Transcritoma da resposta de Klebsiella pneumoniae à polimixina B e abordagem computacional para priorização de alvos moleculares

• Autor(a) principal: Ramos, Pablo Ivan Pereira
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do LNCC
• Texto Completo: https://tede.lncc.br/handle/tede/257
• topic: Bioinformática; Biologia de sistemas; Modelagem molecular; Transcritoma; System biology; Molecular modeling; Bioinformatics; CNPQ::CIENCIAS BIOLOGICAS::MICROBIOLOGIA

The emergence of clinically important bacteria presenting a wide spectrum of antibiotic resistance represents a global concern. Although bacterial resistance was reported in the literature from the beggining of antibiotic use, early in the 20th century, we currently face the threat of pan-resistance, pathogens that can escape the action of all currently available antibiotic classes. A better understanding of virulence and resistance mechanisms, as well as new therapeutic options, are of paramount importance. This thesis proposal is based on the study of Klebsiella pneumoniae Kp13, a clinical isolate obtained in 2009 during a clonal outbreak in South Brazil which had its complete genome determined by our group. This strain is multidrug resistant and presents high-level resistance against polymyxin B (MIC 32 mg L􀀀1), a \last resort" drug for the treatment of Gram-negative multidrug-resistant bacteria. Using techniques from bioinformatics, transcriptomics (RNA-seq), systems biology and molecular modeling, we sought to better understand the gene expression response in K. pneumoniae in face of changes in abiotic characteristics and polymyxin B exposure. How these factors influence the metabolic repertoire of K. pneumoniae was also object of research. We also aimed to delineate a computational strategy for priorization of metabolic pathways that could serve as new targets for therapeuticals, by integrating expression, metabolic and structural reconstruction data. In parallel, this strategy was also applied to the study of Mycobacterium tubercluosis H37Rv (Mtb), the best characterized strain of this bacteria. E orts were made to study the metabolic complement of this pathogen, identifying important pathways related to its growth and correlating to molecular targets from a structural standview. The transcriptomic analyses allowed the identi cation of novel intracellular targets (such as ArcA-ArcB) that go beyond the \classic" e ect of polymyxin B mode of action, based in membrane interaction, besides drug-induced metabolic modulation which may lead to fermentative pathways of growth. The computational strategy for whole-genome target priorization led to the nding of pathways already known as druggable, such as S-methyl 5'-adenosin, as well as pathways not previously classi ed as druggable, but which could serve as candidates for future development of therapeutical compounds.