Tagging the vanA gene in wastewater microbial communities for cell sorting and taxonomy of vanA carrying cells.

2020 
Abstract Failure to understand the microbial ecology driving the proliferation of antibiotic resistance in the environment prevents us from developing strategies to limit the spread of antibiotic resistant infectious disease. In this study, we developed for the first time a tyramide signal amplification-fluorescence in situ hybridization-fluorescence-activated cell sorting protocol (TSA-FISH-FACS) for the characterization of all vanA carrying bacteria in wastewater samples. Firstly, we validated the TSA-FISH protocol through microscopy in pure cultures and wastewater influent. Then, samples were sorted and quantified by FACS and qPCR. Significantly higher percentage tagging of cells was detected in vanA carrying pure cultures and wastewater samples spiked with vanA carrying cells as compared to vanA negative Gram positive strains and non-spiked wastewater samples respectively. qPCR analysis targeting vanZ, a regulating gene in the vanA cluster, showed its relative abundance was significantly greater in Enterococcus faecium ATCC 700221-spiked and positively sorted samples compared to the E. faecium spiked and negatively sorted samples. Phylogenetic analysis was then performed. Although further efforts are needed to overcome technical problems, we have, for the first time, demonstrated sorting bacterial-cells carrying antibiotic resistance genes from wastewater samples through a TSA-FISH-FACS protocol and provided insight into the microbial ecology of vancomycin resistant bacteria. Future potential applications using this approach will include the separation of members of an environmental microbial community (cultured and hard-to-culture) to allow for metagenomics on single cells or, in the case of clumping, targeting a smaller portion of the community with a priori knowledge that the target gene is present.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    70
    References
    2
    Citations
    NaN
    KQI
    []
    Baidu
    map