Quantitative Transformation Efficiency Assay for Bacillus subtilis

Phase Variation of Flagella and Toxins in Clostridioides difficile is Mediated by Selective Rho-dependent Termination

Clostridioides difficile is an intestinal pathogen that exhibits phase variation of flagella and toxins through inversion of the flagellar (flg) switch controlling flagellar and toxin gene expression. The transcription termination factor Rho preferentially inhibits swimming motility of bacteria with the 'flg-OFF' switch sequence. How C. difficile Rho mediates this selectivity was unknown. C. difficile Rho contains an N-terminal insertion domain (NID) which is found in a subset of Rho orthologues and confers diverse functions. Here we determined how Rho distinguishes between flg-ON and -OFF mRNAs and the roles of the NID and other domains of C. difficile Rho. Using in vitro ATPase assays, we determined that Rho specifically binds a region containing the left inverted repeat of the flg switch, but only of flg-OFF mRNA, indicating that differential termination is mediated by selective Rho binding. Using a suite of in vivo and in vitro assays in C. difficile, we determined that the NID is essential for Rho termination of flg-OFF mRNA, likely by influencing the ability to form stable hexamers, and the RNA binding domain is critical for flg-OFF specific termination. This work gives insight into the novel mechanism by which Rho interacts with flg mRNA to mediate phase variation of flagella and toxins in C. difficile and broadens our understanding of Rho-mediated termination in an organism with an AT-rich genome.

Microbial Interaction Network Inference in Microfluidic Droplets

Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.