Linking genomics and metabolomics to chart specialized metabolic diversity

Rule-based omics mining reveals antimicrobial macrocyclic peptides against drug-resistant clinical isolates

Antimicrobial resistance remains a significant global threat, driving up mortality rates worldwide. Ribosomally synthesized and post-translationally modified peptides have emerged as a promising source of novel peptide antibiotics due to their diverse chemical structures. Here, we report the discovery of new aminovinyl-(methyl)cysteine (Avi(Me)Cys)-containing peptide antibiotics through a synergistic approach combining biosynthetic rule-based omics mining and heterologous expression. We first bioinformatically identify 1172 RiPP biosynthetic gene clusters (BGCs) responsible for Avi(Me)Cys-containing peptides formation from a vast pool of over 50,000 bacterial genomes. Subsequently, we successfully establish the connection between three identified BGCs and the biosynthesis of five peptide antibiotics via biosynthetic rule-guided metabolic analysis. Notably, we discover a class V lanthipeptide, massatide A, which displays excellent activity against gram-positive pathogens, including drug-resistant clinical isolates like linezolid-resistant S. aureus and methicillin-resistant S. aureus, with a minimum inhibitory concentration of 0.25 μg/mL. The remarkable performance of massatide A in an animal infection model, coupled with a relatively low risk of resistance and favorable safety profile, positions it as a promising candidate for antibiotic development. Our study highlights the potential of Avi(Me)Cys-containing peptides in expanding the arsenal of antibiotics against multi-drug-resistant bacteria, offering promising drug leads in the ongoing battle against infectious diseases.

Charting the Cannabis plant chemical space with computational metabolomics

INTRODUCTION

The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.

OBJECTIVES

Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.

METHODS

Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).

RESULTS

The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.

CONCLUSION

These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

Nucleolin myocardial-specific knockout exacerbates glucose metabolism disorder in endotoxemia-induced myocardial injury

Background

Sepsis-induced myocardial injury, as one of the important complications of sepsis, can significantly increase the mortality of septic patients. Our previous study found that nucleolin affected mitochondrial function in energy synthesis and had a protective effect on septic cardiomyopathy in mice. During sepsis, glucose metabolism disorders aggravated myocardial injury and had a negative effect on septic patients.

Objectives

We investigated whether nucleolin could regulate glucose metabolism during endotoxemia-induced myocardial injury.

Methods

The study tested whether the nucleolin cardiac-specific knockout in the mice could affect glucose metabolism through untargeted metabolomics, and the results of metabolomics were verified experimentally in H9C2 cells. The ATP content, lactate production, and oxygen consumption rate (OCR) were evaluated.

Results

The metabolomics results suggested that glycolytic products were increased in endotoxemia-induced myocardial injury, and that nucleolin myocardial-specific knockout altered oxidative phosphorylation-related pathways. The experiment data showed that TNF-α combined with LPS stimulation could increase the lactate content and the OCR values by about 25%, and decrease the ATP content by about 25%. However, interference with nucleolin expression could further decrease ATP content and OCR values by about 10-20% and partially increase the lactate level in the presence of TNF-α and LPS. However, nucleolin overexpression had the opposite protective effect, which partially reversed the decrease in ATP content and the increase in lactate level.

Conclusion

Down-regulation of nucleolin can exacerbate glucose metabolism disorders in endotoxemia-induced myocardial injury. Improving glucose metabolism by regulating nucleolin was expected to provide new therapeutic ideas for patients with septic cardiomyopathy.

Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes

Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40-60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.

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.

The insect microbiome is a vast source of bioactive small molecules

Covering: September 1964 to June 2023Bacteria and fungi living in symbiosis with insects have been studied over the last sixty years and found to be important sources of bioactive natural products. Not only classic producers of secondary metabolites such as Streptomyces and other members of the phylum Actinobacteria but also numerous bacteria from the phyla Proteobacteria and Firmicutes and an impressive array of fungi (usually pathogenic) serve as the source of a structurally diverse number of small molecules with important biological activities including antimicrobial, cytotoxic, antiparasitic and specific enzyme inhibitors. The insect niche is often the exclusive provider of microbes producing unique types of biologically active compounds such as gerumycins, pederin, dinactin, and formicamycins. However, numerous insects still have not been described taxonomically, and in most cases, the study of their microbiota is completely unexplored. In this review, we present a comprehensive survey of 553 natural products produced by microorganisms isolated from insects by collating and classifying all the data according to the type of compound (rather than the insect or microbial source). The analysis of the correlations among the metadata related to insects, microbial partners, and their produced compounds provides valuable insights into the intricate dynamics between insects and their symbionts as well as the impact of their metabolites on these relationships. Herein, we focus on the chemical structure, biosynthesis, and biological activities of the most relevant compounds.

Unlocking the potential of bacterial endophytes from medicinal plants for drug discovery

Among the plant-associated microorganisms, the so-called endophytes continue to attract much attention because of their ability not only to protect host plants from biotic and abiotic stress factors, but also the potential to produce bioactive secondary metabolites. The latter property can elicit growth-promoting effects on plants, as well as boost the production of plant-specific secondary metabolites with valuable pharmacological properties. In addition, endophyte-derived secondary metabolites may be a rich source for the discovery of drugs to treat various diseases, including infections and cancer. However, the full potential of endophytes to produce bioactive secondary metabolites is often not revealed upon conventional cultivation in the laboratory. New advances in genomics and metabolic engineering offer exciting opportunities for the exploration and exploitation of endophytes' biosynthetic potential. This review focuses on bacterial endophytes of medicinal plants, some of their secondary metabolites and recent advances in deciphering their biosynthesis. The latter may assist in genetic engineering efforts aimed at the discovery of novel bioactive compounds with the potential to be developed into drugs.

Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

In this editorial I comment on the article "Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer" published in the recent issue of the World Journal of Clinical Cases 2023 November 6; 11 (31): 7593-7609. Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats. Numerous methods for chemical warfare, communication, nutrition acquisition, and stress prevention are made possible by these specialized metabolites. Metabolomics is a popular technique for collecting direct measurements of metabolic activity from many biological systems. However, confusing metabolite identification is a typical issue, and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity. Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologenomics. This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes. Combining metabolomics and genomics data is beneficial. Furthermore, computer networking proposes that using metabolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.

Fast mass spectrometry search and clustering of untargeted metabolomics data

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

Rapid Discovery of Substances with Anticancer Potential from Marine Fungi Based on a One Strain-Many Compounds Strategy and UPLC-QTOF-MS

The secondary metabolites of marine fungi with rich chemical diversity and biological activity are an important and exciting target for natural product research. This study aimed to investigate the fungal community in Quanzhou Bay, Fujian, and identified 28 strains of marine fungi. A total of 28 strains of marine fungi were screened for small-scale fermentation by the OSMAC (One Strain-Many Compounds) strategy, and 77 EtOAc crude extracts were obtained and assayed for cancer cell inhibition rate. A total of six strains of marine fungi (P-WZ-2, P-WZ-3-2, P-WZ-4, P-WZ-5, P56, and P341) with significant changes in cancer cell inhibition induced by the OSMAC strategy were analysed by UPLC-QTOF-MS. The ACD/MS Structure ID Suite software was used to predict the possible structures with inhibitory effects on cancer cells. A total of 23 compounds were identified, of which 10 compounds have been reported to have potential anticancer activity or cytotoxicity. In this study, the OSMAC strategy was combined with an untargeted metabolomics approach based on UPLC-QTOF-MS to efficiently analyse the effect of changes in culture conditions on anticancer potentials and to rapidly find active substances that inhibit cancer cell growth.

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.

Tuning Expectations to Reality: Don't Expect Increased Gut Microbiota Diversity with Dietary Fiber

Dietary approaches, particularly those including fiber supplementation, can be used to promote health benefits by shaping gut microbial communities. Whereas community diversity measures, such as richness and evenness, are often used in microbial ecology to make sense of these complex and vast microbial ecosystems, it is less clear how these concepts apply when dietary fiber supplementation is given. In this perspective, we summarize and demonstrate how factors including experimental approach, number of bacteria sharing a dietary fiber, and initial relative abundances of bacteria that use a fiber can significantly affect diversity outcomes in fiber fermentation studies. We also show that a reduction in alpha diversity is possible, and perhaps expected, for most approaches that use fermentable fibers to beneficially shape the gut microbial community while still achieving health-related improvements.

Exploring the biosynthetic gene clusters in Brevibacterium: a comparative genomic analysis of diversity and distribution

Exploring Brevibacterium strains from various ecosystems may lead to the discovery of new antibiotic-producing strains. Brevibacterium sp. H-BE7, a strain isolated from marine sediments from Northern Patagonia, Chile, had its genome sequenced to study the biosynthetic potential to produce novel natural products within the Brevibacterium genus. The genome sequences of 98 Brevibacterium strains, including strain H-BE7, were selected for a genomic analysis. A phylogenomic cladogram was generated, which divided the Brevibacterium strains into four major clades. A total of 25 strains are potentially unique new species according to Average Nucleotide Identity (ANIb) values. These strains were isolated from various environments, emphasizing the importance of exploring diverse ecosystems to discover the full diversity of Brevibacterium. Pangenome analysis of Brevibacterium strains revealed that only 2.5% of gene clusters are included within the core genome, and most gene clusters occur either as singletons or as cloud genes present in less than ten strains. Brevibacterium strains from various phylogenomic clades exhibit diverse BGCs. Specific groups of BGCs show clade-specific distribution patterns, such as siderophore BGCs and carotenoid-related BGCs. A group of clade IV-A Brevibacterium strains possess a clade-specific Polyketide synthase (PKS) BGCs that connects with phenazine-related BGCs. Within the PKS BGC, five genes, including the biosynthetic PKS gene, participate in the mevalonate pathway and exhibit similarities with the phenazine A BGC. However, additional core biosynthetic phenazine genes were exclusively discovered in nine Brevibacterium strains, primarily isolated from cheese. Evaluating the antibacterial activity of strain H-BE7, it exhibited antimicrobial activity against Salmonella enterica and Listeria monocytogenes. Chemical dereplication identified bioactive compounds, such as 1-methoxyphenazine in the crude extracts of strain H-BE7, which could be responsible of the observed antibacterial activity. While strain H-BE7 lacks the core phenazine biosynthetic genes, it produces 1-methoxyphenazine, indicating the presence of an unknown biosynthetic pathway for this compound. This suggests the existence of alternative biosynthetic pathways or promiscuous enzymes within H-BE7's genome.

Highlighting the Phototherapeutical Potential of Fungal Pigments in Various Fruiting Body Extracts with Informed Feature-Based Molecular Networking

Fungal pigments are characterized by a diverse set of chemical backbones, some of which present photosensitizer-like structures. From the genus Cortinarius, for example, several biologically active photosensitizers have been identified leading to the hypothesis that photoactivity might be a more general phenomenon in the kingdom Fungi. This paper aims at testing the hypothesis. Forty-eight fruiting body-forming species producing pigments from all four major biosynthetic pathways (i.e., shikimate-chorismate, acetate-malonate, mevalonate, and nitrogen heterocycles) were selected and submitted to a workflow combining in vitro chemical and biological experiments with state-of-the-art metabolomics. Fungal extracts were profiled by high-resolution mass spectrometry and subsequently explored by spectral organization through feature-based molecular networking (FBMN), including advanced metabolite dereplication techniques. Additionally, the photochemical properties (i.e., light-dependent production of singlet oxygen), the phenolic content, and the (photo)cytotoxic activity of the extracts were studied. Different levels of photoactivity were found in species from all four metabolic groups, indicating that light-dependent effects are common among fungal pigments. In particular, extracts containing pigments from the acetate-malonate pathway, e.g., extracts from Bulgaria inquinans, Daldinia concentrica, and Cortinarius spp., were not only efficient producers of singlet oxygen but also exhibited photocytotoxicity against three different cancer cell lines. This study explores the distribution of photobiological traits in fruiting body forming fungi and highlights new sources for phototherapeutics.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

2-Furoic acid associated with the infection of nematodes by Dactylellina haptotyla and its biocontrol potential on plant root-knot nematodes

Dactylellina haptotyla is a typical nematode-trapping fungus that has garnered the attention of many scholars for its highly effective lethal potential for nematodes. Secondary metabolites play an important role in D. haptotyla-nematode interactions, but which metabolites perform which function remains unclear. We report the metabolic functions based on high-quality, chromosome-level genome assembly of wild D. haptotyla YMF1.03409. The results indicate that a large variety of secondary metabolites and their biosynthetic genes were significantly upregulated during the nematode-trapping stage. In parallel, we identified that 2-furoic acid was specifically produced during nematode trapping by D. haptotyla YMF1.03409 and isolated it from fermentation production. 2-Furoic acid demonstrated strong nematicidal activity with an LD50 value of 55.05 µg/mL against Meloidogyne incognita at 48 h. Furthermore, the pot experiment showed that the number of galls of tomato root was significantly reduced in the experimental group treated with 2-furoic acid. The considerable increase in the 2-furoic acid content during the infection process and its virulent nematicidal activity revealed an essential synergistic effect during the process of nematode-trapping fungal infection. IMPORTANCE Dactylellina haptotyla have significant application potential in nematode biocontrol. In this study, we determined the chromosome-level genome sequence of D. haptotyla YMF1.03409 by long-read sequencing technology. Comparative genomic analysis identified a series of pathogenesis-related genes and revealed significant gene family contraction events during the evolution of D. haptotyla YMF1.03409. Combining transcriptomic and metabolomic data as well as in vitro activity test results, a compound with important application potential in nematode biocontrol, 2-furoic acid, was identified. Our result expanded the genetic resource of D. haptotyla and identified a previously unreported nematicidal small molecule, which provides new options for the development of plant biocontrol agents.

Structural Diversity and Biological Activity of Cyanopeptolins Produced by Nostoc edaphicum CCNP1411

Cyanopeptolins (CPs) are one of the most commonly occurring class of cyanobacterial nonribosomal peptides. For the majority of these compounds, protease inhibition has been reported. In the current work, the structural diversity of cyanopeptolins produced by Nostoc edaphicum CCNP1411 was explored. As a result, 93 CPs, including 79 new variants, were detected and structurally characterized based on their mass fragmentation spectra. CPs isolated in higher amounts were additionally characterized by NMR. To the best of our knowledge, this is the highest number of cyanopeptides found in one strain. The biological assays performed with the 34 isolated CPs confirmed the significance of the amino acid located between Thr and the unique 3-amino-6-hydroxy-2-piperidone (Ahp) on the activity of the compounds against serine protease and HeLa cancer cells.

Whole genome sequencing and analysis of multiple isolates of Ceratocystis destructans, the causal agent of Ceratocystis canker of almond in California

Ceratocystis canker caused by Ceratocystis destructans is a severe disease of almond, reducing the longevity and productivity of infected trees. Once the disease has established in an individual tree, there is no cure, and management efforts are often limited to removing the infected area of cankers. In this study, we present the genome assemblies of five C. destructans isolates isolated from symptomatic almond trees. The genomes were assembled into a genome size of 27.2 ± 0.9 Mbp with an average of 6924 ± 135 protein-coding genes and an average GC content of 48.8 ± 0.02%. We concentrated our efforts on identifying putative virulence factors of canker pathogens. Analysis of the secreted carbohydrate-active enzymes showed that the genomes harbored 83.4 ± 1.8 secreted CAZymes. The secreted CAZymes covered all the known categories of CAZymes. AntiSMASH revealed that the genomes had at least 7 biosynthetic gene clusters, with one of the non-ribosomal peptide synthases encoding dimethylcoprogen, a conserved virulence determinant of plant pathogenic ascomycetes. From the predicted proteome, we also annotated cytochrome P450 monooxygenases, and transporters, these are well-established virulence determinants of canker pathogens. Moreover, we managed to identify 57.4 ± 2.1 putative effector proteins. Gene Ontology (GO) annotation was applied to compare gene content with two closely related species C. fimbriata, and C. albifundus. This study provides the first genome assemblies for C. destructans, expanding genomic resources for an important almond canker pathogen. The acquired knowledge provides a foundation for further advanced studies, such as molecular interactions with the host, which is critical for breeding for resistance.

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.

A metabologenomics approach to unlock the metabolome of the novel Antarctic deep-sea isolate Lacinutrix shetlandiensis sp. nov. WUR7

The South Shetland Trough, Antarctica, is an underexplored region for microbiological and biotechnological exploitation. Herein, we describe the isolation and characterization of the novel bacterium Lacinutrix shetlandiensis sp. nov. WUR7 from a deep-sea environment. We explored its chemical diversity via a metabologenomics approach, wherein the OSMAC strategy was strategically employed to upregulate cryptic genes for secondary metabolite production. Based on hybrid de novo whole genome sequencing and digital DNA-DNA hybridization, isolate WUR7 was identified as a novel species from the Gram-negative genus Lacinutrix. Its genome was mined for the presence of biosynthetic gene clusters with limited results. However, extensive investigation of its metabolism uncovered an unusual tryptophan decarboxylase with high sequence homology and conserved structure of the active site as compared to ZP_02040762, a highly specific tryptophan decarboxylase from Ruminococcus gnavus. Therefore, WUR7's metabolism was directed toward indole-based alkaloid biosynthesis by feeding it with L-tryptophan. As expected, its metabolome profile changed dramatically, by triggering the extracellular accumulation of a massive array of metabolites unexpressed in the absence of tryptophan. Untargeted LC-MS/MS coupled with molecular networking, followed along with chemoinformatic dereplication, allowed for the annotation of 10 indole alkaloids, belonging to β-carboline, bisindole, and monoindole classes, alongside several unknown alkaloids. These findings guided us to the isolation of a new natural bisindole alkaloid 8,9-dihydrocoscinamide B (1), as the first alkaloid from the genus Lacinutrix, whose structure was elucidated on the basis of extensive 1D and 2D NMR and HR-ESIMS experiments. This comprehensive strategy allowed us to unlock the previously unexploited metabolome of L. shetlandiensis sp. nov. WUR7.

Draft genome sequence of potato crop bacterial isolates and nanoparticles-intervention for the induction of secondary metabolites biosynthesis

Introduction

Insights about the effects of gold nanoparticles (AuNPs) on the biosynthetic manipulation of unknown microbe secondary metabolites could be a promising technique for prospective research on nano-biotechnology.

Aim

In this research, we aimed to isolate a fresh, non-domesticated unknown bacterium strain from a common scab of potato crop located in Saudi Arabia and study the metabolic profile.

Methodology

This was achieved through genomic DNA (gDNA) sequencing using Oxford Nanopore Technology. The genomic data were subjected to several bioinformatics tools, including canu-1.9 software, Prokka, DFAST, Geneious Prime, and AntiSMASH. We exposed the culture of the bacterial isolate with different concentrations of AuNPs and investigated the effects of AuNPs on secondary metabolites biosynthesis using several analytical techniques. Furthermore, Tandem-mass spectrometric (MS/MS) technique was optimized for the characterization of several significant sub-classes.

Results

The genomic draft sequence assembly, alignment, and annotation have verified the bacterial isolate as Priestia megaterium. This bacterium has secondary metabolites related to different biosynthetic gene clusters. AuNPs intervention showed an increase in the production of compounds with the molecular weights of 254 and 270 Da in a direct-dependent manner with the increase of the AuNPs concentrations.

Conclusion

The increase in the yields of compound 1 and 2 concomitantly with the increase in the concentration of the added AuNPs provide evidences about the effects of nanoparticles on the biosynthesis of the secondary metabolites. It contributes to the discovery of genes involved in different biosynthetic gene clusters (BGCs) and prediction of the structures of the natural products.

Uncovering Phytotoxic Compounds Produced by Colletotrichum spp. Involved in Legume Diseases Using an OSMAC-Metabolomics Approach

Different fungal species belonging to the Colletotrichum genus cause anthracnose disease in a range of major crops, resulting in huge economic losses worldwide. Typical symptoms include dark, sunken lesions on leaves, stems, or fruits. Colletotrichum spp. have synthesized, in vitro, a number of biologically active and structurally unusual metabolites that are involved in their host's infection process. In this study, we applied a one strain many compounds (OSMAC) approach, integrated with targeted and non-targeted metabolomics profiling, to shed light on the secondary phytotoxic metabolite panels produced by pathogenic isolates of Colletotrichum truncatum and Colletotrichum trifolii. The phytotoxicity of the fungal crude extracts was also assessed on their primary hosts and related legumes, and the results correlated with the metabolite profile that arose from the different cultural conditions. To the best of our knowledge, this is the first time that the OSMAC strategy integrated with metabolomics approaches has been applied to Colletotrichum species involved in legume diseases.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Metabolomics Reveals Strain-Specific Cyanopeptide Profiles and Their Production Dynamics in Microcystis aeruginosa and M. flos-aquae

Cyanobacterial blooms that release biologically active metabolites into the environment are increasing in frequency as a result of the degradation of freshwater ecosystems globally. The microcystins are one group of cyanopeptides that are extensively studied and included in water quality risk management frameworks. Common bloom-forming cyanobacteria produce incredibly diverse mixtures of other cyanopeptides; however, data on the abundance, distribution, and biological activities of non-microcystin cyanopeptides are limited. We used non-targeted LC-MS/MS metabolomics to study the cyanopeptide profiles of five Microcystis strains: four M. aeruginosa and one M. flos-aquae. Multivariate analysis and GNPS molecular networking demonstrated that each Microcystis strain produced a unique mixture of cyanopeptides. In total, 82 cyanopeptides from the cyanopeptolin (n = 23), microviridin (n = 18), microginin (n = 12), cyanobactin (n = 14), anabaenopeptin (n = 6), aeruginosin (n = 5), and microcystin (n = 4) classes were detected. Microcystin diversity was low compared with the other detected cyanopeptide classes. Based on surveys of the literature and spectral databases, most cyanopeptides represented new structures. To identify growth conditions yielding high amounts of multiple cyanopeptide groups, we next examined strain-specific cyanopeptide co-production dynamics for four of the studied Microcystis strains. When strains were cultivated in two common Microcystis growth media (BG-11 and MA), the qualitative cyanopeptides profiles remained unchanged throughout the growth cycle. For each of the cyanopeptide groups considered, the highest relative cyanopeptide amounts were observed in the mid-exponential growth phase. The outcomes of this study will guide the cultivation of strains producing common and abundant cyanopeptides contaminating freshwater ecosystems. The synchronous production of each cyanopeptide group by Microcystis highlights the need to make more cyanopeptide reference materials available to investigate their distributions and biological functions.

MS2Query: reliable and scalable MS2 mass spectra-based analogue search

Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library matches generally returns a few hits. An attractive alternative is searching for so-called analogues as a starting point for structural annotations; analogues are library molecules which are not exact matches but display a high chemical similarity. However, current analogue search implementations are not yet very reliable and relatively slow. Here, we present MS2Query, a machine learning-based tool that integrates mass spectral embedding-based chemical similarity predictors (Spec2Vec and MS2Deepscore) as well as detected precursor masses to rank potential analogues and exact matches. Benchmarking MS2Query on reference mass spectra and experimental case studies demonstrate improved reliability and scalability. Thereby, MS2Query offers exciting opportunities to further increase the annotation rate of metabolomics profiles of complex metabolite mixtures and to discover new biology.

Constructing a Myxobacterial Natural Product Database to Facilitate NMR-Based Metabolomics Bioprospecting of Myxobacteria

Myxobacteria are fascinating prokaryotes featuring a potent capacity for producing a wealth of bioactive molecules with intricate chemical topology as well as intriguing enzymology, and thus it is critical to developing an efficient pipeline for bioprospecting. Herein, we construct the database MyxoDB, the first public compendium solely dedicated to myxobacteria, which enabled us to provide an overview of the structural diversity and taxonomic distribution of known myxobacterial natural products. Moreover, we demonstrated that the cutting-edge NMR-based metabolomics was effective to differentiate the biosynthetic priority of myxobacteria, whereby MyxoDB could greatly streamline the dereplication of multifarious known compounds and accordingly speed up the discovery of new compounds. This led to the rapid identification of a class of linear di-lipopeptides (archangimins) and a rare rearranged sterol (corasterol) that were endowed with unique chemical architectures and/or biosynthetic enzymology. We also showcased that NMR-based metabolomics, MyxoDB, and genomics can also work concertedly to accelerate the targeted discovery of a polyketidic compound pyxipyrrolone C. All in all, this study sets the stage for the discovery of many more novel natural products from underexplored myxobacterial resources.

Impact of Marine Chemical Ecology Research on the Discovery and Development of New Pharmaceuticals

Diverse ecologically important metabolites, such as allelochemicals, infochemicals and volatile organic chemicals, are involved in marine organismal interactions. Chemically mediated interactions between intra- and interspecific organisms can have a significant impact on community organization, population structure and ecosystem functioning. Advances in analytical techniques, microscopy and genomics are providing insights on the chemistry and functional roles of the metabolites involved in such interactions. This review highlights the targeted translational value of several marine chemical ecology-driven research studies and their impact on the sustainable discovery of novel therapeutic agents. These chemical ecology-based approaches include activated defense, allelochemicals arising from organismal interactions, spatio-temporal variations of allelochemicals and phylogeny-based approaches. In addition, innovative analytical techniques used in the mapping of surface metabolites as well as in metabolite translocation within marine holobionts are summarized. Chemical information related to the maintenance of the marine symbioses and biosyntheses of specialized compounds can be harnessed for biomedical applications, particularly in microbial fermentation and compound production. Furthermore, the impact of climate change on the chemical ecology of marine organisms—especially on the production, functionality and perception of allelochemicals—and its implications on drug discovery efforts will be presented.

Cost-effective hybrid long-short read assembly delineates alternative GC-rich Streptomyces hosts for natural product discovery

With the advent of rapid automated in silico identification of biosynthetic gene clusters (BGCs), genomics presents vast opportunities to accelerate natural product (NP) discovery. However, prolific NP producers, Streptomyces, are exceptionally GC-rich (>80%) and highly repetitive within BGCs. These pose challenges in sequencing and high-quality genome assembly which are currently circumvented via intensive sequencing. Here, we outline a more cost-effective workflow using multiplex Illumina and Oxford Nanopore sequencing with hybrid long-short read assembly algorithms to generate high quality genomes. Our protocol involves subjecting long read-derived assemblies to up to 4 rounds of polishing with short reads to yield accurate BGC predictions. We successfully sequenced and assembled 8 GC-rich Streptomyces genomes whose lengths range from 7.1 to 12.1 Mb with a median N50 of 8.2 Mb. Taxonomic analysis revealed previous misrepresentation among these strains and allowed us to propose a potentially new species, Streptomyces sydneybrenneri. Further comprehensive characterization of their biosynthetic, pan-genomic and antibiotic resistance features especially for molecules derived from type I polyketide synthase (PKS) BGCs reflected their potential as alternative NP hosts. Thus, the genome assemblies and insights presented here are envisioned to serve as gateway for the scientific community to expand their avenues in NP discovery.

Segregation of the genus Parahypoxylon (Hypoxylaceae, Xylariales) from Hypoxylon by a polyphasic taxonomic approach

During a mycological survey of the Democratic Republic of the Congo, a fungal specimen that morphologically resembled the American species Hypoxylon papillatum was encountered. A polyphasic approach including morphological and chemotaxonomic together with a multigene phylogenetic study (ITS, LSU, tub2, and rpb2) of Hypoxylon spp. and representatives of related genera revealed that this strain represents a new species of the Hypoxylaceae. However, the multi-locus phylogenetic inference indicated that the new fungus clustered with H. papillatum in a separate clade from the other species of Hypoxylon. Studies by ultrahigh performance liquid chromatography coupled to diode array detection and ion mobility tandem mass spectrometry (UHPLC-DAD-IM-MS/MS) were carried out on the stromatal extracts. In particular, the MS/MS spectra of the major stromatal metabolites of these species indicated the production of hitherto unreported azaphilone pigments with a similar core scaffold to the cohaerin-type metabolites, which are exclusively found in the Hypoxylaceae. Based on these results, the new genus Parahypoxylon is introduced herein. Aside from P. papillatum, the genus also includes P. ruwenzoriensesp. nov., which clustered together with the type species within a basal clade of the Hypoxylaceae together with its sister genus Durotheca.

iPRESTO: Automated discovery of biosynthetic sub-clusters linked to specific natural product substructures

Microbial specialised metabolism is full of valuable natural products that are applied clinically, agriculturally, and industrially. The genes that encode their biosynthesis are often physically clustered on the genome in biosynthetic gene clusters (BGCs). Many BGCs consist of multiple groups of co-evolving genes called sub-clusters that are responsible for the biosynthesis of a specific chemical moiety in a natural product. Sub-clusters therefore provide an important link between the structures of a natural product and its BGC, which can be leveraged for predicting natural product structures from sequence, as well as for linking chemical structures and metabolomics-derived mass features to BGCs. While some initial computational methodologies have been devised for sub-cluster detection, current approaches are not scalable, have only been run on small and outdated datasets, or produce an impractically large number of possible sub-clusters to mine through. Here, we constructed a scalable method for unsupervised sub-cluster detection, called iPRESTO, based on topic modelling and statistical analysis of co-occurrence patterns of enzyme-coding protein families. iPRESTO was used to mine sub-clusters across 150,000 prokaryotic BGCs from antiSMASH-DB. After annotating a fraction of the resulting sub-cluster families, we could predict a substructure for 16% of the antiSMASH-DB BGCs. Additionally, our method was able to confirm 83% of the experimentally characterised sub-clusters in MIBiG reference BGCs. Based on iPRESTO-detected sub-clusters, we could correctly identify the BGCs for xenorhabdin and salbostatin biosynthesis (which had not yet been annotated in BGC databases), as well as propose a candidate BGC for akashin biosynthesis. Additionally, we show for a collection of 145 actinobacteria how substructures can aid in linking BGCs to molecules by correlating iPRESTO-detected sub-clusters to MS/MS-derived Mass2Motifs substructure patterns. This work paves the way for deeper functional and structural annotation of microbial BGCs by improved linking of orphan molecules to their cognate gene clusters, thus facilitating accelerated natural product discovery.

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

BACKGROUND

It is well-known that the microbiome produces a myriad of specialised metabolites with diverse functions. To better characterise their structures and identify their producers in complex samples, integrative genome and metabolome mining is becoming increasingly popular. Metabologenomic co-occurrence-based correlation scoring methods facilitate the linking of metabolite mass fragmentation spectra (MS/MS) to their cognate biosynthetic gene clusters (BGCs) based on shared absence/presence patterns of metabolites and BGCs in paired omics datasets of multiple strains. Recently, these methods have been made more readily accessible through the NPLinker platform. However, co-occurrence-based approaches usually result in too many candidate links to manually validate. To address this issue, we introduce a generic feature-based correlation method that matches chemical compound classes between BGCs and MS/MS spectra.

RESULTS

To automatically reduce the long lists of potential BGC-MS/MS spectrum links, we match natural product (NP) ontologies previously independently developed for genomics and metabolomics and developed NPClassScore: an empirical class matching score that we also implemented in the NPLinker platform. By applying NPClassScore on three paired omics datasets totalling 189 bacterial strains, we show that the number of links is reduced by on average 63% as compared to using a co-occurrence-based strategy alone. We further demonstrate that 96% of experimentally validated links in these datasets are retained and prioritised when using NPClassScore.

CONCLUSION

The matching genome-metabolome class ontologies provide a starting point for selecting plausible candidates for BGCs and MS/MS spectra based on matching chemical compound class ontologies. NPClassScore expedites genome/metabolome data integration, as relevant BGC-metabolite links are prioritised, and researchers are faced with substantially fewer proposed BGC-MS/MS links to manually inspect. We anticipate that our addition to the NPLinker platform will aid integrative omics mining workflows in discovering novel NPs and understanding complex metabolic interactions in the microbiome. Video Abstract.

Microbial Genomics: Innovative Targets and Mechanisms

Multidrug resistance (MDR) has become an increasing threat to global health because bacteria can develop resistance to antibiotics over time. Scientists worldwide are searching for new approaches that go beyond traditional antibiotic discovery and development pipelines. Advances in genomics, however, opened up an unexplored therapeutic opportunity for the discovery of new antibacterial agents. Genomic approaches have been used to discover several novel antibiotics that target critical processes for bacterial growth and survival, including histidine kinases (HKs), LpxC, FabI, peptide deformylase (PDF), and aminoacyl-tRNA synthetases (AaRS). In this review, we will discuss the use of microbial genomics in the search for innovative and promising drug targets as well as the mechanisms of action for novel antimicrobial agents. We will also discuss future directions on how the utilization of the microbial genomics approach could improve the odds of antibiotic development having a more successful outcome.

Metabolomics and Genomics Enable the Discovery of a New Class of Nonribosomal Peptidic Metallophores from a Marine Micromonospora

Although microbial genomes harbor an abundance of biosynthetic gene clusters, there remain substantial technological gaps that impair the direct correlation of newly discovered gene clusters and their corresponding secondary metabolite products. As an example of one approach designed to minimize or bridge such gaps, we employed hierarchical clustering analysis and principal component analysis (hcapca, whose sole input is MS data) to prioritize 109 marine Micromonospora strains and ultimately identify novel strain WMMB482 as a candidate for in-depth "metabologenomics" analysis following its prioritization. Highlighting the power of current MS-based technologies, not only did hcapca enable the discovery of one new, nonribosomal peptide bearing an incredible diversity of unique functional groups, but metabolomics for WMMB482 unveiled 16 additional congeners via the application of Global Natural Product Social molecular networking (GNPS), herein named ecteinamines A-Q (1-17). The ecteinamines possess an unprecedented skeleton housing a host of uncommon functionalities including a menaquinone pathway-derived 2-naphthoate moiety, 4-methyloxazoline, the first example of a naturally occurring Ψ[CH2NH] "reduced amide", a methylsulfinyl moiety, and a d-cysteinyl residue that appears to derive from a unique noncanonical epimerase domain. Extensive in silico analysis of the ecteinamine (ect) biosynthetic gene cluster and stable isotope-feeding experiments helped illuminate the novel enzymology driving ecteinamine assembly as well the role of cluster collaborations or "duets" in producing such structurally complex agents. Finally, ecteinamines were found to bind nickel, cobalt, zinc, and copper, suggesting a possible biological role as broad-spectrum metallophores.

Untargeted metabolomic profiling of sepsis-induced cardiac dysfunction

OBJECTIVE

Sepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction.

METHOD

Plasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group.

RESULTS

Two metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism.

CONCLUSION

Metabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.

Metabolomics Applied to Cyanobacterial Toxins and Natural Products

The biological and chemical diversity of Cyanobacteria is remarkable. These ancient prokaryotes are widespread in nature and can be found in virtually every habitat on Earth where there is light and water. They are producers of an array of secondary metabolites with important ecological roles, toxic effects, and biotechnological applications. The investigation of cyanobacterial metabolites has benefited from advances in analytical tools and bioinformatics that are employed in metabolomic analyses. In this chapter, we review selected articles highlighting the use of targeted and untargeted metabolomics in the analyses of secondary metabolites produced by cyanobacteria. Here, cyanobacterial secondary metabolites have been didactically divided into toxins and natural products according to their relevance to toxicological studies and drug discovery, respectively. This review illustrates how metabolomics has improved the chemical analysis of cyanobacteria in terms of speed, sensitivity, selectivity, and/or coverage, allowing for broader and more complex scientific questions.

Cross Cultivation on Homologous/Heterologous Plant-Based Culture Media Empowers Host-Specific and Real Time In Vitro Signature of Plant Microbiota

Alliances of microbiota with plants are masked by the inability of in vitro cultivation of their bulk. Pure cultures piled in international centers originated from dissimilar environments/hosts. Reporting that plant root/leaf-based culture media support the organ-specific growth of microbiota, it was of interest to further investigate if a plant-based medium prepared from homologous (maize) supports specific/adapted microbiota compared to another prepared from heterologous plants (sunflower). The culture-independent community of maize phyllosphere was compared to communities cross-cultivated on plant broth-based media: CFU counts and taxa prevalence (PCR-DGGE; Illumina MiSeq amplicon sequencing). Similar to total maize phyllospheric microbiota, culture-dependent communities were overwhelmed by Proteobacteria (>94.3–98.3%); followed by Firmicutes (>1.3–3.7%), Bacteroidetes (>0.01–1.58%) and Actinobacteria (>0.06–0.34%). Differential in vitro growth on homologous versus heterologous plant-media enriched/restricted various taxa. In contrast, homologous cultivation over represented members of Proteobacteria (ca. > 98.0%), mainly Pseudomonadaceae and Moraxellaceae; heterologous cultivation and R2A enriched Firmicutes (ca. > 3.0%). The present strategy simulates/fingerprints the chemical composition of host plants to expand the culturomics of plant microbiota, advance real-time in vitro cultivation and lab-keeping of compatible plant microbiota, and identify preferential pairing of plant-microbe partners toward future synthetic community (SynComs) research and use in agriculture.

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.

Integrative omics approaches for biosynthetic pathway discovery in plants

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Biosynthesis of Sesquiterpenes in Basidiomycetes: A Review

Sesquiterpenes are common small-molecule natural products with a wide range of promising applications and are biosynthesized by sesquiterpene synthase (STS). Basidiomycetes are valuable and important biological resources. To date, hundreds of related sesquiterpenoids have been discovered in basidiomycetes, and the biosynthetic pathways of some of these compounds have been elucidated. This review summarizes 122 STSs and 2 fusion enzymes STSs identified from 26 species of basidiomycetes over the past 20 years. The biological functions of enzymes and compound structures are described, and related research is discussed.

Organo-mineral complexes alter bacterial composition and induce carbon and nitrogen cycling in the rhizosphere

It is widely thought that organo-mineral complexes (OMCs) stabilize organic matter via mineral adsorption. Recent studies have demonstrated that root exudates can activate OMCs, but the influence of OMCs on plant rhizosphere, which is among the most active areas for microbes, has not been thoroughly researched. In this study, a pot experiment using Brassica napus was conducted to investigate the effects of OMCs on plant rhizosphere. The result showed that OMC addition significantly promoted the growth of B. napus compared to the prevalent fertilization (PF, chemical fertilizer + chicken compost) treatment. Specifically, OMC addition increased the relative abundance (RA) of nitrogen-fixing bacteria and the bacterial α-diversity, and the operational taxonomic unit (OTU) group with RA > 0.5% in the OMC-treated rhizosphere was the result of a deterministic assembly process with homogeneous selection. Gene abundance related to nitrogen cycling and the soil chemical analysis demonstrated that the OMC-altered bacterial community induced nitrogen fixation and converted nitrate to ammonium. The upregulated carbon sequestration pathway genes and the increased soil microbial biomass carbon (23.68%) demonstrated that the bacterial-induced carbon storage in the rhizosphere was activated. This study shows that the addition of OMCs can influence the biogeochemical carbon and nitrogen cycling via regulating microorganisms in the rhizosphere. The findings provide fresh insights into the effects of OMCs on the biogeochemical cycling of important elements and suggest a promising strategy for improving soil productivity.

A brief guide to machine learning for antibiotic discovery

Rising antibiotic resistance and an alarmingly lean antibiotic pipeline require the adoption of novel approaches to rapidly discover new structural and functional classes of antibiotics. Excitingly, algorithmic approaches to antibiotic discovery are sufficiently advanced to meaningfully influence the antibiotic discovery process. Indeed, once trained on high-quality datasets, contemporary machine-learning and deep-learning models can be used to perform predictions for new antibiotics across vast chemical spaces, orders of magnitude more rapidly than compounds can be screened in the laboratory. This increases the probability of discovering new antibiotics with desirable properties. In this short review, we briefly describe the utility of contemporary machine-learning and deep-learning approaches to guide the discovery of new small-molecule antibiotics and unidentified natural products. We then propose a call to action for more open sharing of high-quality screening datasets to accelerate the rate at which forthcoming antibiotic-prediction models can be trained. Together, we aim to introduce antibiotic discoverers to a sample of recent applications of contemporary algorithmic methods to facilitate the wider adoption of these powerful computational approaches.

Chemometrics and genome mining reveal an unprecedented family of sugar acid-containing fungal nonribosomal cyclodepsipeptides

Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)-based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d-xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d-xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N-methylated amino acid residues in positions where the corresponding NRPS modules lack N-methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.

Native metabolomics identifies the rivulariapeptolide family of protease inhibitors

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

Bioactivity-guided isolation of specialized metabolites is an iterative process. Here, the authors demonstrate a native metabolomics approach that allows for fast screening of complex metabolite extracts against a protein of interest and simultaneous structure annotation.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Genomic and Chemical Decryption of the Bacteroidetes Phylum for Its Potential to Biosynthesize Natural Products

With progress in genome sequencing and data sharing, 1,000s of bacterial genomes are publicly available. Genome mining-using bioinformatics tools in terms of biosynthetic gene cluster (BGC) identification, analysis, and rating-has become a key technology to explore the capabilities for natural product (NP) biosynthesis. Comprehensively, analyzing the genetic potential of the phylum Bacteroidetes revealed Chitinophaga as the most talented genus in terms of BGC abundance and diversity. Guided by the computational predictions, we conducted a metabolomics and bioactivity driven NP discovery program on 25 Chitinophaga strains. High numbers of strain-specific metabolite buckets confirmed the upfront predicted biosynthetic potential and revealed a tremendous uncharted chemical space. Mining this data set, we isolated the new iron chelating nonribosomally synthesized cyclic tetradeca- and pentadecalipodepsipeptide antibiotics chitinopeptins with activity against Candida, produced by C. eiseniae DSM 22224 and C. flava KCTC 62435, respectively. IMPORTANCE The development of pipelines for anti-infectives to be applied in plant, animal, and human health management are dried up. However, the resistance development against compounds in use calls for new lead structures. To fill this gap and to enhance the probability of success for the discovery of new bioactive natural products, microbial taxa currently underinvestigated must be mined. This study investigates the potential within the bacterial phylum Bacteroidetes. A combination of omics-technologies revealed taxonomical hot spots for specialized metabolites. Genome- and metabolome-based analyses showed that the phylum covers a new chemical space compared with classic natural product producers. Members of the Bacteroidetes may thus present a promising bioresource for future screening and isolation campaigns.

The Potential Use of Antibiotics Against Helicobacter pylori Infection: Biopharmaceutical Implications

Helicobacter pylori (H. pylori) is a notorious, recalcitrant and silent germ, which can cause a variety of debilitating stomach diseases, including gastric and duodenal ulcers and gastric cancer. This microbe predominantly colonizes the mucosal layer of the human stomach and survives in the inhospitable gastric microenvironment, by adapting to this hostile milieu. In this review, we first discuss H. pylori colonization and invasion. Thereafter, we provide a survey of current curative options based on polypharmacy, looking at pharmacokinetics, pharmacodynamics and pharmaceutical microbiology concepts, in the battle against H. pylori infection.

Integrated Metabolomic, Molecular Networking, and Genome Mining Analyses Uncover Novel Angucyclines From Streptomyces sp. RO-S4 Strain Isolated From Bejaia Bay, Algeria

Multi-omic approaches have recently made big strides toward the effective exploration of microorganisms, accelerating the discovery of new bioactive compounds. We combined metabolomic, molecular networking, and genomic-based approaches to investigate the metabolic potential of the Streptomyces sp. RO-S4 strain isolated from the polluted waters of Bejaia Bay in Algeria. Antagonistic assays against methicillin-resistant Staphylococcus aureus with RO-S4 organic extracts showed an inhibition zone of 20 mm by using the agar diffusion method, and its minimum inhibitory concentration was 16 μg/ml. A molecular network was created using GNPS and annotated through the comparison of MS/MS spectra against several databases. The predominant compounds in the RO-S4 extract belonged to the angucycline family. Three compounds were annotated as known metabolites, while all the others were putatively new to Science. Notably, all compounds had fridamycin-like aglycones, and several of them had a lactonized D ring analogous to that of urdamycin L. The whole genome of Streptomyces RO-S4 was sequenced to identify the biosynthetic gene cluster (BGC) linked to these angucyclines, which yielded a draft genome of 7,497,846 bp with 72.4% G+C content. Subsequently, a genome mining analysis revealed 19 putative biosynthetic gene clusters, including a grincamycin-like BGC with high similarity to that of Streptomyces sp. CZN-748, that was previously reported to also produce mostly open fridamycin-like aglycones. As the ring-opening process leading to these compounds is still not defined, we performed a comparative analysis with other angucycline BGCs and advanced some hypotheses to explain the ring-opening and lactonization, possibly linked to the uncoupling between the activity of GcnE and GcnM homologs in the RO-S4 strain. The combination of metabolomic and genomic approaches greatly improved the interpretation of the metabolic potential of the RO-S4 strain.

Diversity of an uncommon elastic hypersaline microbial mat along a small-scale transect

We evaluated the microbial diversity and metabolome profile of an uncommon hypersaline elastic microbial mat from Cuatro Ciénegas Basin (CCB) in the Chihuahuan Desert of Coahuila, México. We collected ten samples on a small scale transect (1.5-m) and described its microbial diversity through NGS-based ITS and 16S rDNA gene sequencing. A very low number of taxa comprised a considerable proportion of the mat and were shared across all sampling points, whereas the rare biosphere was more phylogenetically diverse (Faith's Phylogenetic Diversity (FPD) index) and phylogenetically disperse (using a null model distribution of Phylogenetic Species Clustering (nmdPSC)) than the abundant (high read count) taxa for both analyzed libraries. We also found a distinctive metabolome profile for each sample and were able to tentatively annotate several classes of compounds with relevant biological properties.

Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions

Drought is one of the major abiotic stresses causing severe damage and losses in economically important crops worldwide. Drought decreases the plant water status, leading to a disruptive metabolic reprogramming that negatively affects plant growth and yield. Seaweed extract-based biostimulants show potential as a sustainable strategy for improved crop health and stress resilience. However, cellular, biochemical, and molecular mechanisms governing the agronomically observed benefits of the seaweed extracts on plants are still poorly understood. In this study, a liquid chromatography–mass spectrometry-based untargeted metabolomics approach combined with computational metabolomics strategies was applied to unravel the molecular ‘stamps’ that define the effects of seaweed extracts on greenhouse-grown maize (Zea mays) under drought conditions. We applied mass spectral networking, substructure discovery, chemometrics, and metabolic pathway analyses to mine and interpret the generated mass spectral data. The results showed that the application of seaweed extracts induced alterations in the different pathways of primary and secondary metabolism, such as phenylpropanoid, flavonoid biosynthesis, fatty acid metabolism, and amino acids pathways. These metabolic changes involved increasing levels of phenylalanine, tryptophan, coumaroylquinic acid, and linolenic acid metabolites. These metabolic alterations are known to define some of the various biochemical and physiological events that lead to enhanced drought resistance traits. The latter include root growth, alleviation of oxidative stress, improved water, and nutrient uptake. Moreover, this study demonstrates the use of molecular networking in annotating maize metabolome. Furthermore, the results reveal that seaweed extract-based biostimulants induced a remodeling of maize metabolism, subsequently readjusting the plant towards stress alleviation, for example, by increasing the plant height and diameter through foliar application. Such insights add to ongoing efforts in elucidating the modes of action of biostimulants, such as seaweed extracts. Altogether, our study contributes to the fundamental scientific knowledge that is necessary for the development of a biostimulants industry aiming for a sustainable food security.

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

Fungi produce several bioactive metabolites, pigments, dyes, antioxidants, polysaccharides, and industrial enzymes. Fungal products are also the primary sources of functional food and nutrition, and their pharmacological products are used for healthy aging. Their molecular properties are validated through the use of recent high-throughput genomic, transcriptomic, and metabolomic tools and techniques. Together, these updated multi-omic tools have been used to study fungal metabolites structure and their mode of action on biological and cellular processes. Diverse groups of fungi produce different proteins and secondary metabolites, which possess tremendous biotechnological and pharmaceutical applications. Furthermore, its use and acceptability can be accelerated by adopting multi-omics, bioinformatics, and machine learning tools that generate a huge amount of molecular data. The integration of artificial intelligence and machine learning tools in the era of omics and big data has opened up a new outlook in both basic and applied researches in the area of nutraceuticals and functional food and nutrition. KEY POINTS: • Multi-omic tool helps in the identification of novel fungal metabolites • Intra-omic data from genomics to bioinformatics • Novel metabolites and application in human health.