Changes in lipid distribution in E. coli strains in response to norfloxacin.

Bacterial resistance to antibiotics has become an increasing threat, requiring not only the development of new targets in drug discovery, but more importantly, a better understanding of cellular response. In the current study, three closely related Escherichia coli strains, a wild type (MG1655) and an isogenic pair derived from the wild type (DPB635 and DPB636) are studied following exposure to sub lethal concentrations of antibiotic (norfloxacin) over time. In particular, genotype similarities between the three strains were assessed based on the lipid regulation response (e.g. presence/absence and up/down regulation). Lipid identification was performed using direct surface probe analysis (matrix-assisted laser desorption/ionization, MALDI), coupled to high-resolution mass spectrometry (Fourier transform ion cyclotron resonance mass spectrometry, FT-ICR MS) followed by statistical analysis of variability and reproducibility across batches using internal standards. Inspection of the lipid profile showed that for the MG1655, DPB635 and DPB636 E. coli strains, a similar distribution of the altered lipids was observed after exposure to norfloxacin antibiotic (e.g. fatty acids and glycerol phospholipids are up and down regulated, respectively). Additionally, variations in the lipid distribution resemble the extent to which each strain can combat the antibiotic exposure. That is, the topA66 topoisomerase I mutation of DPB636 translates into diminished response related to antibiotic sensitivity when compared to MG1655 and the DPB635 strains.

Theoretical predictor for candidate structure assignment from IMS data of biomolecule-related conformational space.

The ability to correlate experimental ion mobility data with candidate structures from theoretical modeling provides a powerful analytical and structural tool for the characterization of biomolecules. In the present paper, a theoretical workflow is described to generate and assign candidate structures for experimental trapped ion mobility and H/D exchange (HDX-TIMS-MS) data following molecular dynamics simulations and statistical filtering. The applicability of the theoretical predictor is illustrated for a peptide and protein example with multiple conformations and kinetic intermediates. The described methodology yields a low computational cost and a simple workflow by incorporating statistical filtering and molecular dynamics simulations. The workflow can be adapted to different IMS scenarios and CCS calculators for a more accurate description of the IMS experimental conditions. For the case of the HDX-TIMS-MS experiments, molecular dynamics in the "TIMS box" accounts for a better sampling of the molecular intermediates and local energy minima.