Enhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching

New Cytotoxic γ-Lactam Alkaloids from the Mangrove-Derived Fungus Talaromyces hainanensis sp. nov. Guided by Molecular Networking Strategy

The fungus Talaromyces hainanensis, isolated from the mangrove soil, was characterized as a novel species by morphology observation and phylogenetic analyses. Four new γ-lactam alkaloids talaroilactams A-D (1-4) and two reported compounds harzianic acid (5) and isoharzianic acid (6) were identified from the fungus T. hainanensis WHUF0341, assisted by OSMAC along with molecular networking approaches. Their structures were determined through ECD calculations and spectroscopic analyses. Moreover, the biosynthetic route of 1-4 was also proposed. Compound 1 displayed potent cytotoxicity against HepG2 cell lines, with an IC50 value of 10.75 ± 1.11 μM. In addition, network pharmacology was employed to dissect the probable mechanisms contributing to the antihepatocellular carcinoma effects of compound 1, revealing that cytotoxicity was mainly associated with proteolysis, negative regulation of autophagy, inflammatory response, and the renin-angiotensin system. These results not only expanded the chemical space of natural products from the mangrove associated fungi but also afforded promising lead compounds for developing the antihepatocellular carcinoma agents.

Bioactivity-driven fungal metabologenomics identifies antiproliferative stemphone analogs and their biosynthetic gene cluster

INTRODUCTION

Fungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways.

OBJECTIVES

To prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences.

METHODS

The 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics.

RESULTS

We isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated-Omics datasets.

CONCLUSIONS

This work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.

Unveiling biosynthetic potential of an Arctic marine-derived strain Aspergillus sydowii MNP-2

BACKGROUND

A growing number of studies have demonstrated that the polar regions have the potential to be a significant repository of microbial resources and a potential source of active ingredients. Genome mining strategy plays a key role in the discovery of bioactive secondary metabolites (SMs) from microorganisms. This work highlighted deciphering the biosynthetic potential of an Arctic marine-derived strain Aspergillus sydowii MNP-2 by a combination of whole genome analysis and antiSMASH as well as feature-based molecular networking (MN) in the Global Natural Products Social Molecular Networking (GNPS).

RESULTS

In this study, a high-quality whole genome sequence of an Arctic marine strain MNP-2, with a size of 34.9 Mb was successfully obtained. Its total number of genes predicted by BRAKER software was 13,218, and that of non-coding RNAs (rRNA, sRNA, snRNA, and tRNA) predicted by using INFERNAL software was 204. AntiSMASH results indicated that strain MNP-2 harbors 56 biosynthetic gene clusters (BGCs), including 18 NRPS/NRPS-like gene clusters, 10 PKS/PKS-like gene clusters, 8 terpene synthse gene clusters, 5 indole synthase gene clusters, 10 hybrid gene clusters, and 5 fungal-RiPP gene clusters. Metabolic analyses of strain MNP-2 grown on various media using GNPS networking revealed its great potential for the biosynthesis of bioactive SMs containing a variety of heterocyclic and bridge-ring structures. For example, compound G-8 exhibited a potent anti-HIV effect with an IC50 value of 7.2 nM and an EC50 value of 0.9 nM. Compound G-6 had excellent in vitro cytotoxicities against the K562, MCF-7, Hela, DU145, U1975, SGC-7901, A549, MOLT-4, and HL60 cell lines, with IC50 values ranging from 0.10 to 3.3 µM, and showed significant anti-viral (H1N1 and H3N2) activities with IC50 values of 15.9 and 30.0 µM, respectively.

CONCLUSIONS

These findings definitely improve our knowledge about the molecular biology of genus A. sydowii and would effectively unveil the biosynthetic potential of strain MNP-2 using genomics and metabolomics techniques.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Cyclic dipeptides and the human microbiome: Opportunities and challenges

Research into the human microbiome has implicated its constituents in a variety of non-communicable diseases, with certain microbes found to promote health and others leading to dysbiosis and pathogenesis.Microbes communicate and coordinate their behaviour through the secretion of small molecules, such as cyclic dipeptides (CDPs), into their surrounding environment. CDPs are ubiquitous signalling molecules thatexhibit a wide range of biological activities, with particular relevance to human health due to their potential to act as microbiome modulators.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.