O estudo da diversidade viral em amostras ambientais tem se tornado cada vez mais importante devido a funções-chave desempenhadas por esses organismos. Estudos recentes têm fornecido evidências de que vírus de bactérias (bacteriófagos) podem ser os principais determinantes em ciclos biogeoquímicos de grandes ecossistemas, além de atuarem no fluxo de genes entre comunidades ambientais e na plasticidade funcional das mesmas frente a estresses ambientais. Neste trabalho, propomos a investigação e caracterização da diversidade viral presente em amostras de compostagem através de abordagens não dependentes e dependentes de cultivo. Na primeira abordagem, coletamos amostras seriadas de uma unidade de compostagem do zoológico de São Paulo para realização de sequenciamento metagenômico. O conjunto de sequências gerado foi extensivamente minerado (data-mining) para a produção de resultados de diversidade e abundância de táxons virais ao longo do processo de compostagem. Adicionalmente, procedemos com a montagem e recuperação de sequências virais candidatas a genomas completos e/ou parciais de novos vírus ambientais. Os dois protocolos computacionais utilizados para a mineração de dados encontram-se definidos e automatizados, podendo ser aplicados em quaisquer conjuntos de dados de sequenciamento metagenômico ou metatranscritômico obtidos através da plataforma Illumina. A segunda abordagem correspondeu ao isolamento e caracterização de novos fagos de Pseudomonas obtidos de amostras de compostagem. Três novos fagos foram identificados e tiveram os seus genomas sequenciados. A caracterização genômica desses fagos revelou genomas com alto grau de novidade, insights sobre a evolução de Caudovirales e a presença de genes de tRNA, cuja função pode estar relacionada com um mecanismo dos fagos para contornar o viés traducional apresentado pela bactéria hospedeira. A caracterização experimental dos novos fagos isolados demonstrou grande potencial para lise e dissolução de biofilme da cepa Pseudomonas aeruginosa PA14, conhecida como agente causador de infecções hospitalares em pacientes imunodeprimidos. Em suma, os dados reunidos nesta dissertação caracterizam a diversidade presente no viroma da compostagem e contribuem para o entendimento dos perfis taxonômico, funcional e ecológico do processo. / The study of the viral diversity in environmental samples has become increasingly important due to key-roles that are performed by these organisms in our ecosystems. Recent publications provide evidence that viruses of bacteria (bacteriophages) may be key-players in biogeochemical cycles of large ecosystems, as oceans and forests. Besides, they may also be determinant in the genes flux among populations and in the plasticity of the communities face to environmental stresses. In this work, we propose the investigation and characterization of the viral diversity in composting samples through non-culturable and culturable-dependent approaches. In the first approach, we sampled a composting unit from the Sao Paulo Zoo Park in different time points and proceeded with metagenomic sequencing. The dataset generated was extensively mined to provide results of diversity and abundance of viral taxa through the composting process. Additionally, we proceeded with the assembly and retrieval of candidate sequences to partial or/and complete viral genomes. The two computational protocols were automatized as pipelines and can be applied to any metagenomic dataset of illumina reads. The second approach refers to the isolation and characterization of new Pseudomonas phages obtained from composting samples. Three new phages were identified and their genomes were sequenced. A detailed characterization of these genomes revealed high degree of novelty, insights about evolution of tailed-phages and the presence of tRNA genes, which may be related to a mechanism to bypass host translational bias. The experimental characterization of the new phages demonstrated great potential to lyse bacterial cells and to degrade Pseudomonas aeruginosa PA14 biofilms. In short, the data presented in this dissertation shed light to the composting virome diversity, as well as to the functional and ecological profiles of viruses in the composting environment.

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Doctor of Philosophy / Antibiotic resistance is ability of bacteria to withstand an antibiotic to which they were once sensitive. Antibiotic resistance is a global threat that can pose a serious threat to public health. In order to curb the spread of antibiotic resistance, it is imperative that efforts commensurate with the "One Health" approach. Since ecosystem networks can act as channels for the spread and spread of antibiotic resistance, there is growing recognition that a robust global environmental monitoring framework is required to promote a true one-health approach. The ideal goal would be to develop approaches that can inform the global spread of antibiotic resistance, help prioritize monitoring objectives and present robust data analysis frameworks for resistance profiling, and ultimately help develop strategies to contain the spread of antibiotic resistance. The objective of the work described in this thesis was to evaluate and develop different data analysis paradigms and their applications in the study and characterization of antibiotic resistance in different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. The Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. The results of Chapters 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.

Dans les maladies neurodégénératives comme la sclérose en plaques, la neuro-inflammation modifie l'activité de la barrière hémato-encéphalique (BHE) par des altérations cellulaires et moléculaires complexes. La caractérisation de tels changements moléculaires par une approche d'étiquetage in vivo justifie la recherche d’outils de ciblage fiables et de biomarqueurs. Les stratégies pour définir in vivo ces marqueurs sont cependant compliquées par la pléthore de molécules cibles accessibles, par l’intrication des régions atteintes au sein du tissu sain et par les altérations structurales potentielles des molécules cibles étudiées par histopathologie. Le but de ce travail est de rationaliser la découverte de biomarqueurs des altérations moléculaires dans les tissus par une stratégie de sélection in vivo de répertoires de phages présentant des peptides à leur surface (phage display), les ligands présents dans les deux répertoires (sain et pathologique) étant ensuite soustraits physiquement. Cette stratégie de soustraction (« PhiSSH ») permettant d’enrichir un répertoire en ligands spécifiques est d’un intérêt majeur dans le cas de répertoires complexes tels ceux obtenus dans des sélections in vivo.Nous présentons l'application de cette stratégie dans le modèle de rat de la sclérose en plaques, l’Encéphalomyélite Autoimmune Expérimentale (EAE), où les lésions disséminées dans le système nerveux central engendrent la sélection d’une grande quantité de clones s’associant au tissu sain, par comparaison avec les rats témoins en bonne santé. L'efficacité de la technique de soustraction a été contrôlée par séquençage massif des trois repertoires, «EAE», «SAIN», et «SOUSTRACTION». Plus de 95 % des clones communs aux répertoires EAE et contrôle sont absents du répertoire de la soustraction. Un ensemble de clones de phages et des peptides synthétisés chimiquement dessinés après l’analyse bioinformatique du répertoire de soustraction a été testé a) sur des tissus de rats EAE et sains et b) sur des cellules humaines en culture (HCMEC/D3) constituant un modèle de BHE, dans des conditions inflammatoires, (activation IL- 1ß) ou non activées. Un des clones et quatre peptide testés ont montré une association spécifique sur les cellules endothéliales de BHE dans des conditions inflammatoires. Pour identifier la cible d’un phage spécifique des lésions neuro-inflammatoires, nous avons mis en œuvre un procédé de création de liaison covalente entre ce phage et les protéines exprimées par des cellules de BHE cultivées en présence d’IL-1ß, puis effectué une analyse par spectométrie de masse. La galectine-1 est apparue comme une cible potential de ce phage. La découverte de biomarqueurs spécifiques de modifications moléculaires et cellulaires de régions inflammatoires disséminées dans les tissus sains, comme c’est le cas dans la plupart des pathologies présentant une activité neuro–inflammatoire, sera facilitée par l’utilisation de la stratégie de soustraction PhiSSH décrite dans ce document. / In neurodegenerative disorders like multiple sclerosis, neuroinflammation modifies the blood brain barrier (BBB) status by causing complex cellular and molecular alterations. Characterization of such molecular changes by an in vivo labeling approach is most challenging to generate reliable in vivo targeting tools and biomarkers. In vivo strategies to define such markers are, however, hampered by the plethora of the accessible target molecules, the vicinity of diseased target expression among healthy tissue and the potentially structural alterations of target molecules when studied by histopathology. The aim of this work is to streamline the biomarker discovery of pathological molecular tissue alterations by in vivo selection of phage displayed peptide repertoires that are further submitted to physical DNA subtraction (“PhiSSH”) of sequences encoding common peptides in both repertoires (HEALTHY and PATHOLOGY). The strategy of Subtraction allows thus the enrichment of clones specific for one repertoire and is of particular interest for complex repertoires produced by in vivo selection. We present the application of this strategy in the multiple sclerosis rat model, Experimental Autoimmune Encephalomyelitis (EAE) pathology, where target lesions are disseminated in the central nervous system (CNS) generating a large amount of clones binding to healthy tissue among the recovered repertoire clones binding to the lesions by comparison with healthy control rats. The efficiency of the subtraction was monitored by massive sequencing of the three repertoires, «EAE», «HEALTHY», and «SUBTRACTION». More than 95% of the clones common to EAE and Healthy repertoires were shown to be absent from the Subtraction repertoire. A set of randomly chosen clones and synthesized peptides from the EAE and subtraction repertoires were tested for differential labeling of a) diseased and healthy animal tissues and b) an in vitro BBB model, in IL-1ß challenged and resting control state culture human cells (hCMEC/D3). One of the phage clones and 4 chemically synthesized peptides showed specific binding to brain ECs in neuro-inflammatory conditions. Using a strategy of crosslinking of an EAE specific phage clone on protein targets expressed by IL-1ß activated ECs followed by mass spectrometry, we propose hypothetically Galectin-1 as a possible target of this phage. PhiSSH will be useful for in vivo screening of small peptide combinatorial libraries for the discovery of biomarkers specific of molecular and cellular alterations untangled with healthy tissues, as in most pathologies presenting neuroinflammatory activity.

Utilistions d'anticorps monoclonaux est une partie prometteuse de la thérapie du cancer. À ce jour, il existe plus de 30 anticorps monoclonaux approuvés pour la thérapie contre le cancer. Plus de 350 anticorps se situent également dans différentes phases du développement clinique. La mésothéline est l'une des cibles les plus prometteuses pour l'immunothérapie. La mésothéline est présente à des niveaux relativement faibles dans les cellules mésothéliales de la plèvre, du péritonéum et du péricarde normaux, mais est fortement exprimée dans un certain nombre de cancers différents, y compris les mésothéliomes, le cancer de l'estomac, les carcinomes à cellules squameuses, le cancer de la prostate, le cancer du pancréas, le cancer du poumon et le cancer de l'ovaire. La mésothéline est une glycoprotéine liée au glycosylphosphatidylinositol (GPI) synthétisée sous la forme d'un précurseur de 69 kDa et transformée de façon protéolytique en une forme sécrétée à 30 kDa (anciennement appelée Facteur de potentialisation des mégacaryocytes (MPF)) et une forme liée à la membrane de 40 kDa. Par ailleurs, il peut être clivé par une protéase et peut produire une forme de mésothéline soluble. Il a été déjà montré que cette forme soluble de mésothéline agit comme un ligand et neutralise les anticorps thérapeutiques ciblant la mésothéline. Par conséquent, les anticorps ne pouvaient pas atteindre les cellules cancéreuses et reste inefficaces. Dans notre travail, nous avons décidé de développer un anticorps discriminant spécifique à la forme associée à la membrane pour surmonter l'antagonisme produit par les formes solubles de mésothéline. Pour ce but, nous avons utilisé une nouvelle méthode d'immunisation de souris, que nous avons d'abord toléré la souris avec une mésothéline soluble et ensuite ré-immunisée avec des cellules exprimant la mésothéline. En utilisant la technologie de phage display, nous avons obtenu près de 150 clones de ciblant mésothéline dans 34 familles de VH-CDR3 parmi lesquelles nous avons identifié seulement 2 familles qui se lient à la mésothéline membranaire avec une affinité élevée et ne reconnaissent aucune autre forme soluble de mésothéline. Ici, nous proposons qu'ils puissent être des bons candidats pour être utilisés pour la thérapie contre le cancer de qui permet de passer à travers la barrière de mésothéline soluble. Pour démontrer leur efficacité pour une utilisation thérapeutique, nous avons construit une CAR avec le sc-Fv d'un anticorps discriminant de la forme membranaire. / Antibody based immune treatment is a promising component of cancer therapy. To date there are more than 30 approved monoclonal antibodies for cancer therapy. More than 350 antibodies are also in different phases of clinical development. Mesothelin is one of the most promising targets for immunotherapy. It is present at relatively low levels in mesothelial cells of the pleura, peritoneum and pericardium of healthy individuals, but is highly expressed in a number of different cancers, including mesotheliomas, stomach cancers, squamous cell carcinomas, as well as prostate, pancreatic, lung, and ovarian cancers. Mesothelin is a glycosylphosphatidylinositol (GPI)-linked glycoprotein synthesized as a 69 kDa precursor and proteolytically processed into a 30 kDa NH2-terminal secreted form (formerly referred to as Megakaryocyte Potentiating Factor (MPF)) and a 40 kDa membrane-bound form. Besides that it can be cleaved by a protease leading to the production of a soluble, shedded, form of mesothelin. It has already been shown that this soluble form of mesothelin acts as a ligand and neutralizes the mesothelin targeting therapeutic antibodies. Therefore antibodies could not reach cancer cells and remained inefficient. In our work we decided to develop discriminating antibodies specific to a membrane associated form so as to overcome the antagonism produced by soluble forms of mesothelin. To this aim we used a novel method of mouse immunization, in which we first tolerized the mouse with soluble mesothelin before immunization with mesothelin expressing cells. By using phage display technology we obtained nearly 150 mesothelin recognizing clones in 34 VH-CDR3 families, among which we identified only 2 families that bind membrane mesothelin with high affinity and do not recognize any other soluble form of mesothelin. Here we suggest that this Fab can be effective candidates to be used for mesothelin expressing cancer therapy being allowed to pass through the soluble mesothelin barrier. To show their efficacy for therapeutic use we constructed a CAR with the sc-Fv of a membrane-form discriminating antibody

Les interactions font partie intégrante des écosystèmes. Que ce soit aux niveaux les plus fins, comme les protéines, ou les plus larges, comme les méta-communautés, il est possible de les regrouper en réseaux et d’en étudier la structure. Cela a permis de mettre en évidence que certaines structures sont observables à différents niveaux, c’est le cas par exemple des réseaux emboîtés. De plus, les réseaux d’interaction ont la spécificité de ne pas être fixes dans le temps et l’espace, ce qui leur confère un avantage de taille pour l’étude de l’évolution. Ils peuvent ainsi servir de support à l’études des mécanismes intervenants dans les processus évolutifs. Cependant, il n’existe pas encore de méthodologie ayant fait consensus sur l’utilisation des réseaux et leur analyse à différentes échelles d’organisation. Cette thèse se base sur l’hypothèse que les réseaux, de par leurs propriétés, sont pertinents à considérer pour comprendre l’évolution et ce à différentes échelles d’organisation, et offrent la possibilité de faire des liens entre chacune d’entre elles. L’approche basée sur les réseaux, combinée à l’utilisation de modèles théorique serait donc un outil méthodologique puissant dans l’élargissement des connaissances concernant les processus sous-jacents à l’évolution. La thèse qui suit composée de six chapitres dont le contenu est le suivant. Elle commence par un chapitre d’introduction aux concepts d’intérêts, notamment sur l’évolution et la coévolution. Le deuxième chapitre est une introduction à l’utilisation des réseaux en écologie, suivit par le troisième chapitre qui effectue une revue non exhaustive des méthodologies développées autour des réseaux d’interactions. Les chapitres suivants sont en quelque sorte une mise en pratique de ces méthodes et ce à différents niveaux d’organisation. Le quatrième chapitre revient sur une étape avortée de ce doctorat qui servira tout de même à la construction du modèle du chapitre suivant. Le cinquième chapitre se concentre sur la coévolution et son suivit au travers des réseaux d’interaction entre les bactéries et leurs virus. Enfin, le sixième chapitre traque l’évolution des communautés grâce à la structure des arbres phylogénétiques et structure des réseaux d’interactions au cours du temps. / Interactions are an integral part of ecosystems. Whether at the finest levels, such as proteins, or the broadest, such as meta-communities, it is possible to group them into networks and study their structure. This made it possible to demonstrate that certain structures can be observed at different levels, such as nested networks, for example. In addition, interaction networks have the property of not being fixed in time and space, which gives them a major advantage for the study of evolution. They can thus serve as a support for the study of the mechanisms involved in the evolutionary processes. However, there is not yet a methodology that has achieved consensus on the use of networks and their analysis at different organizational scales. This thesis is based on the hypothesis that networks, by virtue of their properties, are relevant to consider in order to understand evolution at different organizational scales, and offer the possibility of making links between each of them. The network-based approach, combined with the use of theoretical models, would therefore be a powerful methodological tool in expanding knowledge about the processes underlying evolution. The thesis which follows consists of six chapters whose content is as follows. It begins with an introductory chapter to the concepts of interest, in particular on evolution and coevolution. The second chapter is an introduction to the use of networks in ecology, followed by the third chapter which performs a non-exhaustive review of the methodologies developed around interaction networks. The following chapters are in a way a practical application of these methods at different levels of organization. The fourth chapter returns to an aborted stage of this doctorate which will nevertheless be used to construct the model of the following chapter. The fifth chapter focuses on coevolution and its follow-up through the interaction networks between bacteria and their viruses. Finally, the sixth chapter tracks the evolution of communities thanks to the structure of phylogenetic trees and the structure of interaction networks over time.

Les communautés bactériennes sont constituées d’un grand éventail d’espèces pouvant interagir entre elles dans des environnements spatialement hétérogènes tels que le sol, les plantes ou l'intestin humain. À quel point ces interactions stimulent ou entravent la diversité du microbiome demeure inconnu. Historiquement, deux hypothèses ont été proposées pour expliquer comment les interactions interespèces pourraient influencer la diversité. L’hypothèse ‘l’écologie contrôle’ (EC) prédit une relation négative, dans laquelle l'évolution ou la migration de nouvelles espèces est freinée à mesure que les niches se saturent. En revanche, l’hypothèse ‘la diversité engendre la diversité’ (DBD) prédit une relation positive, où la diversité existante favorise l'accumulation d'une plus grande diversité à travers des interactions telles que la construction de niche. De nombreuses études ont investigué ces modèles chez les vertébrés ou les plantes, et certaines les ont testés sur des bactéries en culture ; mais le modèle qui régit les communautés bactériennes naturelles demeure inconnu. En utilisant les données du gène ARN ribosomique 16S provenant d’un large éventail de microbiomes, j'ai montré une relation positive générale entre la diversité des taxons et la diversité des communautés de niveaux taxonomiques plus élevés. Cette observation est conforme à l’hypothèse du DBD, mais cette tendance positive plafonne à des niveaux élevés de diversité en raison des limites physiques de la niche. Ensuite, j'ai observé que le modèle DBD restait valide à une résolution plus fine, en analysant la variation génétique intra espèce dans les métagénomes des microbiomes intestinaux humains. Conformément au DBD, j'ai observé que le polymorphisme génétique ainsi que le nombre de souches intra espèces étaient positivement corrélés avec la diversité Shannon de la communauté. Dans le chapitre 3, j'ai examiné les interactions antagonistes entre V. cholerae et ses phages virulents et la manière dont ces interactions affectaient le cours de l’infection et la diversité génétique de V. cholerae chez les patients infectés. J'ai quantifié les abondances relatives de V. cholerae et des phages virulents associés dans plus de 300 métagénomes provenant de selles de patients atteints de choléra, tout en tenant compte de leur exposition aux antibiotiques. Les phages et les antibiotiques ont supprimé V. cholerae et ont été associés à une déshydratation légère chez les patients. J'ai également investigué les mécanismes de défense contre les phages dans V. cholerae et découvert que les éléments connus de résistance aux phages (integrative conjugative elements, ICEs) étaient associés à de faibles rapports phage: V. cholerae. J’ai pu montrer aussi que lorsque les ICEs ne sont pas détectés, la résistance aux phages semble être acquise par l’accumulation de mutations ponctuelles non synonymes. Mes résultats valident que les phages virulents sont un facteur qui protège contre le choléra tout en sélectionnant la résistance dans le génome de V. cholerae. / Bacterial communities harbor a broad range of species interacting within spatially heterogeneous environments such as soil, plants or the human gut. The extent to which these interactions drive or impede microbiome diversity is not well understood. Historically, two hypotheses have been suggested to explain how species interactions could influence diversity. The ‘Ecological Controls’ (EC) hypothesis predicts a negative relationship, where the evolution or migration of novel species is constrained as niches become filled. In contrast, the ‘Diversity Begets Diversity’ (DBD) hypothesis predicts a positive relationship, with existing diversity promoting the accumulation of further diversity via niche construction and other interactions. Many studies investigated these models in vertebrates or plants, some focused on cultured bacteria, but we still lack insights into how natural communities are assembled in the context of these two hypotheses. Using 16S RNA gene amplicon data across a broad range of microbiomes, I showed a general positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with DBD. Due to niche’ limits, this positive trend plateaus at high levels of community diversity. Then, I found that DBD holds at a finer resolution by analyzing intra-species strain and nucleotide variation in sampled metagenomes from human gut microbiomes. Consistent with DBD, I observed that both intra-species polymorphism and strain number were positively correlated with community Shannon diversity. In Chapter 3, I investigated the antagonistic interactions between V. cholerae and its virulent phages and how these interactions affect the course of the infection and the within V. cholerae genetic diversity in natural infections. I quantified relative abundances of Vibrio cholerae (Vc) and associated phages in 300 metagenomes from cholera patients stool, while accounting for antibiotic exposure. Both phages and antibiotics suppressed V. cholerae and were inversely associated with severe dehydration. I also looked at V. cholerae phage-defense mechanisms and found that known phage-resistance elements (integrative conjugative elements, ICEs) were associated with lower phage:V. cholerae ratios. In the absence of detectable ICEs, phages selected for nonsynonymous point mutations in the V. cholerae genome. My findings validate that phages may protect against severe cholera while also selecting for resistance in the V. cholerae genome within infected patients.

This study examined students' perception between short research experiences (SRE) courses and full-semester course research experiences (CRE) using the Persistence in the Sciences (PITS) survey and the interview questionnaire. The study also aimed to correlate the influence of student's demographic as a predictive indicator for Project Ownership Scores (POS) and Quantitative Literacy (QL) score means. The three courses studied at the University of North Texas were Biology for Science Majors Laboratory (BIOL 1760 SRE), Microbiology with Tiny Earth (BIOL 2042 Tiny Earth SRE), and Introductory Biology Research Laboratory I (BIOL 1750 SEA-PHAGES CRE). The mean scores for the PITS categories leaned favorably towards the research component of each laboratory course assessed in this study. The interview questionnaire showed 66% of the students in the SRE courses and 90% of the students in the CRE course preferred the research component of the lab. Paired survey demographic analysis for BIOL 1760 SRE showed significance for the Science Community Values with associate/bachelor's degree. BIOL 1750 SEA-PHAGES CRE showed significance in three of the six categories when comparing means for Project Ownership Emotion, Self-Efficacy, and Science Identity with Gender. Binary logistics was used to build a regression model to predict demographics with approximately 65% to 75% accuracy for each course. When analyzing students' QL score, the demographic category "Ethnicity" showed significance for BIOL 2042 Tiny Earth SRE. Categorizing the correct response into two categories for the QL test scores, the SRE and CRE courses, and analyzing the PITS scores for paired data sets showed that there was significance in the Networking category for the question "I have discussed my research in this course with professors other than my course instructor." The validated PITS, POS, and interview questionnaire could be a tool for use to analyze laboratories at UNT that offer a SRE or CRE component and to understand students' perceptions on the effectiveness of the laboratory.