ARTS-DB: a database for antibiotic resistant targets

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.

Accessing hidden microbial biosynthetic potential from underexplored sources for novel drug discovery

Microbial natural products and their structural analogues have widely used as pharmaceutical agents, especially for infectious diseases and cancer. Despite this success, new structural classes with innovative chemistry and modes of action are urgently needed to be developed to combat the growing antimicrobial resistance and other public health problems. The advances in next-generation sequencing technologies and powerful computational tools open up new opportunities to explore microbial biosynthetic potential from underexplored sources, with millions of secondary metabolites awaiting discovery. The review highlights challenges associated with discovery of new chemical entities, rich reservoirs provided by untapped taxa, ecological niches or host microbiomes, emerging synthetic biotechnologies to unearth the hidden microbial biosynthetic potential for novel drug discovery at scale and speed.

CAGECAT: The CompArative GEne Cluster Analysis Toolbox for rapid search and visualisation of homologous gene clusters

BACKGROUND

Co-localized sets of genes that encode specialized functions are common across microbial genomes and occur in genomes of larger eukaryotes as well. Important examples include Biosynthetic Gene Clusters (BGCs) that produce specialized metabolites with medicinal, agricultural, and industrial value (e.g. antimicrobials). Comparative analysis of BGCs can aid in the discovery of novel metabolites by highlighting distribution and identifying variants in public genomes. Unfortunately, gene-cluster-level homology detection remains inaccessible, time-consuming and difficult to interpret.

RESULTS

The comparative gene cluster analysis toolbox (CAGECAT) is a rapid and user-friendly platform to mitigate difficulties in comparative analysis of whole gene clusters. The software provides homology searches and downstream analyses without the need for command-line or programming expertise. By leveraging remote BLAST databases, which always provide up-to-date results, CAGECAT can yield relevant matches that aid in the comparison, taxonomic distribution, or evolution of an unknown query. The service is extensible and interoperable and implements the cblaster and clinker pipelines to perform homology search, filtering, gene neighbourhood estimation, and dynamic visualisation of resulting variant BGCs. With the visualisation module, publication-quality figures can be customized directly from a web-browser, which greatly accelerates their interpretation via informative overlays to identify conserved genes in a BGC query.

CONCLUSION

Overall, CAGECAT is an extensible software that can be interfaced via a standard web-browser for whole region homology searches and comparison on continually updated genomes from NCBI. The public web server and installable docker image are open source and freely available without registration at: https://cagecat.bioinformatics.nl .

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Prevalence and heterogeneity of antibiotic resistance genes in Orientia tsutsugamushi and other rickettsial genomes

Despite a million infections every year and an estimated one billion people at risk, scrub typhus is regarded as a neglected tropical disease. The causative bacterium Orientia tsutsugamushi, a member of rickettsiae, seems to be intrinsically resistant to several classes of antibiotics. The emergence of antibiotic-resistant scrub typhus is likely to become a global public health concern. Yet, it is unknown as to how common antibiotic resistance genes are in O. tsutsugamushi, and how variable these loci are among the genomes of rickettsiae. By using the comprehensive antibiotic resistance database, we explored 79 complete genomes from 24 species of rickettsiae for antibiotic resistance loci. There were 244 unique antibiotic resistance genes in rickettsiae. Both the total and unique antibiotic resistance genes in O. tsutsugamushi were significantly less compared to other members of rickettsiae. However, antibiotic resistance genes in O. tsutsugamushi genomes were more unique and highly variable. Many genes such as resistant variants of evgS, and vanS A/G were present in numerous copies. These results will have important implications in the context of antibiotic-resistant scrub typhus.

Antibiotic-Derived Radiotracers for Positron Emission Tomography: Nuclear or "Unclear" Infection Imaging?

The excellent features of non-invasive molecular imaging, its progressive technology (real-time, whole-body imaging and quantification), and global impact by a growing infrastructure for positron emission tomography (PET) scanners are encouraging prospects to investigate new concepts, which could transform clinical care of complex infectious diseases. Researchers are aiming towards the extension beyond the routinely available radiopharmaceuticals and are looking for more effective tools that interact directly with causative pathogens. We reviewed and critically evaluated (challenges or pitfalls) antibiotic-derived PET radiopharmaceutical development efforts aimed at infection imaging. We considered both radiotracer development for infection imaging and radio-antibiotic PET imaging supplementing other tools for pharmacologic drug characterization; overall, a total of 20 original PET radiotracers derived from eleven approved antibiotics.

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.