Characterization of Peptaibols Produced by a Marine Strain of the Fungus Trichoderma endophyticum via Mass Spectrometry, Genome Mining and Phylogeny-Based Prediction

Metabolites. 2023 Feb 3;13(2):221. doi: 10.3390/metabo13020221.

Abstract

Trichoderma is recognized as a prolific producer of nonribosomal peptides (NRPs) known as peptaibols, which have remarkable biological properties, such as antimicrobial and anticancer activities, as well as the ability to promote systemic resistance in plants against pathogens. In this study, the sequencing of 11-, 14- and 15-res peptaibols produced by a marine strain of Trichoderma isolated from the ascidian Botrylloides giganteus was performed via liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS). Identification, based on multilocus phylogeny, revealed that our isolate belongs to the species T. endophyticum, which has never been reported in marine environments. Through genome sequencing and genome mining, 53 biosynthetic gene clusters (BGCs) were identified as being related to bioactive natural products, including two NRP-synthetases: one responsible for the biosynthesis of 11- and 14-res peptaibols, and another for the biosynthesis of 15-res. Substrate prediction, based on phylogeny of the adenylation domains in combination with molecular networking, permitted extensive annotation of the mass spectra related to two new series of 15-res peptaibols, which are referred to herein as "endophytins". The analyses of synteny revealed that the origin of the 15-module peptaibol synthetase is related to 18, 19 and 20-module peptaibol synthetases, and suggests that the loss of modules may be a mechanism used by Trichoderma species for peptaibol diversification. This study demonstrates the importance of combining genome mining techniques, mass spectrometry analysis and molecular networks for the discovery of new natural products.

Keywords: de novo sequencing; marine fungus; molecular networking; nonribosomal peptides; synteny analysis.

Grants and funding

This work was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-grant number 88881.200469/2018-01 Procad AmazonMicro and Grant Nº 88887.510218-2020-00 CAPES-Amazônia-Legal) and CAPES—Finance code 001. We also would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the productivity scholarship grant provided to H.H.F.K. (No. 305942/2020-4). The authors H.H.F.K and G.F.S. also acknowledge Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) for the funding via the projects PROSPAM (call 008/2021), AMAZONAS ESTRATÉGICO (call 004/2018), ÁREAS PRIORITÁRIAS (call 010/2021) and POSGRAD 2022/2023 (call 005/2022). In addition, A.H.J. would like to thank Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the grants 2014/19184-7 and 2017/14261-1.