Recent development of antiSMASH and other computational approaches to mine secondary metabolite biosynthetic gene clusters

A treasure trove of 1034 actinomycete genomes

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from >1000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

A Practical Toolkit for the Detection, Isolation, Quantification, and Characterization of Siderophores and Metallophores in Microorganisms

Siderophores are well-recognized low-molecular-weight compounds produced by numerous microorganisms to acquire iron from the surrounding environments. These secondary metabolites can form complexes with other metals besides iron, forming soluble metallophores; because of that, they are widely investigated in either the medicinal or environmental field. One of the bottlenecks of siderophore research is related to the identification of new siderophores from microbial sources. Herein we have compiled a comprehensive range of standard and updated methodologies that have been developed over the past few years to provide a comprehensive toolbox in this area to current researchers.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Plasmid-Borne Biosynthetic Gene Clusters within a Permanently Stratified Marine Water Column

Plasmids are mobile genetic elements known to carry secondary metabolic genes that affect the fitness and survival of microbes in the environment. Well-studied cases of plasmid-encoded secondary metabolic genes in marine habitats include toxin/antitoxin and antibiotic biosynthesis/resistance genes. Here, we examine metagenome-assembled genomes (MAGs) from the permanently-stratified water column of the Cariaco Basin for integrated plasmids that encode biosynthetic gene clusters of secondary metabolites (smBGCs). We identify 16 plasmid-borne smBGCs in MAGs associated primarily with Planctomycetota and Pseudomonadota that encode terpene-synthesizing genes, and genes for production of ribosomal and non-ribosomal peptides. These identified genes encode for secondary metabolites that are mainly antimicrobial agents, and hence, their uptake via plasmids may increase the competitive advantage of those host taxa that acquire them. The ecological and evolutionary significance of smBGCs carried by prokaryotes in oxygen-depleted water columns is yet to be fully elucidated.

Exploring halophilic environments as a source of new antibiotics

Microbial natural products from microbes in extreme environments, including haloarchaea, and halophilic bacteria, possess a huge capacity to produce novel antibiotics. Additionally, enhanced isolation techniques and improved tools for genomic mining have expanded the efficiencies in the antibiotic discovery process. This review article provides a detailed overview of known antimicrobial compounds produced by halophiles from all three domains of life. We summarize that while halophilic bacteria, in particular actinomycetes, contribute the vast majority of these compounds the importance of understudied halophiles from other domains of life requires additional consideration. Finally, we conclude by discussing upcoming technologies- enhanced isolation and metagenomic screening, as tools that will be required to overcome the barriers to antimicrobial drug discovery. This review highlights the potential of these microbes from extreme environments, and their importance to the wider scientific community, with the hope of provoking discussion and collaborations within halophile biodiscovery. Importantly, we emphasize the importance of bioprospecting from communities of lesser-studied halophilic and halotolerant microorganisms as sources of novel therapeutically relevant chemical diversity to combat the high rediscovery rates. The complexity of halophiles will necessitate a multitude of scientific disciplines to unravel their potential and therefore this review reflects these research communities.

Sinomonas terricola sp. nov., a plant-beneficial bacterium isolated from litchi rhizosphere soil in Guangdong, China

A novel plant-beneficial bacterium strain, designated as JGH33T, which inhibited Peronophythora litchii sporangia germination, was isolated on Reasoner's 2A medium from a litchi rhizosphere soil sample collected in Gaozhou City, Guangdong Province, PR China. Cells of strain JGH33T were Gram-stain-positive, aerobic, non-motile, bent rods. The strain grew optimally at 30-37 °C and pH 6.0-8.0. Sequence similarity analysis based on 16S rRNA genes indicated that strain JGH33T exhibited highest sequence similarity to Sinomonas albida LC13T (99.2 %). The genomic DNA G+C content of the isolate was 69.1 mol%. The genome of JGH33T was 4.7 Mbp in size with the average nucleotide identity value of 83.45 % to the most related reference strains, which is lower than the species delineation threshold of 95 %. The digital DNA-DNA hybridization of the isolate resulted in a relatedness value of 24.9 % with its closest neighbour. The predominant respiratory quinone of JGH33T was MK-9(H2). The major fatty acids were C15 : 0 anteiso (43.4 %), C16 : 0 iso (19.1 %) and C17 : 0 anteiso (19.3 %), and the featured component was C18 : 3 ω6c (1.01 %). The polar lipid composition of strain JGH33T included diphosphatidylglycerol, phosphatidylglycerol, dimannosylglyceride, phosphatidylinositol and glycolipids. On the basis of polyphasic taxonomy analyses data, strain JGH33T represents a novel species of the genus Sinomonas, for which the name Sinomonas terricola sp. nov. is proposed, with JGH33T (=JCM 35868T=GDMCC 1.3730T) as the type strain.

Antimicrobial Action Mechanisms of Natural Compounds Isolated from Endophytic Microorganisms

Infectious diseases are a significant challenge to global healthcare, especially in the face of increasing antibiotic resistance. This urgent issue requires the continuous exploration and development of new antimicrobial drugs. In this regard, the secondary metabolites derived from endophytic microorganisms stand out as promising sources for finding antimicrobials. Endophytic microorganisms, residing within the internal tissues of plants, have demonstrated the capacity to produce diverse bioactive compounds with substantial pharmacological potential. Therefore, numerous new antimicrobial compounds have been isolated from endophytes, particularly from endophytic fungi and actinomycetes. However, only a limited number of these compounds have been subjected to comprehensive studies regarding their mechanisms of action against bacterial cells. Furthermore, the investigation of their effects on antibiotic-resistant bacteria and the identification of biosynthetic gene clusters responsible for synthesizing these secondary metabolites have been conducted for only a subset of these promising compounds. Through a comprehensive analysis of current research findings, this review describes the mechanisms of action of antimicrobial drugs and secondary metabolites isolated from endophytes, antibacterial activities of the natural compounds derived from endophytes against antibiotic-resistant bacteria, and biosynthetic gene clusters of endophytic fungi responsible for the synthesis of bioactive secondary metabolites.

Genomics-based analysis of four porcine-derived lactic acid bacteria strains and their evaluation as potential probiotics

The search for probiotics and exploration of their functions are crucial for livestock farming. Recently, porcine-derived lactic acid bacteria (LAB) have shown great potential as probiotics. However, research on the evaluation of porcine-derived LAB as potential probiotics through genomics-based analysis is relatively limited. The present study analyzed four porcine-derived LAB strains (Lactobacillus johnsonii L16, Latilactobacillus curvatus ZHA1, Ligilactobacillus salivarius ZSA5 and Ligilactobacillus animalis ZSB1) using genomic techniques and combined with in vitro tests to evaluate their potential as probiotics. The genome sizes of the four strains ranged from 1,897,301 bp to 2,318,470 bp with the GC contents from 33.03 to 41.97%. Pan-genomic analysis and collinearity analysis indicated differences among the genomes of four strains. Carbohydrate active enzymes analysis revealed that L. johnsonii L16 encoded more carbohydrate active enzymes than other strains. KEGG pathway analysis and in vitro tests confirmed that L. johnsonii L16 could utilize a wide range of carbohydrates and had good utilization capacity for each carbohydrate. The four strains had genes related to acid tolerance and were tolerant to low pH, with L. johnsonii L16 showing the greatest tolerance. The four strains contained genes related to bile salt tolerance and were able to tolerate 0.1% bile salt. Four strains had antioxidant related genes and exhibited antioxidant activity in in vitro tests. They contained the genes linked with organic acid biosynthesis and exhibited antibacterial activity against enterotoxigenic Escherichia coli K88 (ETEC K88) and Salmonella 6,7:c:1,5, wherein, L. johnsonii L16 and L. salivarius ZSA5 had gene clusters encoding bacteriocin. Results suggest that genome analysis combined with in vitro tests is an effective approach for evaluating different strains as probiotics. The findings of this study indicate that L. johnsonii L16 has the potential as a probiotic strain among the four strains and provide theoretical basis for the development of probiotics in swine production.

Global characterization of biosynthetic gene clusters in non-model eukaryotes using domain architectures

The majority of pharmaceuticals are derived from natural products, bioactive compounds naturally synthesized by organisms to provide evolutionary advantages. Although the rich evolutionary history of eukaryotic algal species implicates a high potential for natural product-based drug discovery, it remains largely untouched. This study investigates 2762 putative biosynthetic gene clusters (BGCs) from 212 eukaryotic algal genomes. To analyze a vast set of structurally diverse BGCs, we employed comparative analysis based on the vectorization of biosynthetic domains, referred to as biosynthetic domain architecture (BDA). By characterizing core biosynthetic machineries through BDA, we identified key BDAs of modular BGCs in diverse eukaryotes and introduced 16 candidate modular BGCs with similar BDAs to previously validated BGCs. This study provides a global characterization of eukaryotic algal BGCs, offering an alternative to laborious manual curation for BGC prioritization.

Effective binning of metagenomic contigs using contrastive multi-view representation learning

Contig binning plays a crucial role in metagenomic data analysis by grouping contigs from the same or closely related genomes. However, existing binning methods face challenges in practical applications due to the diversity of data types and the difficulties in efficiently integrating heterogeneous information. Here, we introduce COMEBin, a binning method based on contrastive multi-view representation learning. COMEBin utilizes data augmentation to generate multiple fragments (views) of each contig and obtains high-quality embeddings of heterogeneous features (sequence coverage and k-mer distribution) through contrastive learning. Experimental results on multiple simulated and real datasets demonstrate that COMEBin outperforms state-of-the-art binning methods, particularly in recovering near-complete genomes from real environmental samples. COMEBin outperforms other binning methods remarkably when integrated into metagenomic analysis pipelines, including the recovery of potentially pathogenic antibiotic-resistant bacteria (PARB) and moderate or higher quality bins containing potential biosynthetic gene clusters (BGCs).

MAGICIAN: MAG simulation for investigating criteria for bioinformatic analysis

BACKGROUND

The possibility of recovering metagenome-assembled genomes (MAGs) from sequence reads allows for further insights into microbial communities and their members, possibly even analyzing such sequences with tools designed for single-isolate genomes. As result quality depends on sequence quality, performance of tools for single-isolate genomes on MAGs should be tested beforehand. Bioinformatics can be leveraged to quickly create varied synthetic test sets with known composition for this purpose.

RESULTS

We present MAGICIAN, a flexible, user-friendly pipeline for the simulation of MAGs. MAGICIAN combines a synthetic metagenome simulator with a metagenomic assembly and binning pipeline to simulate MAGs based on user-supplied input genomes, allowing users to test performance of tools on MAGs while having a ground truth to compare results to. Using MAGICIAN, we found that even very slight (1%) changes in depth of coverage can drastically affect whether a genome can be recovered. We also demonstrate the use of simulated MAGs by evaluating the suitability of such genomes obtained with MAGICIAN's current default pipeline for analysis with the antimicrobial resistance gene identification tool ResFinder.

CONCLUSIONS

Using MAGICIAN, it is possible to simulate MAGs which, while generally high in quality, reflect issues encountered with real-world data, thus providing realistic best-case data. Evaluating the results of ResFinder analysis of these genomes revealed a risk for plausible-looking false positives, which underlines the need for pipeline validation so that researchers are aware of the potential issues when interpreting real-world data. Furthermore, the effects of fluctuations in depth of coverage on genome recovery in our simulated "random sequencing" warrant further investigation and indicate random subsampling of reads may affect discovery of more genomes.

The antiSMASH database version 4: additional genomes and BGCs, new sequence-based searches and more

Many microorganisms produce natural products that are frequently used in the development of medicines and crop protection agents. Genome mining has evolved into a prominent method to access this potential. antiSMASH is the most popular tool for this task. Here we present version 4 of the antiSMASH database, providing biosynthetic gene clusters detected by antiSMASH 7.1 in publicly available, dereplicated, high-quality microbial genomes via an interactive graphical user interface. In version 4, the database contains 231 534 high quality BGC regions from 592 archaeal, 35 726 bacterial and 236 fungal genomes and is available at https://antismash-db.secondarymetabolites.org/.

Identification of the p-coumaric acid biosynthetic gene cluster in Kutzneria albida: insights into the diazotization-dependent deamination pathway

Recently, we identified the biosynthetic gene cluster of avenalumic acid (ava cluster) and revealed its entire biosynthetic pathway, resulting in the discovery of a diazotization-dependent deamination pathway. Genome database analysis revealed the presence of more than 100 ava cluster-related biosynthetic gene clusters (BGCs) in actinomycetes; however, their functions remained unclear. In this study, we focused on an ava cluster-related BGC in Kutzneria albida (cma cluster), and revealed that it is responsible for p-coumaric acid biosynthesis by heterologous expression of the cma cluster and in vitro enzyme assays using recombinant Cma proteins. The ATP-dependent diazotase CmaA6 catalyzed the diazotization of both 3-aminocoumaric acid and 3-aminoavenalumic acid using nitrous acid in vitro. In addition, the high efficiency of the CmaA6 reaction enabled us to perform a kinetic analysis of AvaA7, which confirmed that AvaA7 catalyzes the denitrification of 3-diazoavenalumic acid in avenalumic acid biosynthesis. This study deepened our understanding of the highly reducing type II polyketide synthase system as well as the diazotization-dependent deamination pathway for the production of avenalumic acid or p-coumaric acid.

Two distinct non-ribosomal peptide synthetase-independent siderophore synthetase gene clusters identified in Armillaria and other species in the Physalacriaceae

Siderophores are important for ferric iron solubilization, sequestration, transportation, and storage, especially under iron-limiting conditions such as aerobic conditions at high pH. Siderophores are mainly produced by non-ribosomal peptide synthetase-dependent siderophore pathway, non-ribosomal peptide synthetase-independent siderophore synthetase pathway, or the hybrid non-ribosomal peptide synthetases/non-ribosomal peptide synthetases-independent siderophore pathway. Outcompeting or inhibition of plant pathogens, alteration of host defense mechanisms, and alteration of plant-fungal interactions have been associated with fungal siderophores. To understand these mechanisms in fungi, studies have been conducted on siderophore biosynthesis by ascomycetes with limited focus on the basidiomycetes. Armillaria includes several species that are pathogens of woody plants and trees important to agriculture, horticulture, and forestry. The aim of this study was to investigate the presence of non-ribosomal peptide synthetases-independent siderophore synthetase gene cluster(s) in genomes of Armillaria species using a comparative genomics approach. Iron-dependent growth and siderophore biosynthesis in strains of selected Armillaria spp. were also evaluated in vitro. Two distinct non-ribosomal peptide synthetases-independent siderophore synthetase gene clusters were identified in all the genomes. All non-ribosomal peptide synthetases-independent siderophore synthetase genes identified putatively encode Type A' non-ribosomal peptide synthetases-independent siderophore synthetases, most of which have IucA_IucC and FhuF-like transporter domains at their N- and C-terminals, respectively. The effect of iron on culture growth varied among the strains studied. Bioassays using the CAS assay on selected Armillaria spp. revealed in vitro siderophore biosynthesis by all strains irrespective of added FeCl3 concentration. This study highlights some of the tools that Armillaria species allocate to iron homeostasis. The information generated from this study may in future aid in developing molecular based methods to control these phytopathogens.

A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters

Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space. In this multi-label learning framework, a joint representation of features (BGCs domains) and labels (natural products annotations) is efficiently learnt in an integral and low-dimensional space to accurately define the inter-class boundaries and scale to the learning problem of many imbalanced labels. Computational results on experimental data show that the proposed framework achieves satisfactory multi-label learning performance, and the learnt patterns of BGCs domains are transferrable across bacteria, or even across kingdom, for instance, from bacteria to Arabidopsis thaliana. Lastly, take Arabidopsis thaliana and its rhizosphere microbiome for example, we propose a pipeline combining existing BGCs identification tools and this proposed framework to find and functionally annotate novel natural products for downstream bioecological studies in terms of plant-microbiota-soil interactions and plant environmental adaption.

SOCfinder: a genomic tool for identifying social genes in bacteria

Bacteria cooperate by working collaboratively to defend their colonies, share nutrients, and resist antibiotics. Nevertheless, our understanding of these remarkable behaviours primarily comes from studying a few well-characterized species. Consequently, there is a significant gap in our understanding of microbial social traits, particularly in natural environments. To address this gap, we can use bioinformatic tools to identify genes that control cooperative or otherwise social traits. Existing tools address this challenge through two approaches. One approach is to identify genes that encode extracellular proteins, which can provide benefits to neighbouring cells. An alternative approach is to predict gene function using annotation tools. However, these tools have several limitations. Not all extracellular proteins are cooperative, and not all cooperative behaviours are controlled by extracellular proteins. Furthermore, existing functional annotation methods frequently miss known cooperative genes. We introduce SOCfinder as a new tool to find bacterial genes that control cooperative or otherwise social traits. SOCfinder combines information from several methods, considering if a gene is likely to [1] code for an extracellular protein [2], have a cooperative functional annotation, or [3] be part of the biosynthesis of a cooperative secondary metabolite. We use data on two extensively-studied species (P. aeruginosa and B. subtilis) to show that SOCfinder is better at finding known cooperative genes than existing tools. We also use theory from population genetics to identify a signature of kin selection in SOCfinder cooperative genes, which is lacking in genes identified by existing tools. SOCfinder opens up a number of exciting directions for future research, and is available to download from https://github.com/lauriebelch/SOCfinder.

Deep learning for metabolic pathway design

The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.

Paenibacillus polymyxa YLC1: a promising antagonistic strain for biocontrol of Pseudomonas syringae pv. actinidiae, causing kiwifruit bacterial canker

BACKGROUND

Kiwifruit bacterial canker (KBC) caused by Pseudomonas syringae pv. actinidiae (Psa) is the main limiting factor in the kiwifruit industry. This study aimed to identify bacterial strains with antagonistic activity against Psa, analyze antagonistically active substances and provide a new basis for the biological control of KBC.

RESULTS

A total of 142 microorganisms were isolated from the rhizosphere soil of asymptomatic kiwifruit. Among them, an antagonistic bacterial strain was identified as Paenibacillus polymyxa YLC1 by 16S rRNA sequencing. KBC control by strain YLC1 (85.4%) was comparable to copper hydroxide treatment (81.8%) under laboratory conditions and field testing. Active substances of strain YLC1 were identified by genetic sequence analysis using antiSMASH. Six biosynthetic active compound gene clusters were identified as encoding ester peptide synthesis, such as polymyxins. An active fraction was purified and identified as polymyxin B1 using chromatography, hydrogen nuclear magnetic resonance (NMR), and liquid chromatography-mass spectrometry. In addition, polymyxin B1 also was found significantly to suppress the expression of T3SS-related genes, but did not affect the growth of Psa at low concentrations.

CONCLUSION

In this study, a biocontrol strain P. polymyxa YLC1 obtained from kiwifruit rhizosphere soil exhibited excellent control effects on KBC in vitro and in field tests. Its active compound was identified as polymyxin B1, which inhibits a variety of pathogenic bacteria. We conclude that P. polymyxa YLC1 is a biocontrol strain with excellent prospects for development and application. © 2023 Society of Chemical Industry.

Metabolic flux phenotyping of secondary metabolism in cyanobacteria

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

Heterologous overproduction of oviedomycin by refactoring biosynthetic gene cluster and metabolic engineering of host strain Streptomyces coelicolor

BACKGROUND

Oviedomycin is one among several polyketides known for their potential as anticancer agents. The biosynthetic gene cluster (BGC) for oviedomycin is primarily found in Streptomyces antibioticus. However, because this BGC is usually inactive under normal laboratory conditions, it is necessary to employ systematic metabolic engineering methods, such as heterologous expression, refactoring of BGCs, and optimization of precursor biosynthesis, to allow efficient production of these compounds.

RESULTS

Oviedomycin BGC was captured from the genome of Streptomyces antibioticus by a newly constructed plasmid, pCBA, and conjugated into the heterologous strain, S. coelicolor M1152. To increase the production of oviedomycin, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) system was utilized in an in vitro setting to refactor the native promoters within the ovm BGC. The target promoters of refactoring were selected based on examination of factors such as transcription levels and metabolite profiling. Furthermore, genome-scale metabolic simulation was applied to find overexpression targets that could enhance the biosynthesis of precursors or cofactors related to oviedomycin production. The combined approach led to a significant increase in oviedomycin production, reaching up to 670 mg/L, which is the highest titer reported to date. This demonstrates the potential of the approach undertaken in this study.

CONCLUSIONS

The metabolic engineering approach used in this study led to the successful production of a valuable polyketide, oviedomycin, via BGC cloning, promoter refactoring, and gene manipulation of host metabolism aided by genome-scale metabolic simulation. This approach can be also useful for the efficient production of other secondary molecules encoded by 'silent' BGCs.

Ultra-high-depth macrogenomic sequencing revealed differences in microbial composition and function between high temperature and medium-high temperature Daqu

Complex microorganisms in Daqu of different temperatures play a vital role in the taste, flavor and quality of Baijiu during fermentation. However, understanding the functional diversity of the whole microbial community between the Daqus of two different temperatures (high temperature Daqu, HD and medium-high temperature Daqu, MD) remains a major challenge. Here, a systematic study of the microbial diversity, functions as well as physiological and biochemical indexes of Daqu are described. The results revealed that the Daqu exhibited unique characteristics. In particular, the diversity of microorganisms in HD and MD was high, with 44 species including 14 novel species (Sphingomonas sp. is the main novel species) detected in all samples. Their profiles of carbohydrate-active enzymes and specific functional components supported the fact that these species were involved in flavor formation. The Daqu microbiome consisted of a high proportion of phage, providing evidence of phage infection/genome integration and horizontal gene transfer from phage to bacteria. Such processes would also regulate Daqu microbiomes and thus flavor quality. These results enrich current knowledge of Daqu and can be used to promote the development of Baijiu fermentation technology.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

The Cytotoxic Properties of Extreme Fungi's Bioactive Components-An Updated Metabolic and Omics Overview

Fungi are the most diverse living organisms on planet Earth, where their ubiquitous presence in various ecosystems offers vast potential for the research and discovery of new, naturally occurring medicinal products. Concerning human health, cancer remains one of the leading causes of mortality. While extensive research is being conducted on treatments and their efficacy in various stages of cancer, finding cytotoxic drugs that target tumor cells with no/less toxicity toward normal tissue is a significant challenge. In addition, traditional cancer treatments continue to suffer from chemical resistance. Fortunately, the cytotoxic properties of several natural products derived from various microorganisms, including fungi, are now well-established. The current review aims to extract and consolidate the findings of various scientific studies that identified fungi-derived bioactive metabolites with antitumor (anticancer) properties. The antitumor secondary metabolites identified from extremophilic and extremotolerant fungi are grouped according to their biological activity and type. It became evident that the significance of these compounds, with their medicinal properties and their potential application in cancer treatment, is tremendous. Furthermore, the utilization of omics tools, analysis, and genome mining technology to identify the novel metabolites for targeted treatments is discussed. Through this review, we tried to accentuate the invaluable importance of fungi grown in extreme environments and the necessity of innovative research in discovering naturally occurring bioactive compounds for the development of novel cancer treatments.

Genomic Characterization and Functional Description of Beauveria bassiana Isolates from Latin America

Beauveria bassiana is an entomopathogenic fungus used in agriculture as a biological controller worldwide. Despite being a well-studied organism, there are no genomic studies of B. bassiana isolates from Central American and Caribbean countries. This work characterized the functional potential of eight Neotropical isolates and provided an overview of their genomic characteristics, targeting genes associated with pathogenicity, the production of secondary metabolites, and the identification of CAZYmes as tools for future biotechnological applications. In addition, a comparison between these isolates and reference genomes was performed. Differences were observed according to geographical location and the lineages of the B. bassiana complex to which each isolate belonged.

Whole-genome sequence analysis reveals phenanthrene and pyrene degradation pathways in newly isolated bacteria Klebsiella michiganensis EF4 and Klebsiella oxytoca ETN19

Polycyclic aromatic hydrocarbons (PAHs) are diverse pollutants of significant environmental concerns, requiring effective biodegradation. This study used different bioinformatics tools to conduct whole-genome sequencing of two novel bacterial strains, Klebsiella michiganensis EF4 and K. oxytoca ETN19, to improve our understanding of their many genomic functions and degradation pathways of phenanthrene and pyrene. After 28 days of cultivation, strain EF4 degraded approximately 80% and 60% of phenanthrene and pyrene, respectively. However, their combinations (EF4 +ETN19) showed tremendous phenanthrene degradation efficiency, supposed to be at the first-level kinetic model with a t_1/2 value of approximately 6 days. In addition, the two bacterial genomes contained carbohydrate-active enzymes and secondary metabolites biosynthetic gene clusters associated with PAHs degradation. The two genomes contained the bZIP superfamily of transcription factors, primarily the cAMP-response element-binding protein (CREB), which could regulate the expression of several PAHs degradation genes and enzymes. Interestingly, the two genomes were found to uniquely degrade phenanthrene through a putative pathway that catabolizes 2-carboxybenzalpyruvate into the TCA cycle. An operon containing multicomponent proteins, including a novel gene (JYK05_14550) that could initiate the beginning step of phenanthrene and pyrene degradation, was found in the EF4 genome. However, the degradation pathway of ETN19 showed that the yhfP gene encoding putative quinone oxidoreductase was associated with phenanthrene and pyrene catabolic processes. Furthermore, the significant expression of catechol 1,2-dioxygenase and quinone oxidoreductase genes in EF4 +ETN19 and ETN19 following the quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis confirmed the ability of the bacteria combination to degrade pyrene and phenanthrene effectively. These findings present new insight into the possible co-metabolism of the two bacterial species in the rapid biodegradation of phenanthrene and pyrene in soil environments.

antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation

Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.

Deciphering mechanisms of production of natural compounds using inducer-producer microbial consortia

Living organisms produce a wide range of metabolites. Because of their potential antibacterial, antifungal, antiviral, or cytostatic properties, such natural molecules are of high interest to the pharmaceutical industry. In nature, these metabolites are often synthesized via secondary metabolic biosynthetic gene clusters that are silent under the typical culturing conditions. Among different techniques used to activate these silent gene clusters, co-culturing of "producer" species with specific "inducer" microbes is a particularly appealing approach due to its simplicity. Although several "inducer-producer" microbial consortia have been reported in the literature and hundreds of different secondary metabolites with attractive biopharmaceutical properties have been described as a result of co-cultivating inducer-producer consortia, less attention has been devoted to the understanding of the mechanisms and possible means of induction for production of secondary metabolites in co-cultures. This lack of understanding of fundamental biological functions and inter-species interactions significantly limits the diversity and yield of valuable compounds using biological engineering tools. In this review, we summarize and categorize the known physiological mechanisms of production of secondary metabolites in inducer-producer consortia, and then discuss approaches that could be exploited to optimize the discovery and production of secondary metabolites.

Tahibacter harae sp. nov., isolated from pig farm soil in Guangdong, PR China

In the process of exploring the microbial diversity of pig farms, a Gram-stain-negative, aerobic, rod-shaped, non-spore-forming, non-motile bacterial strain, designated P2K^T, was isolated from soil sample collected at a pig farm, Guangdong Province, PR China. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain P2K^T belonged to the genus Tahibacter, with the highest sequence similarity to Tahibacter aquaticus PYM5-11^T (98.6%) and Tahibacter caeni BUT-6^T (98.3 %). The genome size of strain P2K^T was 6.0 Mb with a DNA G+C content of 68.3 mol%. Average nucleotide identity values between strain P2K^T and the type strains of the genus Tahibacter were 81.1-81.6 %. The digital DNA-DNA hybridization values between P2K^T and these relative species were 24.5-25.6%. The polar lipids were phosphatidylethanolamine, phosphatidylglycerol, an unknown aminolipids, two unknown lipids and three unknown phospholipids. The major respiratory quinone of strain P2K^T was ubiquinone Q-8, and the main fatty acids (>10.0 % of total fatty acids) of strain P2K^T were iso-C_15:0, iso-C_16:0 and summed feature 9 (C_16:0 10-methyl and/or iso-C_17:1 ω9c). Based on phenotypic and genotypic data, strain P2K^T represents a novel species within the genus Tahibacter, for which the name Tahibacter harae sp. nov. is proposed, with the type strain P2K^T (=GDMCC 1.3107^T=JCM 35231^T).

Diverse secondary metabolites are expressed in particle-associated and free-living microorganisms of the permanently anoxic Cariaco Basin

Secondary metabolites play essential roles in ecological interactions and nutrient acquisition, and are of interest for their potential uses in medicine and biotechnology. Genome mining for biosynthetic gene clusters (BGCs) can be used for the discovery of new compounds. Here, we use metagenomics and metatranscriptomics to analyze BGCs in free-living and particle-associated microbial communities through the stratified water column of the Cariaco Basin, Venezuela. We recovered 565 bacterial and archaeal metagenome-assembled genomes (MAGs) and identified 1154 diverse BGCs. We show that differences in water redox potential and microbial lifestyle (particle-associated vs. free-living) are associated with variations in the predicted composition and production of secondary metabolites. Our results indicate that microbes, including understudied clades such as Planctomycetota, potentially produce a wide range of secondary metabolites in these anoxic/euxinic waters.

Characterization of Peptaibols Produced by a Marine Strain of the Fungus Trichoderma endophyticum via Mass Spectrometry, Genome Mining and Phylogeny-Based Prediction

Trichoderma is recognized as a prolific producer of nonribosomal peptides (NRPs) known as peptaibols, which have remarkable biological properties, such as antimicrobial and anticancer activities, as well as the ability to promote systemic resistance in plants against pathogens. In this study, the sequencing of 11-, 14- and 15-res peptaibols produced by a marine strain of Trichoderma isolated from the ascidian Botrylloides giganteus was performed via liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS). Identification, based on multilocus phylogeny, revealed that our isolate belongs to the species T. endophyticum, which has never been reported in marine environments. Through genome sequencing and genome mining, 53 biosynthetic gene clusters (BGCs) were identified as being related to bioactive natural products, including two NRP-synthetases: one responsible for the biosynthesis of 11- and 14-res peptaibols, and another for the biosynthesis of 15-res. Substrate prediction, based on phylogeny of the adenylation domains in combination with molecular networking, permitted extensive annotation of the mass spectra related to two new series of 15-res peptaibols, which are referred to herein as "endophytins". The analyses of synteny revealed that the origin of the 15-module peptaibol synthetase is related to 18, 19 and 20-module peptaibol synthetases, and suggests that the loss of modules may be a mechanism used by Trichoderma species for peptaibol diversification. This study demonstrates the importance of combining genome mining techniques, mass spectrometry analysis and molecular networks for the discovery of new natural products.

A new isolate of Streptomyces lateritius (Z1-26) with antibacterial activity against fish pathogens and immune enhancement effects on crucian carp (Carassius auratus)

The Streptomyces lateritius Z1-26 was isolated from soil samples which showed broad-spectrum antibacterial activity against a broad range of fish pathogens. The In Vivo Imaging System (IVIS) monitored that strain Z1-26 could survive and colonize in the gills and abdomen of crucian carp. The effects of dietary supplementation with strain Z1-26 were evaluated with respect to the growth performance, antioxidant capacity, and immune response of crucian carp. The results showed that the Z1-26-fed fish had a significantly higher growth rate than the fish fed the control diet. The immune and antioxidant parameters revealed that the non-specific immune indicators (AKP, SOD, and LZM) of the serum, the expression of immune-related genes (IgM, C3, and LZM), and antioxidant-related genes (Nrf2 and Keap1) of the immune organs were significantly increased, whereas the expression of pro-inflammatory factors (IL-1β, IL-8, and TNF-α) of the immune organs was significantly down-regulated in crucian carp fed strain Z1-26 compared with fish fed a control diet. Moreover, fish fed with Z1-26 supplemented diets showed a significantly improved survival rate after Aeromonas hydrophila infection. In addition, the whole genome analysis showed that strain Z1-26 possesses 28 gene clusters, including 6 polyketide synthetase (PKS), 4 non-ribosomal peptide-synthetase (NRPS), 1 bacteriocin, and 1 lantipeptide. In summary, these results indicated that strain Z1-26 could improve the growth performance and disease resistance in crucian carp, and has the potential to be developed as a candidate probiotics for the control of bacterial diseases in aquaculture.

CusProSe: a customizable protein annotation software with an application to the prediction of fungal secondary metabolism genes

We report here a new application, CustomProteinSearch (CusProSe), whose purpose is to help users to search for proteins of interest based on their domain composition. The application is customizable. It consists of two independent tools, IterHMMBuild and ProSeCDA. IterHMMBuild allows the iterative construction of Hidden Markov Model (HMM) profiles for conserved domains of selected protein sequences, while ProSeCDA scans a proteome of interest against an HMM profile database, and annotates identified proteins using user-defined rules. CusProSe was successfully used to identify, in fungal genomes, genes encoding key enzyme families involved in secondary metabolism, such as polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), hybrid PKS-NRPS and dimethylallyl tryptophan synthases (DMATS), as well as to characterize distinct terpene synthases (TS) sub-families. The highly configurable characteristics of this application makes it a generic tool, which allows the user to refine the function of predicted proteins, to extend detection to new enzymes families, and may also be applied to biological systems other than fungi and to other proteins than those involved in secondary metabolism.

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.

Biological potential of Bacillus subtilis BS45 to inhibit the growth of Fusarium graminearum through oxidative damage and perturbing related protein synthesis

Fusarium root rot (FRR) caused by Fusarium graminearum poses a threat to global food security. Biological control is a promising control strategy for FRR. In this study, antagonistic bacteria were obtained using an in-vitro dual culture bioassay with F. graminearum. Molecular identification of the bacteria based on the 16S rDNA gene and whole genome revealed that the species belonged to the genus Bacillus. We evaluated the strain BS45 for its mechanism against phytopathogenic fungi and its biocontrol potential against FRR caused by F. graminearum. A methanol extract of BS45 caused swelling of the hyphal cells and the inhibition of conidial germination. The cell membrane was damaged and the macromolecular material leaked out of cells. In addition, the mycelial reactive oxygen species level increased, mitochondrial membrane potential decreased, oxidative stress-related gene expression level increased and oxygen-scavenging enzyme activity changed. In conclusion, the methanol extract of BS45 induced hyphal cell death through oxidative damage. A transcriptome analysis showed that differentially expressed genes were significantly enriched in ribosome function and various amino acid transport pathways, and the protein contents in cells were affected by the methanol extract of BS45, indicating that it interfered with mycelial protein synthesis. In terms of biocontrol capacity, the biomass of wheat seedlings treated with the bacteria increased, and the BS45 strain significantly inhibited the incidence of FRR disease in greenhouse tests. Therefore, strain BS45 and its metabolites are promising candidates for the biological control of F. graminearum and its related root rot diseases.

Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial Activated Sludge Ecosystem

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Navigating and expanding the roadmap of natural product genome mining tools

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

Comparative Transcriptome Analysis of Two Chrysomycin-Producing Wild-Type and Mutant Strains of Streptomyces sp. 891

Chrysomycin A (CA), a promising antibiotic agent, usually coexists with two analog chrysomycins B (CB) and C (CC) produced by several wild-type (WT) Streptomyces strains. With the aim to increase CA production, UV mutagenesis-based breeding had been employed on a marine-derived strain Streptomyces sp. 891 in our previous study and afforded an improved strain 891-B6 with enhanced CA yield. By comparative transcriptome analysis, significant differences in chrysomycin BGC-related gene expression between the WT strain 891 and the mutant strain 891-B6 were unveiled in the current study. Among 25 up-regulated genes in mutant 891-B6, chryA, chryB, chryC, chryF, chryG, chryK, chryP, and chryQ, responsible for the biosynthesis of benzonaphthopyranone aglycone, and chryD, chryE, and chryU in charge of production of its deoxyglycoside, were characterized. Furthermore, the expression of genes chryOII, chryOIII, and chryOIV responsible for the formation of 8-vinyl in CA from 8-ethyl in CB were greatly enhanced in strain 891-B6. These findings provide molecular mechanisms for increased yield of CA and decreased yield of CB for mutant 891-B6, which has potential application in industrial CA production.

Breaking the Bottleneck in Anticancer Drug Development: Efficient Utilization of Synthetic Biology

Natural products have multifarious bioactivities against bacteria, fungi, viruses, cancers and other diseases due to their diverse structures. Nearly 65% of anticancer drugs are natural products or their derivatives. Thus, natural products play significant roles in clinical cancer therapy. With the development of biosynthetic technologies, an increasing number of natural products have been discovered and developed as candidates for clinical cancer therapy. Here, we aim to summarize the anticancer natural products approved from 1950 to 2021 and discuss their molecular mechanisms. We also describe the available synthetic biology tools and highlight their applications in the development of natural products.

Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.

Genomic Comparative Analysis of Cordyceps pseudotenuipes with Other Species from Cordyceps

The whole genome of Cordyceps pseudotenuipes was sequenced, annotated, and compared with three related species to characterize the genome. The antibiotics and Secondary Metabolites Analysis Shell (antiSMASH) and local BLAST analysis were used to explore the secondary metabolites (SMs) and biosynthesis gene clusters (BGCs) of the genus Cordyceps. The genome-wide basic characteristics of C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris revealed unequal genome size, with C. cicadae as the largest (34.11 Mb), followed by C. militaris (32.27 Mb). However, the total gene lengths of C. pseudotenuipes and C. tenuipes were similar (30.1 Mb and 30.06 Mb). The GC contents of C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris genomes differed slightly (51.40% to 54.11%). AntiSMASH and local BLAST analysis showed that C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris had 31, 28, 31, and 29 putative SM BGCs, respectively. The SM BGCs contained different quantities of polyketide synthetase (PKS), nonribosomal peptide synthetase (NRPS), terpene, hybrid PKS + NRPS, and hybrid NRPS + Other. Moreover, C. pseudotenuipes, C. tenuipes, C. cicadae, and C. militaris had BGCs for the synthesis of dimethylcoprogen. C. pseudotenuipes, C. tenuipes, and C. cicadae had BGCs for the synthesis of leucinostatin A/B, neosartorin, dimethylcoprogen, wortmanamide A/B, and beauvericin. In addition, the SM BGCs unique to C. pseudotenuipes were clavaric acid, communesin, and deoxynivalenol. Synteny analysis indicated that the scaffolds where the SM BGC was located were divided into more than 70 collinear blocks, and there might be rearrangements. Altogether, these findings improved our understanding of the molecular biology of the genus Cordyceps and will facilitate the discovery of new biologically active SMs from the genus Cordyceps using heterologous expression and gene knockdown methods.

How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances

Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial "weapon systems", signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

Genome Features and AntiSMASH Analysis of an Endophytic Strain Fusarium sp. R1

Endophytic fungi are one of the most prolific sources of functional biomolecules with therapeutic potential. Besides playing an important role in serious plant diseases, Fusarium strains possess the powerful capability to produce a diverse array of bioactive secondary metabolites (SMs). In order to in-depth mine gene clusters for SM biosynthesis of the genus Fusarium, an endophytic strain Fusarium sp. R1 isolated from Rumex madaio Makino was extensively investigated by whole-genome sequencing and in-depth bioinformatic analysis, as well as antiSMASH annotation. The results displayed that strain R1 harbors a total of 51.8 Mb genome, which consists of 542 contigs with an N50 scaffold length of 3.21 Mb and 50.4% GC content. Meanwhile, 19,333 functional protein-coding genes, 338 tRNA and 111 rRNA were comprehensively predicted and highly annotated using various BLAST databases including non-redundant (Nr) protein sequence, nucleotide (Nt) sequence, Swiss-Prot, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Clusters of Orthologous Groups (COG), as well as Pathogen Host Interactions (PHI) and Carbohydrate-Active enzymes (CAZy) databases. Antibiotics and Secondary Metabolites Analysis Shell (AntiSMASH) results showed that strain R1 has 37 SM biosynthetic gene clusters (BGCs), including 17 nonribosomal peptide synthetases (NRPSs), 13 polyketide synthetases (PKSs), 3 terpene synthases (Ts), 3 hybrid NRPS + PKS and 1 hybrid indole + NRPS. These findings improve our knowledge of the molecular biology of the genus Fusarium and would promote the discovery of new bioactive SMs from strain R1 using gene mining strategies including gene knockout and heteroexpression.

Exploring Newer Biosynthetic Gene Clusters in Marine Microbial Prospecting

Marine microbes genetically evolved to survive varying salinity, temperature, pH, and other stress factors by producing different bioactive metabolites. These microbial secondary metabolites (SMs) are novel, have high potential, and could be used as lead molecule. Genome sequencing of microbes revealed that they have the capability to produce numerous novel bioactive metabolites than observed under standard in vitro culture conditions. Microbial genome has specific regions responsible for SM assembly, termed biosynthetic gene clusters (BGCs), possessing all the necessary genes to encode different enzymes required to generate SM. In order to augment the microbial chemo diversity and to activate these gene clusters, various tools and techniques are developed. Metagenomics with functional gene expression studies aids in classifying novel peptides and enzymes and also in understanding the biosynthetic pathways. Genome shuffling is a high-throughput screening approach to improve the development of SMs by incorporating genomic recombination. Transcriptionally silent or lower level BGCs can be triggered by artificially knocking promoter of target BGC. Additionally, bioinformatic tools like antiSMASH, ClustScan, NAPDOS, and ClusterFinder are effective in identifying BGCs of existing class for annotation in genomes. This review summarizes the significance of BGCs and the different approaches for detecting and elucidating BGCs from marine microbes.

Comparative genomics reveals the organic acid biosynthesis metabolic pathways among five lactic acid bacterial species isolated from fermented vegetables

Lactic acid bacteria (LAB) comprise a widespread bacterial group, inhabiting the niches of fermented vegetables and capable of producing beneficial organic acids. In the present study, several bioinformatics approaches were used to perform whole-genome sequencing and comparative genomics of five LAB species, Lactobacillus plantarum PC1-1, Pediococcus pentosaceus PC2-1(F2), Weissella hellenica PC1A, Lactobacillus buchneri PC-C1, and Enterococcus sp. YC2-6, to enhance understanding of their different genetic functionalities and organic acid biosynthesis. The results revealed major carbohydrate-active enzymes, putative operons and unique mobile genetic elements, including plasmids, resistance genes, insertion sequences and composite transposons involved in organic acid biosynthesis. The metabolic pathways of organic acid biosynthesis emphasize the key genes encoding specific enzymes required for organic acid metabolism. The five genomes were found to contain various regions of secondary metabolite biosynthetic gene clusters, including the type III polyketide synthases (T3PKS) enriched with unique genes encoding a hydroxymethylglutaryl-CoA synthase, capable of exhibiting specific antimicrobial activity with biopreservative potential, and a cyclic AMP receptor protein (CPR) transcription factor acting as a glucose sensor in organic acid biosynthesis. This could enable the organisms to prevail in the fermentation process, suggesting potential industrial applications.

Bioinformatic comparison of three Embleya species and description of steffimycins production by Embleya sp. NF3

The Embleya genus is a new member of the Streptomycetaceae family formed by only two species isolated from soil (Embleya scabrispora and Embleya hyalina). Strain NF3 is an endophytic actinobacterium obtained from the medicinal tree Amphipterygium adstringens. By 16S rRNA gene analysis, NF3 strain was identified as Embleya sp., closely related to E. hyalina. In our interest to deep into the NF3 strain features, a bioinformatic study was performed on the Embleya genus based on their genome information to produce secondary metabolites. A comparative analysis of the biosynthetic gene clusters (BGCs) of NF3 with the two released Embleya genomes revealed that NF3 has 49 BGCs, E. scabrispora DSM41855 has 50 BGCs, and E. hyalina NBRC13850 has 46 BGCs. Although bearing similar cluster numbers, the three strains shared only 25% of the BGCs information. NF3 encoded the nybomycin cluster detected in E. hyalina NBRC13850 and lacked the hitachimycin cluster present in E. scabrispora DSM41855. On the contrary, strain NF3 contained a cluster for the anthracycline steffimycin, neither encoded by E. hyalina NBRC13850 nor by E. scabrispora DSM41855. Our results and previous characterization studies supported strain NF3 as a new member of the genus Embleya. The chemical analysis of the steffimycins produced by strain NF3 showed the production of eight compounds of the steffimycins and steffimycinone families. Four of these molecules have already been described: steffimycin B, steffimycin C, 8-demethoxy-10-deoxysteffimycinone, and 7-deoxiesteffimycinone, and four are new natural products: 8-demethoxysteffimycin B, 8-demethoxy-10-deoxysteffimycin B, 7-deoxy-8-demethoxysteffimycinone, and 7-deoxy-10-deoxysteffimycinone. With this information, we proposed an alternative pathway to produce StefB. Among steffimycins, StefB was the main compound produced by this strain (29.8%) and showed the best cytotoxic activity. KEY POINTS: • The Embleya genus and its biosynthetic potential • An alternative biosynthetic pathway for steffimycins biosynthesis • Four new natural products of the steffimycin family.

Uncovering the biodiversity and biosynthetic potentials of rare actinomycetes

Background

Antibiotic resistance is on the rise, and new antibiotic research has slowed in recent years, necessitating the discovery of possibly novel microbial resources capable of producing bioactive compounds. Microbial infections are gaining resistance to existing antibiotics, emphasizing the need for novel medicinal molecules to be discovered as soon as possible. Because the possibilities of isolating undiscovered actinomycetes strains have decreased, the quest for novel products has shifted to rare actinomycetes genera from regular environments or the identification of new species identified in unusual habitats.

Main body of the abstract

The non-streptomyces actinobacteria are known as rare actinomycetes that are extremely difficult to cultivate. Rare actinomycetes are known to produce a variety of secondary metabolites with varying medicinal value. In this review, we reported the diversity of rare actinomycetes in several habitat including soil, plants, aquatic environment, caves, insects and extreme environments. We also reported some isolation methods to easily recover rare Actinobacteria from various sources guided with some procedures to identify the rare Actinobacteria isolates. Finally, we reported the biosynthetic potential of rare actinomycetes and its role in the production of unique secondary metabolites that could be used in medicine, agriculture, and industry. These microbial resources will be of interest to humanity, as antibiotics, insecticides, anticancer, antioxidants, to mention but a few.

Short conclusion

Rare actinomycetes are increasingly being investigated for new medicinal compounds that could help to address existing human health challenges such as newly emerging infectious illnesses, antibiotic resistance, and metabolic disorders. The bioactive secondary metabolites from uncommon actinomycetes are the subject of this review, which focuses on their diversity in different habitats, isolation, identification and biosynthetic potentials.

Genome Analysis of the Broad Host Range Necrotroph Nalanthamala psidii Highlights Genes Associated With Virulence

Guava wilt disease is caused by the fungus Nalanthamala psidii. The wilt disease results in large-scale destruction of orchards in South Africa, Taiwan, and several Southeast Asian countries. De novo assembly, annotation, and in-depth analysis of the N. psidii genome were carried out to facilitate the identification of characteristics associated with pathogenicity and pathogen evolution. The predicted secretome revealed a range of CAZymes, proteases, lipases and peroxidases associated with plant cell wall degradation, nutrient acquisition, and disease development. Further analysis of the N. psidii carbohydrate-active enzyme profile exposed the broad-spectrum necrotrophic lifestyle of the pathogen, which was corroborated by the identification of putative effectors and secondary metabolites with the potential to induce tissue necrosis and cell surface-dependent immune responses. Putative regulatory proteins including transcription factors and kinases were identified in addition to transporters potentially involved in the secretion of secondary metabolites. Transporters identified included important ABC and MFS transporters involved in the efflux of fungicides. Analysis of the repetitive landscape and the detection of mechanisms linked to reproduction such as het and mating genes rendered insights into the biological complexity and evolutionary potential of N. psidii as guava pathogen. Hence, the assembly and annotation of the N. psidii genome provided a valuable platform to explore the pathogenic potential and necrotrophic lifestyle of the guava wilt pathogen.