Dans le cadre du diagnostic biologique des maladies auto-immunes, les autoanticorpssont généralement recherchés par des méthodes qui utilisent des antigènes(ou peptides) présélectionnés. De telles techniques ont des limites qui pourraient êtrecontournées par de nouvelles approches. Dans le premier temps de ce travail, nousrapportons l'intérêt d’évaluer les réactivités d'auto-anticorps vis-à-vis de ciblesmodifiées par des processus post traductionnels, comme la citrullination dans lecadre diagnostique de la polyarthrite rhumatoïde. Cette approche ne peut toutefoispas être transposée à toutes les pathologies auto-immunes, et notamment lasclérose en plaques. Dans un deuxième temps, notre démarche s’est élargie àl’analyse globale de la réactivité des immunoglobulines G sériques dirigée contredifférents extraits d’antigènes tissulaires, notamment issus du cerveau. Malgré lagrande hétérogénéité des réponses interindividuelles, il a été possible d’observer desprofils de réactivités distinguant les sujets sains de patients souffrant de différentespathologies à composantes auto-immunes. Cette signature sérologique permetégalement de distinguer les profils auto-immuns associés à différentes maladies etmême de distinguer leurs formes cliniques. Dans le modèle expérimental développéau laboratoire, nous avons observé que des modifications de ces profils apparaissentde façon précoce et peuvent être liés à une évolution péjorative ou favorable de lamaladie auto-immune. Ces résultats ont été confirmés dans la sclérose en plaques,où l’étude menée chez des patients ne présentant qu’un syndrome clinique isolé, amontré que les profils de réactivité sérique des IgG sont déjà marqués de l’empreintede la maladie qui se révèlera cliniquement en moyenne trois ans plus tard. De façonintéressante, d'autres travaux ont montré que le répertoire des IgM pouvaitégalement être perturbé au cours d’un processus auto-immun dont la pathogénierepose sur un dysfonctionnement des lymphocytes T (syndrome APECED). Cesrésultats suggèrent que des mécanismes T dépendants mais aussi T indépendantspèsent sur la constitution et l’entretien du répertoire auto-immun pathologique.Pour identifier les cibles des réactivités discriminantes révélées par cette premièreapproche, nous avons développé une technique de caractérisation moléculairefaisant appel à la technique immunoprotéomique. Alors que les pathologieshumaines et les modèles animaux étudiés sont principalement des pathologiesspécifiques d’organe, les antigènes tissulaires qui ont été identifiés comme cibles desréactivités spécifiquement associées à ces pathologies sont des antigènesubiquitaires, et non des antigènes spécifiques d’organe. Ces résultats posent laquestion de l’implication réelle de ces cibles dans la physiopathologie des maladiesauto-immunes. Ils illustrent également l’impérative nécessité de connaître les limitesdes résultats apportés par les méthodes d’immunoprotéomique.La caractérisation de la signature sérologique d’un processus pathologique, autravers de l’analyse des perturbations globales des réactivités sériques qui lui sontassociées, offre des perspectives intéressantes tant en termes de prise en charge dupatient qu’en termes de compréhension physiopathologique des maladies autoimmunes.Elle pourrait aboutir à d’utiles débouchés thérapeutiques. Ces attentesjustifient pleinement l’investissement qui a été mis en place par notre laboratoiredans le cadre de sa validation méthodologique. / In autoimmune diseases, specific autoantibodies detected in patients’ sera areusually investigated by techniques using purified self-antigens and/or relevantpeptides from preselected targets. Such a restrictive view may be overcome by usingnew biological techniques to improve the diagnostic procedure. In a first step, weevaluated the impact of slight changes in target self-antigens related to posttranslationalmodifications, such as citrullination. In view of the weak resultsobtained, we further focused on some properties of the humoral response. Westudied the global self-reactive IgG antibody patterns against a large panel ofantigens derived from different target tissue extracts, especially brain antigens.Despite inter-individual differences, some reactivities allowed us to discriminatebetween the immune profiles of healthy individuals and those of patients. The selfreactivefootprints can also differentiate distinct autoimmune diseases and theirclinical forms. When we induced experimental autoimmune diseases, dynamicchanges occurred at the early phases with significant patterns related to pathogenicor protective events. A pathological distortion of the self-reactive antibodyrepertoire was also found in clinically isolated syndromes predictive of multiplesclerosis. Despite the predominant organ-specific symptoms in the clinical andexperimental situations studied, discriminant self-IgG reactivities mostly involvedubiquitous antigens rather than organ specific targets. Interestingly, discriminantIgM reactivities targeting both tissue-specific and ubiquitous antigens were alsospecifically observed in a T-dependent autoimmune disease (autoimmunepolyendocrinopathy syndrome), suggesting that T-cell-dependent but also T-cellindependentmechanisms might be involved in pathological changes in the selfreactiverepertoire. Although these footprints have allowed the identification ofuseful new biomarkers, their pathophysiological relevance remains to be defined.The molecular characterization of specific antigenic targets in autoimmune disease isa critical step towards understanding the pathological mechanisms and developinguseful diagnostic and therapeutic tools. In this perspective, we emphasize the needfor accurate methodological approaches. Our analysis of self-reactive footprintshighlights the potent role of complementary events related to putative dysfunctionin the innate/natural immune response in autoimmune diseases.

BACKGROUND:Mycoplasma hyopneumoniae causes respiratory disease in swine and contributes to the porcine respiratory disease complex, a major disease problem in the swine industry. The M. hyopneumoniae strain 232 genome is one of the smallest and best annotated microbial genomes, containing only 728 annotated genes and 691 known proteins. Standard protein databases for mass spectrometry only allow for the identification of known and predicted proteins, which if incorrect can limit our understanding of the biological processes at work. Proteogenomic mapping is a methodology which allows the entire 6-frame genome translation of an organism to be used as a mass spectrometry database to help identify unknown proteins as well as correct and confirm existing annotations. This methodology will be employed to perform an in-depth analysis of the M. hyopneumoniae proteome.RESULTS:Proteomic analysis indicates 483 of 691 (70%) known M. hyopneumoniae strain 232 proteins are expressed under the culture conditions given in this study. Furthermore, 171 of 328 (52%) hypothetical proteins have been confirmed. Proteogenomic mapping resulted in the identification of previously unannotated genes gatC and rpmF and 5-prime extensions to genes mhp063, mhp073, and mhp451, all conserved and annotated in other M. hyopneumoniae strains and Mycoplasma species. Gene prediction with Prodigal, a prokaryotic gene predicting program, completely supports the new genomic coordinates calculated using proteogenomic mapping.CONCLUSIONS:Proteogenomic mapping showed that the protein coding genes of the M. hyopneumoniae strain 232 identified in this study are well annotated. Only 1.8% of mapped peptides did not correspond to genes defined by the current genome annotation. This study also illustrates how proteogenomic mapping can be an important tool to help confirm, correct and append known gene models when using a genome sequence as search space for peptide mass spectra. Using a gene prediction program which scans for a wide variety of promoters can help ensure genes are accurately predicted or not missed completely. Furthermore, protein extraction using differential detergent fractionation effectively increases the number of membrane and cytoplasmic proteins identifiable my mass spectrometry.

L'uranium est un radionucléide qui possède une toxicité radiologique et chimique, causant des problèmes pour l’environnement et la santé humaine. Les micro-organismes du sol et l'uranium ont des relations complexes. L'objectif de cette étude est de décrire les interactions bactéries-uranium par l'utilisation de souches isolées d’environnements contaminés. Les quatre souches sont apparentées au genre Microbacterium et présentent un phénotype de tolérance à l'uranium contrasté. Différents mécanismes d'interaction avec l'uranium se produisent séquentiellement : une première étape de séquestration rapide de l'uranium due à de la biosorption passive, suivie d'une étape active d’efflux d'uranium et de phosphate, observée uniquement dans les souches tolérantes, et enfin, une accumulation intracellulaire de précipités de phosphate d'uranyle. Afin d'identifier les acteurs moléculaires impliqués dans les interactions cellule-uranium, une analyse comparative basée sur une approche protéogénomique a été réalisée. Les analyses statistiques sur les protéines identifiées ont révélé que l'exposition à l'uranium a un impact sur les métabolismes du phosphate et du fer. La protéine ayant le fold-change positif le plus élevé a fait l'objet d'études plus poussées. La protéine UipA est très affine pour l'uranium et le fer. Les analyses biophysiques ont révélé une coordination mono et bidentale pour l'uranium et le fer. En amont du gène uipA, deux gènes partageant l'homologie de séquence avec le système czcRS à deux composants ont été détectés. Les protéines UipRSA ne sont présentes que dans les souches tolérantes suggérant que ce cluster est impliqué dans la tolérance à l'uranium. / Uranium is a radionuclide used in nuclear energy. It has radiological and chemical toxicity, causing environmental and human health problems. Soil microorganisms and uranium have complex relationships. The goal of this study is to describe bacterium-uranium interactions through the use of bacteria isolated from contaminated environments. The four strains are related to the bacterial genus Microbacterium. They present a contrasted uranium tolerance phenotype from tolerant (ViU2a and Hg3) to sensitive (ViU22) and intermediate (A9). During exposure to uranium, different mechanisms of interaction with uranium occur sequentially: a first step of rapid sequestration of uranium due to passive biosorption, followed by an active step of uranium and phosphate efflux, observed only in tolerant strains, and finally, an active intracellular accumulation of uranyl phosphate precipitates. In order to identify the molecular actors involved in cell-uranium interactions, a comparative analysis based on a proteogenomic approach was performed. Between 1 100 and 2 000 proteins were identified. Statistical analyses revealed that uranium exposure impacts phosphate and iron metabolisms. The protein with the highest positive fold-change has been further studied. The UipA protein is a very affine and specific for uranium and iron, with Kd of the nanomolar order. Biophysic analyses revealed mono- and bidental coordination for uranium and iron. Upstream of the uipA gene, two genes sharing sequence homology with the two-component czcRS system were detected. The UipRSA cluster is only present in the tolerant strains. These data suggest that the uipRSA cluster is involved in uranium tolerance.

Anotação proteogenômica é uma abordagem que une a análise proteômica com a anotação genômica. O intuito de tal abordagem é prover uma anotação mais detalhada ao gene. Intuito esse, que nem sempre é possível quando se trata apenas de genes, uma vez que produtos gênicos, com funções importantes preditas, somente passam a ter papel na fisiologia do organismo quando expressos e traduzidos. Com todo o avanço atual de estudos na área proteogenômica, a geração de dados tem crescido de modo exponencial e, com esse crescimento, nota-se a necessidade cada vez maior da criação de sistemas capazes de processar, armazenar e gerenciar essas novas informações produzidas. Assim, é descrito nesse trabalho o desenvolvimento do ProGen AP , sendo constituído de uma interface web construída em HTML/PHP5, um banco de dados cujo SGBD é o mySQL e de módulos de processamento de dados proteômicos, neste caso o LabKey (com o core Xtandem!) e o QuickMod. Todos os módulos são open source e comunicam entre si através de scripts PERL. Nesse sistema, o pesquisador fornece dados de experimentos proteômicos e o sistema, então, os processa e retorna ao usuário informações sobre o gene expresso, a localização dos peptídeos dentro do gene aos quais pertencem e, ainda, informações quantitativas sobre o peptídeo e a proteína identificados. Além disso, o uso de um processamento esquematizado reduz a possibilidade de erro de entrada/saída de dados nos módulos intermediários do processamento. Aqui, o ProGen AP foi aplicado no estudo proteômico do Mycobacterium tuberculosis (MTb). Na literatura, o genoma do MTb cepa H37Rv contém apenas 4062 open reading frames (ORFs) preditos e o complemento funcional desse genoma, o proteoma, ainda não está totalmente elucidado. A análise do proteoma do MTb, com o uso do ProGen AP, resultou em uma lista total de 154.982 identificações de peptídeos, representando um total de 147.334 peptídeos únicos. Até o momento, foram identificadas 2.369 proteínas, cobrindo aproximadamente 58% de todo o genoma do MTB. É importante ressaltar que, dentre todas as proteínas identificadas até o momento, a maioria delas está anotada como proteinas hipotéticas em seu genoma, e, por consequência, os resultados obtidos nesse projeto confirmam e validam a existência de tais produtos gênicos. Além disso, 567 peptídeos foram identificados como N-terminal e 1229 como C-terminal, o que indica a correta predição do início e do término da tradução de tais genes. Todos esses resultados positivos confirmam que a abordagem utilizada no ProGen AP é eficiente e pode ser usada em vários outros organismos de interesse do pesquisador. / Proteogenomic annotation is an approach that combines proteomic analysis and genomic annotation. The aim of this approach is to provide a more detailed annotation, which is not possible in most of the times when dealing mostly with genes, once that genomic products, with important predicted functions are only important in the organism physiology when they are expressed and translated. There have been occurring several advances in proteogenomic studies and the generation of new data sets has been growing in an exponential wave. With all this growth, the creation of systems able to storing, processing and analyzing all the new knowledge produced is eminent. This study presents the deployment of ProGen AP, a system built with a HTML/PHP5 web interface, a mySQL data management system to store the data and two processing modules (LabKey, with core X!Tandem and QuickMod). In this system, the researcher provides a data set from a proteomic experiment and then the system processes it and returns to the researcher information about the expressed gene, the peptides localization inside the gene that they belong and, also, quantitative information about the peptide and the protein that were identified. Also, the use of an automated pipeline reduces the possibility of making mistakes in input/output of the data when using the intermediate modules. Here, the ProGen AP were applied to perform a proteogenomic annotation of Mycobacterium tuberculosis (MTb). In literature, the MTb genome, strain H37RV, have only 4062 predicted open reading frames (ORFs) and the functional complement of this genome is not completely known. The MTb analysis using ProGen AP, resulted in a list of 154.982 peptides identification, representing a total of 147.334 single peptides. Until now, were identified 2.369 proteins, covering nearly of 58% of the whole MTb genome. Is very important to highlight that, among all the identified proteins until now, most of them are annotated as hypothetical proteins in the MTb genome, so can be affirmed that the results of this project can confirm and validate the existence of all these genomic products. Beside this, 567 peptides were identified as been an N-terminal peptide and 1229 were identified as been a C-terminal, this fact indicates that the prediction of the beginning and the end of translation of those genes are right. All these positive results corroborate that the approach utilized in the ProGen AP is efficient and can be used in studies of other organisms.

Anotação proteogenômica é uma abordagem que une a análise proteômica com a anotação genômica. O intuito de tal abordagem é prover uma anotação mais detalhada ao gene. Intuito esse, que nem sempre é possível quando se trata apenas de genes, uma vez que produtos gênicos, com funções importantes preditas, somente passam a ter papel na fisiologia do organismo quando expressos e traduzidos. Com todo o avanço atual de estudos na área proteogenômica, a geração de dados tem crescido de modo exponencial e, com esse crescimento, nota-se a necessidade cada vez maior da criação de sistemas capazes de processar, armazenar e gerenciar essas novas informações produzidas. Assim, é descrito nesse trabalho o desenvolvimento do ProGen AP , sendo constituído de uma interface web construída em HTML/PHP5, um banco de dados cujo SGBD é o mySQL e de módulos de processamento de dados proteômicos, neste caso o LabKey (com o core Xtandem!) e o QuickMod. Todos os módulos são open source e comunicam entre si através de scripts PERL. Nesse sistema, o pesquisador fornece dados de experimentos proteômicos e o sistema, então, os processa e retorna ao usuário informações sobre o gene expresso, a localização dos peptídeos dentro do gene aos quais pertencem e, ainda, informações quantitativas sobre o peptídeo e a proteína identificados. Além disso, o uso de um processamento esquematizado reduz a possibilidade de erro de entrada/saída de dados nos módulos intermediários do processamento. Aqui, o ProGen AP foi aplicado no estudo proteômico do Mycobacterium tuberculosis (MTb). Na literatura, o genoma do MTb cepa H37Rv contém apenas 4062 open reading frames (ORFs) preditos e o complemento funcional desse genoma, o proteoma, ainda não está totalmente elucidado. A análise do proteoma do MTb, com o uso do ProGen AP, resultou em uma lista total de 154.982 identificações de peptídeos, representando um total de 147.334 peptídeos únicos. Até o momento, foram identificadas 2.369 proteínas, cobrindo aproximadamente 58% de todo o genoma do MTB. É importante ressaltar que, dentre todas as proteínas identificadas até o momento, a maioria delas está anotada como proteinas hipotéticas em seu genoma, e, por consequência, os resultados obtidos nesse projeto confirmam e validam a existência de tais produtos gênicos. Além disso, 567 peptídeos foram identificados como N-terminal e 1229 como C-terminal, o que indica a correta predição do início e do término da tradução de tais genes. Todos esses resultados positivos confirmam que a abordagem utilizada no ProGen AP é eficiente e pode ser usada em vários outros organismos de interesse do pesquisador. / Proteogenomic annotation is an approach that combines proteomic analysis and genomic annotation. The aim of this approach is to provide a more detailed annotation, which is not possible in most of the times when dealing mostly with genes, once that genomic products, with important predicted functions are only important in the organism physiology when they are expressed and translated. There have been occurring several advances in proteogenomic studies and the generation of new data sets has been growing in an exponential wave. With all this growth, the creation of systems able to storing, processing and analyzing all the new knowledge produced is eminent. This study presents the deployment of ProGen AP, a system built with a HTML/PHP5 web interface, a mySQL data management system to store the data and two processing modules (LabKey, with core X!Tandem and QuickMod). In this system, the researcher provides a data set from a proteomic experiment and then the system processes it and returns to the researcher information about the expressed gene, the peptides localization inside the gene that they belong and, also, quantitative information about the peptide and the protein that were identified. Also, the use of an automated pipeline reduces the possibility of making mistakes in input/output of the data when using the intermediate modules. Here, the ProGen AP were applied to perform a proteogenomic annotation of Mycobacterium tuberculosis (MTb). In literature, the MTb genome, strain H37RV, have only 4062 predicted open reading frames (ORFs) and the functional complement of this genome is not completely known. The MTb analysis using ProGen AP, resulted in a list of 154.982 peptides identification, representing a total of 147.334 single peptides. Until now, were identified 2.369 proteins, covering nearly of 58% of the whole MTb genome. Is very important to highlight that, among all the identified proteins until now, most of them are annotated as hypothetical proteins in the MTb genome, so can be affirmed that the results of this project can confirm and validate the existence of all these genomic products. Beside this, 567 peptides were identified as been an N-terminal peptide and 1229 were identified as been a C-terminal, this fact indicates that the prediction of the beginning and the end of translation of those genes are right. All these positive results corroborate that the approach utilized in the ProGen AP is efficient and can be used in studies of other organisms.

L’exploitation conjointe des données transcriptomiques et protéomiques permet l’étude détaillée des mécanismes moléculaires induits lors de perturbations environnementales. L’assemblage de données issues du séquençage des ARNs d’organismes dit « non-modèle » permet de produire la base de données pour l’interprétation des spectres générés en protéomique shotgun. Dans ce contexte, les travaux de thèse avaient pour objectif d’optimiser l’interprétation et l’analyse des données protéomiques par le développement de concepts innovants pour la construction de bases de données protéiques et l’exploration de la biodiversité. La première étape s’est concentrée sur la mise au point d’une méthode de pré-traitement des données de séquençage basée sur les résultats d’attribution protéomique. La deuxième étape a consisté à travailler sur la réduction de la taille des bases de données en optimisant les paramètres de la recherche automatisée des régions codantes. La méthode optimisée a permis l’analyse de 7 groupes taxonomiques de Gammaridés représentatifs de la diversité retrouvée in natura. Les bases de données protéomiques ainsi produites ont permis l’analyse inter-population de 40 protéomes individuels de Gammarus pulex répartis sur deux sites de prélèvement (pollué vs référence). L’analyse statistique basée sur une approche « individu-centré » a montré une hétérogénéité de la réponse biologique au sein d’une population d’organismes suite à une perturbation environnementale. Différents sous-groupes de mécanismes moléculaires induits ont été identifiés. Enfin, l’étude de la transversalité de biomarqueurs peptidiques identifiés chez Gammarus fossarum a permis de définir les peptides communs à l’aide de l’ensemble des données protéomiques et transcriptomiques. Pour cela, un logiciel d’exploration des séquences peptidiques a été développé permettant de proposer de potentiels biomarqueurs substituts dans le cas où les peptides définis ne sont pas applicables à certaines espèces de gammare. Tous ces concepts s’intègrent dans une démarche pour améliorer et approfondir l’interprétation des données par protéogénomique. Ces travaux entrouvrent la porte à l’analyse multi-omique d’individus prélevés in natura en considérant la biodiversité inter-espèce et intra-population. / The exploitation of omics data combining transcriptomic and proteomic enables the detailed study of the molecular mechanisms of non-model organisms exposed to an environmental stress. The assembly of data from the RNA-seq of non-model organism enables to produce the protein database for the interpretation of spectra generated in shotgun proteomics. In this context, the aim of the PhD work was to optimize the interpretation and analysis of proteomic data through the development of innovative concepts for the construction of protein databases and the exploration of biodiversity. The first step focused on the development of a pretreatment method for RNA-seq data based on proteomic attribution results. The second step was to work on reducing the size of the databases by optimizing the parameters of the automated coding region search. The optimized method enabled the analysis of 7 taxonomic groups of Gammarids representative of the diversity found in natura. The proteomic databases thus produced enabled the inter-population analysis of 40 individual Gammarus pulex proteomes from two sampling sites (polluted vs reference). Statistical analysis based on an "individual" approach has shown an heterogeneity of the biological response within a population of organisms induced by an environmental stress. Different subclusters of molecular mechanisms response have been identified. Finally, the study of the transversality of the biomarkers peptides identified with Gammarus fossarum revealed which are the common ones using both proteomic and transcriptomic data. For this purpose, a software for the exploration of peptide sequences has been developed suggesting potential substitute biomarkers when the defined peptides are not available for some species of gammarids. All these concepts aim to improve the interpretation of data by proteogenomics. This work opens the door to the multi-omic analysis of individuals collected in natura by considering inter-species and intra-population biodiversity.

Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.

In this dissertation, we describe a new approach for gene finding that can utilize proteomics information in addition to DNA and RNA to identify new genes in prokaryote genomes. Proteomics processing pipelines require identification of small pieces of proteins called peptides. Peptide identification is a very error-prone process and we have developed a new algorithm for validating peptide identifications using a distance-based outlier detection method. We demonstrate that our method identifies more peptides than other popular methods using standard mixtures of known proteins. In addition, our algorithm provides a much more accurate estimate of the false discovery rate than other methods. Once peptides have been identified and validated, we use a second algorithm, proteogenomic mapping (PGM) to map these peptides to the genome to find the genetic signals that allow us to identify potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs). We then collect and combine evidence for ePSTs we generated, and evaluate the likelihood that each ePST represents a true new protein coding gene using supervised machine learning techniques. We use machine learning approaches to evaluate the likelihood that the ePSTs represent new genes. Finally, we have developed new approaches to Bayesian learning that allow us to model the knowledge domain from sparse biological datasets. We have developed two new bootstrap approaches that utilize resampling to build networks with the most robust features that reoccur in many networks. These bootstrap methods yield improved prediction accuracy. We have also developed an unsupervised Bayesian network structure learning method that can be used when training data is not available or when labels may not be reliable.

Among dinitrogen (N2)-fixing some cyanobacteria can establish symbiosis with a broad range of host plants from all plant lineages including bryophytes, ferns, gymnosperms, and angiosperms. In the boreal forests, the symbiosis between epiphytic cyanobacteria and feathermosses Hylocomium splendens and Pleurozium schreberi is ecologically important. The main input of biological N to the boreal forests is through these cyanobacteria, and thus, they greatly contribute to the productivity of this ecosystem. Despite the ecological relevance of the feathermoss symbiosis, our knowledge about the establishment and maintenance of cyanobacterial-plant partnerships in general is limited, and particularly our understanding of the feathermoss symbiosis is rudimentary. The first aim of this thesis was to gain insight on the genomic rearrangements that enabled cyanobacteria to form a symbiosis with feathermosses, and their genomic diversity and similarities with other plant-symbiotic cyanobacteria partnerships. Genomic comparison of the feathermoss isolates with the genomes of free-living cyanobacteria highlighted that functions such as chemotaxis and motility, the transport and metabolism of organic sulfur, and the uptake of phosphate and amino acids were enriched in the genome of plant-symbiotic cyanobacteria. The second aim of this PhD study was to identify cyanobacterial molecular pathways involved in forming the feathermoss symbiosis and the regulatory rewiring needed to maintain it. Global transcriptional and post-transcriptional regulation in cyanobacteria during the early phase of establishment of the feathermoss symbiosis, and after colonization of the moss were investigated. The results revealed that the putative symbiotic gene repertoire includes pathways never before associated with cyanobacteria-plant symbioses, such as nitric-oxide sensing and regulation, and the transport and metabolism of aliphatic sulfonate. The third aim was to explore the role of the cyanobacterial community in contributing to the temporal variability of N2-fixation activity. Results from a field-study showed that temporal variation in N2-fixation rates could be explained to a high degree by changes in cyanobacterial community composition and activity. In particular, the cyanobacteria belonging to the genus Stigonema - although not dominating the community- appeared to be the main contributors to the N2-fixation activities. Based on this result, it is suggested that this genus is responsible for the main input of N in the boreal forest ecosystems. The last aim was to understand how the relationship between cyanobacterial community composition and N2-fixation activity will be affected by climatic changes such as, increased temperature (11oC compared to 19oC) and CO2 level (500 ppm compared to 1000 ppm). Laboratory experiments highlighted that 30 weeks of combined elevation of temperature and CO2 resulted in increased N2-fixation activity and moss growth rates. The observed increases were suggested to be allocated to reduced cyanobacterial diversity and changes in community composition, resulting in the dominance of cyanobacteria adapted to the future abiotic condition. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 4: Manuscript.</p>

Le carcinome séreux de haut grade (CSHG) est le sous-type histologique le plus courant du cancer de l'ovaire et demeure le cancer le plus meurtrier de l'appareil reproducteur féminin. Les récents progrès de l'immunothérapie contre le cancer ont mis en évidence un énorme potentiel thérapeutique pour les patientes confrontées à des besoins non satisfaits de soins de santé pour le traitement des CSHG. Une étape cruciale pour le développement de nouvelles stratégies thérapeutiques consiste à identifier les antigènes spécifiques des tumeurs (TSA), c’est-à-dire des antigènes majeurs d'histocompatibilité de classe I présents à la surface des cellules cancéreuses mais absents des cellules normales. Ces TSA peuvent être reconnus par les cellules T et sont des éléments essentiels dans la conception de vaccins destinés à stimuler les réponses immunitaires anticancéreuses. Les travaux menés dans le cadre de mon programme de doctorat ont porté sur l'identification et la caractérisation des TSAs dans les CSHG. Les premières études sur les TSAs ont été dirigées uniquement vers l’identification de TSAs dérivés de mutations dans les régions codantes, menant à l’identification d’antigènes très rares et privés dans les CSHG. Dans notre première étude, nous avons analysé 23 échantillons de cancer de l'ovaire en utilisant une approche protéogénomique nous permettant d'étudier à la fois les régions génomiques codantes et non codantes. Ce faisant, nous avons identifié un total de 103 TSAs, dont 91 qui ne portaient pas de mutations et dérivaient de séquences présumées non-codantes. Nous appelons ces antigènes « TSAs exprimés de manière aberrante (aeTSAs) » puisqu’ils sont exprimés de manière aberrante dans les échantillons de cancer mais absents dans les tissus normaux. Contrairement aux TSAs mutés, qui sont davantage patient- spécifiques, l’ARN codant pour les aeTSAs est exprimé par plusieurs patientes atteints d’un CSHG, celles-ci exprimant individuellement une médiane de 5 aeTSAs. De par leur nombre élevé et du fait que leur expression soit partagée dans une large population de patientes atteints de CSHG, les aeTSAs représentent des cibles intéressantes pour l'immunothérapie du cancer. En raison de leur origine principalement non codante, la nature et la régulation des aeTSAs sont peu connues. Notre seconde étude a donc porté sur la régulation de la biogenèse des aeTSAs. L’analyse de données de séquençage de l'ARN de CSHG en cellule unique a montré une expression enrichie et spécifique des gènes sources des aeTSAs dans les cellules cancéreuses. Grâce à une analyse transcriptomique plus poussée, nous avons identifié de nouveaux transcrits récurrents codant pour les aeTSAs et avons déterminé que ces transcrits sont largement surexprimés dans les CSHG. De plus, nous avons déterminé que les aeTSAs issus d'événements de traduction non canonique sont codés préférentiellement par des cadres de lecture ouverts courts et générés à partir de régions près de l'extrémité C-terminale. Finalement, nos analyses sur l'accessibilité de la chromatine et les modifications des histones supportent l’hypothèse que les transcrits codant pour les aeTSAs ont recourt à des promoteurs alternatifs, ce qui suggère un rôle important de l'épigénome du cancer dans la genèse du paysage antigénique. Nos travaux représentent la première analyse complète des antigènes provenant des régions codantes et non codantes dans les tumeurs ovariennes. Nous avons pu dresser une liste exhaustive de cibles thérapeutiques potentielles et mieux comprendre leur biogenèse. Nos travaux portant sur la découverte et la caractérisation des aeTSAs favoriseront la conception de nouvelles immunothérapies ciblant ces antigènes et ce qui sera bénéfique pour un plus grand nombre de patientes atteintes de CSHG. / High-grade serous carcinoma (HGSC) is the most common histologic subtype of ovarian cancer and the most lethal cancer in the female reproductive system. Recent advances in cancer immunotherapy have highlighted enormous therapeutical potential for patients facing unmet clinical needs in HGSC treatment. A crucial step for developing new therapeutic strategies is identifying targetable tumor-specific antigens (TSAs), namely the major histocompatibility class I antigens presented on the cancer cell surface but absent from normal cells. These TSAs can be recognized by T cells and are essential elements in designing vaccines to stimulate anti-cancer immune responses. The goal of my Ph.D. thesis was to identify and characterize TSAs in HGSC. In early studies of TSAs, most groups focused solely on TSAs derived from mutations in coding regions, which reported very rare and private antigens in HGSCs. In our first study, we used a proteogenomic workflow that enabled us to survey both coding and noncoding sequences to analyse 23 ovarian cancer samples. We uncovered 103 TSAs, 91 of which were not mutated and derived mainly from allegedly noncoding sequences. We call these antigens aberrantly expressed TSAs (aeTSAs), for their lack of expression in normal tissues while being aberrantly expressed in cancer samples. Unlike the mutated TSAs unique to individual tumors, the aeTSAs have shared RNA expression in HGSC tumors, with an estimated median presentation of five per patient. Because of their number and shared expression in a large population, we consider aeTSAs attractive targets for immunotherapy. Due to their primarily noncoding origin, little is known about the nature and regulation of aeTSAs. In our second study, we explored the biogenesis of the aeTSAs. HGSC single-cell RNA sequencing data showed a malignant cell-specific/enriched expression of aeTSA source genes. Further transcriptomic profiling identified novel recurrent transcripts coding for aeTSAs and revealed broad overexpression of aeTSA-coding transcripts in HGSC. Moreover, we showed that aeTSAs derived from noncanonical translation events are coded preferably by short open reading frames and generated from regions close to the C-terminus. Our analysis of chromatin accessibility and histone modifications support a differential promoter activity for aeTSA-coding transcripts, suggesting an important role of cancer epigenome in shaping the antigen landscape. Our work is the first comprehensive analysis of ovarian tumor antigens derived from both coding and noncoding regions. We reported an extensive list of potential therapeutic targets and provided insights into their biogenesis. Our work on discovering and characterizing shared TSAs shall promote the design of new antigen-targeting immunotherapy that benefits more HGSC patients.