A Computational Screen for Type I Polyketide Synthases in Metagenomics Shotgun Data

Abstract

BackgroundPolyketides are a diverse group of biotechnologically important secondary metabolites that are produced by multi domain enzymes called polyketide synthases (PKS).Methodology/Principal FindingsWe have estimated frequencies of type I PKS (PKS I) – a PKS subgroup – in natural environments by using Hidden-Markov-Models of eight domains to screen predicted proteins from six metagenomic shotgun data sets. As the complex PKS I have similarities to other multi-domain enzymes (like those for the fatty acid biosynthesis) we increased the reliability and resolution of the dataset by maximum-likelihood trees. The combined information of these trees was then used to discriminate true PKS I domains from evolutionary related but functionally different ones. We were able to identify numerous novel PKS I proteins, the highest density of which was found in Minnesota farm soil with 136 proteins out of 183,536 predicted genes. We also applied the protocol to UniRef database to improve the annotation of proteins with so far unknown function and identified some new instances of horizontal gene transfer.Conclusions/SignificanceThe screening approach proved powerful in identifying PKS I sequences in large sequence data sets and is applicable to many other protein families.

A functional screen for recovery of 4′‐phosphopantetheinyl transferase and associated natural product biosynthesis genes from metagenome libraries

“…This can be achieved without complete sequence data for a metagenome sample by using degenerate primers targeted toward conserved genes regions in genes of interest (Moffitt and Neilan, 2001; Fisch et al ., 2009; Kim et al ., 2010). Alternatively, sequences of interest can be identified by in silico mining of sequence data from large‐scale genomic or metagenomic sequencing projects (Schirmer et al ., 2005; Foerstner et al ., 2008). Functional metagenomic screens on the other hand do not rely on sequenceidentity and instead search for heterologous expression of a gene or genes of interest by virtue of a readily detectable phenotype in the host organism (Uchiyama and Miyazaki, 2009).…”

Phylogenetic Approaches to Natural Product Structure Prediction

“…These domains have been used to fingerprint PKS genes from individual strains (Edlund, Loesgen, Fenical, & Jensen, 2011) and environmental DNA (Wawrik et al, 2007). KS phylogeny has even been used to predict secondary metabolite diversity (Foerstner, Doerks, Creevey, Doerks, & Bork, 2008; Metsa-Ketela et al, 1999), structures (Freel, Nam, Fenical, & Jensen, 2011; Gontang, Gaudencio, Fenical, & Jensen, 2010), and the evolutionary processes that generate new structural diversity (Freel et al, 2011)…”

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

“…The collection of raw sequencing reads was first quality filtered and trimmed, and the resulting pool of clean sequences was then clustered at 95% identity to compensate for potential sequencing error and natural-sequence polymorphisms (20). Following these steps, 16,949 unique A-domain clusters and 4,167 unique KS-domain clusters remained, the latter number representing 30-fold more unique KS domains than were identified in a previous analysis of the largest available shotgun metagenomic sequencing datasets (21). The resulting consensus sequence from each 95% cluster was taken to be representative of a unique A-or KS-domain sequence within the library, with position information assigned based on the associated row and column barcodes.…”