Estudos metabolômicos de fungos associados a plantas: desenvolvimento de métodos para o aumento da produção e identificação de metabólitos secundários bioativos microbianos
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Data
2019-03-29
Autores
Selegato, Denise Medeiros [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
A descoberta de novos alvos farmacêuticos derivados de fontes naturais vem diminuindo ao longo das últimas décadas, impulsionando o uso de matrizes naturais novas e incomuns para a busca de metabólitos secundários bioativos. Dentre essas, a microbiota de plantas e os microrganismos marinhos demonstraram um grande potencial para fornecer novos metabólitos biologicamente ativos, produzindo uma alta diversidade de estruturas químicas encontradas em populações microbianas extensas e pouco exploradas. Convencionalmente, a triagem de metabólitos microbianos é realizada em monoculturas, na ausência de interações bióticas e abióticas. No entanto, a falta dessas interações encontradas na natureza limita a diversidade química que pode ser obtida por uma única cepa, permanecendo silenciadas diversos outros genes que codificam novos metabólitos secundários. Durante a última década, vários métodos têm sido desenvolvidos para compreender as condições sob as quais os genes cripticos são ativados. Dentre elas, as estratégias pós-genômicas revelaram um amplo potencial para o aumento da diversidade química, modificando diferentes níveis da maquinaria celular para uma regulação holística do metaboloma microbiano. Estas estratégias incluem co-cultivo, alimentação por metabólitos e One Strain Many Compounds (OSMAC) e têm sido utilizados com sucesso como uma alternativa rápida e barata para a indução de novos metabólitos. Apesar da eficácia das estratégias pós-genômicas na expansão do metaboloma microbiano, durante a avaliação dessas matrizes complexas, a grande dinâmica e diversidade química ainda dificulta sua identificação e correlação biológica dos metabólitos secundários. Como consequência, esses obstáculos analíticos exigem o uso de técnicas cada vez mais robustas e aplicação de algoritmos para uma análise mais precisa e multivariada dos dados. O presente trabalho descreve a integração de estratégias biológicas e analíticas para a aumento da identificação e detecção de metabólitos secundários produzidos por fungus associados à plantas. Para tanto, diferentes estratégias pós-genômicas e ferramentas computacionais foram incorporadas para identificar metabólitos conhecidos e inéditos diretamente em extratos brutos. Os resultados mostraram que as estratégias pós-genômicas proporcionaram um aumento significativo na diversidade química de todos os fungos selecionados, em que a co-cultura resultou na maior e mais ampla indução metabólica. Adicionalmente, a aplicação das metodologias de desreplicação por ressonância magnética nuclear (RMN) e espectrometria de massas (MS), alinhado ao uso de ferramentas de deconvolução e análise de redes de interações moleculares, permitiram a extração de dados químicos confiáveis e a identificação de diversos metabólitos secundários nunca relatados para as espécies estudadas. De maneira geral, os micro-organismos demonstraram possuir uma infinidade de caminhos biossintéticos. Sendo assim, a ativação dos genes cripticos levaram não apenas à identificação de moléculas promissoras, mas também a um melhor entendimento das interações bióticas e abióticas dessas espécies de fungos e do ambiente que habitam.
The discovery of new drug leads from natural sources has decreased over the last decades, emerging the use of new and uncommon matrices for the screening of bioactive secondary metabolites. Among those, the plant microbiota and marine microorganisms have demonstrated a great potential to provide new pharmaceutical leads, producing a high diversity of chemical structures encountered in little-explored and extensive microbial population. Conventionally, microbial metabolite screening is performed in monocultures, in the absence of biotic and abiotic interactions. However, the lack of these communications commonly found in nature seriously limit the chemical diversity that can be obtained by one single strain, remaining silenced many other genes encoded for new secondary metabolites. Over the last decade, several methods have been developed aiming to understand the conditions under which biosynthetic cryptic genes are activated. Among those, the post genomic strategies have revealed widespread potential for the enhancement of chemical diversity, modifying different levels of the cellular machinery for a comprehensive regulation of the microbial metabolome. These strategies include co-cultivation, biotransformation and One Strain Many Compounds (OSMAC) and have been successfully used as a fast and inexpensive alternative for the induction of new bioactive compounds. Notwithstanding these strategies for the expansion of the microbial metabolome, in the screening of these complex matrices, the large dynamic range and diversity of metabolites still hamper their identification and biological correlation. These analytical obstacles require increasingly robust techniques and the use of algorithms for a more accurate and multivariate analysis of the chemical data. In this work, we present an integrative strategy for the enhancement of the identification and detection of secondary metabolites in plant-associated microbes. For this, we have applied different post-genomic strategies and computational tools to accurately elucidate known and novel bioactive metabolites in crude extracts. Results showed that post genomic strategies provided a significant increase in the chemical diversity of all selected fungi, in which co-culture resulted in the highest and more diverse metabolic induction. Moreover, the application of Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS)-dereplication methodologies, combined with deconvolution-based 1HNMR quantification and MS/MS Molecular Networking, enabled the extraction of a broader chemical data, accurate biological correlation and the identification of several secondary metabolites never reported for the targeted species. Overall, microbes have demonstrated a multitude of biosynthetic pathways. Hence, the activation of cryptic genes led not only to the identification of novel biologically promising leads, but also a better understanding of how the biotic and abiotic interactions can interfere on these fungal metabolomes.
The discovery of new drug leads from natural sources has decreased over the last decades, emerging the use of new and uncommon matrices for the screening of bioactive secondary metabolites. Among those, the plant microbiota and marine microorganisms have demonstrated a great potential to provide new pharmaceutical leads, producing a high diversity of chemical structures encountered in little-explored and extensive microbial population. Conventionally, microbial metabolite screening is performed in monocultures, in the absence of biotic and abiotic interactions. However, the lack of these communications commonly found in nature seriously limit the chemical diversity that can be obtained by one single strain, remaining silenced many other genes encoded for new secondary metabolites. Over the last decade, several methods have been developed aiming to understand the conditions under which biosynthetic cryptic genes are activated. Among those, the post genomic strategies have revealed widespread potential for the enhancement of chemical diversity, modifying different levels of the cellular machinery for a comprehensive regulation of the microbial metabolome. These strategies include co-cultivation, biotransformation and One Strain Many Compounds (OSMAC) and have been successfully used as a fast and inexpensive alternative for the induction of new bioactive compounds. Notwithstanding these strategies for the expansion of the microbial metabolome, in the screening of these complex matrices, the large dynamic range and diversity of metabolites still hamper their identification and biological correlation. These analytical obstacles require increasingly robust techniques and the use of algorithms for a more accurate and multivariate analysis of the chemical data. In this work, we present an integrative strategy for the enhancement of the identification and detection of secondary metabolites in plant-associated microbes. For this, we have applied different post-genomic strategies and computational tools to accurately elucidate known and novel bioactive metabolites in crude extracts. Results showed that post genomic strategies provided a significant increase in the chemical diversity of all selected fungi, in which co-culture resulted in the highest and more diverse metabolic induction. Moreover, the application of Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS)-dereplication methodologies, combined with deconvolution-based 1HNMR quantification and MS/MS Molecular Networking, enabled the extraction of a broader chemical data, accurate biological correlation and the identification of several secondary metabolites never reported for the targeted species. Overall, microbes have demonstrated a multitude of biosynthetic pathways. Hence, the activation of cryptic genes led not only to the identification of novel biologically promising leads, but also a better understanding of how the biotic and abiotic interactions can interfere on these fungal metabolomes.
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Palavras-chave
Metabolomics, Natural products, Coculture technique, Microbiology, Chemometrics