Antimicrobial resistance is a global health crisis and few novel antimicrobials have been discovered in recent decades. Natural products, particularly from Streptomyces, are the source of most antimicrobials, yet discovery campaigns focusing on Streptomyces from the soil largely rediscover known compounds. Investigation of understudied and symbiotic sources has seen some success, yet no studies have systematically explored microbiomes for antimicrobials. Here we assess the distinct evolutionary lineages of Streptomyces from insect microbiomes as a source of new antimicrobials through large-scale isolations, bioactivity assays, genomics, metabolomics, and in vivo infection models. Insect-associated Streptomyces inhibit antimicrobial-resistant pathogens more than soil Streptomyces. Genomics and metabolomics reveal their diverse biosynthetic capabilities. Further, we describe cyphomycin, a new molecule active against multidrug resistant fungal pathogens. The evolutionary trajectories of Streptomyces from the insect microbiome influence their biosynthetic potential and ability to inhibit resistant pathogens, supporting the promise of this source in augmenting future antimicrobial discovery.

Prospecting for antifungal molecules: Marine bacteria produce a plethora of natural products that often have unusual chemical structures and corresponding reactivity, which sometimes translate into a valuable biological function. Zhang et al. used a metabolomic screen to zero in on microbial strains from the microbiome of a sea squirt that produces a high diversity of chemical structures. They then screened these molecules for inhibition of fungi (see the Perspective by Cowen). A polycyclic molecule dubbed turbinmicin possessed potent antifungal activity against the multidrug-resistant fungal pathogens Candida auris and Aspergillus fumigatus . Preliminary mechanism-of-action and mouse toxicity studies suggest that this molecule works though a fungus-specific pathway and is well tolerated at therapeutic doses. Science , this issue p. 974 ; see also p. 906

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.