Unlocking the biosynthetic potential and taxonomy of the Antarctic microbiome along temporal and spatial gradients.

Extreme environments, such as Antarctica, select microbial communities that display a range of evolutionary strategies to survive and thrive under harsh environmental conditions. These include a diversity of specialized metabolites, which have the potential to be a source for new natural product discovery. Efforts using (meta)genome mining approaches to identify and understand biosynthetic gene clusters in Antarctica are still scarce, and the extent of their diversity and distribution patterns in the environment have yet to be discovered. Herein, we investigated the biosynthetic gene diversity of the biofilm microbial community of Whalers Bay, Deception Island, in the Antarctic Peninsula and revealed its distribution patterns along spatial and temporal gradients by applying metagenome mining approaches and multivariable analysis. The results showed that the Whalers Bay microbial community harbors a great diversity of biosynthetic gene clusters distributed into seven classes, with terpene being the most abundant. The phyla Proteobacteria and Bacteroidota were the most abundant in the microbial community and contributed significantly to the biosynthetic gene abundances in Whalers Bay. Furthermore, the results highlighted a significant correlation between the distribution of biosynthetic genes and taxonomic diversity, emphasizing the intricate interplay between microbial taxonomy and their potential for specialized metabolite production.IMPORTANCEThis research on antarctic microbial biosynthetic diversity in Whalers Bay, Deception Island, unveils the hidden potential of extreme environments for natural product discovery. By employing metagenomic techniques, the research highlights the extensive diversity of biosynthetic gene clusters and identifies key microbial phyla, Proteobacteria and Bacteroidota, as significant contributors. The correlation between taxonomic diversity and biosynthetic gene distribution underscores the intricate interplay governing specialized metabolite production. These findings are crucial for understanding microbial adaptation in extreme environments and hold significant implications for bioprospecting initiatives. The study opens avenues for discovering novel bioactive compounds with potential applications in medicine and industry, emphasizing the importance of preserving and exploring these polyextreme ecosystems to advance biotechnological and pharmaceutical research.

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.

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Progression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate. METHODS: In all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS-R scores and sub-scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables. RESULTS: Disease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub-score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub-score has little influence in all groups. ALSFRS-R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively. CONCLUSIONS: The sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.

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.

Portobelamides A and B and Caciqueamide, Cytotoxic Peptidic Natural Products from a Caldora sp. Marine Cyanobacterium.

Three new compounds, portobelamides A and B (1 and 2), 3-amino-2-methyl-7-octynoic acid (AMOYA) and hydroxyisovaleric acid (Hiva) containing cyclic depsipeptides, and one long chain lipopeptide caciqueamide (3), were isolated from a field-collection of a Caldora sp. marine cyanobacterium obtained from Panama as part of the Panama International Cooperative Biodiversity Group Program. Their planar structures were elucidated through analysis of 2D NMR and MS data, especially high resolution (HR) MS2/MS3 fragmentation methods. The absolute configurations of compounds 1 and 2 were deduced by traditional hydrolysis, derivative formation, and chromatographic analyses compared with standards. Portobelamide A (1) showed good cytotoxicity against H-460 human lung cancer cells (33% survival at 0.9 µM).

Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.

A Multi-Omics Characterization of the Natural Product Potential of Tropical Filamentous Marine Cyanobacteria.

Microbial natural products are important for the understanding of microbial interactions, chemical defense and communication, and have also served as an inspirational source for numerous pharmaceutical drugs. Tropical marine cyanobacteria have been highlighted as a great source of new natural products, however, few reports have appeared wherein a multi-omics approach has been used to study their natural products potential (i.e., reports are often focused on an individual natural product and its biosynthesis). This study focuses on describing the natural product genetic potential as well as the expressed natural product molecules in benthic tropical cyanobacteria. We collected from several sites around the world and sequenced the genomes of 24 tropical filamentous marine cyanobacteria. The informatics program antiSMASH was used to annotate the major classes of gene clusters. BiG-SCAPE phylum-wide analysis revealed the most promising strains for natural product discovery among these cyanobacteria. LCMS/MS-based metabolomics highlighted the most abundant molecules and molecular classes among 10 of these marine cyanobacterial samples. We observed that despite many genes encoding for peptidic natural products, peptides were not as abundant as lipids and lipopeptides in the chemical extracts. Our results highlight a number of highly interesting biosynthetic gene clusters for genome mining among these cyanobacterial samples.

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.

A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.

This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.

Pagoamide A, a Cyclic Depsipeptide Isolated from a Cultured Marine Chlorophyte, Derbesia sp., Using MS/MS-Based Molecular Networking.

A thiazole-containing cyclic depsipeptide with 11 amino acid residues, named pagoamide A (1), was isolated from laboratory cultures of a marine Chlorophyte, Derbesia sp. This green algal sample was collected from America Samoa, and pagoamide A was isolated using guidance by MS/MS-based molecular networking. Cultures were grown in a light- and temperature-controlled environment and harvested after several months of growth. The planar structure of pagoamide A (1) was characterized by detailed 1D and 2D NMR experiments along with MS and UV analysis. The absolute configurations of its amino acid residues were determined by advanced Marfey's analysis following chemical hydrolysis and hydrazinolysis reactions. Two of the residues in pagoamide A (1), phenylalanine and serine, each occurred twice in the molecule, once in the d- and once in the l-configuration. The biosynthetic origin of pagoamide A (1) was considered in light of other natural products investigations with coenocytic green algae.

MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

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.

Ketoreductase Domain Dysfunction Expands Chemodiversity: Malyngamide Biosynthesis in the Cyanobacterium Okeania hirsuta.

Dozens of type A malyngamides, principally identified by a decorated six-membered cyclohexanone headgroup and methoxylated lyngbic acid tail, have been isolated over several decades. Their environmental sources include macro- and microbiotic organisms, including sea hares, red alga, and cyanobacterial assemblages, but the true producing organism has remained enigmatic. Many type A analogues display potent bioactivity in human-health related assays, spurring an interest in this molecular class and its biosynthetic pathway. Here, we present the discovery of the type A malyngamide biosynthetic pathway in the first sequenced genome of the cyanobacterial genus Okeania. Bioinformatic analysis of two cultured Okeania genome assemblies identified 62 and 68 kb polyketide synthase/nonribosomal peptide synthetase (PKS/NRPS) pathways with unusual loading and termination genes. NMR data of malyngamide C acetate derived from 13C-substrate-fed cultures provided evidence that an intact octanoate moiety is transferred to the first KS module via a LipM homologue originally associated with lipoic acid metabolism and implicated an inactive ketoreductase (KR0) as critical for six-membered ring formation, a hallmark of the malyngamide family. Phylogenetic analysis and homology modeling of the penultimate KR0 domain inferred structural cofactor binding and active site alterations as contributory to domain dysfunction, which was confirmed by recombinant protein expression and NADPH binding assay. The carbonyl retained from this KR0 ultimately enables an intramolecular Knoevenagel condensation to form the characteristic cyclohexanone ring. Understanding this critical step allows assignment of a biosynthetic model for all type A malyngamides, whereby well-characterized tailoring modifications explain the surprising proliferation and diversity of analogues.

Collection, Culturing, and Genome Analyses of Tropical Marine Filamentous Benthic Cyanobacteria.

Decreasing sequencing costs has sparked widespread investigation of the use of microbial genomics to accelerate the discovery and development of natural products for therapeutic uses. Tropical marine filamentous cyanobacteria have historically produced many structurally novel natural products, and therefore present an excellent opportunity for the systematic discovery of new metabolites via the information derived from genomics and molecular genetics. Adequate knowledge transfer and institutional know-how are important to maintain the capability for studying filamentous cyanobacteria due to their unusual microbial morphology and characteristics. Here, we describe workflows, procedures, and commentary on sample collection, cultivation, genomic DNA generation, bioinformatics tools, and biosynthetic pathway analysis concerning filamentous cyanobacteria.

A Maldiisotopic Approach to Discover Natural Products: Cryptomaldamide, a Hybrid Tripeptide from the Marine Cyanobacterium Moorea producens.

Genome sequencing of microorganisms has revealed a greatly increased capacity for natural products biosynthesis than was previously recognized from compound isolation efforts alone. Hence, new methods are needed for the discovery and description of this hidden secondary metabolite potential. Here we show that provision of heavy nitrogen 15N-nitrate to marine cyanobacterial cultures followed by single-filament MALDI analysis over a period of days was highly effective in identifying a new natural product with an exceptionally high nitrogen content. The compound, named cryptomaldamide, was subsequently isolated using MS to guide the purification process, and its structure determined by 2D NMR and other spectroscopic and chromatographic methods. Bioinformatic analysis of the draft genome sequence identified a 28.7 kB gene cluster that putatively encodes for cryptomaldamide biosynthesis. Notably, an amidinotransferase is proposed to initiate the biosynthetic process by transferring an amidino group from arginine to serine to produce the first residue to be incorporated by the hybrid NRPS-PKS pathway. The maldiisotopic approach presented here is thus demonstrated to provide an orthogonal method by which to discover novel chemical diversity from Nature.

Comparative genomics uncovers the prolific and distinctive metabolic potential of the cyanobacterial genus Moorea.

Cyanobacteria are major sources of oxygen, nitrogen, and carbon in nature. In addition to the importance of their primary metabolism, some cyanobacteria are prolific producers of unique and bioactive secondary metabolites. Chemical investigations of the cyanobacterial genus Moorea have resulted in the isolation of over 190 compounds in the last two decades. However, preliminary genomic analysis has suggested that genome-guided approaches can enable the discovery of novel compounds from even well-studied Moorea strains, highlighting the importance of obtaining complete genomes. We report a complete genome of a filamentous tropical marine cyanobacterium, Moorea producens PAL, which reveals that about one-fifth of its genome is devoted to production of secondary metabolites, an impressive four times the cyanobacterial average. Moreover, possession of the complete PAL genome has allowed improvement to the assembly of three other Moorea draft genomes. Comparative genomics revealed that they are remarkably similar to one another, despite their differences in geography, morphology, and secondary metabolite profiles. Gene cluster networking highlights that this genus is distinctive among cyanobacteria, not only in the number of secondary metabolite pathways but also in the content of many pathways, which are potentially distinct from all other bacterial gene clusters to date. These findings portend that future genome-guided secondary metabolite discovery and isolation efforts should be highly productive.