Marine-derived actinomycetes have demonstrated an ability to produce novel compounds with medically relevant biological activity. Studying the diversity and biogeographical patterns of marine actinomycetes offers an opportunity to identify genera that are under environmental pressures, which may drive adaptations that yield specific biosynthetic capabilities. The present study describes research efforts to explore regions of the Atlantic Ocean, specifically around the Madeira Archipelago, where knowledge of the indigenous actinomycete diversity is scarce. A total of 400 actinomycetes were isolated, sequenced, and screened for antimicrobial and anticancer activities. The three most abundant genera identified were Streptomyces, Actinomadura, and Micromonospora. Phylogenetic analyses of the marine OTUs isolated indicated that the Madeira Archipelago is a new source of actinomycetes adapted to life in the ocean. Phylogenetic differences between offshore (>100 m from shore) and nearshore (< 100 m from shore) populations illustrates the importance of sampling offshore in order to isolate new and diverse bacterial strains. Novel phylotypes from chemically rich marine actinomycete groups like MAR4 and the genus Salinispora were isolated. Anticancer and antimicrobial assays identified Streptomyces, Micromonospora, and Salinispora as the most biologically active genera. This study illustrates the importance of bioprospecting efforts at unexplored regions of the ocean to recover bacterial strains with the potential to produce novel and interesting chemistry.

The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.

Salinispora (Micromonosporaceae) is an obligate marine bacterium genus consisting of three species that share over 99% 16S rRNA identity. The genome and biosynthetic pathways of the members of this genus have been widely investigated due to their production of species-specific metabolites. However, despite the species’ high genetic similarity, site-specific secondary metabolic gene clusters have been found in Salinispora strains collected at different locations. Therefore, exploring the metabolic expression of Salinispora recovered from different sites may furnish insights into their environmental adaptation or their chemical communication and, further, may lead to the discovery of new natural products. We describe the first occurrence of Salinispora strains in sediments from the Saint Peter and Saint Paul Archipelago (a collection of islets in Brazil) in the Atlantic Ocean, and we investigate the metabolic profiles of these strains by employing mass-spectrometry-based metabolomic approaches, including molecular networking from the Global Natural Products Social Molecular Networking platform. Furthermore, we analyze data from Salinispora strains recovered from sediments from the Madeira Archipelago (Portugal, Macaronesia) in order to provide a wider metabolomic investigation of Salinispora strains from the Atlantic Oceanic islands. Overall, our study evidences a broader geographic influence on the secondary metabolism of Salinispora than was previously proposed. Still, some biosynthetic gene clusters, such as those corresponding to typical chemical signatures of S. arenicola, like saliniketals and rifamycins, are highly conserved among the assessed strains.