Back

Co-dependent formation of the Toxoplasma gondii subpellicular microtubules and inner membrane skeleton

Engelberg, K.; Bauwens, C.; Ferguson, D.; Gubbels, M.-J.

2024-05-25 microbiology
10.1101/2024.05.25.595886 bioRxiv
Show abstract

One of the defining features of apicomplexan parasites is their cytoskeleton composed of alveolar vesicles, known as the inner membrane complex (IMC) undergirded by intermediate-like filament network and an array of subpellicular microtubules (SPMTs). In Toxoplasma gondii, this specialized cytoskeleton is involved in all aspects of the disease-causing lytic cycle, and notably acting as a scaffold for parasite offspring in the internal budding process. Despite advances in our understanding of the architecture and molecular composition, insights pertaining to the coordinated assembly of the scaffold are still largely elusive. Here, T. gondii tachyzoites were dissected by advanced, iterative expansion microscopy (pan-ExM) revealing new insights into the very early sequential formation steps of the tubulin scaffold. A comparative study of the related parasite Sarcocystis neurona revealed that different MT bundling organizations of the nascent SPMTs correlate with the number of central and basal alveolar vesicles. In absence of a so far identified MT nucleation mechanism, we genetically dissected T. gondii {gamma}-tubulin and {gamma}-tubulin complex protein 4 (GCP4). While {gamma}-tubulin depletion abolished the formation of the tubulin scaffold, a set of MTs still formed that suggests SPMTs are nucleated at the outer core of the centrosome. Depletion of GCP4 interfered with the correct assembly of SPMTs into the forming daughter buds, further indicating that the parasite utilizes the {gamma}-tubulin complex in tubulin scaffold formation.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.