Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics.

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Untargeted metabolomics approach and molecular networking analysis reveal changes in chemical composition under the influence of altitudinal variation in bamboo species.

Bamboo species have traditionally been used as building material and potential source of bioactive substances, as they produce a wide variety of phenolic compounds, including flavonoids and cinnamic acid derivatives that are considered biologically active. However, the effects of growth conditions such as location, altitude, climate, and soil on the metabolome of these species still need to be fully understood. This study aimed to evaluate variations in chemical composition induced by altitudinal gradient (0-3000 m) by utilizing an untargeted metabolomics approach and mapping chemical space using molecular networking analysis. We analyzed 111 samples from 12 bamboo species collected from different altitudinal ranges using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). We used multivariate and univariate statistical analyses to identify the metabolites that showed significant differences in the altitude environments. Additionally, we used the Global Natural Products Social Molecular Networking (GNPS) web platform to perform chemical mapping by comparing the metabolome among the studied species and the reference spectra from its database. The results showed 89 differential metabolites between the altitudinal ranges investigated, wherein high altitude environments significantly increased the profile of flavonoids. While, low altitude environments significantly boosted the profile of cinnamic acid derivatives, particularly caffeoylquinic acids (CQAs). MolNetEnhancer networks confirmed the same differential molecular families already found, revealing metabolic diversity. Overall, this study provides the first report of variations induced by altitude in the chemical profile of bamboo species. The findings may possess fascinating active biological properties, thus offering an alternative use for bamboo.

Targeted Isolation of Two New Anti-inflammatory and UV-A Protective Dipyrroloquinones from the Sponge-associated Fungus Aspergillus tamarii MCCF102.

In following up on observed in vitro anti-inflammatory activity of the organic extract of the marine sponge-derived fungus Aspergillus tamarii MCCF102, two new dipyrrolobenzoquinones, terreusinone B and C (1: and 2: ), were discovered along with the known analogue, terreusinone (3: ). The structures of 1: -3: were determined by spectroscopic and spectrometric analyses, along with chemical inter-conversion. In vitro testing on lipopolysaccharide (LPS) stimulated RAW 264.7 murine macrophage cells revealed that 1: -3: exhibit anti-inflammatory activity by inhibiting nitric oxide production in a dose-dependent manner (IC50 < 1 µM) without any cytotoxicity observed at the same concentrations. Due to this and the UV-A absorptive properties imparted by the highly conjugated structures of these molecules, the potential for using 1: -3: or related analogues as natural sunscreen components is suggested. Gene sequencing and informatics biosynthetic gene cluster comparisons were insufficient to confidently elucidate the biosynthetic origins of these compounds, possibly suggesting the occurrence of a gene cluster not detected in the initial sequencing or a non-canonical pathway that should be further investigated.

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters.

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Heterologous Expression of Cryptomaldamide in a Cyanobacterial Host.

Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, nonribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drug leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers. Here we report a set of genetic tools for the heterologous expression of biosynthetic gene clusters in the cyanobacteria Synechococcus elongatus PCC 7942 and Anabaena (Nostoc) PCC 7120. To facilitate the transfer of gene clusters in both strains, we engineered a strain of Anabaena that contains S. elongatus homologous sequences for chromosomal recombination at a neutral site and devised a CRISPR-based strategy to efficiently obtain segregated double recombinant clones of Anabaena. These genetic tools were used to express the large 28.7 kb cryptomaldamide biosynthetic gene cluster from the marine cyanobacterium Moorena (Moorea) producens JHB in both model strains. S. elongatus did not produce cryptomaldamide; however, high-titer production of cryptomaldamide was obtained in Anabaena. The methods developed in this study will facilitate the heterologous expression of biosynthetic gene clusters isolated from marine cyanobacteria and complex metagenomic samples.

Nature's Combinatorial Biosynthesis Produces Vatiamides A-F.

Hybrid type I PKS/NRPS biosynthetic pathways typically proceed in a collinear manner wherein one molecular building block is enzymatically incorporated in a sequence that corresponds to gene arrangement. In this work, genome mining combined with the use of a fluorogenic azide-based click probe led to the discovery and characterization of vatiamides A-F, three structurally diverse alkynylated lipopeptides, and their brominated analogues, from the cyanobacterium Moorea producens ASI16Jul14-2. These derive from a unique combinatorial non-collinear PKS/NRPS system encoded by a 90 kb gene cluster in which an upstream PKS cassette interacts with three separate cognate NRPS partners. This is facilitated by a series of promiscuous intermodule PKS-NRPS docking motifs possessing identical amino acid sequences. This interaction confers a new type of combinatorial capacity for creating molecular diversity in microbial systems.

Integrating mass spectrometry and genomics for cyanobacterial metabolite discovery.

Filamentous marine cyanobacteria produce bioactive natural products with both potential therapeutic value and capacity to be harmful to human health. Genome sequencing has revealed that cyanobacteria have the capacity to produce many more secondary metabolites than have been characterized. The biosynthetic pathways that encode cyanobacterial natural products are mostly uncharacterized, and lack of cyanobacterial genetic tools has largely prevented their heterologous expression. Hence, a combination of cutting edge and traditional techniques has been required to elucidate their secondary metabolite biosynthetic pathways. Here, we review the discovery and refined biochemical understanding of the olefin synthase and fatty acid ACP reductase/aldehyde deformylating oxygenase pathways to hydrocarbons, and the curacin A, jamaicamide A, lyngbyabellin, columbamide, and a trans-acyltransferase macrolactone pathway encoding phormidolide. We integrate into this discussion the use of genomics, mass spectrometric networking, biochemical characterization, and isolation and structure elucidation techniques.