Identification of protein complexes in Alzheimer's disease

A Doença de Alzheimer (AD) é a maior doença neurodegenerativa a nível mundial, e a principal causa de demência na população idosa. O processamento da proteína precursora de amilóide (APP) pelas β- e g- secretases origina o peptídeo Aβ, que agrega em oligómeros neurotóxicos e em placas senis. Estes são eventos-chave na patogénese da DA que levam à rutura da neurotransmissão sináptica, morte neuronal e inflamação neuronal do hipocampo e córtex cerebral, causando perda de memória disfunção cognitiva geral. Apesar dos grandes avanços no conhecimento do papel do processamento da APP na DA, a sua função fisiológica ainda não foi totalmente elucidada. Os mapas de interações proteína-proteína (PPI) humanos têm desempenhado um papel importante na investigação biomédica, em particular no estudo de vias de sinalização e de doenças humanas. O método dois-híbrido em levedura (YTH) consiste numa plataforma para a produção rápida de redes de PPI em larga-escala. Neste trabalho foram realizados vários rastreios YTH com o objetivo de identificar proteínas específicas de cérebro humano que interagissem com a APP, ou com o seu domínio intracelular (AICD), tanto o tipo selvagem como com os mutantes Y687F, que mimetizam o estado desfosforilado do resíduo Tyr-687. De facto, a endocitose da APP e a produção de Aβ estão dependentes do estado de fosforilação da Tyr-687. Os rastreios YTH permitiram assim obter de redes proteínas que interagem com a APP, utilizando como “isco” a APP, APPY687F e AICDY687F. Os clones positivos foram isolados e identificados através de sequenciação do cDNA. A maior parte dos clones identificados, 118, correspondia a sequências que codificam para proteínas conhecidas, resultando em 31 proteínas distintas. A análise de proteómica funcional das proteínas identificadas neste estudo e em dois projetos anteriores (AICDY687E, que mimetiza a fosforilação, e AICD tipo selvagem), permitiram avaliar a relevância da fosforilação da Tyr-687. Três clones provenientes do rastreio YTH com a APPY687F foram identificados como um novo transcrito da proteína Fe65, resultante de splicing alternativo, a Fe65E3a (GenBank Accession: EF103274), que codifica para a isoforma p60Fe65. A p60Fe65 está enriquecida no cérebro e os seus níveis aumentam durante a diferenciação neuronal de células PC12, evidenciando o potencial papel que poderá desempenhar na patologia da DA. A RanBP9 é uma proteína nuclear e citoplasmática envolvida em diversas vias de sinalização celulares. Neste trabalho caracterizou-se a nova interação entre a RanBP9 e o AICD, que pode ser regulada pela fosforilação da Tyr-687. Adicionalmente, foi identificada uma nova interação entre a RanBP9 e a acetiltransferase de histonas Tip60. Demonstrou-se ainda que a RanBP9 tem um efeito de regulação inibitório na transcrição mediada por AICD, através da interação com a Tip60, afastando o AICD dos locais de transcrição ativos. O estudo do interactoma da APP/AICD, modelado pela fosforilação da Tyr-687, revela que a APP poderá estar envolvida em novas vias celulares, contribuindo não só para o conhecimento do papel fisiológico da APP, como também auxilia a revelar as vias que levam à agregação de Aβ e neurodegeneração. A potencial relevância deste trabalho relaciona-se com a descoberta de algumas interações proteicas/vias de sinalização que podem que podem ser relevantes para o desenvolvimento de novas estratégias terapêuticas na DA.

Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition - a crucial initial task - with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.

Identification of molecules and pathways regulating mistranslation in Candida albicans

Candida albicans is the major fungal pathogen in humans, causing diseases ranging from mild skin infections to severe systemic infections in immunocompromised individuals. The pathogenic nature of this organism is mostly due to its capacity to proliferate in numerous body sites and to its ability to adapt to drastic changes in the environment. Candida albicans exhibit a unique translational system, decoding the leucine-CUG codon ambiguously as leucine (3% of codons) and serine (97%) using a hybrid serine tRNA (tRNACAGSer). This tRNACAGSer is aminoacylated by two aminoacyl tRNA synthetases (aaRSs): leucyl-tRNA synthetase (LeuRS) and seryl-tRNA synthetase (SerRS). Previous studies showed that exposure of C. albicans to macrophages, oxidative, pH stress and antifungals increases Leu misincorporation levels from 3% to 15%, suggesting that C. albicans has the ability to regulate mistranslation levels in response to host defenses, antifungals and environmental stresses. Therefore, the hypothesis tested in this work is that Leu and Ser misincorporation at CUG codons is dependent upon competition between the LeuRS and SerRS for the tRNACAGSer. To test this hypothesis, levels of the SerRS and LeuRS were indirectly quantified under different physiological conditions, using a fluorescent reporter system that measures the activity of the respective promoters. Results suggest that an increase in Leu misincorporation at CUG codons is associated with an increase in LeuRS expression, with levels of SerRS being maintained. In the second part of the work, the objective was to identify putative regulators of SerRS and LeuRS expression. To accomplish this goal, C. albicans strains from a transcription factor knock-out collection were transformed with the fluorescent reporter system and expression of both aaRSs was quantified. Alterations in the LeuRS/SerRS expression of mutant strains compared to wild type strain allowed the identification of 5 transcription factors as possible regulators of expression of LeuRS and SerRS: ASH1, HAP2, HAP3, RTG3 and STB5. Globally, this work provides the first step to elucidate the molecular mechanism of regulation of mistranslation in C. albicans.