Glucan and Glycogen Exist As a Covalently Linked Macromolecular Complex in the Cell Wall of Candida Albicans and Other Candida Species
The Cell Surface(2021)
Center of Excellence in Inflammation
Abstract
The fungal cell wall serves as the interface between the organism and its environment. Complex carbohydrates are a major component of the Candida albicans cell wall, i.e., glucan, mannan and chitin. β-Glucan is a pathogen associated molecular pattern (PAMP) composed of β-(1 → 3,1 → 6)-linked glucopyranosyl repeat units. This PAMP plays a key role in fungal structural integrity and immune recognition. Glycogen is an α-(1 → 4,1 → 6)-linked glucan that is an intracellular energy storage carbohydrate. We observed that glycogen was co-extracted during the isolation of β-glucan from C. albicans SC5314. We hypothesized that glucan and glycogen may form a macromolecular species that links intracellular glycogen with cell wall β-(1 → 3,1 → 6)-glucan. To test this hypothesis, we examined glucan-glycogen extracts by multi-dimensional NMR to ascertain if glycogen and β-glucan were interconnected. 1H NMR analyses confirmed the presence of glycogen and β-glucan in the macromolecule. Diffusion Ordered SpectroscopY (DOSY) confirmed that the β-glucan and glycogen co-diffuse, which indicates a linkage between the two polymers. We determined that the linkage is not via peptides and/or small proteins. Our data indicate that glycogen is covalently linked to β-(1 → 3,1 → 6) glucan via the β -(1 → 6)-linked side chain. We also found that the glucan-glycogen complex was present in C. dublinensis, C. haemulonii and C. auris, but was not present in C. glabrata or C. albicans hyphal glucan. These data demonstrate that glucan and glycogen form a novel macromolecular complex in the cell wall of C. albicans and other Candida species. This new and unique structure expands our understanding of the cell wall in Candida species.
MoreTranslated text
Key words
Candida albicans,Glucan,Glycogen,Cell wall
求助PDF
上传PDF
View via Publisher
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
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined