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

Molecular networking: An efficient tool for discovering and identifying natural products

Natural products (NPs), play a crucial role in drug development. However, the discovery of NPs is accidental, and conventional identification methods lack accuracy. To overcome these challenges, an increasing number of researchers are directing their attention to Molecular networking (MN). MN based on secondary mass spectrometry has become an important tool for the separation, purification and structural identification of NPs. However, most new tools are not well known. This review started with the most basic MN tool and explains it from the principle, workflow, and application. Then introduce the principles and workflows of the remaining eight new types of MN tools. The reliability of various MNs is mainly verified based on the application of phytochemistry and metabolomics. Subsequently, the principles and applications of 12 structural annotation tools are introduced. For the first time, the scope of 9 kinds of MN tools is compared horizontally, and 12 kinds of structured annotation tools are classified from the type of compound structure suitable for identification. The advantages and disadvantages of various tools are summarized, and make suggestions for future application directions and the development of computing tools in this review. MN tools are expected to enhance the efficiency of the discovery and identification in NPs.

Unlocking marine treasures: isolation and mining strategies of natural products from sponge-associated bacteria

Covering: 2019 to early 2025Marine sponges form unique ecosystems through symbiosis with diverse microbial communities, producing natural products including bioactive compounds. This review comprehensively addresses the key steps in the discovery of natural products from sponge-associated microorganisms, encompassing microbial isolation and cultivation, compound identification, and characterisation. Various cultivation methods, such as floating filter cultivation, microcapsule-based cultivation, and in situ systems, are examined to highlight their applications and strategies for overcoming limitations of conventional approaches. Additionally, the integration of genome-based methodologies and compound screening is explored to enhance the discovery of novel bioactive substances and establish a sustainable platform for natural product research. This review provides insights into the latest trends in sponge-associated microbial research and offers practical perspectives for expanding the utilization of marine biological resources.

Advances in AI-based strategies and tools to facilitate natural product and drug development

Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.

Last Decade Insights in Exploiting Marine Microorganisms as Sources of New Bioactive Natural Products

Marine microorganisms have emerged as prolific sources of bioactive natural products, offering a large chemical diversity and a broad spectrum of biological activities. Over the past decade, significant progress has been made in discovering and characterizing these compounds, pushed by technological innovations in genomics, metabolomics, and bioinformatics. Furthermore, innovative isolation and cultivation approaches have improved the isolation of rare and difficult-to-culture marine microbes, leading to the identification of novel secondary metabolites. Advances in synthetic biology and metabolic engineering have further optimized natural product yields and the generation of novel compounds with improved bioactive properties. This review highlights key developments in the exploitation of marine bacteria, fungi, and microalgae for the discovery of novel natural products with potential applications in diverse fields, underscoring the immense potential of marine microorganisms in the growing Blue Economy sector.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Advances in lasso peptide discovery, biosynthesis, and function

Lasso peptides are a large and sequence-diverse class of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products characterized by their slip knot-like shape. These unique, highly stable peptides are produced by bacteria for various purposes. Their stability and sequence diversity make them a potentially useful scaffold for biomedically relevant folded peptides. However, many questions remain about lasso peptide biosynthesis, ecological function, and diversification potential for biomedical and agricultural applications. This review discusses new insights and open questions about lasso peptide biosynthesis and biological function. The role that genome mining has played in the development of new methodologies for discovering and diversifying lasso peptides is also discussed.

A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance

Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01355-x.

Bacteriocin diversity, function, discovery and application as antimicrobials

Bacteriocins are potent antimicrobial peptides that are produced by bacteria. Since their discovery almost a century ago, diverse peptides have been discovered and described, and some are currently used as commercial food preservatives. Many bacteriocins exhibit extensively post-translationally modified structures encoded on complex gene clusters, whereas others have simple linear structures. The molecular structures, mechanisms of action and resistance have been determined for a number of bacteriocins, but most remain incompletely characterized. These gene-encoded peptides are amenable to bioengineering strategies and heterologous expression, enabling metagenomic mining and modification of novel antimicrobials. The ongoing global antimicrobial resistance crisis demands that novel therapeutics be developed to combat infectious pathogens. New compounds that are target-specific and compatible with the resident microbiota would be valuable alternatives to current antimicrobials. As bacteriocins can be broad or narrow spectrum in nature, they are promising tools for this purpose. However, few bacteriocins have gone beyond preclinical trials and none is currently used therapeutically in humans. In this Review, we explore the broad diversity in bacteriocin structure and function, describe identification and optimization methods and discuss the reasons behind the lack of translation beyond the laboratory of these potentially valuable antimicrobials.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap

Cyanobacterial blooms, producing toxic secondary metabolites, are becoming increasingly common phenomena in the face of rising global temperatures. They are the world's most abundant photosynthetic organisms, largely owing their success to a range of highly diverse and complex natural products possessing a broad spectrum of different bioactivities. Over 2600 compounds have been isolated from cyanobacteria thus far, and their characterisation has revealed unusual and useful chemistries and motifs including alkynes, halogens, and non-canonical amino acids. Genome sequencing of cyanobacteria lags behind natural product isolation, with only 19% of cyanobacterial natural products associated with a sequenced organism. Recent advances in meta(genomics) provide promise to narrow this gap and has also facilitated the uprise of combined genomic and metabolomic approaches, heralding a new era of discovery of novel compounds. Analyses of the datasets described within this manuscript reveal the asynchrony of current genomic and metabolomic data, highlight the chemical diversity of cyanobacterial natural products. Linked to this manuscript, we make these manually curated datasets freely accessible for the public to facilitate further research in this important area.

Harnessing the potential: advances in cyanobacterial natural product research and biotechnology

Covering: 2000 to 2023Cyanobacteria produce a variety of bioactive natural products that can pose a threat to humans and animals as environmental toxins, but also have potential for or inspire pharmaceutical use. As oxygenic phototrophs, cyanobacteria furthermore hold great promise for sustainable biotechnology. Yet, the necessary tools for exploiting their biotechnological potential have so far been established only for a few model strains of cyanobacteria, while large untapped biosynthetic resources are hidden in slow-growing cyanobacterial genera that are difficult to access by genetic techniques. In recent years, several approaches have been developed to circumvent the bottlenecks in cyanobacterial natural product research. Here, we summarize current progress that has been made in unlocking or characterizing cryptic metabolic pathways using integrated omics techniques, orphan gene cluster activation, use of genetic approaches in original producers, heterologous expression and chemo-enzymatic techniques. We are mainly highlighting genomic mining concepts and strategies towards high-titer production of cyanobacterial natural products from the last 10 years and discuss the need for further research developments in this field.

Learning sequence, structure, and function representations of proteins with language models

The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.

Identification of peptides from honeybee gut symbionts as potential antimicrobial agents against Melissococcus plutonius

Eusocial pollinators are crucial elements in global agriculture. The honeybees and bumblebees are associated with a simple yet host-restricted gut community, which protect the hosts against pathogen infections. Recent genome mining has led to the discovery of biosynthesis pathways of bioactive natural products mediating microbe-microbe interactions from the gut microbiota. Here, we investigate the diversity of biosynthetic gene clusters in the bee gut microbiota by analyzing 477 genomes from cultivated bacteria and metagenome-assembled genomes. We identify 744 biosynthetic gene clusters (BGCs) covering multiple chemical classes. While gene clusters for the post-translationally modified peptides are widely distributed in the bee guts, the distribution of the BGC classes varies significantly in different bee species among geographic locations, which is attributed to the strain-level variation of bee gut members in the chemical repertoire. Interestingly, we find that Gilliamella strains possessing a thiopeptide-like BGC show potent activity against the pathogenic Melissococcus plutonius. The spectrometry-guided genome mining reveals a RiPP-encoding BGC from Gilliamella with a 10 amino acid-long core peptide exhibiting antibacterial potentials. This study illustrates the widespread small-molecule-encoding BGCs in the bee gut symbionts and provides insights into the bacteria-derived natural products as potential antimicrobial agents against pathogenic infections.

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.

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

Correlative metabologenomics of 110 fungi reveals metabolite-gene cluster pairs

Natural products research increasingly applies -omics technologies to guide molecular discovery. While the combined analysis of genomic and metabolomic datasets has proved valuable for identifying natural products and their biosynthetic gene clusters (BGCs) in bacteria, this integrated approach lacks application to fungi. Because fungi are hyper-diverse and underexplored for new chemistry and bioactivities, we created a linked genomics-metabolomics dataset for 110 Ascomycetes, and optimized both gene cluster family (GCF) networking parameters and correlation-based scoring for pairing fungal natural products with their BGCs. Using a network of 3,007 GCFs (organized from 7,020 BGCs), we examined 25 known natural products originating from 16 known BGCs and observed statistically significant associations between 21 of these compounds and their validated BGCs. Furthermore, the scalable platform identified the BGC for the pestalamides, demystifying its biogenesis, and revealed more than 200 high-scoring natural product-GCF linkages to direct future discovery.

Advances in mining and expressing microbial biosynthetic gene clusters

Natural products (NPs) especially the secondary metabolites originated from microbes exhibit great importance in biomedical, industrial and agricultural applications. However, mining biosynthetic gene clusters (BGCs) to produce novel NPs has been hindered owing that a large population of environmental microbes are unculturable. In the past decade, strategies to explore BGCs directly from (meta)genomes have been established along with the fast development of high-throughput sequencing technologies and the powerful bioinformatics data-processing tools, which greatly expedited the exploitations of novel BGCs from unculturable microbes including the extremophilic microbes. In this review, we firstly summarized the popular bioinformatics tools and databases available to mine novel BGCs from (meta)genomes based on either pure cultures or pristine environmental samples. Noticeably, approaches rooted from machine learning and deep learning with focuses on the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs) were dramatically increased in recent years. Moreover, synthetic biology techniques to express the novel BGCs in culturable native microbes or heterologous hosts were introduced. This working pipeline including the discovery and biosynthesis of novel NPs will greatly advance the exploitations of the abundant but unexplored microbial BGCs.

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.

Recent advances in research for potential utilization of unexplored lichen metabolites

Several research studies have shown that lichens are productive organisms for the synthesis of a broad range of secondary metabolites. Lichens are a self-sustainable stable microbial ecosystem comprising an exhabitant fungal partner (mycobiont) and at least one or more photosynthetic partners (photobiont). The successful symbiosis is responsible for their persistence throughout time and allows all the partners (holobionts) to thrive in many extreme habitats, where without the synergistic relationship they would be rare or non-existent. The ability to survive in harsh conditions can be directly correlated with the production of some unique metabolites. Despite the potential applications, these unique metabolites have been underutilised by pharmaceutical and agrochemical industries due to their slow growth, low biomass availability and technical challenges involved in their artificial cultivation. However, recent development of biotechnological tools such as molecular phylogenetics, modern tissue culture techniques, metabolomics and molecular engineering are opening up a new opportunity to exploit these compounds within the lichen holobiome for industrial applications. This review also highlights the recent advances in culturing the symbionts and the computational and molecular genetics approaches of lichen gene regulation recognized for the enhanced production of target metabolites. The recent development of multi-omics novel biodiscovery strategies aided by synthetic biology in order to study the heterologous expressed lichen-derived biosynthetic gene clusters in a cultivatable host offers a promising means for a sustainable supply of specialized metabolites.

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.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Comparative Metagenomic Analysis of Biosynthetic Diversity across Sponge Microbiomes Highlights Metabolic Novelty, Conservation, and Diversification

Marine sponges and their microbial symbiotic communities are rich sources of diverse natural products (NPs) that often display biological activity, yet little is known about the global distribution of NPs and the symbionts that produce them. Since the majority of sponge symbionts remain uncultured, it is a challenge to characterize their NP biosynthetic pathways, assess their prevalence within the holobiont, and measure the diversity of NP biosynthetic gene clusters (BGCs) across sponge taxa and environments. Here, we explore the microbial biosynthetic landscapes of three high-microbial-abundance (HMA) sponges from the Atlantic Ocean and the Mediterranean Sea. This data set reveals striking novelty, with <1% of the recovered gene cluster families (GCFs) showing similarity to any characterized BGC. When zooming in on the microbial communities of each sponge, we observed higher variability of specialized metabolic and taxonomic profiles between sponge species than within species. Nonetheless, we identified conservation of GCFs, with 20% of sponge GCFs being shared between at least two sponge species and a GCF core comprised of 6% of GCFs shared across all species. Within this functional core, we identified a set of widespread and diverse GCFs encoding nonribosomal peptide synthetases that are potentially involved in the production of diversified ether lipids, as well as GCFs putatively encoding the production of highly modified proteusins. The present work contributes to the small, yet growing body of data characterizing NP landscapes of marine sponge symbionts and to the cryptic biosynthetic potential contained in this environmental niche. IMPORTANCE Marine sponges and their microbial symbiotic communities are a rich source of diverse natural products (NPs). However, little is known about the sponge NP global distribution landscape and the symbionts that produce them. Here, we make use of recently developed tools to perform untargeted mining and comparative analysis of sponge microbiome metagenomes of three sponge species in the first study considering replicate metagenomes of multiple sponge species. We present an overview of the biosynthetic diversity across these sponge holobionts, which displays extreme biosynthetic novelty. We report not only the conservation of biosynthetic and taxonomic diversity but also a core of conserved specialized metabolic pathways. Finally, we highlight several novel GCFs with unknown ecological function, and observe particularly high biosynthetic potential in Acidobacteriota and Latescibacteria symbionts. This study paves the way toward a better understanding of the marine sponge holobionts' biosynthetic potential and the functional and ecological role of sponge microbiomes.

Strategies to access biosynthetic novelty in bacterial genomes for drug discovery

Bacteria provide a rich source of natural products with potential therapeutic applications, such as novel antibiotic classes or anticancer drugs. Bioactivity-guided screening of bacterial extracts and characterization of biosynthetic pathways for drug discovery is now complemented by the availability of large (meta)genomic collections, placing researchers into the postgenomic, big-data era. The progress in next-generation sequencing and the rise of powerful computational tools provide unprecedented insights into unexplored taxa, ecological niches and 'biosynthetic dark matter', revealing diverse and chemically distinct natural products in previously unstudied bacteria. In this Review, we discuss such sources of new chemical entities and the implications for drug discovery with a particular focus on the strategies that have emerged in recent years to identify and access novelty.

Advances in Microbiome-Derived Solutions and Methodologies Are Founding a New Era in Skin Health and Care

The microbiome, as a community of microorganisms and their structural elements, genomes, metabolites/signal molecules, has been shown to play an important role in human health, with significant beneficial applications for gut health. Skin microbiome has emerged as a new field with high potential to develop disruptive solutions to manage skin health and disease. Despite an incomplete toolbox for skin microbiome analyses, much progress has been made towards functional dissection of microbiomes and host-microbiome interactions. A standardized and robust investigation of the skin microbiome is necessary to provide accurate microbial information and set the base for a successful translation of innovations in the dermo-cosmetic field. This review provides an overview of how the landscape of skin microbiome research has evolved from method development (multi-omics/data-based analytical approaches) to the discovery and development of novel microbiome-derived ingredients. Moreover, it provides a summary of the latest findings on interactions between the microbiomes (gut and skin) and skin health/disease. Solutions derived from these two paths are used to develop novel microbiome-based ingredients or solutions acting on skin homeostasis are proposed. The most promising skin and gut-derived microbiome interventional strategies are presented, along with regulatory, safety, industrial, and technical challenges related to a successful translation of these microbiome-based concepts/technologies in the dermo-cosmetic industry.

Genome-Guided Discovery of Natural Products through Multiplexed Low-Coverage Whole-Genome Sequencing of Soil Actinomycetes on Oxford Nanopore Flongle

Genome mining is an important tool for discovery of new natural products; however, the number of publicly available genomes for natural product-rich microbes such as actinomycetes, relative to human pathogens with smaller genomes, is small. To obtain contiguous DNA assemblies and identify large (ca. 10 to greater than 100 kb) biosynthetic gene clusters (BGCs) with high GC (>70%) and high-repeat content, it is necessary to use long-read sequencing methods when sequencing actinomycete genomes. One of the hurdles to long-read sequencing is the higher cost. In the current study, we assessed Flongle, a recently launched platform by Oxford Nanopore Technologies, as a low-cost DNA sequencing option to obtain contiguous DNA assemblies and analyze BGCs. To make the workflow more cost-effective, we multiplexed up to four samples in a single Flongle sequencing experiment while expecting low-sequencing coverage per sample. We hypothesized that contiguous DNA assemblies might enable analysis of BGCs even at low sequencing depth. To assess the value of these assemblies, we collected high-resolution mass spectrometry data and conducted a multi-omics analysis to connect BGCs to secondary metabolites. In total, we assembled genomes for 20 distinct strains across seven sequencing experiments. In each experiment, 50% of the bases were in reads longer than 10 kb, which facilitated the assembly of reads into contigs with an average N50 value of 3.5 Mb. The programs antiSMASH and PRISM predicted 629 and 295 BGCs, respectively. We connected BGCs to metabolites for N,N-dimethyl cyclic-di-tryptophan, two novel lasso peptides, and three known actinomycete-associated siderophores, namely, mirubactin, heterobactin, and salinichelin. IMPORTANCE Short-read sequencing of GC-rich genomes such as those from actinomycetes results in a fragmented genome assembly and truncated biosynthetic gene clusters (often 10 to >100 kb long), which hinders our ability to understand the biosynthetic potential of a given strain and predict the molecules that can be produced. The current study demonstrates that contiguous DNA assemblies, suitable for analysis of BGCs, can be obtained through low-coverage, multiplexed sequencing on Flongle, which provides a new low-cost workflow ($30 to 40 per strain) for sequencing actinomycete strain libraries.

Systematic analysis of the lysine malonylome in Sanghuangporus sanghuang

Background

Sanghuangporus sanghuang is a well-known traditional medicinal mushroom associated with mulberry. Despite the properties of this mushroom being known for many years, the regulatory mechanisms of bioactive compound biosynthesis in this medicinal mushroom are still unclear. Lysine malonylation is a posttranslational modification that has many critical functions in various aspects of cell metabolism. However, at present we do not know its role in S. sanghuang. In this study, a global investigation of the lysine malonylome in S. sanghuang was therefore carried out.

Results

In total, 714 malonyl modification sites were matched to 255 different proteins. The analysis indicated that malonyl modifications were involved in a wide range of cellular functions and displayed a distinct subcellular localization. Bioinformatics analysis indicated that malonylated proteins were engaged in different metabolic pathways, including glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid (TCA) cycle. Notably, a total of 26 enzymes related to triterpene and polysaccharide biosynthesis were found to be malonylated, indicating an indispensable role of lysine malonylation in bioactive compound biosynthesis in S. sanghuang.

Conclusions

These findings suggest that malonylation is associated with many metabolic pathways, particularly the metabolism of the bioactive compounds triterpene and polysaccharide. This paper represents the first comprehensive survey of malonylation in S. sanghuang and provides important data for further study on the physiological function of lysine malonylation in S. sanghuang and other medicinal mushrooms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08120-0.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Mining genomes to illuminate the specialized chemistry of life

All organisms produce specialized organic molecules, ranging from small volatile chemicals to large gene-encoded peptides, that have evolved to provide them with diverse cellular and ecological functions. As natural products, they are broadly applied in medicine, agriculture and nutrition. The rapid accumulation of genomic information has revealed that the metabolic capacity of virtually all organisms is vastly underappreciated. Pioneered mainly in bacteria and fungi, genome mining technologies are accelerating metabolite discovery. Recent efforts are now being expanded to all life forms, including protists, plants and animals, and new integrative omics technologies are enabling the increasingly effective mining of this molecular diversity.

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation

Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated.

Results

We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA.

Availability and implementation

MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mining and unearthing hidden biosynthetic potential

Genetically encoded small molecules (secondary metabolites) play eminent roles in ecological interactions, as pathogenicity factors and as drug leads. Yet, these chemical mediators often evade detection, and the discovery of novel entities is hampered by low production and high rediscovery rates. These limitations may be addressed by genome mining for biosynthetic gene clusters, thereby unveiling cryptic metabolic potential. The development of sophisticated data mining methods and genetic and analytical tools has enabled the discovery of an impressive array of previously overlooked natural products. This review shows the newest developments in the field, highlighting compound discovery from unconventional sources and microbiomes.

Natural products are an important source of bioactive compounds and have versatile applications in different fields, but their discovery is challenging. Here, the authors review the recent developments in genome mining for discovery of natural products, focusing on compounds from unconventional microorganisms and microbiomes.

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

Coupling Mass Spectral and Genomic Information to Improve Bacterial Natural Product Discovery Workflows

Bacterial natural products possess potent bioactivities and high structural diversity and are typically encoded in biosynthetic gene clusters. Traditional natural product discovery approaches rely on UV- and bioassay-guided fractionation and are limited in terms of dereplication. Recent advances in mass spectrometry, sequencing and bioinformatics have led to large-scale accumulation of genomic and mass spectral data that is increasingly used for signature-based or correlation-based mass spectrometry genome mining approaches that enable rapid linking of metabolomic and genomic information to accelerate and rationalize natural product discovery. In this mini-review, these approaches are presented, and discovery examples provided. Finally, future opportunities and challenges for paired omics-based natural products discovery workflows are discussed.

Leveraging orthogonal mass spectrometry based strategies for comprehensive sequencing and characterization of ribosomal antimicrobial peptide natural products

Covering: Up to July 2020Ribosomal antimicrobial peptide (AMP) natural products, also known as ribosomally synthesized and post-translationally modified peptides (RiPPs) or host defense peptides, demonstrate potent bioactivities and impressive complexity that complicate molecular and biological characterization. Tandem mass spectrometry (MS) has rapidly accelerated bioactive peptide sequencing efforts, yet standard workflows insufficiently address intrinsic AMP diversity. Herein, orthogonal approaches to accelerate comprehensive and accurate molecular characterization without the need for prior isolation are reviewed. Chemical derivatization, proteolysis (enzymatic and chemical cleavage), multistage MS fragmentation, and separation (liquid chromatography and ion mobility) strategies can provide complementary amino acid composition and post-translational modification data to constrain sequence solutions. Examination of two complex case studies, gomesin and styelin D, highlights the practical implementation of the proposed approaches. Finally, we emphasize the importance of heterogeneous AMP peptidoforms that confer varying biological function, an area that warrants significant further development.

The year 2020 in natural product bioinformatics: an overview of the latest tools and databases

Covering: 2020 Bioinformatic approaches to document and analyse chemical structures, biosynthetic gene clusters and analytical data play an important role in the study of natural products. Every year, such a large number of new algorithms, tools and databases are released, that it is difficult to keep track of all the latest developments. The aim of this short article is to provide a concise overview of and reference to the major tools, methods and databases that have been released in the past year.

Omics-based strategies to discover novel classes of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) form a highly diverse class of natural products, with various biotechnologically and clinically relevant activities. A recent increase in discoveries of novel RiPP classes suggests that currently known RiPPs constitute just the tip of the iceberg. Genome mining has been a driving force behind these discoveries, but remains challenging due to a lack of universal genetic markers for RiPP detection. In this review, we discuss how various genome mining methodologies contribute towards the discovery of novel RiPP classes. Some methods prioritize novel biosynthetic gene clusters (BGCs) based on shared modifications between RiPP classes. Other methods identify RiPP precursors using machine-learning classifiers. The integration of such methods as well as integration with other types of omics data in more comprehensive pipelines could help these tools reach their potential, and keep pushing the boundaries of the chemical diversity of this important class of molecules.

Bioinformatic and Reactivity-Based Discovery of Linaridins

Linaridins are members of the ribosomally synthesized and post-translationally modified peptide (RiPP) family of natural products. Five linaridins have been reported, which are defined by the presence of dehydrobutyrine, a dehydrated, alkene-containing amino acid derived from threonine. This work describes the development of a linaridin-specific scoring module for Rapid ORF Description and Evaluation Online (RODEO), a genome-mining tool tailored toward RiPP discovery. Upon mining publicly accessible genomes available in the NCBI database, RODEO identified 561 (382 nonredundant) linaridin biosynthetic gene clusters. Linaridin BGCs with unique gene architectures and precursor sequences markedly different from previous predictions were uncovered during these efforts. To aid in data set validation, two new linaridins, pegvadin A and B, were detected through reactivity-based screening and isolated from Streptomyces noursei and Streptomyces auratus, respectively. Reactivity-based screening involves the use of a probe that chemoselectively modifies an organic functional group present in the natural product. The dehydrated amino acids present in linaridins as α/β-unsaturated carbonyls were appropriate electrophiles for nucleophilic 1,4-addition using a thiol-functionalized probe. The data presented within significantly expand the number of predicted linaridin biosynthetic gene clusters and serve as a roadmap for future work in the area. The combination of bioinformatics and reactivity-based screening is a powerful approach to accelerate natural product discovery.

Genome mining strategies for ribosomally synthesised and post-translationally modified peptides

Genome mining is a computational method for the automatic detection and annotation of biosynthetic gene clusters (BGCs) from genomic data. This approach has been increasingly utilised in natural product (NP) discovery due to the large amount of sequencing data that is now available. Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a class of structurally complex NP with diverse bioactivities. RiPPs have recently been shown to occupy a much larger expanse of genomic and chemical space than previously appreciated, indicating that annotation of RiPP BGCs in genomes may have been overlooked in the past. This review provides an overview of the genome mining tools that have been specifically developed to aid in the discovery of RiPP BGCs, which have been built from an increasing knowledgebase of RiPP structures and biosynthesis. Given these recent advances, the application of targeted genome mining has great potential to accelerate the discovery of important molecules such as antimicrobial and anticancer agents whilst increasing our understanding about how these compounds are biosynthesised in nature.

Advances in actinomycete research: an ActinoBase review of 2019

The actinomycetes are Gram-positive bacteria belonging to the order Actinomycetales within the phylum Actinobacteria. They include members with significant economic and medical importance, for example filamentous actinomycetes such as Streptomyces species, which have a propensity to produce a plethora of bioactive secondary metabolites and form symbioses with higher organisms, such as plants and insects. Studying these bacteria is challenging, but also fascinating and very rewarding. As a Microbiology Society initiative, members of the actinomycete research community have been developing a Wikipedia-style resource, called ActinoBase, the purpose of which is to aid in the study of these filamentous bacteria. This review will highlight 10 publications from 2019 that have been of special interest to the ActinoBase community, covering 4 major components of actinomycete research: (i) development and regulation; (ii) specialized metabolites; (iii) ecology and host interactions; and (iv) technology and methodology.

Linking genomics and metabolomics to chart specialized metabolic diversity

Microbial and plant specialized metabolites constitute an immense chemical diversity, and play key roles in mediating ecological interactions between organisms. Also referred to as natural products, they have been widely applied in medicine, agriculture, cosmetic and food industries. Traditionally, the main discovery strategies have centered around the use of activity-guided fractionation of metabolite extracts. Increasingly, omics data is being used to complement this, as it has the potential to reduce rediscovery rates, guide experimental work towards the most promising metabolites, and identify enzymatic pathways that enable their biosynthetic production. In recent years, genomic and metabolomic analyses of specialized metabolic diversity have been scaled up to study thousands of samples simultaneously. Here, we survey data analysis technologies that facilitate the effective exploration of large genomic and metabolomic datasets, and discuss various emerging strategies to integrate these two types of omics data in order to further accelerate discovery.

Genomic and Metabolomic Analysis of Antarctic Bacteria Revealed Culture and Elicitation Conditions for the Production of Antimicrobial Compounds

Concern about finding new antibiotics against drug-resistant pathogens is increasing every year. Antarctic bacteria have been proposed as an unexplored source of bioactive metabolites; however, most biosynthetic gene clusters (BGCs) producing secondary metabolites remain silent under common culture conditions. Our work aimed to characterize elicitation conditions for the production of antibacterial secondary metabolites from 34 Antarctic bacterial strains based on MS/MS metabolomics and genome mining approaches. Bacterial strains were cultivated under different nutrient and elicitation conditions, including the addition of lipopolysaccharide (LPS), sodium nitroprusside (SNP), and coculture. Metabolomes were obtained by HPLC-QTOF-MS/MS and analyzed through molecular networking. Antibacterial activity was determined, and seven strains were selected for genome sequencing and analysis. Biosynthesis pathways were activated by all the elicitation treatments, which varies among strains and dependents of culture media. Increased antibacterial activity was observed for a few strains and addition of LPS was related with inhibition of Gram-negative pathogens. Antibiotic BGCs were found for all selected strains and the expressions of putative actinomycin, carotenoids, and bacillibactin were characterized by comparison of genomic and metabolomic data. This work established the use of promising new elicitors for bioprospection of Antarctic bacteria and highlights the importance of new "-omics" comparative approaches for drug discovery.

Software tools, databases and resources in metabolomics: updates from 2018 to 2019

Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.

The manifold roles of microbial ribosomal peptide-based natural products in physiology and ecology

The ribosomally synthesized and posttranslationally modified peptides (RiPPs), also called ribosomal peptide natural products (RPNPs), form a growing superfamily of natural products that are produced by many different organisms and particularly by bacteria. They are derived from precursor polypeptides whose modification by various dedicated enzymes helps to establish a vast array of chemical motifs. RiPPs have attracted much interest as a source of potential therapeutic agents, and in particular as alternatives to conventional antibiotics to address the bacterial resistance crisis. However, their ecological roles in nature are poorly understood and explored. The present review describes major RiPP actors in competition within microbial communities, the main ecological and physiological functions currently evidenced for RiPPs, and the microbial ecosystems that are the sites for these functions. We envision that the study of RiPPs may lead to discoveries of new biological functions and highlight that a better knowledge of how bacterial RiPPs mediate inter-/intraspecies and interkingdom interactions will hold promise for devising alternative strategies in antibiotic development.