Genome Neighborhood Networks Research Articles

Genome neighborhood network reveals insights into enediyne biosynthesis and facilitates prediction and prioritization for discovery.

The enediynes are one of the most fascinating families of bacterial natural products given their unprecedented molecular architecture and extraordinary cytotoxicity. Enediynes are rare with only 11 structurally characterized members and four additional members isolated in their cycloaromatized form. Recent advances in DNA sequencing have resulted in an explosion of microbial genomes. A virtual survey of the GenBank and JGI genome databases revealed 87 enediyne biosynthetic gene clusters from 78 bacteria strains, implying that enediynes are more common than previously thought. Here we report the construction and analysis of an enediyne genome neighborhood network (GNN) as a high-throughput approach to analyze secondary metabolite gene clusters. Analysis of the enediyne GNN facilitated rapid gene cluster annotation, revealed genetic trends in enediyne biosynthetic gene clusters resulting in a simple prediction scheme to determine 9- versus 10-membered enediyne gene clusters, and supported a genomic-based strain prioritization method for enediyne discovery.

A General Strategy for the Discovery of Metabolic Pathways: d-Threitol, l-Threitol, and Erythritol Utilization in Mycobacterium smegmatis.

We describe a general integrated bioinformatic and experimental strategy to discover the in vitro enzymatic activities and in vivo functions (metabolic pathways) of uncharacterized enzymes discovered in microbial genome projects using the ligand specificities of the solute binding proteins (SBPs) for ABC transporters. Using differential scanning fluorimetry, we determined that the SBP for an ABC transporter encoded by the genome of Mycobacterium smegmatis is stabilized by d-threitol. Using sequence similarity networks and genome neighborhood networks to guide selection of target proteins for pathway enzymes, we applied both in vitro and in vivo experimental approaches to discover novel pathways for catabolism of d-threitol, l-threitol, and erythritol.

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks.

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ∼85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.