Computational strategies for genome-based natural product discovery and engineering in fungi

Abstract

Fungal natural products possess biological activities that are of great value to medicine, agriculture and manufacturing. Recent metagenomic studies accentuate the vastness of fungal taxonomic diversity, and the accompanying specialized metabolic diversity offers a great and still largely untapped resource for natural product discovery. Although fungal natural products show an impressive variation in chemical structures and biological activities, their biosynthetic pathways share a number of key characteristics. First, genes encoding successive steps of a biosynthetic pathway tend to be located adjacently on the chromosome in biosynthetic gene clusters (BGCs). Second, these BGCs are often are located on specific regions of the genome and show a discontinuous distribution among evolutionarily related species and isolates. Third, the same enzyme (super)families are often involved in the production of widely different compounds. Fourth, genes that function in the same pathway are often co-regulated, and therefore co-expressed across various growth conditions. In this mini-review, we describe how these partly interlinked characteristics can be exploited to computationally identify BGCs in fungal genomes and to connect them to their products. Particular attention will be given to novel algorithms to identify unusual classes of BGCs, as well as integrative pan-genomic approaches that use a combination of genomic and metabolomic data for parallelized natural product discovery across multiple strains. Such novel technologies will not only expedite the natural product discovery process, but will also allow the assembly of a high-quality toolbox for the re-design or even de novo design of biosynthetic pathways using synthetic biology approaches.

Engineering Mycorrhizal Symbioses to Alter Plant Metabolism and Improve Crop Health

“…To initiate this process R. irregularis (formerly Glomus intraradices ) could serve as an initial chassis because it is the only mycorrhizae with a fully sequenced genome (Franz Lang and Hijri, 2009; Tisserant et al, 2013). This extensive knowledge of this mycorrhizal host genome could overcome the challenges bioengineering fungi face including (1) the location of biosynthetic gene clusters (BGCs) on multiple loci and (2) the control of BGCs by shared cis -regulatory elements (van der Lee and Medema, 2016). Promoters and other regulators of gene expression (e.g., transcription factors) currently developed for use in Aspergillus niger and Penicillium chrysogenum could be trialed out in these mycorrhizae (Polli et al, 2016; Wanka et al, 2016).…”

Predominately Uncultured Microbes as Sources of Bioactive Agents

“…These include, but are not limited to: discussion of eliciting secondary metabolism in actinomycetes (Seyedsayamdost et al, 2012; Abdelmohsen et al, 2015; Rutledge and Challis, 2015); methodologies for determining the compounds expressed (Adnani et al, 2014; Luzzatto-Knaan et al, 2015; Medema and Fischbach, 2015; Mohimani and Pevzner, 2016); analyses of the biosynthetic clusters identified in fungi to published natural product structures (Li et al, 2016b); small-scale plate-based techniques for fungal co-culture (Bertrand et al, 2014); on-demand production of secondary metabolites (Bode et al, 2015); mixed culture of endophytes (Chagas et al, 2013); metabolomics in induced cultures (Derewacz et al, 2015; van der Lee and Medema, 2016; Zhang et al, 2016a,b; Ziemert et al, 2016); use of synthetic biological techniques to further expand the chemical biodiversity discovered (Smanski et al, 2016); and a recent review of the array of approaches to study such cryptic cluster expression by Zarins-Tutt et al (2016).…”

Natural products: a strategic lead generation approach in crop protection discovery

“…59 But the full potential of this approach has not yet been fulfilled because of the bioinformatic challenges in identifying novel biosynthetic gene clusters (BGC) and in subsequently predicting chemical structures. 60 A more actionable approach is the combination of genomic analyses and metabolomics data to more accurately allow the linkage of BGC to known and unknown NP families. 61 The success of this approach is directly linked to wileyonlinelibrary.com/journal/ps the scale of generated data sets, as well as to a diverse strain collection, as there is a strong connection between phylogeny and NP BGCs.…”