Heparan Sulfate Proteoglycans: Mediators of Cellular and Molecular Alzheimer's Disease Pathogenic Factors Via Tunnelling Nanotubes?

Duy L. B. Nguyen,Rachel K. Okolicsanyi,Larisa M. Haupt

MOLECULAR AND CELLULAR NEUROSCIENCE（2024）

Stem Cell and Neurogenesis Group

Cited 0|Views10

Abstract

Neurological disorders impact around one billion individuals globally (15 % approx.), with significant implications for disability and mortality with their impact in Australia currently amounts to 6.8 million deaths annually. Heparan sulfate proteoglycans (HSPGs) are complex extracellular molecules implicated in promoting Tau fibril formation resulting in Tau tangles, a hallmark of Alzheimer's disease (AD). HSPG-Tau protein interactions contribute to various AD stages via aggregation, toxicity, and clearance, largely via interactions with the glypican 1 and syndecan 3 core proteins. The tunnelling nanotubes (TNTs) pathway is emerging as a facilitator of intercellular molecule transport, including Tau and Amyloid ( proteins, across extensive distances. While current TNT-associated evidence primarily stems from cancer models, their role in Tau propagation and its effects on recipient cells remain unclear. This review explores the interplay of TNTs, HSPGs, and AD-related factors and proposes that HSPGs influence TNT formation in neurodegenerative conditions such as AD.

Translated text

Key words

Neurological disorders,Tunnelling nanotubes,Alzheimer's disease,Heparan sulfate proteoglycans,Tau,P -tau,A(

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper