Large-scale Genome Mining Research Articles

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets.

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as:(i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant,and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge fromthe ever-growing genomic datasets of biotechnologically relevant bacterial species.

A Comprehensive Self-Resistance Gene Database for Natural-Product Discovery with an Application to Marine Bacterial Genome Mining.

In the world of microorganisms, the biosynthesis of natural products in secondary metabolism and the self-resistance of the host always occur together and complement each other. Identifying resistance genes from biosynthetic gene clusters (BGCs) helps us understand the self-defense mechanism and predict the biological activity of natural products synthesized by microorganisms. However, a comprehensive database of resistance genes is still lacking, which hinders natural product annotation studies in large-scale genome mining. In this study, we compiled a resistance gene database (RGDB) by scanning the four available databases: CARD, MIBiG, NCBIAMR, and UniProt. Every resistance gene in the database was annotated with resistance mechanisms and possibly involved chemical compounds, using manual annotation and transformation from the resource databases. The RGDB was applied to analyze resistance genes in 7432 BGCs in 1390 genomes from a marine microbiome project. Our calculation showed that the RGDB successfully identified resistance genes for more than half of the BGCs, suggesting that the database helps prioritize BGCs that produce biologically active natural products.

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

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

Discovery of novel papillomaviruses in the critically endangered Malayan and Chinese pangolins.

Pangolins are scaly and toothless mammals which are distributed across Africa and Asia. Currently, the Malayan, Chinese and Philippine pangolins are designated as critically endangered species. Although few pangolin viruses have been described, their viromes have received more attention following the discovery that they harbour sarbecoviruses related to SARS-CoV-2. Using large-scale genome mining, we discovered novel lineages of papillomaviruses infecting the Malayan and Chinese pangolins. We were able to assemble three complete circular papillomavirus genomes with an intact coding capacity and five additional L1 genes encoding the major capsid protein. Phylogenetic analysis revealed that seven out of eight L1 sequences formed a monophyletic group which is the sister lineage to the Tupaia belangeri papillomavirus 1, isolated from Yunnan province in China. Additionally, a single L1 sequence assembled from a Chinese pangolin was placed in a clade closer to Alphapapillomavirus and Omegapapillomavirus. Examination of the SRA data from 95 re-sequenced genomes revealed that 49.3% of Malayan pangolins and 50% of Chinese pangolins were positive for papillomavirus reads. Our results indicate that pangolins in South-East Asia are the hosts of diverse and highly prevalent papillomaviruses, and highlight the value of in silico mining of host sequencing data for the discovery of novel viruses.

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery.

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

Synthaser: a CD-Search enabled Python toolkit for analysing domain architecture of fungal secondary metabolite megasynth(et)ases

BackgroundFungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome.ResultsHere we report synthaser, a Python package leveraging the NCBI’s conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. Synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. Synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application (http://gamcil.github.io/synthaser). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network.ConclusionsSynthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. It is freely available under a MIT license from PyPI (https://pypi.org/project/synthaser) and GitHub (https://github.com/gamcil/synthaser).

Palantir: a springboard for the analysis of secondary metabolite gene clusters in large-scale genome mining projects.

To support small and large-scale genome mining projects, we present Post-processing Analysis tooLbox for ANTIsmash Reports (Palantir), a dedicated software suite for handling and refining secondary metabolite biosynthetic gene cluster (BGC) data annotated with the popular antiSMASH pipeline. Palantir provides new functionalities building on NRPS/PKS predictions from antiSMASH, such as improved BGC annotation, module delineation and easy access to sub-sequences at different levels (cluster, gene, module and domain). Moreover, it can parse user-provided antiSMASH reports and reformat them for direct use or storage in a relational database. Palantir is released both as a Perl API available on CPAN (https://metacpan.org/release/Bio-Palantir) and as a web application (http://palantir.uliege.be). As a practical use case, the web interface also features a database built from the mining of 1616 cyanobacterial genomes, of which 1488 were predicted to encode at least one BGC. Supplementary data are available at Bioinformatics online.

Large-scale genome mining allows identification of neutral polymorphisms and novel resistance mutations in genes involved in Candida albicans resistance to azoles and echinocandins.

The genome of Candida albicans displays significant polymorphism. Point mutations in genes involved in resistance to antifungals may either confer phenotypic resistance or be devoid of phenotypic consequences. To catalogue polymorphisms in azole and echinocandin resistance genes occurring in susceptible strains in order to rapidly pinpoint relevant mutations in resistant strains. Genome sequences from 151 unrelated C. albicans strains susceptible to fluconazole and caspofungin were used to create a catalogue of non-synonymous polymorphisms in genes involved in resistance to azoles (ERG11, TAC1, MRR1 and UPC2) or echinocandins (FKS1). The potential of this catalogue to reveal putative resistance mutations was tested in 10 azole-resistant isolates, including 1 intermediate to caspofungin. Selected mutations were analysed by mutagenesis experiments or mutational prediction effect. In the susceptible strains, we identified 126 amino acid substitutions constituting the catalogue of phenotypically neutral polymorphisms. By excluding these neutral substitutions, we identified 22 additional substitutions in the 10 resistant strains. Among these substitutions, 10 had already been associated with resistance. The remaining 12 were in Tac1p (n = 6), Upc2p (n = 2) and Erg11p (n = 4). Four out of the six homozygous substitutions in Tac1p (H263Y, A790V, H839Y and P971S) conferred increases in azole MICs, while no effects were observed for those in Upc2p. Additionally, two homozygous substitutions (Y64H and P236S) had a predicted conformation effect on Erg11p. By establishing a catalogue of neutral polymorphisms occurring in genes involved in resistance to antifungal drugs, we provide a useful resource for rapid identification of mutations possibly responsible for phenotypic resistance in C. albicans.

Discovery of endogenous retroviruses with mammalian envelopes in avian genomes uncovers long-term bird-mammal interaction

Endogenous retroviruses (ERVs) arise from the infection and integration of past retroviruses into animal hosts. We performed large-scale genomic mining of 101 avian genomes for discovery of ERVs having none-avian origin and investigated the cross-species transmission events. Phylogenetic analysis of the reverse transcriptase (RT) of polymerase gene (pol) and the transmembrane subunit (TM) of the envelope gene (env) supported that avian ERVs with a mammalian env gene existed in at least 15 avian species and can be divided into two major groups: Group-1 were of recombinant ERVs with an alpha-like pol gene and a gamma-like env gene, and Group-2 included ERVs with both gamma-like pol and env genes. Group-1 represented the avian alpharetroviral/mammalian gammaretroviral recombinant while Group-2 documented viral jump from mammals to birds. Molecular dating analysis suggested that Group-1 ERVs had integrated into avian genomes continuously, until recent past. We have expanded the knowledge of ERVs with cross-order transmission.

Mining bacterial genomes to reveal secret synergy

The medical treatment of infectious diseases often requires combination therapies that blend two molecules to enhance drug efficacy. Nature does the same. In a new article, Mrak et al. identify and functionally characterize natural products from Streptomyces rapamycinicus that show synergistic antifungal activity with the well-known immunosuppressant metabolite rapamycin, produced by the same strain. The genomic co-association of the two biosynthetic gene clusters paves the way toward new strategies to discover synergistic pairs of antibiotics through large-scale genome mining.