High-throughput transcriptomics of 409 bacteria-drug pairs reveals drivers of gut microbiota perturbation.

Many drugs can perturb the gut microbiome, potentially leading to negative health consequences. However, mechanisms of most microorganism-drug responses have not been elucidated at the genetic level. Using high-throughput bacterial transcriptomics, we systematically characterized the gene expression profiles of prevalent human gut bacteria exposed to the most frequently prescribed orally administered pharmaceuticals. Across >400 drug-microorganism pairs, significant and reproducible transcriptional responses were observed, including pathways involved in multidrug resistance, metabolite transport, tartrate metabolism and riboflavin biosynthesis. Importantly, we discovered that statin-mediated upregulation of the AcrAB-TolC efflux pump in Bacteroidales species enhances microbial sensitivity to vitamin A and secondary bile acids. Moreover, gut bacteria carrying acrAB-tolC genes are depleted in patients taking simvastatin, suggesting that drug-efflux interactions generate collateral toxicity that depletes pump-containing microorganisms from patient microbiomes. This study provides a resource to further understand the drivers of drug-mediated microbiota shifts for better informed clinical interventions.

High-throughput microbial culturomics using automation and machine learning.

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

A standardized workflow for surveying recombinases expands bacterial genome-editing capabilities.

Bacterial recombineering typically relies on genomic incorporation of synthetic oligonucleotides as mediated by Escherichia coli λ phage recombinase ß - an occurrence largely limited to enterobacterial strains. While a handful of similar recombinases have been documented, recombineering efficiencies usually fall short of expectations for practical use. In this work, we aimed to find an efficient Recß homologue demonstrating activity in model soil bacterium Pseudomonas putida EM42. To this end, a genus-wide protein survey was conducted to identify putative recombinase candidates for study. Selected novel proteins were assayed in a standardized test to reveal their ability to introduce the K43T substitution into the rpsL gene of P. putida. An ERF superfamily protein, here termed Rec2, exhibited activity eightfold greater than that of the previous leading recombinase. To bolster these results, we demonstrated Rec2 ability to enter a range of mutations into the pyrF gene of P. putida at similar frequencies. Our results not only confirm the utility of Rec2 as a Recß functional analogue within the P. putida model system, but also set a complete workflow for deploying recombineering in other bacterial strains/species. Implications range from genome editing of P. putida for metabolic engineering to extended applications within other Pseudomonads - and beyond.

Flow environment and matrix structure interact to determine spatial competition in Pseudomonas aeruginosa biofilms.

Bacteria often live in biofilms, which are microbial communities surrounded by a secreted extracellular matrix. Here, we demonstrate that hydrodynamic flow and matrix organization interact to shape competitive dynamics in Pseudomonas aeruginosa biofilms. Irrespective of initial frequency, in competition with matrix mutants, wild-type cells always increase in relative abundance in planar microfluidic devices under simple flow regimes. By contrast, in microenvironments with complex, irregular flow profiles - which are common in natural environments - wild-type matrix-producing and isogenic non-producing strains can coexist. This result stems from local obstruction of flow by wild-type matrix producers, which generates regions of near-zero shear that allow matrix mutants to locally accumulate. Our findings connect the evolutionary stability of matrix production with the hydrodynamics and spatial structure of the surrounding environment, providing a potential explanation for the variation in biofilm matrix secretion observed among bacteria in natural environments.