TOUCAN: a framework for fungal biosynthetic gene cluster discovery

Discovery of fungal onoceroid triterpenoids through domainless enzyme-targeted global genome mining

Genomics-guided methodologies have revolutionized the discovery of natural products. However, a major challenge in the field of genome mining is determining how to selectively extract biosynthetic gene clusters (BGCs) for untapped natural products from numerous available genome sequences. In this study, we developed a fungal genome mining tool that extracts BGCs encoding enzymes that lack a detectable protein domain (i.e., domainless enzymes) and are not recognized as biosynthetic proteins by existing bioinformatic tools. We searched for BGCs encoding a homologue of Pyr4-family terpene cyclases, which are representative examples of apparently domainless enzymes, in approximately 2000 fungal genomes and discovered several BGCs with unique features. The subsequent characterization of selected BGCs led to the discovery of fungal onoceroid triterpenoids and unprecedented onoceroid synthases. Furthermore, in addition to the onoceroids, a previously unreported sesquiterpene hydroquinone, of which the biosynthesis involves a Pyr4-family terpene cyclase, was obtained. Our genome mining tool has broad applicability in fungal genome mining and can serve as a beneficial platform for accessing diverse, unexploited natural products.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Fungal BGCs for Production of Secondary Metabolites: Main Types, Central Roles in Strain Improvement, and Regulation According to the Piano Principle

Filamentous fungi are one of the most important producers of secondary metabolites. Some of them can have a toxic effect on the human body, leading to diseases. On the other hand, they are widely used as pharmaceutically significant drugs, such as antibiotics, statins, and immunosuppressants. A single fungus species in response to various signals can produce 100 or more secondary metabolites. Such signaling is possible due to the coordinated regulation of several dozen biosynthetic gene clusters (BGCs), which are mosaically localized in different regions of fungal chromosomes. Their regulation includes several levels, from pathway-specific regulators, whose genes are localized inside BGCs, to global regulators of the cell (taking into account changes in pH, carbon consumption, etc.) and global regulators of secondary metabolism (affecting epigenetic changes driven by velvet family proteins, LaeA, etc.). In addition, various low-molecular-weight substances can have a mediating effect on such regulatory processes. This review is devoted to a critical analysis of the available data on the "turning on" and "off" of the biosynthesis of secondary metabolites in response to signals in filamentous fungi. To describe the ongoing processes, the model of "piano regulation" is proposed, whereby pressing a certain key (signal) leads to the extraction of a certain sound from the "musical instrument of the fungus cell", which is expressed in the production of a specific secondary metabolite.

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

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

FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi

There is an urgent need to diversify the pipeline for discovering novel natural products due to the increase in multi-drug resistant infections. Like bacteria, fungi also produce secondary metabolites that have potent bioactivity and rich chemical diversity. To avoid self-toxicity, fungi encode resistance genes which are often present within the biosynthetic gene clusters (BGCs) of the corresponding bioactive compounds. Recent advances in genome mining tools have enabled the detection and prediction of BGCs responsible for the biosynthesis of secondary metabolites. The main challenge now is to prioritize the most promising BGCs that produce bioactive compounds with novel modes of action. With target-directed genome mining methods, it is possible to predict the mode of action of a compound encoded in an uncharacterized BGC based on the presence of resistant target genes. Here, we introduce the 'fungal bioactive compound resistant target seeker' (FunARTS) available at https://funarts.ziemertlab.com. This is a specific and efficient mining tool for the identification of fungal bioactive compounds with interesting and novel targets. FunARTS rapidly links housekeeping and known resistance genes to BGC proximity and duplication events, allowing for automated, target-directed mining of fungal genomes. Additionally, FunARTS generates gene cluster networking by comparing the similarity of BGCs from multi-genomes.

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

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

Advances in mining and expressing microbial biosynthetic gene clusters

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

Application of Gene Knockout and Heterologous Expression Strategy in Fungal Secondary Metabolites Biosynthesis

The in-depth study of fungal secondary metabolites (SMs) over the past few years has led to the discovery of a vast number of novel fungal SMs, some of which possess good biological activity. However, because of the limitations of the traditional natural product mining methods, the discovery of new SMs has become increasingly difficult. In recent years, with the rapid development of gene sequencing technology and bioinformatics, new breakthroughs have been made in the study of fungal SMs, and more fungal biosynthetic gene clusters of SMs have been discovered, which shows that the fungi still have a considerable potential to produce SMs. How to study these gene clusters to obtain a large number of unknown SMs has been a research hotspot. With the continuous breakthrough of molecular biology technology, gene manipulation has reached a mature stage. Methods such as gene knockout and heterologous expression techniques have been widely used in the study of fungal SM biosynthesis and have achieved good effects. In this review, the representative studies on the biosynthesis of fungal SMs by gene knockout and heterologous expression under the fungal genome mining in the last three years were summarized. The techniques and methods used in these studies were also briefly discussed. In addition, the prospect of synthetic biology in the future under this research background was proposed.

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.

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.

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.

antiSMASH 6.0: improving cluster detection and comparison capabilities

Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. 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 license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.

Identification of a Novel Biosynthetic Gene Cluster in Aspergillus niger Using Comparative Genomics

Previously, DNA microarrays analysis showed that, in co-culture with Bacillus subtilis, a biosynthetic gene cluster anchored with a nonribosomal peptides synthetase of Aspergillus niger is downregulated. Based on phylogenetic and synteny analyses, we show here that this gene cluster, NRRL3_00036-NRRL3_00042, comprises genes predicted to encode a nonribosomal peptides synthetase, a FAD-binding domain-containing protein, an uncharacterized protein, a transporter, a cytochrome P450 protein, a NAD(P)-binding domain-containing protein and a transcription factor. We overexpressed the in-cluster transcription factor gene NRRL3_00042. The overexpression strain, NRRL3_00042OE, displays reduced growth rate and production of a yellow pigment, which by mass spectrometric analysis corresponds to two compounds with masses of 409.1384 and 425.1331. We deleted the gene encoding the NRRL3_00036 nonribosomal peptides synthetase in the NRRL3_00042OE strain. The resulting strain reverted to the wild-type phenotype. These results suggest that the biosynthetic gene cluster anchored by the NRRL3_00036 nonribosomal peptides synthetase gene is regulated by the in-cluster transcriptional regulator gene NRRL3_00042, and that it is involved in the production of two previously uncharacterized compounds.