It is well established that changes in the underlying architecture of the cell's microtubule (MT) network can affect organelle organization within the cytoplasm, but it remains unclear whether the spatial arrangement of organelles reciprocally influences the MT network. Here we use a combination of cell-free extracts and hydrogel microenclosures to characterize the relationship between membranes and MTs during MT aster centration. We found that initially disperse ER membranes are collected by the aster and compacted near its nucleating center, all while the whole ensemble moves toward the geometric center of its confining enclosure. Once there, aster MTs adopt a bull's-eye pattern with a high-density annular ring of MTs surrounding the compacted membrane core of lower MT density. Formation of this pattern was inhibited when dynein-dependent transport was perturbed or when membranes were depleted from the extracts. Asters in membrane-depleted extracts were able to move away from the most proximal wall but failed to center in cylindrical enclosures with diameters greater than or equal to 150 µm. Taken as whole, our data suggest that the dynein-dependent transport of membranes buttresses MTs near the aster center and that this plays an important role in modulating aster architecture and position.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582798PMC
http://dx.doi.org/10.1091/mbc.E22-03-0074DOI Listing

Publication Analysis

Top Keywords

architecture position
8
cell-free extracts
8
mts aster
8
dynein-dependent transport
8
membranes
5
aster
5
dynein-dependent collection
4
collection membranes
4
membranes defines
4
defines architecture
4

Similar Publications

This study proposes a novel, highly sensitive neutron detector design utilizing a unique multi-layered configuration. Each layer consists of a LiF: ZnS(Ag) scintillator coupled with a transparent neutron moderator that also functions as a light guide for the Silicon Photomultiplier (SiPM) light sensor. This design offers a cost-effective and readily available alternative for existing neutron detectors.

View Article and Find Full Text PDF

Microtubules are dynamic cytoskeletal structures essential for cell architecture, cellular transport, cell motility, and cell division. Due to their dynamic nature, known as dynamic instability, microtubules can spontaneously switch between phases of growth and shortening. Disruptions in microtubule functions have been implicated in several diseases, including cancer, neurodegenerative disorders such as Alzheimer's and Parkinson's disease, and birth defects.

View Article and Find Full Text PDF

In the twenty-first century, maritime routes are crucial for geographical and financial reasons in riverine countries. Compared to the available technology abroad, Bangladesh has insufficient monitoring of water vessels to tackle any possible disaster, such as vessel collisions for inland water transportation. One of the frequent outcomes of this architecture is regular capsizing, which sometimes leads to loss of lives.

View Article and Find Full Text PDF

Identifying influential nodes in brain networks via self-supervised graph-transformer.

Comput Biol Med

December 2024

Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China. Electronic address:

Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.

View Article and Find Full Text PDF

Objective: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.

Methods: In this study, a total of 546 IMMs from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!