. This event is coordinated with the inverse activation curve of Sic1, which has an inhibitory role in the system. To generate minimal qualitative models that can explain this phenomenon, we selected well-defined experiments and constructed all possible minimal models that, when simulated, reproduce the expected results. The models were filtered using standardized qualitative ODE simulations; only the ones reproducing the wave-like phenotype were kept. The set of minimal models can be used to suggest regulatory relations among the participating molecules, which will subsequently be tested experimentally. Finally, during my PhD I participated in the SBV Improver Challenge. The goal was to infer species-specific (human and rat) networks, using phosphoprotein, gene expression and cytokine data and a reference network provided as prior knowledge. Our solution to the challenge was selected as in the final chapter of the thesis.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Combined loss of the BH3-only proteins Bim and Bmf restores B-cell development and function in TACI-Ig transgenic mice.

Terminal differentiation of B cells depends on two interconnected survival pathways, elicited by the B-cell receptor (BCR) and the BAFF receptor (BAFF-R), respectively. Loss of either signaling pathway arrests B-cell development. Although BCR-dependent survival depends mainly on the activation of the v-AKT murine thymoma viral oncogene homolog 1 (AKT)/PI3-kinase network, BAFF/BAFF-R-mediated survival engages non-canonical NF-κB signaling as well as MAPK/extracellular-signal regulated kinase and AKT/PI3-kinase modules to allow proper B-cell development. Plasma cell survival, however, is independent of BAFF-R and regulated by APRIL that signals NF-κB activation via alternative receptors, that is, transmembrane activator and CAML interactor (TACI) or B-cell maturation (BCMA). All these complex signaling events are believed to secure survival by increased expression of anti-apoptotic B-cell lymphoma 2 (Bcl2) family proteins in developing and mature B cells. Curiously, how lack of BAFF- or APRIL-mediated signaling triggers B-cell apoptosis remains largely unexplored. Here, we show that two pro-apoptotic members of the 'Bcl2 homology domain 3-only' subgroup of the Bcl2 family, Bcl2 interacting mediator of cell death (Bim) and Bcl2 modifying factor (Bmf), mediate apoptosis in the context of TACI-Ig overexpression that effectively neutralizes BAFF as well as APRIL. Surprisingly, although Bcl2 overexpression triggers B-cell hyperplasia exceeding the one observed in Bim(-/-)Bmf(-/-) mice, Bcl2 transgenic B cells remain susceptible to the effects of TACI-Ig expression in vivo, leading to ameliorated pathology in Vav-Bcl2 transgenic mice. Together, our findings shed new light on the molecular machinery restricting B-cell survival during development, normal homeostasis and under pathological conditions. Our data further suggest that Bcl2 antagonists might improve the potency of BAFF/APRIL-depletion strategies in B-cell-driven pathologies.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior

Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.

In Vivo Longitudinal (1)H MRS Study of Transgenic Mouse Models of Prion Disease in the Hippocampus and Cerebellum at 14.1 T.

In vivo (1)H MR spectroscopy allows the non invasive characterization of brain metabolites and it has been used for studying brain metabolic changes in a wide range of neurodegenerative diseases. The prion diseases form a group of fatal neurodegenerative diseases, also described as transmissible spongiform encephalopathies. The mechanism by which prions elicit brain damage remains unclear and therefore different transgenic mouse models of prion disease were created. We performed an in vivo longitudinal (1)H MR spectroscopy study at 14.1 T with the aim to measure the neurochemical profile of Prnp -/- and PrPΔ32-121 mice in the hippocampus and cerebellum. Using high-field MR spectroscopy we were able to analyze in details the in vivo brain metabolites in Prnp -/- and PrPΔ32-121 mice. An increase of myo-inositol, glutamate and lactate concentrations with a decrease of N-acetylaspartate concentrations were observed providing additional information to the previous measurements.