Advances in mining and expressing microbial biosynthetic gene clusters

Feature sequence-based genome mining uncovers the hidden diversity of bacterial siderophore pathways

Microbial secondary metabolites are a rich source for pharmaceutical discoveries and play crucial ecological functions. While tools exist to identify secondary metabolite clusters in genomes, precise sequence-to-function mapping remains challenging because neither function nor substrate specificity of biosynthesis enzymes can accurately be predicted. Here, we developed a knowledge-guided bioinformatic pipeline to solve these issues. We analyzed 1928 genomes of Pseudomonas bacteria and focused on iron-scavenging pyoverdines as model metabolites. Our pipeline predicted 188 chemically different pyoverdines with nearly 100% structural accuracy and the presence of 94 distinct receptor groups required for the uptake of iron-loaded pyoverdines. Our pipeline unveils an enormous yet overlooked diversity of siderophores (151 new structures) and receptors (91 new groups). Our approach, combining feature sequence with phylogenetic approaches, is extendable to other metabolites and microbial genera, and thus emerges as powerful tool to reconstruct bacterial secondary metabolism pathways based on sequence data.

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.

FGCD: a database of fungal gene clusters related to secondary metabolism

Fungal secondary metabolites are not necessary for growth, but they are important for fungal metabolism and ecology because they provide selective advantages for competition, survival and interactions with the environment. These various metabolites are widely used as medicinal precursors and insecticides. Secondary metabolism genes are commonly arranged in clusters along chromosomes, which allow for the coordinate control of complete pathways. In this study, we created the Fungal Gene Cluster Database to store, retrieve, and visualize secondary metabolite gene cluster information across fungal species. The database was created by merging data from RNA sequencing, Basic Local Alignment Search Tool, genome browser, enrichment analysis and the R Shiny web framework to visualize and query putative gene clusters. This database facilitated the rapid and thorough examination of significant gene clusters across fungal species by detecting, defining and graphically displaying the architecture, organization and expression patterns of secondary metabolite gene clusters. In general, this genomic resource makes use of the tremendous chemical variety of the products of these ecologically and biotechnologically significant gene clusters to our further understanding of fungal secondary metabolism. Database URL: https://www.hebaubioinformatics.cn/FungalGeneCluster/.

Investigating the Stirred Tank Bioreactor Co-Cultures of the Secondary Metabolite Producers Streptomyces noursei and Penicillium rubens

The stirred tank bioreactor co-cultures of the filamentous fungus Penicillium rubens and actinomycete Streptomyces noursei were studied with regard to secondary metabolite (SM) production, sugar consumption, and dissolved oxygen levels. In addition to the quantitative analysis of penicillin G and nystatin A1, the broad repertoire of 22 putatively identified products was semi-quantitatively evaluated with the use of UPLC-MS. Three co-cultivation variants differing with respect to the co-culture initiation method (i.e., the simultaneous inoculation of P. rubens and S. noursei and the 24 or 48 h inoculation delay of S. noursei relative to P. rubens) were investigated. All the co-cultures were carried out in parallel with the corresponding monoculture controls. Even though S. noursei showed the tendency to outperform P. rubens and inhibit the production of fungal secondary metabolites, the approach of simultaneous inoculation was effective in terms of enhancing the production of some S. noursei SMs, namely desferrioxamine E, deshydroxynocardamine, and argvalin. S. noursei displayed the capability of adaptation and SM production even after being inoculated into the 24 or 48 h culture of P. rubens. Interestingly, S. noursei turned out to be more efficient in terms of secondary metabolite production when its inoculation time relative to P. rubens was delayed by 48 h rather than by 24 h. The study demonstrated that the prolongation of inoculation delays can be beneficial for production-related performance in some co-culture systems.

Computation-aided studies related to the induction of specialized metabolite biosynthesis in microbial co-cultures: An introductory overview

Co-cultivation is an effective method of inducing the production of specialized metabolites (SMs) in microbial strains. By mimicking the ecological interactions that take place in natural environment, this approach enables to trigger the biosynthesis of molecules which are not formed under monoculture conditions. Importantly, microbial co-cultivation may lead to the discovery of novel chemical entities of pharmaceutical interest. The experimental efforts aimed at the induction of SMs are greatly facilitated by computational techniques. The aim of this overview is to highlight the relevance of computational methods for the investigation of SM induction via microbial co-cultivation. The concepts related to the induction of SMs in microbial co-cultures are briefly introduced by addressing four areas associated with the SM induction workflows, namely the detection of SMs formed exclusively under co-culture conditions, the annotation of induced SMs, the identification of SM producer strains, and the optimization of fermentation conditions. The computational infrastructure associated with these areas, including the tools of multivariate data analysis, molecular networking, genome mining and mathematical optimization, is discussed in relation to the experimental results described in recent literature. The perspective on the future developments in the field, mainly in relation to the microbiome-related research, is also provided.

Bacterial natural product discovery by heterologous expression

Natural products have found important applications in the pharmaceutical and agricultural sectors. In bacteria, the genes that encode the biosynthesis of natural products are often colocalized in the genome, forming biosynthetic gene clusters. It has been predicted that only 3% of natural products encoded in bacterial genomes have been discovered thus far, in part because gene clusters may be poorly expressed under laboratory conditions. Heterologous expression can help convert bioinformatics predictions into products. However, challenges remain, such as gene cluster prioritization, cloning of the complete gene cluster, high level expression, product identification, and isolation of products in practical yields. Here we reviewed the literature from the past 5 years (January 2018 to June 2023) to identify studies that discovered natural products by heterologous expression. From the 50 studies identified, we present analyses of the rationale for gene cluster prioritization, cloning methods, biosynthetic class, source taxa, and host choice. Combined, the 50 studies led to the discovery of 63 new families of natural products, supporting heterologous expression as a promising way to access novel chemistry. However, the success rate of natural product detection varied from 11% to 32% based on four large-scale studies that were part of the reviewed literature. The low success rate makes it apparent that much remains to be improved. The potential reasons for failure and points to be considered to improve the chances of success are discussed.

ONE-SENTENCE SUMMARY

At least 63 new families of bacterial natural products were discovered using heterologous expression in the last 5 years, supporting heterologous expression as a promising way to access novel chemistry; however, the success rate is low (11-32%) making it apparent that much remains to be improved-we discuss the potential reasons for failure and points to be considered to improve the chances of success. BioRender was used to generate the graphical abstract figure.