Metabolismo secundário em fungos e análises in silico: o papel da Bioinformática na predição de clusters de genes biossintéticos

Nicole Anne Modena; Fernanda Pessi de Abreu; Pedro Lenz Casa; Nikael Souza de Oliveira; Julia Gabriele Dani; Joséli Schwambach; Scheila de Ávila e Silva

doi:10.22481/exon.v14i1.20528

Autores

Nicole Anne Modena Universidade de Caxias do Sul
Fernanda Pessi de Abreu Universidade de Caxias do Sul
Pedro Lenz Casa Universidade de Caxias do Sul
Nikael Souza de Oliveira Universidade de Caxias do Sul
Julia Gabriele Dani Universidade de Caxias do Sul
Joséli Schwambach Universidade de Caxias do Sul
Scheila de Ávila e Silva Universidade de Caxias do Sul

DOI:

https://doi.org/10.22481/exon.v14i1.20528

Palavras-chave:

clusters de genes biossintéticos, metabolismo secundário, fungos filamentosos, AntiSMASH, bioinformática

Resumo

Os fungos filamentosos geram uma série de produtos não essenciais, dentre eles os metabólitos secundários. Esses, além de possuírem importância nas relações ecológicas, também são alvo de pesquisas na área da biotecnologia. Para a produção desses compostos, é muitas vezes necessária a atuação de clusters de genes biossintéticos (BGCs). O avanço das tecnologias de sequenciamento e o desenvolvimento de softwares de bioinformática tem tornado o uso de dados genômicos mais acessível. Assim, metodologias in silico permitem a inferência de produtos naturais. No presente trabalho foi exemplificada a predição de BGCs de metabólitos secundários a partir do genoma do fungo Penicillium echinulatum, utilizando a ferramenta antiSMASH. As famílias de metabólitos secundários mais encontradas para P. echinulatum incluíram clusters de terpenos, de peptídeos não ribossômicos e similares a peptídeos não ribossômicos. Adicionalmente, a abordagem utilizada permitiu a identificação e caracterização de 26 BGCs para P. echinulatum, dos quais destaca-se ácido clavárico, naftopirano, nidulanin A, oxaleimide C e phyllostictine A. Nesse sentido, é possível observar que as ferramentas de bioinformática são fundamentais para auxiliar na identificação de organismos produtores de compostos de interesse, visto que otimizam processos e reduzem custos.

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