Project description:Vibrio cholerae uses multiple strategies to resist predation by heterotrophic protozoa. For example, V. cholerae releases toxic compounds such as ammonium and pyomelanin, that can kill protists such as Tetrahymena pyriformis. V. cholerae also survives intracellularly and escapes as viable cells inside protozoan expelled food vacuoles (EFVs). We previously reported that V. cholerae encased in EFVs are hyperinfectious, establishing an important link between anti-protozoal strategies and bacterial virulence. Although the intracellular resistance and escape of V. cholerae in EFVs has been reported, the molecular mechanisms behind this remain poorly understood. Here, we used single cell transcriptomics of V. cholerae exposed to T. pyriformis and captured a total of 5,344 bacterial cells with heterogeneous gene expression. Cells with the same pattern of gene expression were grouped, resulting in eleven clusters of cells with a unique gene expression profile. Genes encoding outer membrane proteins, F1F0-Na+/H+ ATPase, metabolites and toxins showed differential expression among the clusters. Furthermore, the motility-associated killing factor (Mak) toxins (makA, makB and makC) were differentially expressed. Individual V. cholerae ΔmakA, ΔmakB, and ΔmakE strains were not capable of killing T. pyriformis and ΔmakA and ΔmakE showed reduced survival inside EFVs compared to the wild type. Our findings reveal new insights into the grazing resistance mechanisms of V. cholerae, identify factors associated with the survival of V. cholerae within EFVs and more broadly, highlight the connection between antiprotozoal and virulence factors displayed by pathogenic bacteria.
Project description:The behavior of ciliates has been studied for many years through environmental biology and the ethology of microorganisms, and recent hydrodynamic studies of microswimmers have greatly advanced our understanding of the behavioral dynamics at the single-cell level. However, the association between single-cell dynamics captured by microscopic observation and pattern dynamics obtained by macroscopic observation is not always obvious. Hence, to bridge the gap between the two, there is a need for experimental results on swarming dynamics at the mesoscopic scale. In this study, we investigated the spatial population dynamics of the ciliate, Tetrahymena pyriformis, based on quantitative data analysis. We combined the image processing of 3D micrographs and machine learning to obtain the positional data of individual cells of T. pyriformis and examined their statistical properties based on spatio-temporal data. According to the 3D spatial distribution of cells and their temporal evolution, cells accumulated both on the solid wall at the bottom surface and underneath the air-liquid interface at the top. Furthermore, we quantitatively clarified the difference in accumulation levels between the bulk and the interface by creating a simple behavioral model that incorporated quantitative accumulation coefficients in its solution. The accumulation coefficients can be compared under different conditions and between different species.