>Magister Scientiae - MSc / Recent advances in the field of plant genetics and application of molecular technologies has lead to greater understanding of various crop genomes and their organization.The applications of these techniques include molecular markers which have been used to examine DNA variation within crop species. This allows for the creation of further genetic variation for new and favourable traits.Molecular markers or DNA markers are short fragments of DNA that can be used to locate desirable genetic traits in the genome or show specific genetic differences. The Maloideae subfamily includes fruit species such as pear. Pears (Pyrus communis L.) are large edible fruit that are grown in cool climates, native to coastal regions in Africa, Asia and Europe. The external appearance of this fruit plays a vital role on its rate of sale potential. Thus it is important for the appearances of the pear to meet the expectations of the consumer.External factors affecting the appearance of fruit, such as shape and colour, can have a large influence on the consumer’s first impression and opinion of what the fruit may taste like(Jaeger and MacFie, et al., 2001). The South African pear industry is the fourth largest in the fruit industry after apple, citrus and grape, exporting 3.8% to Europe (Ferrandi, et al., 2005).Increase in production and export of the pear is dependant on the variety of cultivars with desired traits. New cultivars, especially ranges of new cultivars, with harvest dates from early to late in the season, can fill gaps in the marketing strategy of exporters and in the local markets (Human, et al., 2005) Therefore, development of molecular markers allows for their possible use in maker-assisted selection and for the construction of a genetic linkage map thus leading to the location of favourable traits and ultimately the improvement of the quality of the pear.In this study high throughput genomic DNA extractions were performed. The Cetyltrimethyl ammonium bromide (CTAB) method was employed as the results proved to be most promising. Furthermore the screenings of molecular markers were conducted in order to obtain DNA variation. Molecular markers were used to locate specific genetic differences.Multiplexing PCR was conducted using fluorescent primers for further screening and results proved to be useful as many variations could be observed.

Le microbiote du rumen bovin est un écosystème très diversifié et efficace pour la dégradation de substrats complexes, notamment issus de la biomasse végétale. Composé majoritairement de microorganismes non cultivés, il constitue un réservoir très riche de nouvelles enzymes d’intérêt potentiel pour les biotechnologies industrielles, en particulier les bioraffineries et la bioremédiation. Dans le cadre de cette thèse, nous avons mis en œuvre une approche de criblage fonctionnel du métagenome ruminal pour accélérer la découverte d’enzymes de dégradation des lignocelluloses, mais aussi de divers polluants synthétiques. En particulier, de nouvelles estérases capables de dégrader un insecticide de la famille des carbamates, le fenobucarb, ainsi qu’un polyuréthane commercial, l’Impranil DLN, ont pu être identifiées. De plus, le développement d’une nouvelle stratégie de criblage d’oxydoréductases nous a permis d’isoler trois enzymes bactériennes originales, très polyspécifiques, ne requérant ni cuivre ni manganèse pour dégrader différents substrats polycycliques tels que des polluants majeurs de l’industrie textile, mais aussi des dérivés de lignine. Enfin, le criblage de deux banques issues d’enrichissements in vivo et in vitro du microbiome du rumen sur paille de blé a permis d’isoler des cocktails d’enzymes lignocellulolytiques au profil fonctionnel et d’origine taxonomique différents, constitués de glycoside-hydrolases, estérases et oxydoréductases. Quinze nouveaux modules CAZy, correspondant à des familles enzymatiques jamais caractérisées, ont été identifiés. L’ensemble de ces résultats met en lumière l’immense potentiel d’innovation biotechnologique contenu dans les écosystèmes microbiens, en particulier dans le microbiote du rumen bovin / Bovine rumen microbiota is a highly diverse and efficient ecosystem for the degradation of complex substrates, especially those issued from plant biomass. Predominantly composed of uncultivated microorganisms, it constitutes a rich reservoir of new enzymes of potential interest for industrial biotechnologies, especially biorefineries and bioremediation. As part of this thesis, we used the functional screening of the ruminal metagenome to increase the discovery of enzymes able to degrade lignocelluloses, as well as different synthetic pollutants. In particular, new esterases able to degrade a carbamate insecticide, fenoucarb, and a commercial polyurethane, Impranil DLN, have been identified. Moreover, the development of a new screening strategy for oxidoreductases allowed the isolation of three original bacterial enzymes that are very polyspecific, and do not need copper nor manganese to degrade different polycyclic substrates, like major pollutants of the textile industry, as well as lignin derivatives. Finally, the screening of two libraries from in vivo and in vitro enrichments of the ruminal microbiome on wheat straw allowed the isolation of lignocellulolytic enzymatic cocktails, with different functional profiles and taxonomical origins, comprising glycoside-hydrolases, esterases and oxidoreductases. Fifteen novel CAZy modules, related to enzymatic families never characterized, were identified. All these results highlight the vast potential of microbial ecosystems, in particular the bovine rumen microbiota, for biotechnological innovation

L’innovation thérapeutique progresse traditionnellement par la combinaison du criblage expérimental et de la modélisation moléculaire. En pratique, cette dernière approche est souvent limitée par la pénurie de données expérimentales, particulièrement les informations structurales et biologiques. Aujourd'hui, la situation a complètement changé avec le séquençage à haut débit du génome humain et les avancées réalisées dans la détermination des structures tridimensionnelles des protéines. Cette détermination permet d’avoir accès à une grande quantité de données pouvant servir à la recherche de nouveaux traitements pour un grand nombre de maladies. À cet égard, les approches informatiques permettant de développer des programmes de criblage virtuel à haut débit offrent une alternative ou un complément aux méthodes expérimentales qui font gagner du temps et de l’argent dans la découverte de nouveaux traitements.Cependant, la plupart de ces approches souffrent des mêmes limitations. Le coût et la durée des temps de calcul pour évaluer la fixation d'une collection de molécules à une cible, qui est considérable dans le contexte du haut débit, ainsi que la précision des résultats obtenus sont les défis les plus évidents dans le domaine. Le besoin de gérer une grande quantité de données hétérogènes est aussi particulièrement crucial.Pour surmonter les limitations actuelles du criblage virtuel à haut débit et ainsi optimiser les premières étapes du processus de découverte de nouveaux médicaments, j’ai mis en place une méthodologie innovante permettant, d’une part, de gérer une masse importante de données hétérogènes et d’en extraire des connaissances et, d’autre part, de distribuer les calculs nécessaires sur les grilles de calcul comportant plusieurs milliers de processeurs, le tout intégré à un protocole de criblage virtuel en plusieurs étapes. L’objectif est la prise en compte, sous forme de contraintes, des connaissances sur le problème posé afin d’optimiser la précision des résultats et les coûts en termes de temps et d’argent du criblage virtuel / Therapeutic innovation has traditionally benefited from the combination of experimental screening and molecular modelling. In practice, however, the latter is often limited by the shortage of structural and biological information. Today, the situation has completely changed with the high-throughput sequencing of the human genome, and the advances realized in the three-dimensional determination of the structures of proteins. This gives access to an enormous amount of data which can be used to search for new treatments for a large number of diseases. In this respect, computational approaches have been used for high-throughput virtual screening (HTVS) and offer an alternative or a complement to the experimental methods, which allow more time for the discovery of new treatments.However, most of these approaches suffer the same limitations. One of these is the cost and the computing time required for estimating the binding of all the molecules from a large data bank to a target, which can be considerable in the context of the high-throughput. Also, the accuracy of the results obtained is another very evident challenge in the domain. The need to manage a large amount of heterogeneous data is also particularly crucial.To try to surmount the current limitations of HTVS and to optimize the first stages of the drug discovery process, I set up an innovative methodology presenting two advantages. Firstly, it allows to manage an important mass of heterogeneous data and to extract knowledge from it. Secondly, it allows distributing the necessary calculations on a grid computing platform that contains several thousand of processors. The whole methodology is integrated into a multiple-step virtual screening funnel. The purpose is the consideration, in the form of constraints, of the knowledge available about the problem posed in order to optimize the accuracy of the results and the costs in terms of time and money at various stages of high-throughput virtual screening

Orientador: Gonçalo Amarante Guimarães Pereira / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-22T08:42:43Z (GMT). No. of bitstreams: 1 Tizei_PedroAugustoGalvao_M.pdf: 2670317 bytes, checksum: edbc1214fe54dca40dc54a503a4d4c85 (MD5) Previous issue date: 2013 / Resumo: A produção fermentativa de etanol a partir de substratos chamados de "primeira geração", como cana-de-açúcar e amido de milho, já atingiu níveis muito elevados de eficiência. As linhagens industriais utilizadas nestes processos já são adaptadas ao ambiente industrial e possuem características que dificultam o melhoramento por engenharia genética tradicional. Duas abordagens inovadoras foram utilizadas para buscar processos fermentativos mais eficientes: o uso de reatores bioelétricos para alterar os produtos da fermentação e um ensaio de engenharia evolutiva para otimizar fenótipos heterólogos. Foram feitas fermentações bioelétricas com Saccharomyces cerevisiae, obtendo aumentos de produtividade de etanol, sem alterar o rendimento final, e também mudanças nas proporções dos subprodutos glicerol e acetato. Uma resposta distinta foi observada para uma linhagem industrial cultivada nas mesmas condições. Foram realizadas análises de expressão gênica global de linhagens de laboratório e industrial fermentando sob estímulo bioelétrico. Não foram observadas alterações na via fermentativa, mas houve variações grandes na expressão de genes relacionados a outros aspectos da fisiologia da levedura, como genes para síntese de lipídios de membrana e genes desconhecidos ou com funções aparentemente não-relacionadas ao processo fermentativo. Também foi evidente uma resposta global diferente entre as duas linhagens. Foi estabelecido um método automatizado para ensaios de engenharia evolutiva, que permitiu a seleção de linhagens por crescimento em celobiose. Utilizando apenas a variabilidade presente no genoma de uma linhagem industrial diplóide, foi possível obter linhagens haplóides com desempenho superior à linhagem parental. Portanto, esta estratégia pode ser viável para se obter fenótipos superiores utilizando linhagens distantes de S. cerevisiae / Abstract: Fermentative ethanol production from substrates known as "first generation", such as sugar cane and corn starch, has reached very high levels of efficiency. The industrial strains used in these processes are already adapted to the industrial environment and possess characteristics that hinder further improvement by traditional genetic engineering. Two innovative approaches were used to seek more efficient fermentation processes: the use of bioelectric reactors to alter fermentation products and an evolutionary engineering assay to optimize heterologous phenotypes. Bioelectric fermentations were carried out with Saccharomyces cerevisiae, obtaining increases in ethanol productivity, without changing the final yield, and also changes in the proportions of byproducts glycerol and acetate. A distinct response was observed for an industrial strain cultivated under the same conditions. Global gene expression analyses were carried out for a laboratory and industrial strain under bioelectric stimulus. No changes were observed for the fermentative pathway, but there were large variations in expression for genes related to other aspects of yeast physiology, such as membrane lipid synthesis and unknown genes or genes with functions that are apparently unrelated to the fermentation process. A difference in the global response for the two strains was also evident. An automated method for evolutionary engineering assays was established, which allowed the selection of strains by growth on cellobiose. Using only the genetic variability present within the genome of a diploid industrial strain, it was possible to obtain haploid strains with superior growth rate when compared to the parental strain. Therefore, this strategy may be viable for obtaining superior phenotypes using distant strains of S. cerevisiae / Mestrado / Genetica de Microorganismos / Mestre em Genética e Biologia Molecular

While protein therapeutics are indispensable in the treatment of a variety of diseases, including cancer, rheumatoid arthritis, and diabetes, key limitations including short half-lives, high immunogenicity, protein instability, and centralized production complicate long-term use and on-demand production. Site-specific polymer conjugation provides a method for mitigating these challenges while minimizing negative impacts on protein activity. However, the location-dependent effects of polymer conjugation are not well understood. Cell-free protein synthesis provides direct access to the synthesis environment and rapid synthesis times, enabling rapid evaluation of multiple conjugation sites on a target protein. Here, work is presented towards developing cell-free protein synthesis as a platform for both design and on-demand production of next generation polymer-protein therapeutics, including (1) eliminating endotoxin contamination in cell-free reagents for simplified therapeutic preparation, (2) improving shelf-stability of cell-free reagents via lyophilization for on-demand production, (3) coupling coarse-grain simulation with high-throughput cell-free protein synthesis to enable rapid identification of optimal polymer conjugation sites, and (4) optimizing cell-free protein synthesis for production of therapeutic proteins

<div>Protein N-terminal methyltransferases (NTMTs) are a family of enzymes that methylate the α-N-terminus of a variety of protein substrates. Both NTMT1 and NTMT2 recognize a unique N-terminal X-P-K/R motif (X represents any amino acid other than D/E) to install 1-3 methyl group(s) on the substrates. NTMT1 plays important roles in mitosis regulation, chromatin interactions, and DNA damage repair. Another member NTMT2 shares ~50% sequence similarity and the same substrate recognition motif although NTMT2 was initially characterized as a mono-methyltransferase. To understand the molecular mechanism of NTMT2, we obtained the first co-crystal structure of NTMT2 in complex with its peptide substrate. After an extensive investigation of substrate recognition and methylated products of NTMT1/2, we found out that NTMT2 can fully methylate G/P-PKRIA peptides despite a predominant mono-methyltransferase. Moreover, we identified a gatekeeper N89 in NTMT2 that controls the substrate entry and the product specificity of NTMT2.</div><div>To elucidate the biological functions of NTMT1/2-catalyzed N-terminal methylation, we applied two different strategies to discover cell-potent inhibitors. Guided by the co-crystal structures of NTMT1 in complex with previously reported inhibitors, we designed and synthesized a series of new peptidomimetic inhibitors. By introducing more hydrophobic groups, the most cell-potent peptidomimetic inhibitor GD562 (IC50 = 0.93 ± 0.04 µM) exhibited over 2-fold increased inhibition on cellular N-terminal methylation levels with an IC50 value of ~50 µM compared to previously reported peptidomimetic inhibitor DC541. Meanwhile, we also discovered the first potent small molecule inhibitor Genz-682452 (IC50 = 0.5 ± 0.04 µM) after screening ~58,000 compounds. Subsequent structural modifications led to the discovery of GD433 (IC50 = 27 ± 0.5 nM) with a 20-fold increased potency compared to the initial hit Genz-682452. Inhibition mechanism indicated both inhibitors bind to peptide-binding pocket and co-crystal structures of both Genz-682452 and GD433 with NTMT1 confirmed their binding modes. Furthermore, GD433 shows over 7-fold selectivity over other major 40 protein methyltransferases and DNA methyltransferase and exhibits improved selectivity for NTMT1 over glucosylceramide synthase (GCS). GD433 significantly decreases the cellular N-terminal methylation level of NTMT1 substrates RCC1 and SET at 10 nM in both HEK293 and HCT116 cells, providing a valuable probe for cell-based studies in the future.<br></div><p><br></p>

Influenza virus A circulates in birds and mammals and causes severe infectious disease that affects from 3 to 5 million people each year. There are two classes of anti-influenza drugs currently available: neuraminidase and M2 channel inhibitors. However, increasing resistance against these two types of inhibitors along with the potential emergence of new viral strains and unpredictability of pandemic outbreaks emphasize an unmet need for new types of inhibitors. RNA-dependent influenza polymerase serves as a novel promising target for the development of anti-influenza medications. The aim of this master thesis is to develop in vitro high-throughput assays for screening of compounds targeting influenza RNA polymerase, particularly, its cap binding and endonuclease domains. For cap-binding domain the screening is based on DIANA (DNA-linked Inhibitor ANtibody Assay) method that was recently developed in our laboratory; for endonuclease domain, the method is based on AlphaScreen technology. For the purposes of the methods development, recombinant cap binding domain of PB2 subunit and N-terminal endonuclease domain of PA subunit of influenza polymerase were expressed with appropriate fusion tags and purified using affinity and gel permeation chromatography. The probes for the screening assays were...

This thesis explores the use of computational chemistry and machine learning techniques to aid in the design of Metal-Organic Frameworks (MOFs) for use in postcombustion carbon capture and storage (PoC-CCS). PoC-CCS is an ongoing field of research which aims to selectively remove carbon dioxide, an important greenhouse gas, from the exhaust of fossil-fuel burning powerplants. By using a suite of advanced simulation techniques, high-throughput screenings were performed on thousands of MOFs to study their behaviour in a pressure swing adsorption (PSA) system. To develop a comprehensive picture of a material’s performance, the behaviour of individual gas molecules within the pores of the crystal structures to the material’s performance in industrial scale PSA columns was evaluated. To study the behaviour of individual gas molecules within the pores of a MOF, a new algorithm which can accurately determine the locations of gas binding sites was developed. This algorithm, which relies on probability distributions generated through grand canonical Monte Carlo simulations (GCMC), was optimized for CO2 with the goal of use in high-throughput screening. By tuning the user-controlled parameters for a desired gas, this algorithm, which was named the Guest Atom Localization Algorithm (GALA), was shown to accurately reproduce experimentally determined binding sites while being run in a high-throughput manner with no user intervention. Studying MOFs at the pore or crystal scale in this manner provides valuable insights into the behaviour of gases within the materials. A major shortcoming, however, is the lack of direct insight into the material’s behaviour in industrial systems. Materials scientists and MOF chemists have historically focused on a set of performance metrics measured at this scale; however, no clear connection can be made between such metrics and the performance of that sorbent material in a PSA column. To bridge this gap between MOF chemists and the process engineers studying the PSA systems, a large-scale screening of MOFs was performed using a sophisticated PSA simulator designed to reproduce the performance of an 80 kg PSA column. By supplying isotherms obtained using GCMC simulations to be used as inputs into the PSA simulator, a multi-scale high-throughput screening of MOFs for PoC-CCS was performed for the first time under coal-fired powerplant conditions. This multi-scale screening provided the ideal conditions to study the materials science performance metrics and their relationships to industrial PSA performance. To study this relationship, a series of machine learning and artificial intelligence techniques were employed. The primary goal was to extract important relationships between the materials science and industrial PSA performance metrics, with a secondary goal of developing a predictive model which could be used to accelerate the pace of materials discovery. Through the use of machine learning, several metrics were identified which could be used to predict whether a material could meet the minimum target of 95 % purity of captured CO2, and 90 % removal (or recovery) of CO2 from the flue gas stream. Among them was the isotherm parameters for N2, the most abundant species in the flue gas. This finding was significant as to date the focus among MOF chemists studying the PoC-CCS system was placed primarily on the CO2 metrics, with N2 only implicitly considered when calculating the CO2/N2 selectivity. Although several metrics were identified which could predict the purity and recovery targets, none of the conventional metrics tested could be used to estimate the energetic cost of capture or the size of the capture plant, both important considerations in evaluating the cost of capture. The relationship between N2 binding within the pores of the MOF and its ability to meet the purity-recovery targets was explored using GALA. Using a Tanimoto similarity metric and the ratio of single component and competitive loadings, the CO2 and N2 binding environments were studied. It was determined that when the N2 binding environment was significantly altered by the presence of CO2, the material was more likely to meet the purity-recovery targets. Further analysis found that this change in binding environments was correlated to a reduced N2 uptake in the presence of CO2, implying that the competition for binding sites within the pores of the MOF is an important indicator for the material’s ability to meet the purity-recovery target. For the first time, a direct relationship between the behaviour of individual gas molecules to industrial PSA performance can be reported. Although the PSA simulator used throughout this work has proven to be a powerful tool for materials discovery, several shortcomings still exist. The first is the method used by the simulator to predict the loadings at various points within the column. This method relies on single component isotherm data despite the ability of GCMC to simulate multi-component isotherms. An alternative method to using single component isotherms was proposed which relies on multi-component isotherm data and a linear interpolation model. The existing method was compared to the new proposed interpolation method, and it was found that the loadings predicted using the interpolation method were more accurate. The second shortcoming of the PSA simulator is the computational expense associated with the optimizations. Using the PSA simulator, a single material may take up to a week to be fully optimized on a high-performance computing cluster. To increase the pace of materials discovery, a surrogate model was developed using the data accumulated over the course of the work presented in this thesis. Using artificial neural networks, a suite of models was developed which reproduces the outputs of the PSA simulator and is able to optimize a single MOF in a matter of minutes. This suite of models, known as the Fossil Fuel Combustion for Carbon Capture and Storage (FoCAS) was used to perform a screening of over 4,000 materials.

Les conditions du corps humain ne sont pas reproduites fidèlement par la culture cellulaire traditionnelle en 2D. Dans cette thèse, des cultures cellulaires 3D sont réalisées dans une plateforme microfluidique hautement intégrée. Des cellules mammifères adhérentes sont encapsulées dans des gouttes immobilisées dans un tableau de pièges capillaires à haute densité. Dans chaque goutte, les cellules se réorganisent pour former un unique microtissu 3D et fonctionnel appelé sphéroïde. L'utilisation d'un hydrogel permet d'alonger le temps de culture et de perfuser le tableau avec des solutions aqueuses, par exemple pour de l'immuno-cyto-chimie. Un unique sphéroïde, viable, peut aussi être extrait de cette puce microfluidique. Des données quantitatives sont extraites à haut débit au niveau de la population, du sphéroïde (dizaines de miliers de sphéroïdes) et au niveau cellulaire emph{in situ} (centaines de miliers de cellules) grâce à de l'imagerie de fluorescence et au dévelopement d'un code d'analyse d'image. Une première preuve de concept a été obtenue en démontrant la viabilité, la prolifération et la fonctionalité de sphéroïdes d'hépatocytes et en les corrélant à des paramètres morphologiques. Ensuite, des aggrégats de cellules souches mésenchymales ont été produits et les hétérogénéités spatiales dans l'expression de protéines impliquées dans leurs propriétés thérapeutiques ont été étudiées. Enfin, cette technologie a été encore dévelopée pour permettre d'appliquer des conditions biochimiques différentes dans chaque goutte. La production et la culture de sphéroïdes dans cette plateforme microfluidique peut mener à des dévelopements importants dans beaucoup de domaines tels que l'analyse de la toxicité des médicaments, le criblage de médicaments à haut débit, le traitement personnalisé du cancer, l'ingénierie tissulaire ou la modélisation de maladies. / Conventional 2D cell culture fails to reproduce emph{in vivo} conditions. In this PhD thesis, 3D cell culture is implemented into a highly integrated microfluidic platform. Adherent mammalian cells are encapsulated in droplets immobilized on a high density array of capillary traps called anchors. In each droplet, the cells reorganize into a single functional 3D microtissue called spheroid. The use of an hydrogel allows to extend the culturing time in microdroplets and to perfuse the array with aqueous solutions, for instance for immuno-cyto-chemistry. A single and viable spheroid can also be selectively retrieved from the microfluidic chip. High throughput and quantitative data is extracted at the population, spheroid (tens of thousands of spheroids) and cellular level emph{in situ} (hundreds of thousands of cells) thanks to fluorescent imaging and a custom image analysis software. As a first proof of concept, the viability, proliferation and functionality of hp sh s were demonstrated and correlated with morphological parameters. Drug toxicity experiments were also performed on this liver model. Then, human mesenchymal stem cell aggregates were produced and the spatial heterogeneities of the expression of proteins involved in their therapeutic properties were investigated. Finally, this technology was further developed to enable applying different biochemical conditions in each droplet. The production and culture of spheroids in this microfluidic platform could lead to major advances in many fields such as drug toxicity, high throughput drug screening, personalized cancer treatment, tissue engineering or disease modeling.

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