Unraveling Direct Correlations between Membrane Nanodomain Reorganization and Antimicrobial Resistance Evolution in Bacterial Cells

Bacterial drug resistance is a major global health emergency that requires newer approaches for its detection, especially those that are rapid and sensitive at the single cell level. One of the major limitations of existing antimicrobial resistance (AMR) screening is that it relies on culturing bacterial samples, which is time- and resource-intensive, or on detection of known mutations that impart resistance. Here we provide the first evidence for the existence of direct correlations between nanoscale dynamical reorganization in Gram-negative bacterial cell membranes with their evolution of phenotypic resistance under sublethal dosage of the last-line antibiotic Colistin. While super-resolution fluorescence microscopy in combination with fluorescence correlation spectroscopy enables probing dynamical lipid nanodomains on single E. coli cells undergoing AMR evolution, high-resolution atomic force microscopy provides information on nanoscale morphological changes in the same cell population. Interestingly, our study also reveals intricate correlations between nanoscale bacterial membrane organization and biochemical signaling responses that eventually drive the evolution of antimicrobial resistance. In addition, we detect signatures of cooperative lipid motion and dynamic heterogeneity as quantified through the non-Gaussian parameter, α2, for lipid number fluctuations in the illumination volume. Further, this parameter is also correlated with the evolution of resistance in the strains. Our study suggests a subtle feedback mechanism for the emergence of antimicrobial resistance which is initiated by membrane nanoscale organization and lipid dynamics leading to biochemical signaling that leads to membrane compositional changes. These compositional changes alter these membrane nanoscale parameters to mitigate the antibiotic mediated stress, and they increase the survival probability of the cell population, which thus becomes more resistant. Our study could thus lead to the development of a fundamentally new approach with high resolution and sensitivity that could be used to infer about antimicrobial resistance evolution, which could also be applicable to other Gram-negative strains and membrane-targeting antibiotics.