Modelagem metabólica da cianobactéria Geminocystis sp. GBBB08, isolada no Parque Nacional da Chapada das Mesas, Maranhão

• Autor(a) principal: RIBEIRO, Igor Santana
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/4816
• topic: Mineração genômica;; engenharia metabólica;; modelo metabólico em escala genômica;; reconstrução baseada em restrições;; Flux Balance Analysis.; Genome mining,; metabolic engineering;; genome-scale metabolic model;; constraint-based reconstruction;; FBA.; Genética; Ciências Biológicas

Bioinformatics tools for predictive analysis of omic data have grown exponentially together with the advancement of new sequencing and computing technologies. Genomic scale models are effective tools for metabolic engineering and investigation of metabolic networks. However, these reconstructions can demand a lot of time for broader approaches, and their quality depends on the set of biochemical and phenotypic characteristics to be evaluated, in addition to the available data on the group under study. In this work, we used the integration of genome mining analysis (antiSMASH and MIBiG) for the discovery of biosynthetic natural products combined with genomic-scale metabolic reconstruction tools (CarveMe) of the genome of the cyanobacterium Geminocystis sp. GBBB08. Comparative analyzes led to the in silico reproduction of the Terpene (Non-Melavonate) production pathway with the flow balance analysis providing metabolic characteristics for the biosynthesis of the terpenoids involved. In addition, biosynthetic gene clusters of anabaenopeptin and heptadecene were identified. In general, the genome of Geminocystis sp. GBBB08 provides important data on the metabolic potential of the genus with an in silico approach to a frequent and economically relevant metabolic pathway such as terpenoids. Comparative analyzes in a genomic mining approach and systems biology can favor the reconstruction of metabolic networks and lead to a better understanding of metabolism and its biotechnological potential.