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1.
Nat Microbiol ; 8(10): 1896-1910, 2023 10.
Article in English | MEDLINE | ID: mdl-37679597

ABSTRACT

The order Corynebacteriales includes major industrial and pathogenic Actinobacteria such as Corynebacterium glutamicum or Mycobacterium tuberculosis. These bacteria have multi-layered cell walls composed of the mycolyl-arabinogalactan-peptidoglycan complex and a polar growth mode, thus requiring tight coordination between the septal divisome, organized around the tubulin-like protein FtsZ, and the polar elongasome, assembled around the coiled-coil protein Wag31. Here, using C. glutamicum, we report the discovery of two divisome members: a gephyrin-like repurposed molybdotransferase (Glp) and its membrane receptor (GlpR). Our results show how cell cycle progression requires interplay between Glp/GlpR, FtsZ and Wag31, showcasing a crucial crosstalk between the divisome and elongasome machineries that might be targeted for anti-mycobacterial drug discovery. Further, our work reveals that Corynebacteriales have evolved a protein scaffold to control cell division and morphogenesis, similar to the gephyrin/GlyR system that mediates synaptic signalling in higher eukaryotes through network organization of membrane receptors and the microtubule cytoskeleton.


Subject(s)
Eukaryota , Mycobacterium tuberculosis , Eukaryota/metabolism , Bacterial Proteins/genetics , Bacterial Proteins/metabolism , Cell Division , Mycobacterium tuberculosis/genetics , Mycobacterium tuberculosis/metabolism
2.
Nat Commun ; 13(1): 2199, 2022 04 22.
Article in English | MEDLINE | ID: mdl-35459274

ABSTRACT

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator's potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.


Subject(s)
Microscopy , Software , Image Processing, Computer-Assisted/methods , Machine Learning , Saccharomyces cerevisiae
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