Mass spectral molecular networking of living microbial colonies

TLDR

Integrating chemistry with genomics and phenotypes of microbial colonies has been a “holy grail” in microbiology. This work presents a highly sensitive, broadly applicable, and cost‑effective method for metabolic profiling of live colonies directly from a Petri dish without sample preparation. The platform employs nanospray desorption electrospray ionization MS combined with data alignment and molecular networking to monitor metabolite production in diverse bacteria, and was validated by profiling Pseudomonas sp. SH‑C52, a strain that protects sugar beet plants from soil‑borne fungi. Applying these tools to visualize small molecular changes within bacterial interactions revealed developmental insights, identified the antifungal lipopeptide thanamycin in SH‑C52, and demonstrated that the platform advances understanding of spatiotemporal metabolite dynamics in live colonies.

Abstract

Integrating the governing chemistry with the genomics and phenotypes of microbial colonies has been a “holy grail” in microbiology. This work describes a highly sensitive, broadly applicable, and cost-effective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample preparation. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and molecular networking, enabled monitoring of metabolite production from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis , and Pseudomonas aeruginosa . This work demonstrates that, by using these tools to visualize small molecular changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this experimental platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi [R. Mendes et al. (2011) Science 332:1097–1100]. The antifungal effect of strain SH-C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. Our technology, in combination with our recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in our ability to understand the spatiotemporal dynamics of metabolite production in live microbial colonies and communities.