Achimovich, A. M. | bioRxiv Science

bioRxiv2024

Targeting Iron - Respiratory Reciprocity Promotes Bacterial Death

Discovering new bacterial signaling pathways offers unique antibiotic strategies. Here, through an unbiased resistance screen of 3,884 gene knockout strains, we uncovered a previously unknown non-lytic bactericidal mechanism that sequentially couples three transporters and downstream transcription to lethally suppress respiration of the highly virulent P. aeruginosa strain PA14 - one of three species on the WHOs Priority 1: Critical list. By targeting outer membrane YaiW, cationic lacritin peptide N-104 translocates into the periplasm where it ligates outer loops 4 and 2 of the inner membrane transporters FeoB and PotH, respectively, to suppress both ferrous iron and polyamine uptake. This broadly shuts down transcription of many biofilm-associated genes, including ferrous iron-dependent TauD and ExbB1. The mechanism is innate to the surface of the eye and is enhanced by synergistic coupling with thrombin peptide GKY20. This is the first example of an inhibitor of multiple bacterial transporters.

microbiology↗

bioRxiv2022

Dimerization of iLID Optogenetic Proteins Observed Using 3D Single-Molecule Tracking in Live Bacterial Cells

3D single molecule tracking microscopy has enabled measurements of protein diffusion in living cells, offering information about protein dynamics and cellular environments. For example, different diffusive states can be resolved and assigned to protein complexes of different size and composition. However, substantial statistical power and biological validation, often through genetic deletion of binding partners, are required to support diffusive state assignments. When investigating cellular processes, transient perturbation to protein spatial distributions is preferable to permanent genetic deletion of an essential protein. In this context, optogenetic dimerization systems can be used to manipulate protein spatial distributions which could offer a means to deplete specific diffusive states observed in single molecule tracking experiments. Here, we evaluate the performance of the iLID optogenetic system in living E. coli cells using diffraction-limited microscopy and 3D single-molecule tracking. We observed a robust optogenetic response in protein spatial distributions after 488 nm laser activation. Surprisingly, 3D single-molecule tracking results indicate activation of the optogenetic response when illuminating with high intensity light with wavelengths at which there is minimal photon absorbance by the LOV2 domain. The pre-activation can be minimized through the use of iLID system mutants, and titration of protein expression levels. SIGNIFICANCE STATEMENTWe describe the combination of 3D single-molecule tracking microscopy and optogenetic manipulation of protein spatial distributions in living cells. Such a combination is impactful, because optogenetic systems enable sample perturbation in real time using light, which provides more flexibility than gene deletion and gene editing approaches that result in permanent changes to the specimen. We specifically investigate the performance of the iLID optogenetic system in a knocksideways experiment, in which cytosolic prey proteins (SspB) are sequestered to the membrane by interacting with membrane-anchored bait proteins (iLID). We quantified the magnitude of the optogenetic effect using both diffraction-limited imaging and 3D single-molecule tracking microscopy. We found, surprisingly, that the iLID optogenetic response is activated substantially by high intensity light at wavelengths for which there is negligible photon absorption by the iLID protein. Quantification of this alternative activation mechanism is a necessary component before optogenetic tools, such as iLIDs, are employed in single-molecule knocksideways experiment that are designed to provide new biological insights.

biophysics↗

bioRxiv2020

Non-Invasive Single-Cell Morphometry in Living Bacterial Biofilms

Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.

microbiology↗