387 research outputs found

    Determination of protein localization and RNA kinetics in human cells

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    In dieser Dissertation haben wir das Verhalten menschlicher Zellen in Raum und Zeit untersucht. Hochwertige Datensätze subzellulärer Regionen in HEK293-Zellen wurden mit Hilfe der BirA* Proximity-Labelling-Aktivität erstellt, wobei die Lokalisierung auf zelluläre Regionen beschränkt wurde, die mit herkömmlichen Methoden nur schwer zu reinigen sind (d. h. die dem Zytosol zugewandten Seiten des ER, Mitochondrien und Plasma-membranen). Wir entwickelten daraufhin einen Ansatz zur Kartierung der Verteilung von Proteinen, die aktiv an RNA binden, und nannten ihn f-XRNAX. Wir stellten hintergrundkorrigierte Proteome für Zellkerne, Zytoplasma und Membranen von HEK293-Zellen her. Überraschenderweise wurden viele nicht-kanonische RBPs in der Membranfraktion identifiziert, und ihre Peptidprofile waren in Regionen mit hoher Dichte an intrinsisch ungeordneten Regionen angereichert, was auf eine möglicherweise schwache, durch diese nicht-strukturellen Motive vermittelte Interaktion mit RNA hinweist. Schließlich konnten wir die unterschiedliche Bindung desselben Proteins an RNA in verschiedenen HEK293-Kompartimenten nachweisen. Im zweiten Teil dieser Arbeit konzentrierten wir uns auf die Bestimmung und Quantifizierung von neu transkribierten RNAs auf Einzelzellebene. Die Kinetik der RNA-Transkription und -Degradation war bis vor kurzem auf Einzelzellebene nicht messbar. Daher haben wir einen neuen Ansatz (SLAM-Drop-seq genannt) entwickelt, indem wir die veröffentlichte SLAM-seq-Methode an Einzelzellen angepasst haben. Wir haben SLAM-Drop-seq verwendet, um die zeitabhängigen RNA-Kinetikraten der Transkription und des Umsatzes für Hunderte von oszillierenden Transkripten während des Zellzyklus von HEK293-Zellen zu schätzen. Wir fanden heraus, dass Gene ihre Expression mit unterschiedlichen Strategien regulieren und spezifische Modi zur Feinabstimmung ihrer kinetischen Raten entlang des Zellzyklus haben.In this PhD dissertation we investigated the behaviour of human cells through space and time. High quality datasets of subcellular regions in HEK293 cells were generated using BirA* proximity labelling activity and restricting its localization at cellular regions difficult to purified with traditional methods (i.e., the cytosol-facing sides of the endoplasmic reticulum, mitochondria, and plasma membranes). We then developed an approach to map the distribution of proteins actively binding to RNA, and named it f-XRNAX. We recovered background-corrected proteomes for nuclei, cytoplasm and membranes of HEK293 cells. Surprisingly, many non-canonical RBPs were identified in the membrane fraction, and their peptide profiles were enriched in regions with high density of intrinsically disordered regions, indicating a possibly weak interaction with RNA mediated by these non-structural motives. Lastly, we provided evidence of the differential binding to RNA of the same protein in different HEK293 compartments. In the second part of this thesis, we focused on the determination and quantification of newly transcribed RNAs at the single-cell level. The kinetics of RNA transcription, processing and degradation were until recently not measurable at the single-cell level. Thus, we have developed a novel approach (called SLAM-Drop-seq ) by adapting the published SLAM-seq method to single cells. We used SLAM Drop-seq to estimate time-dependent RNA kinetics rates of transcription and turnover for hundreds of oscillating transcripts during the cell cycle of HEK293 cells. We found that genes regulate their expression with different strategies and have specific modes to fine-tune their kinetic rates along the cell cycle

    From spectrometric data to metabolic networks: an integrated view of cell metabolism

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    La biologia molecular ha avançat considerablement gràcies a importants progressos com la seqüenciació del ADN o la seva modificació per CRISPR. Tot i això, per entendre el metabolisme requerim estudiar els perfils metabòlics i les seves reaccions metabòliques. L™objectiu d™aquesta tesi és contribuir en aquest estudi del metabolism, el qual unifica dels camps de la proteòmica i la metabolòmica. Tradicionalment, l™anàlisi de dades òmiques es basa en el tractament independent de les diferents variables encara que està profundament establert que els mecanismes moleculars són controlats per la interacció de diferents molècules, i per tant seria més correcte tractar les dades de la mateixa manera. Avui dia, s™han descrit una gran quantitat de vies metabòliques, incluint els enzims responsables de les transformacions dels metabòlits que les formen, aquesta informació s™ha recopilat en bases de dades, que a la vegada poden ser utilitzades per a construir xarxes metabòliques. En aquesta tesi, s™han utilitzat xarxes metabòliques per a desenvolupar un algoritme que prediu metabòlits desregulats basant-se en el perfil d™expressió d™enzims gràcies a proteòmica quantitativa. Per a validar tals prediccions, és possible mesurar l™abundància d™aquests metabòlits, o el seu flux, o sigui la velocitat a la que s™han transformat, utilitzant experiments de marcatge amb isòtops estables, mesures completades mitjançant metabolòmica. Aqui, mostrem els productes del desenvolupament de dos mètodes per a l™anàlisi de dades de metabolòmica per a experiments amb isòtops estables: el primer per a la quantificació dirigida del flux en metabòlits del metabolisme central; i un segon, per la detecció no-dirigida de metabòlits marcats amb isòtops en altres vies metabòliques. Aquests mètodes han sigut provats en diferents estudis on han aportat resultats remarcables, revelant nous mecanismes moleculars en una complicació de la diabetes o en relació al metabolisme del càncer.La biología molecular ha avanzado considerablemente gracias a progresos como la secuenciación de ADN o su modificación por CRISPR. Sin embargo, para entender el metabolismo es indispensable estudiar los perfiles metabólicos y sus reacciones metabólicas. El objetivo de esta tesis es contribuir en el estudio del metabolismo, el cual implica los campos de la proteómica y la metabolómica. Tradicionalmente, el análisis de datos ómicas se basa en el tratamiento independiente de las diferentes variables aunque está profundamente aceptado que los mecanismos moleculares son controlados por la interacción de diferentes moléculas, y por lo tanto sería más correcto tratar los datos de esa manera. Hoy día, se han descrito una gran cantidad de vías metabólicas, incluyendo las enzimas responsables de las transformaciones de los metabolitos que las forman, esta información se ha recopilado en bases de datos, que a su vez pueden ser utilizadas para construir redes metabólicas . En esta tesis, se han utilizado redes metabólicas para desarrollar un algoritmo que predice metabolitos desregulados basándose en el perfil de expresión de enzimas por proteómica cuantitativa. Para validar tales predicciones, es posible medir la abundancia de estos metabolitos, o su flujo, o sea la velocidad a la que se han transformado, utilizando experimentos de marcado con isótopos estables, estas medidas se obtienen por metabolómica. Aquí, mostramos los productos del desarrollo de dos métodos para el análisis de datos de metabolómica para experimentos con isótopos estables: el primero para la cuantificación dirigida del flujo en metabolitos del metabolismo central; y un segundo, para la detección no-dirigida de metabolitos marcados con isótopos en otras vías metabólicas. Estos métodos han sido probados en diferentes estudios donde han aportado resultados interesantes, revelando nuevos mecanismos moleculares en una complicación de la diabetes o en relación al metabolismo del cáncer.Understanding the molecular basis of life has been in the spotlight of biochemistry research for more than a century already. Molecular biology has taken medicine forward thanks to technological breakthroughs like DNA sequencing and CRISPR editing. However, in order to understand metabolism we must rely on the study of metabolite profiles and metabolic reactions. The purpose of this thesis to contribute to this area, which unites the fields of proteomics and metabolomics. Traditionally, omics data analysis treats variables independently even if it is strongly settled that molecular mechanisms involve the interaction of diverse pathways, therefore data should be analyzed correspondingly. A vast amount of metabolic pathways have been described, together with enzymes that are responsible for metabolite transformations, this information has been assembled in databases that, in turn, can be used to build metabolic networks. In here, we use metabolic networks to predict metabolite dysregulation based on quantitative proteomics profiles. To validate the predictions, it is possible to measure the abundance of metabolites or their flux, namely the rate at which they are transformed, using stable isotope labelling experiments, both measurements can be performed by metabolomics. In this thesis, two different metabolomics-based stable isotope labelling approaches have been developed, one for the study of central carbon metabolites and one for the unbiased detection of deregulated fluxes in other metabolic pathways. These approaches have been tested on different datasets and have proven valuable to obtain remarkable results, unraveling molecular mechanisms in diabetes complications or novel metabolic hallmarks of cancer

    Transmembrane protein topology prediction using support vector machines

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    Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins

    An exploration of the protein component of scent marks from a range of mammalian species using proteomics approaches

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    Whilst semiochemistry is traditionally associated with volatile cues, the discovery of the major urinary proteins (MUPs) of the house mouse, and their demonstrable ability to convey information such as identity, kinship and mating availability, have identified proteins as an important tool for communication. Whilst protein-mediated scent signalling is understood relatively well in the house mouse and the Norway rat, little is known regarding the roles of proteins in chemical signalling beyond these species. The protein content of scent marks from three species, the closely related bank vole (Myodes glareolus) and field vole (Microtus agrestis), and the much more distantly related marsupial, the brushtail possum (Trichosurus vulpecula), were investigated. Overall protein content was initially assessed using polyacrylamide gel electrophoresis and electrospray ionisation-mass spectrometry of intact proteins. Prominent proteins of interest were separated by anion exchange chromatography and sequenced de novo using liquid chromatography allied to a tandem mass spectrometer. Total proteome analyses were performed by tryptic proteolytic cleavage followed by liquid chromatography-tandem mass spectrometry. Proteins were identified by cross-species matching and quantified using a label-free approach. Previous investigation into urinary protein expression in the bank vole, Myodes glareolus, identified three odorant-binding proteins (OBPs). The discovery of another OBP, glareosin, is described. Glareosin was determined as the single most abundant protein present in male bank vole urine during the breeding season, and the elucidation of its sequence and structure was published in 2017. Additional work continued to explore the total protein content, identifying additional OBP-like proteins at the peptide level in urine and scent marks of both sexes, including previously identified OBPs, although no corresponding intact masses were found. Behavioural research into the field vole (Microtus agrestis) has concentrated on its unusual dynamic population cycles, but olfactory communication remain largely unevaluated. Three abundant male-specific sequence variants, homologous to bank vole glareosin, were identified from male breeding season urine and sequenced de novo. Other proteins, including another OBP-like protein, a putatively glycosylated MUP-like protein, and a lipocalin-11-like protein were partially sequenced from urine and scent marks of both species. Global proteome analysis indicated further unidentified heterogeneity of OBP proteins at the peptide level, indicating a far more complex protein landscape within field vole scent marks when compared to the bank vole. In New Zealand, the status of the brushtail possum, Trichosurus vulpecula, as an invasive pest species has ignited a concerted effort to eradicate the species, despite a deficit in research into marsupial behaviour. Following proteome analyses, a glycosylated lipocalin was identified in the urine of both sexes, and sequenced de novo. Phylogenetic analysis with a range of mammalian lipocalins identified the novel sequence within a new clade of lipocalins unique to marsupials, suggesting that the marsupial lineage diverged prior to the establishment of described lipocalin classes in the placental mammals. The following investigations are three examples of the diversity of scent mark protein expression between both closely- and distantly-related species, and highlight the lack of understanding in this area. However, due to the evolutionary pressure placed on proteins with capacity to influence mate choice and sexual selection, a proteome approach reliant on identification by database matching is difficult, particularly for those species without a comprehensive genome, which will remain a rate-limiting factor until a wider range of genomes are available
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