A genetic screen identifies a role for oprF in Pseudomonas aeruginosa biofilm stimulation by subinhibitory antibiotics.

Biofilms are surface-associated communities of bacteria that grow in a self-produced matrix of polysaccharides, proteins, and extracellular DNA (eDNA). Sub-minimal inhibitory concentrations (sub-MIC) of antibiotics induce biofilm formation, potentially as a defensive response to antibiotic stress. However, the mechanisms behind sub-MIC antibiotic-induced biofilm formation are unclear. We show that treatment of Pseudomonas aeruginosa with multiple classes of sub-MIC antibiotics with distinct targets induces biofilm formation. Further, addition of exogenous eDNA or cell lysate failed to increase biofilm formation to the same extent as antibiotics, suggesting that the release of cellular contents by antibiotic-driven bacteriolysis is insufficient. Using a genetic screen for stimulation-deficient mutants, we identified the outer membrane porin OprF and the ECF sigma factor SigX as important. Similarly, loss of OmpA - the Escherichia coli OprF homolog - prevented sub-MIC antibiotic stimulation of E. coli biofilms. Our screen also identified the periplasmic disulfide bond-forming enzyme DsbA and a predicted cyclic-di-GMP phosphodiesterase encoded by PA2200 as essential for biofilm stimulation. The phosphodiesterase activity of PA2200 is likely controlled by a disulfide bond in its regulatory domain, and folding of OprF is influenced by disulfide bond formation, connecting the mutant phenotypes. Addition of reducing agent dithiothreitol prevented sub-MIC antibiotic biofilm stimulation. Finally, activation of a c-di-GMP-responsive promoter follows treatment with sub-MIC antibiotics in the wild-type but not an oprF mutant. Together, these results show that antibiotic-induced biofilm formation is likely driven by a signaling pathway that translates changes in periplasmic redox state into elevated biofilm formation through increases in c-di-GMP.

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com .

Thiostrepton Hijacks Pyoverdine Receptors To Inhibit Growth of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a biofilm-forming opportunistic pathogen and is intrinsically resistant to many antibiotics. In a high-throughput screen for molecules that modulate biofilm formation, we discovered that the thiopeptide antibiotic thiostrepton (TS), which is considered to be inactive against Gram-negative bacteria, stimulated P. aeruginosa biofilm formation in a dose-dependent manner. This phenotype is characteristic of exposure to antimicrobial compounds at subinhibitory concentrations, suggesting that TS was active against P. aeruginosa Supporting this observation, TS inhibited the growth of a panel of 96 multidrug-resistant (MDR) P. aeruginosa clinical isolates at low-micromolar concentrations. TS also had activity against Acinetobacter baumannii clinical isolates. The expression of Tsr, a 23S rRNA-modifying methyltransferase from TS producer Streptomyces azureus, in trans conferred TS resistance, confirming that the drug acted via its canonical mode of action, inhibition of ribosome function. The deletion of oligopeptide permease systems used by other peptide antibiotics for uptake failed to confer TS resistance. TS susceptibility was inversely proportional to iron availability, suggesting that TS exploits uptake pathways whose expression is increased under iron starvation. Consistent with this finding, TS activity against P. aeruginosa and A. baumannii was potentiated by the FDA-approved iron chelators deferiprone and deferasirox and by heat-inactivated serum. Screening of P. aeruginosa mutants for TS resistance revealed that it exploits pyoverdine receptors FpvA and FpvB to cross the outer membrane. We show that the biofilm stimulation phenotype can reveal cryptic subinhibitory antibiotic activity, and that TS has activity against select multidrug-resistant Gram-negative pathogens under iron-limited growth conditions, similar to those encountered at sites of infection.

An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products.

Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC-MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products.