Chronic liver diseases, which includes alcoholic liver disease (ALD), are consistently among the top 15 leading causes of death in the United States. ALD is characterized by progression from a normal liver to fatty liver disease (hepatic steatosis), which can lead to cirrhosis, alcoholic hepatitis, and liver failure. We have identified a novel role of phosphohistidine signaling, mediated through phosphohistidine phosphatase 1 (PHPT1), in the onset of hepatic steatosis. We have identified PHPT1 as a target of selective oxidation following acute ethanol exposure as well as being downregulated following chronic ethanol exposure. We mapped the oxidative modification site and developed a mass-spectrometry based phosphohistidine phosphatase assay to determine the impact of PHPT1 oxidative modification during acute ethanol exposure. To further understand the role of PHPT1 and phosphohistidine signaling during chronic ethanol exposure, we have developed PHPT1 overexpression and knockout mouse models. These mouse models were characterized using mass spectrometry-based proteomics. They were then utilized in a 10-day chronic ethanol plus binge model to determine the impact of PHPT1 expression on the onset of ethanol-induced hepatic steatosis. In addition, advanced mass spectrometry-based phenotypic characterization was performed on the treated liver tissues to determine the key regulators and canonical pathways influencing phosphohistidine signaling during chronic ethanol exposure. We have evidence to suggest that PHPT1 overexpression plays a protective role in the onset of hepatic steatosis, the PHPT1 heterozygous model is more susceptible to liver damage, and the complete knockout model is embryonically lethal. Additionally, we have identified novel pathways and regulators involved in phosphohistidine signaling during the development of ethanol-induced hepatic steatosis.

Há muito tempo os venenos de abelhas se tornaram objeto de interesse de muitos cientistas, principalmente os venenos daquelas linhagens do gênero Apis, chamadas abelhas europeias e as conhecidas abelhas africanizadas. O foco no desenvolvimento de terapias eficazes que pudessem prevenir ou frear as reações desencadeadas pelas toxinas dos venenos desses insetos foi o principal estimulador do surgimento dessa grande área da pesquisa, uma vez que esses animais causam um grande número de acidentes em animais e seres humanos e os acidentes podem desencadear graves consequências, inclusive óbito. Sendo assim, desenvolvemos o presente trabalho baseado na caracterização da composição proteica dos venenos de abelhas europeias e africanizadas, na análise quantitativa diferencial entre os venenos e, na investigação de fosforilações e a possível relação das mesmas com as funções biológicas. Reunindo todos os elementos que estavam ao nosso alcance para compor o melhor conjunto de etapas para a realização do estudo proteômico de venenos de abelhas, nós atingimos todos os objetivos propostos. Utilizando uma abordagem baseada em espectrometria de massas, consistindo de análise shotgun seguida de LC-MS/MS realizada em um dos mais modernos espectrômetros de massas, nós fomos capazes de obter resultados extremamente confiáveis e interessantes. Comparando-se o efeito da extensão do gradiente de separação na cromatografia líquida, nós fomos capazes de atingir uma maior cobertura da amostra, uma vez que identificamos um maior número de proteínas após a utilização do gradiente mais longo. A identificação de fosforilações foi favorecida pela combinação de fragmentação por \"Colisão Induzida por Dissociação\" e medidas de massa de alta acurácia realizadas no instrumento LTQ-Orbitrap-Velos. Enquanto apenas 29 proteínas foram identificadas após 120 minutos de separação cromatográfica, um total de 51 proteínas foram identificadas aplicando-se gradiente mais longo. Dentre as 51 proteínas totais, 42 são comuns aos venenos das três abelhas. A comparação em pares mostrou que os venenos das abelhas europeias compartilham 44 proteínas e os venenos das abelhas africanizadas compartilham 43 proteínas tanto com o veneno de A. m. carnica quanto com o de A. m. ligustica. Além disso, nós revelamos que existem diferenças quantitativas entre algumas das proteínas dos venenos, sendo muitas dessas proteínas diferenciais toxinas com funções conhecidamente relevantes. A investigação de fosforilação mostrou que duas toxinas apresentam-se na forma fosforilada: melitina e icarapina. Melitina é considerada a principal toxina de venenos de abelhas, sendo bem conhecida por sua ação altamente tóxica e alergenicidade. Este peptídeo foi identificado com fosforilação ocorrendo no sítio Ser18 em todas as amostras de venenos, enquanto somente no veneno da abelha africanizada foi também identificado com sítio de fosforilação no resíduo de Thr10. Icarapina, também já descrita como um alérgeno do veneno, apresentou sítio de fosforilação no resíduo Ser205. Por fim, nós demonstramos o efeito da fosforilação presente em melitina (Ser18) realizando ensaios de atividade biológica do peptídeo fosforilado e nativo, como: hemólise, lise celular e desgranulação de mastócitos e atividade quimiotáctica. Foi observado que a toxicidade do peptídeo fosforilado é reduzida em comparação ao do peptídeo nativo. Sendo assim, nós podemos concluir que a combinação de metodologias eficientes e a utilização de moderna instrumentação nos levou a resultados surpreendentes, os quais se somam a todo o conhecimento já existente acerca de venenos de abelhas / Honeybee venom toxins disturb the activity of critical cellular processes, triggering immunological, physiological, and neurological responses within victims. Studies on venom toxins have provided invaluable knowledge towards elucidating the molecular and functional details of their biological targets, yet there has been no report of a full proteome/phosphoproteome profile of honeybee venom. In this study, we focused on Apis mellifera honeybee venom characterization, including proteins identification, label-free quantitative analysis and phosphorylation identification. Making use of a MS-based proteomic approach, consisting on in-solution digestion followed by LC-MS/MS analysis, we were able to compare the effect of the liquid chromatography gradient length on the sample coverage, consequently, to identify a higher number of proteins using longer separation gradient of the tryptic peptides. Favorable identification of phosphorylations was achieved by the application of a long separation gradient combined with CID fragmentation and high accuracy mass measurement using an LTQ Orbitrap Velos. Here we report on the comparative shotgun proteomics study of the venoms of two Apis mellifera subspecies, A. m. carnica and A. m. ligustica, and the hybrid known as Africanized honey bee (AHB). We identified 51 proteins in total, with 42 of them being common among the three venoms, including many previously unidentified entries. Performing label-free quantification, we observed that few proteins were found with different relative amounts. Additionally, we revealed the phosphorylation of two proteins in all the samples, with two of them being HBV toxins/allergens: melittin and icarapin. Icarapin was identified as phosphorylated at 205Ser. Melittin was identified as phosphorylated at the 18Ser and 10Thr positions in all venoms, as well. Given these novel findings, we then chose to compare the toxicity of the phosphorylated/unphosphorylated forms of the major venom toxin, melittin, considering the most prominent phosphorylation event, the phosphorylated 18Ser position. We showed that the toxicity is in fact decreased when the peptide is phosphorylated. Based on a combination of efficient methodology and state-of-the-art instrumentation, delineated by our Shotgun-NanoESI-Long Gradient-LTQ Orbitrap Velos analysis, we achieved proteomic coverage far surpassing any previous report. Together, these discoveries pave the way for future phosphovenomic studies

Les stratégies de protéomique quantitative sans marquage sont très attractives dans le domaine de la recherche de biomarqueurs de pathologies. Cependant, elles requièrent une pleine maîtrise du schéma analytique et de sa répétabilité. Plus particulièrement, la préparation d’échantillons nécessite d’être suffisamment répétable pour ne pas impacter la qualité et la fiabilité des résultats. Les objectifs de cette thèse étaient de développer et d’optimiser des méthodes analytiques pour la protéomique quantitative, en particulier pour l’étape de préparation d’échantillons. Ainsi, un protocole innovant, simple, rapide et permettant l’analyse quantitative sans marquage d’un grand nombre d’échantillons avec une haute répétabilité a été développé et optimisé : le « Tube-Gel ». Par ailleurs, des préparations d’échantillons adaptées à différentes matrices biologiques pour la recherche de biomarqueurs ont été élaborées. Les méthodes mises au point et leur application ont permis de proposer des candidats biomarqueurs pour plusieurs pathologies : le glioblastome, les lymphomes B diffus à grandes cellules, et les complications survenant sur les greffons rénaux. / Label-free quantitative proteomics strategies are very attractive for diseases biomarkers researches. These approaches require the full control and the repeatability of the analytical workflow. In particular, the sample preparation has to be repeatable enough to ensure the quality and reliability of the results. Objectives of this work were to optimize and develop analytical methods for quantitative proteomics, with a special focus on the sample preparation step. Thus, an innovative, easy and fast protocol allowing the analysis of high sample numbers with high repeatability was developed and further optimized: the “Tube-Gel” protocol. Besides,sample preparations adapted to a variety of biological matrices were developed for the search of biomarkers. The developed methods and their application allowed the identification of potential biomarkers for a variety of diseases: glioblastoma, diffuse large B-cell lymphomas and renal transplants failures.

Há muito tempo os venenos de abelhas se tornaram objeto de interesse de muitos cientistas, principalmente os venenos daquelas linhagens do gênero Apis, chamadas abelhas europeias e as conhecidas abelhas africanizadas. O foco no desenvolvimento de terapias eficazes que pudessem prevenir ou frear as reações desencadeadas pelas toxinas dos venenos desses insetos foi o principal estimulador do surgimento dessa grande área da pesquisa, uma vez que esses animais causam um grande número de acidentes em animais e seres humanos e os acidentes podem desencadear graves consequências, inclusive óbito. Sendo assim, desenvolvemos o presente trabalho baseado na caracterização da composição proteica dos venenos de abelhas europeias e africanizadas, na análise quantitativa diferencial entre os venenos e, na investigação de fosforilações e a possível relação das mesmas com as funções biológicas. Reunindo todos os elementos que estavam ao nosso alcance para compor o melhor conjunto de etapas para a realização do estudo proteômico de venenos de abelhas, nós atingimos todos os objetivos propostos. Utilizando uma abordagem baseada em espectrometria de massas, consistindo de análise shotgun seguida de LC-MS/MS realizada em um dos mais modernos espectrômetros de massas, nós fomos capazes de obter resultados extremamente confiáveis e interessantes. Comparando-se o efeito da extensão do gradiente de separação na cromatografia líquida, nós fomos capazes de atingir uma maior cobertura da amostra, uma vez que identificamos um maior número de proteínas após a utilização do gradiente mais longo. A identificação de fosforilações foi favorecida pela combinação de fragmentação por \"Colisão Induzida por Dissociação\" e medidas de massa de alta acurácia realizadas no instrumento LTQ-Orbitrap-Velos. Enquanto apenas 29 proteínas foram identificadas após 120 minutos de separação cromatográfica, um total de 51 proteínas foram identificadas aplicando-se gradiente mais longo. Dentre as 51 proteínas totais, 42 são comuns aos venenos das três abelhas. A comparação em pares mostrou que os venenos das abelhas europeias compartilham 44 proteínas e os venenos das abelhas africanizadas compartilham 43 proteínas tanto com o veneno de A. m. carnica quanto com o de A. m. ligustica. Além disso, nós revelamos que existem diferenças quantitativas entre algumas das proteínas dos venenos, sendo muitas dessas proteínas diferenciais toxinas com funções conhecidamente relevantes. A investigação de fosforilação mostrou que duas toxinas apresentam-se na forma fosforilada: melitina e icarapina. Melitina é considerada a principal toxina de venenos de abelhas, sendo bem conhecida por sua ação altamente tóxica e alergenicidade. Este peptídeo foi identificado com fosforilação ocorrendo no sítio Ser18 em todas as amostras de venenos, enquanto somente no veneno da abelha africanizada foi também identificado com sítio de fosforilação no resíduo de Thr10. Icarapina, também já descrita como um alérgeno do veneno, apresentou sítio de fosforilação no resíduo Ser205. Por fim, nós demonstramos o efeito da fosforilação presente em melitina (Ser18) realizando ensaios de atividade biológica do peptídeo fosforilado e nativo, como: hemólise, lise celular e desgranulação de mastócitos e atividade quimiotáctica. Foi observado que a toxicidade do peptídeo fosforilado é reduzida em comparação ao do peptídeo nativo. Sendo assim, nós podemos concluir que a combinação de metodologias eficientes e a utilização de moderna instrumentação nos levou a resultados surpreendentes, os quais se somam a todo o conhecimento já existente acerca de venenos de abelhas / Honeybee venom toxins disturb the activity of critical cellular processes, triggering immunological, physiological, and neurological responses within victims. Studies on venom toxins have provided invaluable knowledge towards elucidating the molecular and functional details of their biological targets, yet there has been no report of a full proteome/phosphoproteome profile of honeybee venom. In this study, we focused on Apis mellifera honeybee venom characterization, including proteins identification, label-free quantitative analysis and phosphorylation identification. Making use of a MS-based proteomic approach, consisting on in-solution digestion followed by LC-MS/MS analysis, we were able to compare the effect of the liquid chromatography gradient length on the sample coverage, consequently, to identify a higher number of proteins using longer separation gradient of the tryptic peptides. Favorable identification of phosphorylations was achieved by the application of a long separation gradient combined with CID fragmentation and high accuracy mass measurement using an LTQ Orbitrap Velos. Here we report on the comparative shotgun proteomics study of the venoms of two Apis mellifera subspecies, A. m. carnica and A. m. ligustica, and the hybrid known as Africanized honey bee (AHB). We identified 51 proteins in total, with 42 of them being common among the three venoms, including many previously unidentified entries. Performing label-free quantification, we observed that few proteins were found with different relative amounts. Additionally, we revealed the phosphorylation of two proteins in all the samples, with two of them being HBV toxins/allergens: melittin and icarapin. Icarapin was identified as phosphorylated at 205Ser. Melittin was identified as phosphorylated at the 18Ser and 10Thr positions in all venoms, as well. Given these novel findings, we then chose to compare the toxicity of the phosphorylated/unphosphorylated forms of the major venom toxin, melittin, considering the most prominent phosphorylation event, the phosphorylated 18Ser position. We showed that the toxicity is in fact decreased when the peptide is phosphorylated. Based on a combination of efficient methodology and state-of-the-art instrumentation, delineated by our Shotgun-NanoESI-Long Gradient-LTQ Orbitrap Velos analysis, we achieved proteomic coverage far surpassing any previous report. Together, these discoveries pave the way for future phosphovenomic studies

Biological studies always start from curious observations. This is exemplified by description of cells for the first time by Robert Hooke in 1665, observed using his microscope. Since then the field of microscopy and cell biology grew hand in hand, with one field pushing the growth of the other and vice-versa. From basic description of cells in 1665, with parallel advancements in microscopy, we have travelled a long way to understand sub-cellular processes and molecular mechanisms. With each day, our understanding of cells increases and several questions are being posed and answered. Several high-resolution microscopic techniques are being introduced (PALM, STED, STORM, etc.) that push the resolution limit to few tens of nm, taking us to a new era where ‘seeing is believing'. Having said this, it is to be noted that the world of cells is vast, with information spread from nanometers to millimetres, and also over extended time-period, implying that not just one microscopic technique could acquire all the available information. The knowledge in the field of cell biology comes from a combination of imaging and quantifying techniques that complement one another.Majority of modern-day microscopic techniques focuses on increasing resolution which, is achieved at the expense of cost, compactness, simplicity, and field of view. The substantial decrease in the field of observation limits the visibility to a few single cells at best. Therefore, despite our ability to peer through the cells using increasingly powerful optical instruments, fundamental biology questions remain unanswered at mesoscopic scales. A global view of cell population with significant statistics both in terms of space and time is necessary to understand the dynamics of cell biology, taking in to account the heterogeneity of the population and the cell-cell variability. Mesoscopic information is as important as microscopic information. Although the latter gains access to sub-cellular functions, it is the former that leads to high-throughput, label-free measurements. By focussing on simplicity, cost, feasibility, field of view, and time-lapse in-incubator imaging, we developed ‘Lensfree Video Microscope' based on digital in-line holography that is capable of providing a new perspective to cell culture monitoring by being able to capture the kinetics of thousands of cells simultaneously. In this thesis, we present our lensfree video microscope and its applications in in-vitro cell culture monitoring and quantification.We validated the system by performing more than 20,000 hours of real-time imaging, in diverse conditions (e.g.: 37°C, 4°C, 0% O2, etc.) observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). This permitted us to develop label-free cell based assays to study the major cellular events – cell adhesion and spreading, cell division, cell division orientation, cell migration, cell differentiation, network formation, and cell death. The results that we obtained respect the heterogeneity of the population, cell to cell variability (a raising concern in the biological community) and the massiveness of the population, whilst adhering to the standard cell culture practices - a rare combination that is seldom attained by existing real-time monitoring methods.We believe that our microscope and associated metrics would complement existing techniques by bridging the gap between mesoscopic and microscopic information. / Biological studies always start from curious observations. This is exemplified by description of cells for the first time by Robert Hooke in 1665, observed using his microscope. Since then the field of microscopy and cell biology grew hand in hand, with one field pushing the growth of the other and vice-versa. From basic description of cells in 1665, with parallel advancements in microscopy, we have travelled a long way to understand sub-cellular processes and molecular mechanisms. With each day, our understanding of cells increases and several questions are being posed and answered. Several high-resolution microscopic techniques are being introduced (PALM, STED, STORM, etc.) that push the resolution limit to few tens of nm, taking us to a new era where ‘seeing is believing'. Having said this, it is to be noted that the world of cells is vast, with information spread from nanometers to millimetres, and also over extended time-period, implying that not just one microscopic technique could acquire all the available information. The knowledge in the field of cell biology comes from a combination of imaging and quantifying techniques that complement one another.Majority of modern-day microscopic techniques focuses on increasing resolution which, is achieved at the expense of cost, compactness, simplicity, and field of view. The substantial decrease in the field of observation limits the visibility to a few single cells at best. Therefore, despite our ability to peer through the cells using increasingly powerful optical instruments, fundamental biology questions remain unanswered at mesoscopic scales. A global view of cell population with significant statistics both in terms of space and time is necessary to understand the dynamics of cell biology, taking in to account the heterogeneity of the population and the cell-cell variability. Mesoscopic information is as important as microscopic information. Although the latter gains access to sub-cellular functions, it is the former that leads to high-throughput, label-free measurements. By focussing on simplicity, cost, feasibility, field of view, and time-lapse in-incubator imaging, we developed ‘Lensfree Video Microscope' based on digital in-line holography that is capable of providing a new perspective to cell culture monitoring by being able to capture the kinetics of thousands of cells simultaneously. In this thesis, we present our lensfree video microscope and its applications in in-vitro cell culture monitoring and quantification.We validated the system by performing more than 20,000 hours of real-time imaging, in diverse conditions (e.g.: 37°C, 4°C, 0% O2, etc.) observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). This permitted us to develop label-free cell based assays to study the major cellular events – cell adhesion and spreading, cell division, cell division orientation, cell migration, cell differentiation, network formation, and cell death. The results that we obtained respect the heterogeneity of the population, cell to cell variability (a raising concern in the biological community) and the massiveness of the population, whilst adhering to the standard cell culture practices - a rare combination that is seldom attained by existing real-time monitoring methods.We believe that our microscope and associated metrics would complement existing techniques by bridging the gap between mesoscopic and microscopic information.

Le Benzo(a)pyrène (BaP), appartenant à la famille des hydrocarbures aromatiques polycycliques (HAP) est cancérigène pour l’homme. Nous avons développé une approche protéomique quantitative nanoLC-MS/MS label-free pour identifier des biomarqueurs liés à l’exposition au BaP dans le sécrétome des cellules hépatiques humaines exposées au BaP vs. des cellules non exposées et exposées au Benzo(e)pyrène (BeP). Le BeP, agent non classifié comme cancérigène pour l’homme, est choisi comme contrôle négatif afin de distinguer les protéines spécifiques du BaP de celles des HAP. 847 protéines ont été identifiées et quantifiées, et 55 ont été fortement surexprimées avec un ratio supérieur à 5 : la plupart de ces protéinessurexprimées sont précoces et liées au cancer. Une validation ultérieure de l'expression de ces protéines dans le plasma de la population exposée au BaP aidera dans le développement de biomarqueurs qui permettront d'améliorer la détection précoce, le pronostic et prévention. / Benzo(a)pyrene (BaP) belongs to a class of polycyclic aromatic hydrocarbon and is reported as a potent human carcinogen. We performed a nanoLC-MS/MS-based label-free quantitative proteomics approach to identify potential biomarkers of exposure in the secretome of BaPtreated vs. non-treated and Benzo(e)pyrene (BeP)-treated human hepatoma cell line HepG2.BeP-treated cells were chosen as a negative control to distinguish the BaP-specific from the HAP-specific regulated proteins: BeP is not classifiable as to its carcinogenicity to humans. 847 proteins have been identified and quantified and 55 proteins were seen as being highly upregulated with a fold change of at least 5. Most of these up-regulated proteins were focused incancer-related activities. Further validation of expression of these proteins in the plasma of BaP-exposed population will assist in the development of biomarkers that will greatly improve early detection, prognosis, prediction of treatment response and prevention.

Les cytochromes P450 (CYPs) jouent un rôle essentiel dans le métabolisme oxydatif de nombreux composés endogènes et exogènes. L’expression de CYPs est extrêmement variable en fonction de facteurs physiopathologiques, génétiques et environnementaux. Le métabolisme des xénobiotiques par les CYPs dépend en partie de la nature, de la quantité et de l’activité d’isoformes des CYPs impliqués. L'analyse quantitative de l'expression de CYP dans les organes du métabolisme, tels que le foie, sont d'une importance particulière étant donné que la biotransformation réalisée par les CYPs est souvent un facteur critique qui affecte l'efficacité, la disponibilité et la toxicité des médicaments chez l'homme. La technique actuelle de dosage la plus courante est l’immunoquantification par Western Blot. Cette technique est limitée par la disponibilité et la spécificité de l'anticorps. Les techniques de protéomique par spectrométrie de masse, permettant d’analyser de très faibles quantités de protéines en mélange, sont les méthodes de choix pour l’identification et la quantification des CYPs dans différents organes. Nous avons développé et validé une méthode pour doser 6 CYPs (1A2, 2C9, 2D6, 2J2, 3A4 et 3A5) par spectrométrie de masse en couplage chromatographique. Cette méthode, simple, rapide de sensibilité satisfaisante et peu coûteuse, a été validée dans différents types de matrices biologiques (lignées cellulaires hépatiques et neuronales, baculosomes). Ensuite, elle a été appliquée à grande échelle pour l’analyse de 50 foies humains (microsomes et mitochondries) afin d’étudier la relation phénotype/génotype pour les CYPs. Cette méthode pourra être appliquée à d’autres CYPS, est un outil utile qui permettra d’améliorer la compréhension et la prédiction pharmacocinétique et toxique de médicaments et d’autres produits chimiques. / Cytochromes P450 (CYPs) play a key role in the oxidative metabolism of many endogenous and exogenous compounds. The expression of CYPs is extremely variable depending on patho-physiological, genetic and environmental factors. The metabolism of xenobiotics by CYPs depends on the nature the quantity and the activity of CYP isoforms involved. Quantitative analysis of CYP expression in organs such as liver, are of particular importance since the biotransformation performed by CYPs is often a critical factor that affects the efficiency, availability and drug toxicity in humans. The most common technique is the immune-quantitation (Western Blot). This technique is limited by the availability and specificity of the antibody. Mass spectrometry-based proteomics, able to analyze very small amounts of protein in a mixture, are the methods of choice for identification and quantification of CYPs in different organs. We developed and validated a method for dosing 6 CYPs (1A2, 2C9, 2D6, 2J2, 3A4 and 3A5) by liquid chromatography coupled with mass spectrometry. This simple, rapid, low-cost method has an adequate sensitivity, and has been validated in different types of biological matrices (liver and neuronal cell lines, baculosomes). It has been applied at large-scale to analyze these 6 CYPs in 50 human livers samples (microsomes and mitochondria) to study the phenotype/genotype relationship. This method, which could easily be applied to other CYPs, provides an important tool to improve the understanding and prediction of pharmacokinetics and toxicity profile of drugs and other chemicals.

As high-throughput biology experiments generate increasing amounts of data, the field is naturally turning to data-driven methods for the analysis and extraction of novel insights. These insights into biological systems are crucial for understanding disease progression, drug targets, treatment development, and diagnostics methods, ultimately leading to improving human health and well-being, as well as, deeper insight into cellular biology. Biological data sources such as the genome, transcriptome, proteome, metabolome, and metagenome provide critical information about biological system structure, function, and dynamics. The focus of this licentiate thesis is on proteomics, the study of proteins, which is a natural starting point for understanding biological functions as proteins are crucial functional components of cells. Proteins play a crucial role in enzymatic reactions, structural support, transport, storage, cell signaling, and immune system function. In addition, proteomics has vast data repositories and technical and methodological improvements are continually being made to yield even more data. However, generating proteomic data involves multiple steps, which are prone to errors, making sophisticated models essential to handle technical and biological artifacts and account for uncertainty in the data. In this licentiate thesis, the use of machine learning and probabilistic methods to extract information from mass-spectrometry-based proteomic data is investigated. The thesis starts with an introduction to proteomics, including a basic biological background, followed by a description of how massspectrometry-based proteomics experiments are performed, and challenges in proteomic data analysis. The statistics of proteomic data analysis are also explored, and state-of-the-art software and tools related to each step of the proteomics data analysis pipeline are presented. The thesis concludes with a discussion of future work and the presentation of two original research works. The first research work focuses on adapting Triqler, a probabilistic graphical model for protein quantification developed for data-dependent acquisition (DDA) data, to data-independent acquisition (DIA) data. Challenges in this study included verifying that DIA data conformed with the model used in Triqler, addressing benchmarking issues, and modifying the missing value model used by Triqler to adapt for DIA data. The study showed that DIA data conformed with the properties required by Triqler, implemented a protein inference harmonization strategy, and modified the missing value model to adapt for DIA data. The study concluded by showing that Triqler outperformed current protein quantification techniques. The second research work focused on developing a novel deep-learning based MS2-intensity predictor by incorporating the self-attention mechanism called transformer into Prosit, an established Recurrent Neural Networks (RNN) based deep learning framework for MS2 spectrum intensity prediction. RNNs are a type of neural network that can efficiently process sequential data by capturing information from previous steps, in a sequential manner. The transformer self-attention mechanism allows a model to focus on different parts of its input sequence during processing independently, enabling it to capture dependencies and relationships between elements more effectively. The transformers therefore remedy some of the drawbacks of RNNs, as such, we hypothesized that the implementation of MS2-intensity predictor using transformers rather than RNN would improve its performance. Hence, Prosit-transformer was developed, and the study showed that the model training time and the similarity between the predicted MS2 spectrum and the observed spectrum improved. These original research works address various challenges in computational proteomics and contribute to the development of data-driven life science. / Allteftersom high-throughput experiment genererar allt större mängder data vänder sig området naturligt till data-drivna metoder för analys och extrahering av nya insikter. Dessa insikter om biologiska system är avgörande för att förstå sjukdomsprogression, läkemedelspåverkan, behandlingsutveckling, och diagnostiska metoder, vilket i slutändan leder till en förbättring av människors hälsa och välbefinnande, såväl som en djupare förståelse av cell biologi. Biologiska datakällor som genomet, transkriptomet, proteomet, metabolomet och metagenomet ger kritisk information om biologiska systems struktur, funktion och dynamik. I licentiatuppsats fokusområde ligger på proteomik, studiet av proteiner, vilket är en naturlig startpunkt för att förstå biologiska funktioner eftersom proteiner är avgörande funktionella komponenter i celler. Dessa proteiner spelar en avgörande roll i enzymatiska reaktioner, strukturellt stöd, transport, lagring, cellsignalering och immunsystemfunktion. Dessutom har proteomik har stora dataarkiv och tekniska samt metodologiska förbättringar görs kontinuerligt för att ge ännu mer data. Men för att generera proteomisk data krävs flera steg, som är felbenägna, vilket gör att sofistikerade modeller är väsentliga för att hantera tekniska och biologiska artefakter och för att ta hänsyn till osäkerhet i data. I denna licentiatuppsats undersöks användningen av maskininlärning och probabilistiska metoder för att extrahera information från masspektrometribaserade proteomikdata. Avhandlingen börjar med en introduktion till proteomik, inklusive en grundläggande biologisk bakgrund, följt av en beskrivning av hur masspektrometri-baserade proteomikexperiment utförs och utmaningar i proteomisk dataanalys. Statistiska metoder för proteomisk dataanalys utforskas också, och state-of-the-art mjukvara och verktyg som är relaterade till varje steg i proteomikdataanalyspipelinen presenteras. Avhandlingen avslutas med en diskussion om framtida arbete och presentationen av två original forskningsarbeten. Det första forskningsarbetet fokuserar på att anpassa Triqler, en probabilistisk grafisk modell för proteinkvantifiering som utvecklats för datadependent acquisition (DDA) data, till data-independent acquisition (DIA) data. Utmaningarna i denna studie inkluderade att verifiera att DIA-datas egenskaper överensstämde med modellen som användes i Triqler, att hantera benchmarking-frågor och att modifiera missing-value modellen som användes av Triqler till DIA-data. Studien visade att DIA-data överensstämde med de egenskaper som krävdes av Triqler, implementerade en proteininferensharmoniseringsstrategi och modifierade missing-value modellen till DIA-data. Studien avslutades med att visa att Triqler överträffade nuvarande state-of-the-art proteinkvantifieringsmetoder. Det andra forskningsarbetet fokuserade på utvecklingen av en djupinlärningsbaserad MS2-intensitetsprediktor genom att inkorporera self-attention mekanismen som kallas för transformer till Prosit, en etablerad Recurrent Neural Network (RNN) baserad djupinlärningsramverk för MS2 spektrum intensitetsprediktion. RNN är en typ av neurala nätverk som effektivt kan bearbeta sekventiell data genom att bevara och använda dolda tillstånd som fångar information från tidigare steg på ett sekventiellt sätt. Självuppmärksamhetsmekanismen i transformer tillåter modellen att fokusera på olika delar av sekventiellt data samtidigt under bearbetningen oberoende av varandra, vilket gör det möjligt att fånga relationer mellan elementen mer effektivt. Genom detta lyckas Transformer åtgärda vissa nackdelar med RNN, och därför hypotiserade vi att en implementation av en ny MS2-intensitetprediktor med transformers istället för RNN skulle förbättra prestandan. Därmed konstruerades Prosit-transformer, och studien visade att både modellträningstiden och likheten mellan predicerat MS2-spektrum och observerat spektrum förbättrades. Dessa originalforskningsarbeten hanterar olika utmaningar inom beräkningsproteomik och bidrar till utvecklingen av datadriven livsvetenskap. / <p>QC 2023-05-22</p>

Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total, 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labeled tryptic peptide and analyzed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labeled tryptic peptides and their natural unlabeled analogues quantification could be performed over the range of 0.1-1.5 pmol on column. Liver microsomes from four individuals were analyzed for CYP2E1 giving values of 88-200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 to 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP isoforms in a single sample.