CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes

CLOCI: unveiling cryptic fungal gene clusters with generalized detection

Gene clusters are genomic loci that contain multiple genes that are functionally and genetically linked. Gene clusters collectively encode diverse functions, including small molecule biosynthesis, nutrient assimilation, metabolite degradation, and production of proteins essential for growth and development. Identifying gene clusters is a powerful tool for small molecule discovery and provides insight into the ecology and evolution of organisms. Current detection algorithms focus on canonical 'core' biosynthetic functions many gene clusters encode, while overlooking uncommon or unknown cluster classes. These overlooked clusters are a potential source of novel natural products and comprise an untold portion of overall gene cluster repertoires. Unbiased, function-agnostic detection algorithms therefore provide an opportunity to reveal novel classes of gene clusters and more precisely define genome organization. We present CLOCI (Co-occurrence Locus and Orthologous Cluster Identifier), an algorithm that identifies gene clusters using multiple proxies of selection for coordinated gene evolution. Our approach generalizes gene cluster detection and gene cluster family circumscription, improves detection of multiple known functional classes, and unveils non-canonical gene clusters. CLOCI is suitable for genome-enabled small molecule mining, and presents an easily tunable approach for delineating gene cluster families and homologous loci.

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

DNA Transposons Favor De Novo Transcript Emergence Through Enrichment of Transcription Factor Binding Motifs

De novo genes emerge from noncoding regions of genomes via succession of mutations. Among others, such mutations activate transcription and create a new open reading frame (ORF). Although the mechanisms underlying ORF emergence are well documented, relatively little is known about the mechanisms enabling new transcription events. Yet, in many species a continuum between absent and very prominent transcription has been reported for essentially all regions of the genome. In this study, we searched for de novo transcripts by using newly assembled genomes and transcriptomes of seven inbred lines of Drosophila melanogaster, originating from six European and one African population. This setup allowed us to detect sample specific de novo transcripts, and compare them to their homologous nontranscribed regions in other samples, as well as genic and intergenic control sequences. We studied the association with transposable elements (TEs) and the enrichment of transcription factor motifs upstream of de novo emerged transcripts and compared them with regulatory elements. We found that de novo transcripts overlap with TEs more often than expected by chance. The emergence of new transcripts correlates with regions of high guanine-cytosine content and TE expression. Moreover, upstream regions of de novo transcripts are highly enriched with regulatory motifs. Such motifs are more enriched in new transcripts overlapping with TEs, particularly DNA TEs, and are more conserved upstream de novo transcripts than upstream their 'nontranscribed homologs'. Overall, our study demonstrates that TE insertion is important for transcript emergence, partly by introducing new regulatory motifs from DNA TE families.

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.

Comparative genome analysis of the freshwater fungus Filosporella fistucella indicates potential for plant-litter degradation at cold temperatures

Freshwater fungi play an important role in the decomposition of organic matter of leaf litter in rivers and streams. They also possess the necessary mechanisms to endure lower temperatures caused by habitat and weather variations. This includes the production of cold-active enzymes and antifreeze proteins. To better understand the physiological activities of freshwater fungi in their natural environment, different methods are being applied, and genome sequencing is one in the spotlight. In our study, we sequenced the first genome of the freshwater fungus Filosporella fistucella (45.7 Mb) and compared the genome with the evolutionary close-related species Tricladium varicosporioides (48.2 Mb). The genomes were annotated using the carbohydrate-active enzyme database where we then filtered for leaf-litter degradation-related enzymes (cellulase, hemicellulase, laccase, pectinase, cutinase, amylase, xylanase, and xyloglucanase). Those enzymes were analyzed for antifreeze properties using a machine-learning approach. We discovered that F. fistucella has more enzymes to participate in the breakdown of sugar, leaf, and wood than T. varicosporioides (855 and 719, respectively). Filosporella fistucella shows a larger set of enzymes capable of resisting cold temperatures than T. varicosporioides (75 and 66, respectively). Our findings indicate that in comparison with T. varicosporioides, F. fistucella has a greater capacity for aquatic growth, adaptability to freshwater environments, and resistance to low temperatures.

Mining for a new class of fungal natural products: the evolution, diversity, and distribution of isocyanide synthase biosynthetic gene clusters

The products of non-canonical isocyanide synthase (ICS) biosynthetic gene clusters (BGCs) mediate pathogenesis, microbial competition, and metal-homeostasis through metal-associated chemistry. We sought to enable research into this class of compounds by characterizing the biosynthetic potential and evolutionary history of these BGCs across the Fungal Kingdom. We amalgamated a pipeline of tools to predict BGCs based on shared promoter motifs and located 3800 ICS BGCs in 3300 genomes, making ICS BGCs the fifth largest class of specialized metabolites compared to canonical classes found by antiSMASH. ICS BGCs are not evenly distributed across fungi, with evidence of gene-family expansions in several Ascomycete families. We show that the ICS dit1/2 gene cluster family (GCF), which was prior only studied in yeast, is present in ∼30% of all Ascomycetes. The dit variety ICS exhibits greater similarity to bacterial ICS than other fungal ICS, suggesting a potential convergence of the ICS backbone domain. The evolutionary origins of the dit GCF in Ascomycota are ancient and these genes are diversifying in some lineages. Our results create a roadmap for future research into ICS BGCs. We developed a website (https://isocyanides.fungi.wisc.edu/) that facilitates the exploration and downloading of all identified fungal ICS BGCs and GCFs.

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 virulence features of Beauveria bassiana as a biocontrol agent for the mountain pine beetle population

BACKGROUND

The mountain pine beetle, Dendroctonus ponderosae, is an irruptive bark beetle that causes extensive mortality to many pine species within the forests of western North America. Driven by climate change and wildfire suppression, a recent mountain pine beetle (MPB) outbreak has spread across more than 18 million hectares, including areas to the east of the Rocky Mountains that comprise populations and species of pines not previously affected. Despite its impacts, there are few tactics available to control MPB populations. Beauveria bassiana is an entomopathogenic fungus used as a biological agent in agriculture and forestry and has potential as a management tactic for the mountain pine beetle population. This work investigates the phenotypic and genomic variation between B. bassiana strains to identify optimal strains against a specific insect.

RESULTS

Using comparative genome and transcriptome analyses of eight B. bassiana isolates, we have identified the genetic basis of virulence, which includes oosporein production. Genes unique to the more virulent strains included functions in biosynthesis of mycotoxins, membrane transporters, and transcription factors. Significant differential expression of genes related to virulence, transmembrane transport, and stress response was identified between the different strains, as well as up to nine-fold upregulation of genes involved in the biosynthesis of oosporein. Differential correlation analysis revealed transcription factors that may be involved in regulating oosporein production.

CONCLUSION

This study provides a foundation for the selection and/or engineering of the most effective strain of B. bassiana for the biological control of mountain pine beetle and other insect pests populations.

Dual RNA-Seq Profiling Unveils Mycoparasitic Activities of Trichoderma atroviride against Haploid Armillaria ostoyae in Antagonistic Interaction Assays

Armillaria ostoyae, a species among the destructive forest pathogens from the genus Armillaria, causes root rot disease on woody plants worldwide. Efficient control measures to limit the growth and impact of this severe underground pathogen are under investigation. In a previous study, a new soilborne fungal isolate, Trichoderma atroviride SZMC 24276 (TA), exhibited high antagonistic efficacy, which suggested that it could be utilized as a biocontrol agent. The dual culture assay results indicated that the haploid A. ostoyae-derivative SZMC 23085 (AO) (C18/9) is highly susceptible to the mycelial invasion of TA. In the present study, we analyzed the transcriptome of AO and that of TA in in vitro dual culture assays to test the molecular arsenal of Trichoderma antagonism and the defense mechanisms of Armillaria. We conducted time-course analysis and functional annotation and analyzed enriched pathways and differentially expressed genes including biocontrol-related candidate genes from TA and defense-related candidate genes from AO. The results indicated that TA deployed several biocontrol mechanisms when confronted with AO. In response, AO initiated multiple defense mechanisms to protect against the fungal attack. To our knowledge, the present study offers the first transcriptome analysis of a biocontrol fungus attacking AO. Overall, this study provides insights that aid the further exploration of plant pathogen-biocontrol agent interaction mechanisms. IMPORTANCE Armillaria species can survive for decades in the soil on dead woody debris, develop rapidly under favorable conditions, and harmfully infect newly planted forests. Our previous study found Trichoderma atroviride to be highly effective in controlling Armillaria growth; therefore, our current work explored the molecular mechanisms that might play a key role in Trichoderma-Armillaria interactions. Direct confrontation assays combined with time course-based dual transcriptome analysis provided a reliable system for uncovering the interactive molecular dynamics between the fungal plant pathogen and its mycoparasitic partner. Furthermore, using a haploid Armillaria isolate allowed us to survey the deadly prey-invading activities of the mycoparasite and the ultimate defensive strategies of its prey. Our current study provides detailed insights into the essential genes and mechanisms involved in Armillaria defense against Trichoderma and the genes potentially involved in the efficiency of Trichoderma to control Armillaria. In addition, using a sensitive haploid Armillaria strain (C18/9), with its complete genome data already available, also offers the opportunity to test possible variable molecular responses of Armillaria ostoyae toward diverse Trichoderma isolates with various biocontrol abilities. Initial molecular tests of the dual interactions may soon help to develop a targeted biocontrol intervention with mycoparasites against plant pathogens.

Mining for a New Class of Fungal Natural Products: The Evolution, Diversity, and Distribution of Isocyanide Synthase Biosynthetic Gene Clusters

The products of non-canonical isocyanide synthase (ICS) biosynthetic gene clusters (BGCs) have notable bioactivities that mediate pathogenesis, microbial competition, and metal-homeostasis through metal-associated chemistry. We sought to enable research into this class of compounds by characterizing the biosynthetic potential and evolutionary history of these BGCs across the Fungal Kingdom. We developed the first genome-mining pipeline to identify ICS BGCs, locating 3,800 ICS BGCs in 3,300 genomes. Genes in these clusters share promoter motifs and are maintained in contiguous groupings by natural selection. ICS BGCs are not evenly distributed across fungi, with evidence of gene-family expansions in several Ascomycete families. We show that the ICS dit1 / 2 gene cluster family (GCF), which was thought to only exist in yeast, is present in ∼30% of all Ascomycetes, including many filamentous fungi. The evolutionary history of the dit GCF is marked by deep divergences and phylogenetic incompatibilities that raise questions about convergent evolution and suggest selection or horizontal gene transfers have shaped the evolution of this cluster in some yeast and dimorphic fungi. Our results create a roadmap for future research into ICS BGCs. We developed a website ( www.isocyanides.fungi.wisc.edu ) that facilitates the exploration, filtering, and downloading of all identified fungal ICS BGCs and GCFs.

Comparative genomics of five Valsa species gives insights on their pathogenicity evolution

Valsa is a genus of ascomycetes within the Valsaceae family. This family includes many wood destructive pathogens such as the well known Valsa mali and Valsa pyri which cause canker diseases in fruit trees and threaten the global fruit production. Lack of genomic information of this family is impeding our understandings about their evolution and genetic basis of their pathogenicity divergence. Here, we report genome assemblies of Valsa malicola, Valsa persoonii, and Valsa sordida which represent close relatives of Valsa mali and Valsa pyri with different host preferences. Comparative genomics analysis revealed that segmental rearrangements, inversions, and translocations frequently occurred among Valsa spp. genomes. Gene families that exhibited gene copy expansions tended to be associated with secondary metabolism, transmembrane transport, and pyrophosphatase activities. Orthologous genes in regions lost synteny exhibited significantly higher rate of synonymous substitution (KS) than those in regions retained synteny. Moreover, among these genes, membrane transporter families associated with antidrug (MFS, DHA) activities and nutrient transportation (SP and APCs) activities were significantly over-represented. Lineage specific synonymous substitution (KS) and nonsynonymous substitution (KA) analysis based on the phylogeny constructed from 11 fungal species identified a set of genes with selection signatures in Valsa clade and these genes were significantly enriched in functions associated with fatty acid beta-oxidation, DNA helicase activity, and ATPase activity. Furthermore, unique genes that possessed or retained by each of the five Valsa species are more likely part of the secondary metabolic (SM) gene clusters. SM gene clusters conserved across five Valsa species showed various degrees of diversification in both identity and completeness. All 11 syntenically conserved SM clusters showed differential expression during the infection of apple branch with Valsa mali suggesting involvements of secondary metabolism in the pathogenicity of Valsa species.

Combinatorial biosynthesis: playing chess with the metabolism

Secondary metabolites are a group of natural products that produced by bacteria, fungi and plants. Many applications of these compounds from medicine to industry have been discovered. However, some changes in their structure and biosynthesis mechanism are necessary for their properties to be more suitable and also for their production to be profitable. The main and most useful method to achieve this goal is combinatorial biosynthesis. This technique uses the multi-unit essence of the secondary metabolites biosynthetic enzymes to make changes in their order, structure and also the organism that produces them.

Enhancing chemical and biological diversity by co-cultivation

In natural product research, microbial metabolites have tremendous potential to provide new therapeutic agents since extremely diverse chemical structures can be found in the nearly infinite microbial population. Conventionally, these specialized metabolites are screened by single-strain cultures. However, owing to the lack of biotic and abiotic interactions in monocultures, the growth conditions are significantly different from those encountered in a natural environment and result in less diversity and the frequent re-isolation of known compounds. In the last decade, several methods have been developed to eventually understand the physiological conditions under which cryptic microbial genes are activated in an attempt to stimulate their biosynthesis and elicit the production of hitherto unexpressed chemical diversity. Among those, co-cultivation is one of the most efficient ways to induce silenced pathways, mimicking the competitive microbial environment for the production and holistic regulation of metabolites, and has become a golden methodology for metabolome expansion. It does not require previous knowledge of the signaling mechanism and genome nor any special equipment for cultivation and data interpretation. Several reviews have shown the potential of co-cultivation to produce new biologically active leads. However, only a few studies have detailed experimental, analytical, and microbiological strategies for efficiently inducing bioactive molecules by co-culture. Therefore, we reviewed studies applying co-culture to induce secondary metabolite pathways to provide insights into experimental variables compatible with high-throughput analytical procedures. Mixed-fermentation publications from 1978 to 2022 were assessed regarding types of co-culture set-ups, metabolic induction, and interaction effects.

Bidirectional hybrid erythritol-inducible promoter for synthetic biology in Yarrowia lipolytica

BACKGROUND

The oleaginous yeast Yarrowia lipolytica is increasingly used as a chassis strain for generating bioproducts. Several hybrid promoters with different strengths have been developed by combining multiple copies of an upstream activating sequence (UAS) associated with a TATA box and a core promoter. These promoters display either constitutive, phase-dependent, or inducible strong expression. However, there remains a lack of bidirectional inducible promoters for co-expressing genes in Y. lipolytica.

RESULTS

This study built on our previous work isolating and characterizing the UAS of the erythritol-induced genes EYK1 and EYD1 (UAS-eyk1). We found an erythritol-inducible bidirectional promoter (BDP) located in the EYK1-EYL1 intergenic region. We used the BDP to co-produce YFP and RedStarII fluorescent proteins and demonstrated that the promoter's strength was 2.7 to 3.5-fold stronger in the EYL1 orientation compared to the EYK1 orientation. We developed a hybrid erythritol-inducible bidirectional promoter (HBDP) containing five copies of UAS-eyk1 in both orientations. It led to expression levels 8.6 to 19.2-fold higher than the native bidirectional promoter. While the BDP had a twofold-lower expression level than the strong constitutive TEF promoter, the HBDP had a 5.0-fold higher expression level when oriented toward EYL1 and a 2.4-fold higher expression level when oriented toward EYK1. We identified the optimal media for BDP usage by exploring yeast growth under microbioreactor conditions. Additionally, we constructed novel Golden Gate biobricks and a destination vector for general use.

CONCLUSIONS

In this research, we developed novel bidirectional and hybrid bidirectional promoters of which expression can be fine-tuned, responding to the need for versatile promoters in the yeast Y. lipolytica. This study provides effective tools that can be employed to smoothly adjust the erythritol-inducible co-expression of two target genes in biotechnology applications. BDPs developed in this study have potential applications in the fields of heterologous protein production, metabolic engineering, and synthetic biology.

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.

Review of Escherichia coli O157:H7 Prevalence, Pathogenicity, Heavy Metal and Antimicrobial Resistance, African Perspective

Escherichia coli O157:H7 is an important food-borne and water-borne pathogen that causes hemorrhagic colitis and the hemolytic-uremic syndrome in humans and may cause serious morbidity and large outbreaks worldwide. People with bloody diarrhea have an increased risk of developing serious complications such as acute renal failure and neurological damage. The hemolytic-uremic syndrome (HUS) is a serious condition, and up to 50% of HUS patients can develop long-term renal dysfunction or blood pressure-related complications. Children aged two to six years have an increased risk of developing HUS. Clinical enteropathogenic Escherichia coli (EPEC) infections show fever, vomiting, and diarrhea. The EPEC reservoir is unknown but is suggested to be an asymptomatic or symptomatic child or an asymptomatic adult carrier. Spreading is often through the fecal-oral route. The prevalence of EPEC in infants is low, and EPEC is highly contagious in children. EPEC disease in children tends to be clinically more severe than other diarrheal infections. Some children experience persistent diarrhea that lasts for more than 14 days. Enterotoxigenic Escherichia coli (ETEC) strains are a compelling cause of the problem of diarrheal disease. ETEC strains are a global concern as the bacteria are the leading cause of acute watery diarrhea in children and the leading cause of traveler’s diarrhea. It is contagious to children and can cause chronic diarrhea that can affect the development and well-being of children. Infections with diarrheagenic E. coli are more common in African countries. Antimicrobial agents should be avoided in the acute phase of the disease since studies showed that antimicrobial agents may increase the risk of HUS in children. The South African National Veterinary Surveillance and Monitoring Programme for Resistance to Antimicrobial Drugs has reported increased antimicrobial resistance in E. coli. Pathogenic bacterial strains have developed resistance to a variety of antimicrobial agents due to antimicrobial misuse. The induced heavy metal tolerance may also enhance antimicrobial resistance. The prevalence of antimicrobial resistance depends on the type of the antimicrobial agent, bacterial strain, dose, time, and mode of administration. Developing countries are severely affected by increased resistance to antimicrobial agents due to poverty, lack of proper hygiene, and clean water, which can lead to bacterial infections with limited treatment options due to resistance.

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.

Genome-wide identification of the Penicillium digitatum bZIP gene family and the roles of one key member, PdatfA

Penicillium digitatum is the most common cause of postharvest decay in citrus fruits around the world. Previous studies revealed that the bZIP gene family plays crucial roles in development, stress adaptation, and pathogenicity in fungi. However, little is known about the bZIP genes in P. digitatum. In this study, we systematically identified the bZIP family in 23 Penicillium species and analyzed their evolutionary relationships. We found that gene loss and gene duplication shaped the evolution of the Penicillium bZIP family. P. digitatum experienced 3 bZIP gene loss events, but with no gene duplication. We subsequently characterized the biological functions of one important member, PdatfA in P. digitatum by constructing the deletion mutant. Results showed that ΔPdatfA exhibited a moderate growth defect, reduced pigmentation, and slightly increased resistance to fungicides iprodione and fludioxonil. However, ΔPdatfA displayed similar rot symptoms to that of the wild type. The ΔPdatfA mycelia were not affected in response to oxidative stress while its conidia showed enhanced resistance due to the upregulation of catalases. Our results provide new insights into the evolution and functions of the bZIP gene family in Penicillium.

Transcriptional Activation of Biosynthetic Gene Clusters in Filamentous Fungi

Filamentous fungi are highly productive cell factories, many of which are industrial producers of enzymes, organic acids, and secondary metabolites. The increasing number of sequenced fungal genomes revealed a vast and unexplored biosynthetic potential in the form of transcriptionally silent secondary metabolite biosynthetic gene clusters (BGCs). Various strategies have been carried out to explore and mine this untapped source of bioactive molecules, and with the advent of synthetic biology, novel applications, and tools have been developed for filamentous fungi. Here we summarize approaches aiming for the expression of endogenous or exogenous natural product BGCs, including synthetic transcription factors, assembly of artificial transcription units, gene cluster refactoring, fungal shuttle vectors, and platform strains.

Genomic Characteristics and Comparative Genomics Analysis of Parafenestella ontariensis sp. nov

A new ascomycetous species of Parafenestella was isolated from Acer negundo during the survey of diseased trees in Southern Ontario, Canada. The species is morphologically similar to other taxa of Cucurbitariacea (Pleosporales). The new species is different from the extant species in the morphology of ascospores, culture characteristics and molecular data. The novel species is described as Parafenestella ontariensis sp. nov. based on morphological and multi-gene phylogenetic analyses using a combined set of ITS, LSU, tef1 and tub2 loci. Additionally, the genome of P. ontariensis was sequenced and analyzed. The phylogenomic analysis confirmed the close relationship of the species to the fenestelloid clades of Cucurbitariaceae. The comparative genomics analysis revealed that the species lifestyle appears to be multitrophic (necrotrophic or hemi-biotrophic) with a capability to turn pathogenic on a corresponding plant host.

Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning

MOTIVATION

Precise identification of Biosynthetic Gene Clusters (BGCs) is a challenging task. Performance of BGC discovery tools is limited by their capacity to accurately predict components belonging to candidate BGCs, often overestimating cluster boundaries. To support optimizing the composition and boundaries of candidate BGCs, we propose reinforcement learning approach relying on protein domains and functional annotations from expert curated BGCs.

RESULTS

The proposed reinforcement learning method aims to improve candidate BGCs obtained with state-of-the-art tools. It was evaluated on candidate BGCs obtained for two fungal genomes, Aspergillus niger and Aspergillus nidulans. The results highlight an improvement of the gene precision by above 15% for TOUCAN, fungiSMASH and DeepBGC; and cluster precision by above 25% for fungiSMASH and DeepBCG, allowing these tools to obtain almost perfect precision in cluster prediction. This can pave the way of optimizing current prediction of candidate BGCs in fungi, while minimizing the curation effort required by domain experts.

AVAILABILITY AND IMPLEMENTATION

https://github.com/bioinfoUQAM/RL-bgc-components.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Secondary Metabolite Transcriptomic Pipeline (SeMa-Trap), an expression-based exploration tool for increased secondary metabolite production in bacteria

For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the ‘Secondary Metabolite Transcriptomic Pipeline’ (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest.

Evolutionary Genome Mining for the Discovery and Engineering of Natural Product Biosynthesis

Genome mining has become an invaluable tool in natural products research to quickly identify and characterize the biosynthetic pathways that assemble secondary or specialized metabolites. Recently, evolutionary principles have been incorporated into genome mining strategies in an effort to better assess and prioritize novelty and understand their chemical diversification for engineering purposes. Here, we provide an introduction to the principles underlying evolutionary genome mining, including bioinformatic strategies and natural product biosynthetic databases. We introduce workflows for traditional genome mining, focusing on the popular pipeline antiSMASH, and methods to predict enzyme substrate specificity from genomic information. We then provide an in-depth discussion of evolutionary genome mining workflows, including EvoMining, CORASON, ARTS, and others, as adopted by our group for the discovery and prioritization of natural products biosynthetic gene clusters and their products.

A Bioinformatics Workflow for Investigating Fungal Biosynthetic Gene Clusters

Predicting secondary metabolite biosynthetic gene clusters is a routine analysis performed for each newly sequenced fungal genome. Yet, the usefulness of such predictions remains restricted as they provide total numbers of biosynthetic pathways with only very limited biological significance. In this chapter, we describe a workflow to predict and analyze biosynthetic gene clusters in fungal genomes. It relies on similarity networking and phylogeny to perform genetic dereplication and to prioritize candidate gene clusters that potentially produce new compounds. This basic workflow includes the generation of high-quality figures for publication.

The confluence of big data and evolutionary genome mining for the discovery of natural products

This review covers literature between 2003-2021The development and application of genome mining tools has given rise to ever-growing genetic and chemical databases and propelled natural products research into the modern age of Big Data. Likewise, an explosion of evolutionary studies has unveiled genetic patterns of natural products biosynthesis and function that support Darwin's theory of natural selection and other theories of adaptation and diversification. In this review, we aim to highlight how Big Data and evolutionary thinking converge in the study of natural products, and how this has led to an emerging sub-discipline of evolutionary genome mining of natural products. First, we outline general principles to best utilize Big Data in natural products research, addressing key considerations needed to provide evolutionary context. We then highlight successful examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes. Rather than an exhaustive list of evolution-driven discoveries, we highlight examples where Big Data and evolutionary thinking have been embraced for the evolutionary genome mining of natural products. After reviewing the nascent history of this sub-discipline, we discuss the challenges and opportunities of genomic and metabolomic tools with evolutionary foundations and/or implications and provide a future outlook for this emerging and exciting field of natural product research.

Core Steps to the Azaphilone Family of Fungal Natural Products

Azaphilones are a family of polyketide-based fungal natural products that exhibit interesting and useful bioactivities. This minireview explores the literature on various characterised azaphilone biosynthetic pathways, which allows for a proposed consensus scheme for the production of the core azaphilone structure, as well as identifying early diversification steps during azaphilone biosynthesis. A consensus understanding of the core enzymatic steps towards a particular family of fungal natural products can aid in genome-mining experiments. Genome mining for novel fungal natural products is a powerful technique for both exploring chemical space and providing new insights into fungal natural product pathways.

FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.

The discovery and description of novel fungal secondary metabolites promises novel antibiotics, pharmaceuticals, and other useful compounds. A way to identify novel secondary metabolites is to express the corresponding genes in a suitable expression host. Consequently, a detailed knowledge or an accurate prediction of these genes is necessary. In fungi, the genes are co-localized in so-called biosynthetic gene clusters. Notably, the clusters may also contain genes that are not necessary for the biosynthesis of the secondary metabolites, so-called gap genes. We developed a method to detect co-evolved genes within the clusters and demonstrated that essential genes are co-evolving and can thus be differentiated from the gap genes. This adds an additional layer of information, which can support researchers with their decisions on which genes to study and express for the discovery of novel secondary metabolites.

Nonribosomal peptide synthetases and their biotechnological potential in Penicillium rubens

Nonribosomal peptide synthetases (NRPS) are large multimodular enzymes that synthesize a diverse variety of peptides. Many of these are currently used as pharmaceuticals, thanks to their activity as antimicrobials (penicillin, vancomycin, daptomycin, echinocandin), immunosuppressant (cyclosporin) and anticancer compounds (bleomycin). Because of their biotechnological potential, NRPSs have been extensively studied in the past decades. In this review, we provide an overview of the main structural and functional features of these enzymes, and we consider the challenges and prospects of engineering NRPSs for the synthesis of novel compounds. Furthermore, we discuss secondary metabolism and NRP synthesis in the filamentous fungus Penicillium rubens and examine its potential for the production of novel and modified β-lactam antibiotics.

An Integrated Approach to Determine the Boundaries of the Azaphilone Pigment Biosynthetic Gene Cluster of Monascus ruber M7 Grown on Potato Dextrose Agar

Monascus-type azaphilone pigments (MonAzPs) are produced in multi-thousand ton quantities each year and used as food colorants and nutraceuticals in East Asia. Several groups, including ours, described MonAzPs biosynthesis as a highly complex pathway with many branch points, affording more than 110 MonAzP congeners in a small group of fungi in the Eurotiales order. MonAzPs biosynthetic gene clusters (BGCs) are also very complex and mosaic-like, with some genes involved in more than one pathway, while other genes playing no apparent role in MonAzPs production. Due to this complexity, MonAzPs BGCs have been delimited differently in various fungi. Since most of these predictions rely primarily on bioinformatic analyses, it is possible that genes immediately outside the currently predicted BGC borders are also involved, especially those whose function cannot be predicted from sequence similarities alone. Conversely, some peripheral genes presumed to be part of the BGC may in fact lay outside the boundaries. This study uses a combination of computational and transcriptional analyses to predict the extent of the MonAzPs BGC in Monascus ruber M7. Gene knockouts and analysis of MonAzPs production of the mutants are then used to validate the prediction, revealing that the BGC consists of 16 genes, extending from mrpigA to mrpigP. We further predict that two strains of Talaromyces marneffei, ATCC 18224 and PM1, encode an orthologous but non-syntenic MonAzPs BGC with 14 genes. This work highlights the need to use comprehensive, integrated approaches for the more precise determination of secondary metabolite BGC boundaries.

Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

Genome-based engineering of ligninolytic enzymes in fungi

Background

Many fungi grow as saprobic organisms and obtain nutrients from a wide range of dead organic materials. Among saprobes, fungal species that grow on wood or in polluted environments have evolved prolific mechanisms for the production of degrading compounds, such as ligninolytic enzymes. These enzymes include arrays of intense redox-potential oxidoreductase, such as laccase, catalase, and peroxidases. The ability to produce ligninolytic enzymes makes a variety of fungal species suitable for application in many industries, including the production of biofuels and antibiotics, bioremediation, and biomedical application as biosensors. However, fungal ligninolytic enzymes are produced naturally in small quantities that may not meet the industrial or market demands. Over the last decade, combined synthetic biology and computational designs have yielded significant results in enhancing the synthesis of natural compounds in fungi.

Main body of the abstract

In this review, we gave insights into different protein engineering methods, including rational, semi-rational, and directed evolution approaches that have been employed to enhance the production of some important ligninolytic enzymes in fungi. We described the role of metabolic pathway engineering to optimize the synthesis of chemical compounds of interest in various fields. We highlighted synthetic biology novel techniques for biosynthetic gene cluster (BGC) activation in fungo and heterologous reconstruction of BGC in microbial cells. We also discussed in detail some recombinant ligninolytic enzymes that have been successfully enhanced and expressed in different heterologous hosts. Finally, we described recent advance in CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR associated) protein systems as the most promising biotechnology for large-scale production of ligninolytic enzymes.

Short conclusion

Aggregation, expression, and regulation of ligninolytic enzymes in fungi require very complex procedures with many interfering factors. Synthetic and computational biology strategies, as explained in this review, are powerful tools that can be combined to solve these puzzles. These integrated strategies can lead to the production of enzymes with special abilities, such as wide substrate specifications, thermo-stability, tolerance to long time storage, and stability in different substrate conditions, such as pH and nutrients.

CRISPR-based transcriptional activation tool for silent genes in filamentous fungi

Filamentous fungi are historically known to be a rich reservoir of bioactive compounds that are applied in a myriad of fields ranging from crop protection to medicine. The surge of genomic data available shows that fungi remain an excellent source for new pharmaceuticals. However, most of the responsible biosynthetic gene clusters are transcriptionally silent under laboratory growth conditions. Therefore, generic strategies for activation of these clusters are required. Here, we present a genome-editing-free, transcriptional regulation tool for filamentous fungi, based on the CRISPR activation (CRISPRa) methodology. Herein, a nuclease-defective mutant of Cas9 (dCas9) was fused to a highly active tripartite activator VP64-p65-Rta (VPR) to allow for sgRNA directed targeted gene regulation. dCas9-VPR was introduced, together with an easy to use sgRNA "plug-and-play" module, into a non-integrative AMA1-vector, which is compatible with several filamentous fungal species. To demonstrate its potential, this vector was used to transcriptionally activate a fluorescent reporter gene under the control of the penDE core promoter in Penicillium rubens. Subsequently, we activated the transcriptionally silent, native P. rubens macrophorin biosynthetic gene cluster by targeting dCas9-VPR to the promoter region of the transcription factor macR. This resulted in the production of antimicrobial macrophorins. This CRISPRa technology can be used for the rapid and convenient activation of silent fungal biosynthetic gene clusters, and thereby aid in the identification of novel compounds such as antimicrobials.

Genome Sequence of the Versatile Deadwood Decomposer Xylaria grammica IHIA82

Xylaria grammica is an ascomycetous decomposer of dead hardwood. X. grammica IHIA82 was recovered from the Kakamega Forest, Kenya. The whole genome of this strain was sequenced with a total size of 47.0 Mbp, a G+C content of 48.1%, and 12,126 predicted genes.

Xylaria grammica is an ascomycetous decomposer of dead hardwood. The X. grammica strain IHIA82 was recovered from the Kakamega Forest in Kenya. The whole genome of this strain was sequenced with a total size of 47.0 Mbp, a G+C content of 48.1%, and 12,126 predicted genes.

The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification

Antimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that combines genome mining, comparative genomics, and functional genetics can be used to successfully identify novel biosynthetic gene clusters that produce antimicrobial natural products. Secondary metabolite clusters of an antibiotic producer are first predicted using genome mining tools, generating a list of candidates. Comparative genomic approaches are then used to identify gene suites present in the antibiotic producer that are absent in closely related non-producers. Gene sets that are common to the two lists represent leading candidates, which can then be confirmed using functional genetics approaches. To validate this strategy, we identified the genes responsible for antibiotic production in Pantoea agglomerans B025670, a strain identified in a large-scale bioactivity survey. The genome of B025670 was first mined with antiSMASH, which identified 24 candidate regions. We then used the comparative genomics platform, EDGAR, to identify genes unique to B025670 that were not present in closely related strains with contrasting antibiotic production profiles. The candidate lists generated by antiSMASH and EDGAR were compared with standalone BLAST. Among the common regions was a 14 kb cluster consisting of 14 genes with predicted enzymatic, transport, and unknown functions. Site-directed mutagenesis of the gene cluster resulted in a reduction in antimicrobial activity, suggesting involvement in antibiotic production. An integrated approach that combines genome mining, comparative genomics, and functional genetics yields a powerful, yet simple strategy for identifying potentially novel antibiotics.

TOUCAN: a framework for fungal biosynthetic gene cluster discovery

Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially benefit human health. Identifying biosynthetic gene clusters (BGCs) involved in the biosynthesis of SMs can be a costly and complex task, especially due to the genomic diversity of fungal BGCs. Previous studies on fungal BGC discovery present limited scope and can restrict the discovery of new BGCs. In this work, we introduce TOUCAN, a supervised learning framework for fungal BGC discovery. Unlike previous methods, TOUCAN is capable of predicting BGCs on amino acid sequences, facilitating its use on newly sequenced and not yet curated data. It relies on three main pillars: rigorous selection of datasets by BGC experts; combination of functional, evolutionary and compositional features coupled with outperforming classifiers; and robust post-processing methods. TOUCAN best-performing model yields 0.982 F-measure on BGC regions in the Aspergillus niger genome. Overall results show that TOUCAN outperforms previous approaches. TOUCAN focuses on fungal BGCs but can be easily adapted to expand its scope to process other species or include new features.

FvatfA regulates growth, stress tolerance as well as mycotoxin and pigment productions in Fusarium verticillioides

FvatfA from the maize pathogen Fusarium verticillioides putatively encodes the Aspergillus nidulans AtfA and Schizasaccharomyces pombe Atf1 orthologous bZIP-type transcription factor, FvAtfA. In this study, a ΔFvatfA deletion mutant was constructed and then genetically complemented with the fully functional FvatfA gene. Comparing phenotypic features of the wild-type parental, the deletion mutant and the restored strains shed light on the versatile regulatory functions played by FvAtfA in (i) the maintenance of vegetative growth on Czapek-Dox and Potato Dextrose agars and invasive growth on unwounded tomato fruits, (ii) the preservation of conidiospore yield and size, (iii) the orchestration of oxidative (H₂O₂, menadione sodium bisulphite) and cell wall integrity (Congo Red) stress defences and (iv) the regulation of mycotoxin (fumonisins) and pigment (bikaverin, carotenoid) productions. Expression of selected biosynthetic genes both in the fumonisin (fum1, fum8) and the carotenoid (carRA, carB) pathways were down-regulated in the ΔFvatfA strain resulting in defected fumonisin production and considerably decreased carotenoid yields. The expression of bik1, encoding the polyketide synthase needed in bikaverin biosynthesis, was not up-regulated by the deletion of FvatfA meanwhile the ΔFvatfA strain produced approximately ten times more bikaverin than the wild-type or the genetically complemented strains. The abolishment of fumonisin production of the ΔFvatfA strain may lead to the development of new-type, biology-based mycotoxin control strategies. The novel information gained on the regulation of pigment production by this fungus can be interesting for experts working on new, Fusarium-based biomass and pigment production technologies.

Key points • FvatfA regulates vegetative and invasive growths of F. verticillioides. • FvatfA also orchestrates oxidative and cell wall integrity stress defenses. • The ΔFvatfA mutant was deficient in fumonisin production. • FvatfA deletion resulted in decreased carotenoid and increased bikaverin yields.

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

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.

Genomes and secretomes of Ascomycota fungi reveal diverse functions in plant biomass decomposition and pathogenesis

BACKGROUND

The dominant fungi in arid grasslands and shrublands are members of the Ascomycota phylum. Ascomycota fungi are important drivers in carbon and nitrogen cycling in arid ecosystems. These fungi play roles in soil stability, plant biomass decomposition, and endophytic interactions with plants. They may also form symbiotic associations with biocrust components or be latent saprotrophs or pathogens that live on plant tissues. However, their functional potential in arid soils, where organic matter, nutrients and water are very low or only periodically available, is poorly characterized.

RESULTS

Five Ascomycota fungi were isolated from different soil crust microhabitats and rhizosphere soils around the native bunchgrass Pleuraphis jamesii in an arid grassland near Moab, UT, USA. Putative genera were Coniochaeta, isolated from lichen biocrust, Embellisia from cyanobacteria biocrust, Chaetomium from below lichen biocrust, Phoma from a moss microhabitat, and Aspergillus from the soil. The fungi were grown in replicate cultures on different carbon sources (chitin, native bunchgrass or pine wood) relevant to plant biomass and soil carbon sources. Secretomes produced by the fungi on each substrate were characterized. Results demonstrate that these fungi likely interact with primary producers (biocrust or plants) by secreting a wide range of proteins that facilitate symbiotic associations. Each of the fungal isolates secreted enzymes that degrade plant biomass, small secreted effector proteins, and proteins involved in either beneficial plant interactions or virulence. Aspergillus and Phoma expressed more plant biomass degrading enzymes when grown in grass- and pine-containing cultures than in chitin. Coniochaeta and Embellisia expressed similar numbers of these enzymes under all conditions, while Chaetomium secreted more of these enzymes in grass-containing cultures.

CONCLUSIONS

This study of Ascomycota genomes and secretomes provides important insights about the lifestyles and the roles that Ascomycota fungi likely play in arid grassland, ecosystems. However, the exact nature of those interactions, whether any or all of the isolates are true endophytes, latent saprotrophs or opportunistic phytopathogens, will be the topic of future studies.

ClusterScan: simple and generalistic identification of genomic clusters

Summary

Studies on gene clusters proved to be an excellent source of information to understand genomes evolution and identifying specific metabolic pathways or gene families. Improvements in sequencing methods have resulted in a large increase of sequenced genomes for which cluster annotation could be performed and standardized. Currently available programs are developed to search for specific cluster types and none of them is suitable for a broad range of user-based choices. We have developed ClusterScan which allows identifying clusters of any kind of feature simply based on their genomic coordinates and user-defined categorical annotations.

Availability and implementation

The tool is written in Python, distributed under the GNU General Public License (GPL) and available on Github at http://bit.ly/ClusterScan or as Docker image at sangeslab/clusterscan: latest. It is supported through a mailing-list on http://bit.ly/ClusterScanSupport.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolutionary and functional patterns of shared gene neighbourhood in fungi

Gene clusters comprise genomically co-localized and potentially co-regulated genes that tend to be conserved across species. In eukaryotes, multiple examples of metabolic gene clusters are known, particularly among fungi and plants. However, little is known about how gene clustering patterns vary among taxa or with respect to functional roles. Furthermore, mechanisms of the formation, maintenance and evolution of gene clusters remain unknown. We surveyed 341 fungal genomes to discover gene clusters shared by different species, independently of their functions. We inferred 12,120 cluster families, which comprised roughly one third of the gene space and were enriched in genes associated with diverse cellular functions. Additionally, most clusters did not encode transcription factors, suggesting that they are regulated distally. We used phylogenomics to characterize the evolutionary history of these clusters. We found that most clusters originated once and were transmitted vertically, coupled to differential loss. However, convergent evolution-that is, independent appearance of the same cluster-was more prevalent than anticipated. Finally, horizontal gene transfer of entire clusters was somewhat restricted, with the exception of those associated with secondary metabolism. Altogether, our results provide insights on the evolution of gene clustering as well as a broad catalogue of evolutionarily conserved gene clusters whose function remains to be elucidated.

Nonribosomal peptides in fungal cell factories: from genome mining to optimized heterologous production

Fungi are notoriously prolific producers of secondary metabolites including nonribosomal peptides (NRPs). The structural complexity of NRPs grants them interesting activities such as antibiotic, anti-cancer, and anti-inflammatory properties. The discovery of these compounds with attractive activities can be achieved by using two approaches: either by screening samples originating from various environments for their biological activities, or by identifying the related clusters in genomic sequences thanks to bioinformatics tools. This genome mining approach has grown tremendously due to recent advances in genome sequencing, which have provided an incredible amount of genomic data from hundreds of microbial species. Regarding fungal organisms, the genomic data have revealed the presence of an unexpected number of putative NRP-related gene clusters. This highlights fungi as a goldmine for the discovery of putative novel bioactive compounds. Recent development of NRP dedicated bioinformatics tools have increased the capacity to identify these gene clusters and to deduce NRPs structures, speeding-up the screening process for novel metabolites discovery. Unfortunately, the newly identified compound is frequently not or poorly produced by native producers due to a lack of expression of the related genes cluster. A frequently employed strategy to increase production rates consists in transferring the related biosynthetic pathway in heterologous hosts. This review aims to provide a comprehensive overview about the topic of NRPs discovery, from gene cluster identification by genome mining to the heterologous production in fungal hosts. The main computational tools and methods for genome mining are herein presented with an emphasis on the particularities of the fungal systems. The different steps of the reconstitution of NRP biosynthetic pathway in heterologous fungal cell factories will be discussed, as well as the key factors to consider for maximizing productivity. Several examples will be developed to illustrate the potential of heterologous production to both discover uncharacterized novel compounds predicted in silico by genome mining, and to enhance the productivity of interesting bio-active natural products.

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully.

Draft genome sequence data of Bifidobacterium longum strain VKPM Ac-1636, a prospective probiotic isolated from human gut

Data on the draft genome sequence of Bifidobacterium longumsubsp. longum strain Ac-1636 is presented in this report. This strain, isolated from the digestive tract of one-year old healthy infant, was deposited in the Russian National Collection of Industrial Microorganisms as a prospective candidate for development of probiotics and probiotic foods. The 2,321,741 bp draft genome consists of 73 scaffolds with N50 of 162,253 bp. Genome annotation revealed the presence of multiple determinants of probiotic properties of this strain. The draft genome sequence data of strainAc-1636 is available in DBJ/EMBL/GenBank under the accession nos. RZHL00000000, PRJNA511803 and SAMN10644101 for Genome, Bioproject and Biosample, respectively.

Structural Diversity and Bioactivities of Peptaibol Compounds From the Longibrachiatum Clade of the Filamentous Fungal Genus Trichoderma

This study examined the structural diversity and bioactivity of peptaibol compounds produced by species from the phylogenetically separated Longibrachiatum Clade of the filamentous fungal genus Trichoderma, which contains several biotechnologically, agriculturally and clinically important species. HPLC-ESI-MS investigations of crude extracts from 17 species of the Longibrachiatum Clade (T. aethiopicum, T. andinense, T. capillare, T. citrinoviride, T. effusum, T. flagellatum, T. ghanense, T. konilangbra, T. longibrachiatum, T. novae-zelandiae, T. pinnatum, T. parareesei, T. pseudokoningii, T. reesei, T. saturnisporum, T. sinensis, and T. orientale) revealed several new and recurrent 20-residue peptaibols related to trichobrachins, paracelsins, suzukacillins, saturnisporins, trichoaureocins, trichocellins, longibrachins, hyporientalins, trichokonins, trilongins, metanicins, trichosporins, gliodeliquescins, alamethicins and hypophellins, as well as eight 19-residue sequences from a new subfamily of peptaibols named brevicelsins. Non-ribosomal peptide synthetase genes were mined from the available genome sequences of the Longibrachiatum Clade. Their annotation and product prediction were performed in silico and revealed full agreement in 11 out of 20 positions regarding the amino acids predicted based on the signature sequences and the detected amino acids incorporated. Molecular dynamics simulations were performed for structural characterization of four selected peptaibol sequences: paracelsins B, H and their 19-residue counterparts brevicelsins I and IV. Loss of position R6 in brevicelsins resulted in smaller helical structures with higher atomic fluctuation for every residue than the structures formed by paracelsins. We observed the formation of highly bent, almost hairpin-like, helical structures throughout the trajectory, along with linear conformation. Bioactivity tests were performed on the purified peptaibol extract of T. reesei on clinically and phytopathologically important filamentous fungi, mammalian cells, and Arabidopsis thaliana seedlings. Porcine kidney cells and boar spermatozoa proved to be sensitive to the purified peptaibol extract. Peptaibol concentrations ≥0.3 mg ml-1 deterred the growth of A. thaliana. However, negative effects to plants were not detected at concentrations below 0.1 mg ml-1, which could still inhibit plant pathogenic filamentous fungi, suggesting that those peptaibols reported here may have applications for plant protection.

Psychoactive plant- and mushroom-associated alkaloids from two behavior modifying cicada pathogens

Entomopathogenic fungi routinely kill their hosts before releasing infectious spores, but a few species keep insects alive while sporulating, which enhances dispersal. Transcriptomics- and metabolomics-based studies of entomopathogens with post-mortem dissemination from their parasitized hosts have unraveled infection processes and host responses. However, the mechanisms underlying active spore transmission by Entomophthoralean fungi in living insects remain elusive. Here we report the discovery, through metabolomics, of the plant-associated amphetamine, cathinone, in four Massospora cicadina-infected periodical cicada populations, and the mushroom-associated tryptamine, psilocybin, in annual cicadas infected with Massospora platypediae or Massospora levispora, which likely represent a single fungal species. The absence of some fungal enzymes necessary for cathinone and psilocybin biosynthesis along with the inability to detect intermediate metabolites or gene orthologs are consistent with possibly novel biosynthesis pathways in Massospora. The neurogenic activities of these compounds suggest the extended phenotype of Massospora that modifies cicada behavior to maximize dissemination is chemically-induced.