Les 20 dernières années ont vu l'émergence de technologies de mesure puissantes, permettant l'analyse omique de diverses maladies. Ils fournissent souvent des moyens non invasifs pour étudier l'étiologie des maladies complexes nouvellement émergentes, telles que l'infection de la dengue, transmise par les moustiques. Ma thèse se concentre sur l'adaptation et l'application d'approches utilisant des réseaux d'interaction de gènes et l'apprentissage automatique pour l'analyse de données génomiques et transcriptomiques. La première partie va au-delà d'une analyse pangénomique précédemment publiée de 4 026 personnes en appliquant une analyse de réseaux d'interaction pour trouver des groupes de gènes qui interagissent dans un réseau d'interactions fonctionnelles et qui, pris ensemble, sont associés à la dengue sévère. Dans cette partie, j'ai d'abord recalculé les valeurs-p d'association des polymorphismes séquencés, puis j'ai travaillé sur le mapping des polymorphismes à des gènes fonctionnellement apparentés, et j'ai enfin exploré différentes bases de données de voies métaboliques et d'interactions génétiques pour trouver des groupes de gènes qui, pris ensemble, sont associés à la dengue sévère. La deuxième partie de ma thèse dévoile une approche théorique pour étudier un biais dans les algorithmes de recherche de réseau actifs. Mon analyse théorique suggère que le meilleur score de sous-réseaux d'une taille donnée devrait être normalisé en fonction de la taille, selon l'hypothèse selon laquelle il s'agit d'un échantillon d'une distribution de valeur extrême, et non un échantillon de la distribution normale, comme c'est généralement le cas dans la littérature. Je propose alors une solution théorique à ce biais. La troisième partie présente un nouvel outil de recherche de sous-réseaux que j'ai co-conçu. Son modèle sous-jacent et l'algorithme évite le biais de taille trouvé dans les méthodes existantes et génère des résultats facilement compréhensibles. Je présente une application aux données transcriptomiques de la dengue. Dans la quatrième et dernière partie, je décris l'identification d'un biomarqueur qui détecte la sévérité de la dengue à l'arrivée à l'hôpital en utilisant une nouvelle approche d'apprentissage automatique. Cette approche combine la régression monotone bidimensionnelle avec la sélection des variables. Le modèle sous-jacent va au-delà des approches linéaires couramment utilisées, tout en permettant de contrôler le nombre de transcrits dans le biomarqueur. Le petit nombre de transcrits accompagné de leur représentation visuelle maximisent la compréhension et l'interprétation du biomarqueur par les professionnels de la biomédecine. Je présente un biomarqueur à 18 gènes qui permet de distinguer, à leur arrivée à l'hôpital, les patients qui vont développer des symptômes de dengue sévères de ceux qui auront une dengue non sévère. Ce biomarqueur a une performance prédictive élevée et robuste. La performance prédictive du biomarqueur a été confirmée sur deux ensembles de données qui ont tous deux utilisé différentes technologies transcriptomiques et différents sous-types de cellules sanguines. / The last 20 years have seen the emergence of powerful measurement technologies, enabling omics analysis of diverse diseases. They often provide non-invasive means to study the etiology of newly emerging complex diseases, such as the mosquito-borne infectious dengue disease. My dissertation concentrates on adapting and applying network and machine learning approaches to genomic and transcriptomic data. The first part goes beyond a previously published genome-wide analysis of 4,026 individuals by applying network analysis to find groups of interacting genes in a gene functional interaction network that, taken together, are associated to severe dengue. In this part, I first recalculated association p-values of sequences polymorphisms, then worked on mapping polymorphisms to functionally related genes, and finally explored different pathway and gene interaction databases to find groups of genes together associated to severe dengue. The second part of my dissertation unveils a theoretical approach to study a size bias of active network search algorithms. My theoretical analysis suggests that the best score of subnetworks of a given size should be size-normalized, based on the hypothesis that it is a sample of an extreme value distribution, and not a sample of the normal distribution, as usually assumed in the literature. I then suggest a theoretical solution to this bias. The third part introduces a new subnetwork search tool that I co-designed. Its underlying model and the corresponding efficient algorithm avoid size bias found in existing methods, and generates easily comprehensible results. I present an application to transcriptomic dengue data. In the fourth and last part, I describe the identification of a biomarker that detects dengue severity outcome upon arrival at the hospital using a novel machine learning approach. This approach combines two-dimensional monotonic regression with feature selection. The underlying model goes beyond the commonly used linear approaches, while allowing controlling the number of transcripts in the biomarker. The small number of transcripts along with its visual representation maximize the understanding and the interpretability of the biomarker by biomedical professionals. I present an 18-gene biomarker that allows distinguishing severe dengue patients from non-severe ones upon arrival at the hospital with a unique biomarker of high and robust predictive performance. The predictive performance of the biomarker has been confirmed on two datasets that both used different transcriptomic technologies and different blood cell subtypes.

En cas de carence azotée, les légumineuses sont capables de mettre en place une symbiose avec des bactéries du sol fixatrices d’azote appelées rhizobia. Cette symbiose a lieu dans un organe appelé nodosité où les bactéries sont endocytées et appelées bactéroïdes. Certains clades de légumineuses imposent un processus de différenciation à leurs bactéroïdes qui agrandissent considérablement et deviennent polyploïdes, menant à des morphotypes bactériens allongés ou sphériques. Au cours de cette thèse, j’ai étudié la différenciation des bactéroïdes de Bradyrhizobium spp. en association avec Aeschynomene spp.. Les bactéroïdes de ces plantes présentent des degrés de différenciation distincts qui dépendent de l’espèce hôte. Mes données suggèrent que les bactéroïdes les plus différenciés sont aussi les plus efficaces. J’ai cherché à savoir quels facteurs procaryotes pourraient être impliqués dans les adaptations des bactéroïdes au processus de différenciation et à leurs divers hôtes, le tout en lien avec cette différence d’efficacité symbiotique au travers d’approches globales sans a priori de type -omiques. Les conditions considérées sont des bactéroïdes de différents morphotypes et des cultures libres de référence. Les fonctions activées en conditions symbiotiques ont été identifiées, ainsi que les gènes spécifiques d’un hôte donné. Des analyses fonctionnelles des gènes d’intérêt ont également été menées. Les mutants bactériens n’ont toutefois pas présenté de phénotype symbiotique drastique, montrant ainsi l’existence de réseaux de gènes complexes menant à la résilience des génomes de rhizobia. / In case of nitrogen starvation, legume plants establish a symbiotic interaction with nitrogen-fixing soil bacteria called rhizobia. This interaction takes place in nodules where the symbionts are internalized and become bacteroids. Some legume clades also impose a differentiation process onto the bacteroids which become enlarged and polyploid, leading to elongated or spherical morphotypes. During my PhD work, I have studied bacteroid differentiation of Bradyrhizobium species in association with Aeschynomene spp.. These bacteroids display distinct differentiation levels depending on the plant host, and my analyses suggest that the most differentiated ones are also the most efficient. I investigated the bacterial factors potentially involved in the adaptations to differentiation and host-specificity, and related to the higher efficiency of the most differentiated bacteroids using global-omics approaches without a priori. The analyzed conditions were bacteroids of distinct morphotypes and free-living reference cultures. Activated functions under symbiotic conditions were identified, as well as host-specific ones. Functional analyses were performed on genes of interest. However, the bacterial mutants did not display drastic symbiotic phenotypes, showing the existence of complex gene networks leading to high resilience of rhizobial genomes.

Chronic physiological stress impacts animal fitness by catabolizing metabolic stores and suppressing reproduction and immunity. This can be especially deleterious for capital breeding carnivores, such as marine mammals, which rely on lipid stores accrued during intensive foraging to sustain prolonged periods of fasting associated with reproduction. Therefore, chronic stress may cause a decrease in fitness in these animals, leading to population declines and potentially detrimental shifts in food web dynamics as a result. However, the impacts and indicators of chronic stress in animals are currently poorly understood. To identify downstream mediators of repeated stress responses in marine mammals, adrenocorticotropic hormone (ACTH) was administered once daily for four days to free-ranging juvenile northern elephant seals (Mirounga angustirostris) to stimulate endogenous corticosteroid release. I then compared blubber tissue transcriptome responses to the first and fourth ACTH administrations to determine the effects of acute and chronic endocrine stress, respectively. Gene expression profiles showed differences in responses to single and repeated ACTH administration, despite similarities in circulating cortisol profiles. We identified 61 and 12 differentially expressed genes (DEGs) in response to the first ACTH and fourth administrations, respectively, 24 DEGs between the first and fourth pre-ACTH samples, and 12 DEGs between ACTH response samples from the first and fourth days. Annotated DEGs were associated with functions in redox and lipid homeostasis, suggesting potential negative impacts of repeated stress on marine mammals. In addition, protein expression profiles were discrete between single and repeated ACTH administrations, and identified changes in expression of extracellular proteins that were not detected at the transcriptome level. We identified 8 and 7 differentially expressed proteins (DEPs) in response to the first and fourth ACTH administrations, respectively, including 5 DEPs in the overall ACTH response, 1 DEP between the first and fourth pre-ACTH samples, and 10 DEPs between ACTH response samples from the first and fourth days. Differentially expressed proteins in response to repeated ACTH administrations were associated with extracellular matrix (ECM) remodeling and suggest a link between glucocorticoid-induced adipogenesis and ECM remodeling in blubber. Other differentially expressed proteins were associated with increased lipid metabolism and decreased immunity, consistent with transcriptome data. Together, the use of transcriptomics and proteomics to detect responses to repeated stress provides more comprehensive insight into the marine mammal stress response and highlights the importance of using multiple discovery-driven approaches for understanding stress physiology. The gene and protein markers identified in this study may be used to identify stressed animals and discriminate between acutely and chronically stressed individuals with higher sensitivity than hormone measurements alone.

No description available.

The scope and application of high throughput techniques has expanded from studying a single genome, transcriptome or proteome to understanding complex environments at a greater resolution with the help of novel computational frameworks. Comprehensive structural annotation i.e. description of all functional elements in the genome, is required for measuring genome response accurately, using high throughput methods. Annotation of genome sequences using high throughput data from RNA-seq and proteomics experiments complement computational methods for identifying functional elements and can help validate existing in silico annotation, correct annotation errors, and could potentially identify novel functional elements. Re-annotation studies in recent times have revealed shortcomings of automated methods and the necessity to validate existing annotations using experimental data. This dissertation elucidates re-annotation of Mannheimia haemolytica, Pasteurella multocida and Histophilus somni, bacterial pathogens associated with bovine respiratory disease in cattle. Experimental re-annotation of these bacterial genomes using RNA-seq and proteomics enabled the validation of existing annotation and discovery of novel functional elements that can be utilized in future functional genomics studies. We also addressed the need for developing an automated bioinformatics workflow that is broadly applicable for bacterial genome re-annotation, by developing open source Perl pipeline that can use RNA-seq and proteomics data as input. Simultaneous analysis of host and pathogen gene expression profiling using metatranscriptomics approaches is necessary to improve our understanding of infectious diseases. Traditional methods for analysis of RNA-seq data do not address the impact of cross-mapping of reads to multiple genomes for data originating from a metatranscriptomic study. Analysis of sequence conservation between species can help determine a metric for cross mapping to correct for signal vs. noise. We generated artificial RNA-seq data and evaluated the impact of read length and sequence conservation on cross-mapping. Comparative genomics was used to identify a core and pan-genome for quantifying gene expression. Our results show that cross mapping between genomes can directly be related to evolutionary distance between these genomes and that an increase in RNA-seq read length tends to negate cross mapping.

<p>Nitrogen and phosphorus are the two macronutrients which play important roles in the plant, both structurally and functionally, e.g., starting from being constituents of cellular integrity to being signal molecules in signal transduction. Since they are required by plants in higher concentrations, it becomes indispensable to replenish their pools in soils by the application of chemical fertilizers. However, this practice is not only costly, the sources of Phosphorus and Nitrogen are not renewable and the excessive application in the form of fertilizers is not environmentally sustainable. Therefore, it warrants a better understanding of the plant responses during the nutrient deficiency because such knowledge will help implement strategies for breeding crops with more efficient use of minerals.</p><p>Most prior efforts in studying the molecular and physiological responses to low minerals were focused on roots. However, recently it has been found that shoot-to-root long distance signaling plays an important role in the adaptation of roots to low nitrogen or phosphorus. Here, we measured different physiological and morphological parameters and used RNA-Seq to elucidate the physiological and molecular responses in the vascular tissues of <i>Plantago major</i>, a new model species established in our laboratory, to low nitrogen, low phosphate or combined nitrogen and phosphate starvation<i>. </i>In this study, <i>P major </i>showed reduced photosynthesis and Fv/Fm, increased catalase and ascorbate peroxidase activity, reduced phosphate and nitrate contents in respective treatments. In addition, assessment of root morphological parameters revealed that nutrient deficiencies could lead to higher root densities and increased root to shoot ratios.</p><p>For molecular analysis of transcriptome changes, 24 hours of nutrient starvation exhibited an alteration of 33, 221, and 329 genes for the deficiencies of phosphorus, nitrogen and combined nitrogen and phosphorus, respectively. Our study helped to dissect several novel pathways associated with the vascular system in response to the deficiencies of major macronutrients. </p>

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Gene-expression profiling enables researchers to quantify transcription levels in cells, thus providing insight into functional mechanisms of diseases and other biological processes. However, because of the high dimensionality of these data and the sensitivity of measuring equipment, expression data often contains unwanted confounding effects that can skew analysis. For example, collecting data in multiple runs causes nontrivial differences in the data (known as batch effects), known covariates that are not of interest to the study may have strong effects, and there may be large systemic effects when integrating multiple expression datasets. Additionally, many of these confounding effects represent higher-order interactions that may not be removable using existing techniques that identify linear patterns. We created Confounded to remove these effects from expression data. Confounded is an adversarial variational autoencoder that removes confounding effects while minimizing the amount of change to the input data. We tested the model on artificially constructed data and commonly used gene expression datasets and compared against other common batch adjustment algorithms. We also applied the model to remove cancer-type-specific signal from a pan-cancer expression dataset. Our software is publicly available at https://github.com/jdayton3/Confounded.

No description available.

Marine mammals such as northern elephant seals (NES) routinely experience hypoxemia and ischemia-reperfusion events to many tissues during deep dives with no apparent adverse effects. Adaptations to diving include increased antioxidants and elevated oxygen storage capacity associated with high hemoprotein content in blood and muscle. Despite experiencing decreased oxygen tensions during diving, NES likely rely on the mobilization of large lipids stores and catabolism of fatty acids to provide energy to exercising muscle while diving. To identify potential regulatory mechanisms that may underly hypoxia and exercise tolerance in diving mammals, this study used system-wide approaches to characterize changes in genes and proteins in two metabolically active tissues (skeletal muscle and blubber) and whole blood of NES over development and in response to translocation. Specifically, this study profiled muscle and blood gene expression associated with regulation of oxidative stress and inflammatory pathways in weaned pups, juveniles, and adult NES as well as evaluated muscle and blubber transcriptomic and proteomic responses to swimming and diving in juvenile NES. I found that expression of genes associated with mitochondrial biogenesis (PGC1A, ESRRA, ESRRG), immune system activation (HMOX2, IL1B, NRF2, BVR, IL10), and protection from lipid peroxidation (GPX4, PRDX6, PRDX1, SIRT1) increased over postnatal development in muscle and whole blood of NES, providing a potential ontogenic mechanism for increasing diving capacity and hypoxia and ischemia-reperfusion tolerance. I also found that expression of genes and abundance of proteins associated with lipid transport (APOD, ABCA6, ABCA8, ABCA10, CD1E), lipid catabolism (ADIPOQ , ENPP6), and adipogenesis (DLK1, ADIRF,) increased, while those associated with insulin sensitivity and energy expenditure (APLN, VGF) decreased in response to swimming and diving in juvenile NES blubber and muscle, suggesting potential mechanisms for fuel provisioning to muscle during exercise in hypoxic conditions. Together, these data provide insights into gene activity in muscle, blubber, and blood cells that may provide hypoxia tolerance and regulate energy homeostasis and exercise performance during breath holds in diving mammals.