Mapping gene clusters within arrayed metagenomic libraries to expand the structural diversity of biomedically relevant natural products

Abstract

Complex microbial ecosystems contain large reservoirs of unexplored biosynthetic diversity. Here we provide an experimental framework and data analysis tool to facilitate the targeted discovery of natural-product biosynthetic gene clusters from the environment. Multiplex sequencing of barcoded PCR amplicons is followed by sequence similarity directed data parsing to identify sequences bearing close resemblance to biosynthetically or biomedically interesting gene clusters. Amplicons are then mapped onto arrayed metagenomic libraries to guide the recovery of targeted gene clusters. When applied to adenylation- and ketosynthase-domain amplicons derived from saturating soil DNA libraries, our analysis pipeline led to the recovery of biosynthetic clusters predicted to encode for previously uncharacterized glycopeptide- and lipopeptide-like antibiotics; thiocoraline-, azinomycin-, and bleomycin-like antitumor agents; and a rapamycin-like immunosuppressant. The utility of the approach is demonstrated by using recovered eDNA sequences to generate glycopeptide derivatives. The experiments described here constitute a systematic interrogation of a soil metagenome for gene clusters capable of encoding naturally occurring derivatives of biomedically relevant natural products. Our results show that previously undetected biosynthetic gene clusters with potential biomedical relevance are very common in the environment. This general process should permit the routine screening of environmental samples for gene clusters capable of encoding the systematic expansion of the structural diversity seen in biomedically relevant families of natural products.

Multiplexed metagenome mining using short DNA sequence tags facilitates targeted discovery of epoxyketone proteasome inhibitors

“…Our NPST data comprise ∼1 × 10 6 unique environmental sequences that were amplified from soil metagenomes using degenerate primers targeting two of the most common biosynthetic motifs: nonribosomal peptide synthetase (NRPS) adenylation (A) domains and polyketide synthase (PKS) ketosynthase (KS) domains (12). By targeting these very common biosynthetic domains, sequencing resources are focused on generating only data that are relevant to our search strategy, therefore the raw sequencing power required to generate this dataset was quite modest (∼1.5 Gbps) (8,11). We estimate that the diversity of biosynthetic pathways represented in our NPST dataset is at least 50× larger than the NRPS and PKS pathways contained in all publically available sequenced bacterial genomes, as judged by the number of equivalent domains identified by recent systematic analyses (13)(14)(15).…”

“…A convenient feature of the computational framework is the ability to identify overlapping clones that allow reconstruction of complete pathways by targeting multiple library wells containing the same NPST. The strategy of partially arraying libraries and generating barcoded NPSTs from each library well allows efficient storage and automated in silico screening of cloned metagenomes for diverse biomedically relevant BGCs, as well as facile recovery of entire BGCs identified in computational screens of NPST data (8,9).…”

“…Unfortunately, the large DNA contigs that these search strategies require as input are not readily available from complex metagenomes. In response to the need for a more robust metagenomic search strategy, our group recently developed an informatics platform called eSNaPD (8,9) (environmental Surveyor of Natural Product Diversity) with the specific aim of facilitating sequence-guided discovery of new bacterial natural products from complex metagenomes (Fig. 1).…”

“…NPSTs are used to predict gene content and chemical output of the BGCs present in a metagenome, in a fashion analogous to reconstructing species phylogeny using 16S rRNA sequences (8). Once NPST data are generated from environmental metagenomes or metagenomic libraries, eSNaPD searches each NPST against a curated reference database, and identifies NPSTs whose closest evolutionary relative among all previously characterized reference BGCs encodes a molecule of interest.…”

“…Once NPST data are generated from environmental metagenomes or metagenomic libraries, eSNaPD searches each NPST against a curated reference database, and identifies NPSTs whose closest evolutionary relative among all previously characterized reference BGCs encodes a molecule of interest. This "closest relative" search approach is computationally inexpensive; however, the output it provides is a robust predictor of pathway gene content and chemical output (8).…”