Microbiome composition modulates secondary metabolism in a multispecies bacterial community.

Bacterial secondary metabolites are a major source of antibiotics and other bioactive compounds. In microbial communities, these molecules can mediate interspecies interactions and responses to environmental change. Despite the importance of secondary metabolites in human health and microbial ecology, little is known about their roles and regulation in the context of multispecies communities. In a simplified model of the rhizosphere composed of Bacillus cereus, Flavobacterium johnsoniae, and Pseudomonas koreensis, we show that the dynamics of secondary metabolism depend on community species composition and interspecies interactions. Comparative metatranscriptomics and metametabolomics reveal that the abundance of transcripts of biosynthetic gene clusters (BGCs) and metabolomic molecular features differ between monocultures or dual cultures and a tripartite community. In both two- and three-member cocultures, P. koreensis modified expression of BGCs for zwittermicin, petrobactin, and other secondary metabolites in B. cereus and F. johnsoniae, whereas the BGC transcriptional response to the community in P. koreensis itself was minimal. Pairwise and tripartite cocultures with P. koreensis displayed unique molecular features that appear to be derivatives of lokisin, suggesting metabolic handoffs between species. Deleting the BGC for koreenceine, another P. koreensis metabolite, altered transcript and metabolite profiles across the community, including substantial up-regulation of the petrobactin and bacillibactin BGCs in B. cereus, suggesting that koreenceine represses siderophore production. Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.

Pyridine-2,6-Dithiocarboxylic Acid and Its Metal Complexes: New Inhibitors of New Delhi Metallo ß-Lactamase-1.

Keywords: NDM-1 inhibitors; marine-derived Streptomyces sp.; carbapenem-resistant Enterobacteriaceae; metal chelators.

hcapca: Automated Hierarchical Clustering and Principal Component Analysis of Large Metabolomic Datasets in R.

Microbial natural product discovery programs face two main challenges today: rapidly prioritizing strains for discovering new molecules and avoiding the rediscovery of already known molecules. Typically, these problems have been tackled using biological assays to identify promising strains and techniques that model variance in a dataset such as PCA to highlight novel chemistry. While these tools have shown successful outcomes in the past, datasets are becoming much larger and require a new approach. Since PCA models are dependent on the members of the group being modeled, large datasets with many members make it difficult to accurately model the variance in the data. Our tool, hcapca, first groups strains based on the similarity of their chemical composition, and then applies PCA to the smaller sub-groups yielding more robust PCA models. This allows for scalable chemical comparisons among hundreds of strains with thousands of molecular features. As a proof of concept, we applied our open-source tool to a dataset with 1046 LCMS profiles of marine invertebrate associated bacteria and discovered three new analogs of an established anticancer agent from one promising strain.

Natural Product Discovery Using Planes of Principal Component Analysis in R (PoPCAR).

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming that common actinobacterial metabolites are likely known, focus can be shifted away from dereplication and towards discovery. LC-MS-based principal component analysis (PCA) provides a perfect tool to distinguish unique vs. common metabolites, but the variability inherent within natural products leads to datasets that do not fit ideal standards. To simplify the analysis of PCA models, we developed a script that identifies only those masses or molecules that are unique to each strain within a group, thereby greatly reducing the number of data points to be inspected manually. Since the script is written in R, it facilitates integration with other metabolomics workflows and supports automated mass matching to databases such as Antibase.

Brackish habitat dictates cultivable Actinobacterial diversity from marine sponges.

Bacterial communities associated with marine invertebrates such as sponges and ascidians have demonstrated potential as sources of bio-medically relevant small molecules. Metagenomic analysis has shown that many of these invertebrates harbor populations of Actinobacteria, many of which are cultivable. While some populations within invertebrates are transmitted vertically, others are obtained from the environment. We hypothesized that cultivable diversity from sponges living in brackish mangrove habitats have associations with Actinobacterial populations that differ from those found in clear tropical waters. In this study, we analyzed the cultivable Actinobacterial populations from sponges found in these two distinct habitats with the aim of understanding the secondary metabolite potential. Importantly, we wanted to broadly evaluate the potential differences among these groups to guide future Actinobacterial collection strategies for the purposes of drug discovery.