Peptidoglycan synthesis drives a single population of septal cell wall synthases during division in Bacillus subtilis

Bacterial cell division requires septal peptidoglycan (sPG) synthesis by the divisome complex. Treadmilling of the essential tubulin homologue FtsZ has been implicated in septal constriction, though its precise role remains unclear. Here we used live-cell single-molecule imaging of the divisome transpeptidase PBP2B to investigate sPG synthesis dynamics in Bacillus subtilis. In contrast to previous models, we observed a single population of processively moving PBP2B molecules whose motion is driven by peptidoglycan synthesis and is not associated with FtsZ treadmilling. However, despite the asynchronous motions of PBP2B and FtsZ, a partial dependence of PBP2B processivity on FtsZ treadmilling was observed. Additionally, through single-molecule counting experiments we provide evidence that the divisome synthesis complex is multimeric. Our results support a model for B. subtilis division where a multimeric synthesis complex follows a single track dependent on sPG synthesis whose activity and dynamics are asynchronous with FtsZ treadmilling

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic

Low Resolution Structure of a Bacterial SLC26 Transporter Reveals Dimeric Stoichiometry and Mobile Intracellular Domains

The SLC26/SulP (solute carrier/sulfate transporter) proteins are a superfamily of anion transporters conserved from bacteria to man, of which four have been identified in human diseases. Proteins within the SLC26/SulP family exhibit a wide variety of functions, transporting anions from halides to carboxylic acids. The proteins comprise a transmembrane domain containing between 10-12 transmembrane helices followed a by C-terminal cytoplasmic sulfate transporter and anti-sigma factor antagonist (STAS) domain. These proteins are expected to undergo conformational changes during the transport cycle; however, structural information for this family remains sparse, particularly for the full-length proteins. To address this issue, we conducted an expression and detergent screen on bacterial Slc26 proteins. The screen identified a Yersinia enterocolitica Slc26A protein as the ideal candidate for further structural studies as it can be purified to homogeneity. Partial proteolysis, co-purification, and analytical size exclusion chromatography demonstrate that the protein purifies as stable oligomers. Using small angle neutron scattering combined with contrast variation, we have determined the first low resolution structure of a bacterial Slc26 protein without spectral contribution from the detergent. The structure confirms that the protein forms a dimer stabilized via its transmembrane core; the cytoplasmic STAS domain projects away from the transmembrane domain and is not involved in dimerization. Supported by additional biochemical data, the structure suggests that large movements of the STAS domain underlie the conformational changes that occur during transport

FtsZ treadmilling is essential for Z-ring condensation and septal constriction initiation in Bacillus subtilis cell division

Despite the central role of division in bacterial physiology, how division proteins work together as a nanoscale machine to divide the cell remains poorly understood. Cell division by cell wall synthesis proteins is guided by the cytoskeleton protein FtsZ, which assembles at mid-cell as a dense Z-ring formed of treadmilling filaments. However, although FtsZ treadmilling is essential for cell division, the function of FtsZ treadmilling remains unclear. Here, we systematically resolve the function of FtsZ treadmilling across each stage of division in the Gram-positive model organism Bacillus subtilis using a combination of nanofabrication, advanced microscopy, and microfluidics to measure the division-protein dynamics in live cells with ultrahigh sensitivity. We find that FtsZ treadmilling has two essential functions: mediating condensation of diffuse FtsZ filaments into a dense Z-ring, and initiating constriction by guiding septal cell wall synthesis. After constriction initiation, FtsZ treadmilling has a dispensable function in accelerating septal constriction rate. Our results show that FtsZ treadmilling is critical for assembling and initiating the bacterial cell division machine

FtsZ treadmilling is essential for Z-ring condensation and septal constriction initiation in Bacillus subtilis cell division

Despite the central role of division in bacterial physiology, how division proteins work together as a nanoscale machine to divide the cell remains poorly understood. Cell division by cell wall synthesis proteins is guided by the cytoskeleton protein FtsZ, which assembles at mid-cell as a dense Z-ring formed of treadmilling filaments. However, although FtsZ treadmilling is essential for cell division, the function of FtsZ treadmilling remains unclear. Here, we systematically resolve the function of FtsZ treadmilling across each stage of division in the Gram-positive model organism Bacillus subtilis using a combination of nanofabrication, advanced microscopy, and microfluidics to measure the division-protein dynamics in live cells with ultrahigh sensitivity. We find that FtsZ treadmilling has two essential functions: mediating condensation of diffuse FtsZ filaments into a dense Z-ring, and initiating constriction by guiding septal cell wall synthesis. After constriction initiation, FtsZ treadmilling has a dispensable function in accelerating septal constriction rate. Our results show that FtsZ treadmilling is critical for assembling and initiating the bacterial cell division machine.BN/Cees Dekker La

Democratising deep learning for microscopy with ZeroCostDL4Mic

International audienceDeep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction-fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes