Evidence map›Paper›PMID 35928227›Full record

ArticleFrontiers in molecular biosciences2022

Effect of Cholesterol on C99 Dimerization: Revealed by Molecular Dynamics Simulations.

Cheng-Dong Li, Muhammad Junaid, Xiaoqi Shan, Yanjing Wang, Xiangeng Wang, Abbas Khan, Dong-Qing Wei

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Cheng-Dong LiState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Muhammad JunaidState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Xiaoqi ShanState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Yanjing WangState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Xiangeng WangState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Abbas KhanState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Dong-Qing WeiState Key Laboratory of Microbial Metabolism, Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Jiao Tong University · CNPeng Cheng Laboratory · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

C99 is the immediate precursor for amyloid beta (Aβ) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). It has been suggested that cholesterol is associated with C99, but the dynamic details of how cholesterol affects C99 assembly and the Aβ formation remain unclear. To investigate this question, we employed coarse-grained and all-atom molecular dynamics simulations to study the effect of cholesterol and membrane composition on C99 dimerization. We found that although the existence of cholesterol delays C99 dimerization, there is no direct competition between C99 dimerization and cholesterol association. In contrast, the existence of cholesterol makes the C99 dimer more stable, which presents a cholesterol binding C99 dimer model. Cholesterol and membrane composition change the dimerization rate and conformation distribution of C99, which will subsequently influence the production of Aβ. Our results provide insights into the potential influence of the physiological environment on the C99 dimerization, which will help us understand Aβ formation and AD's etiology.

Indexed as

Alzheimer’s diseaseC99cholesteroldimerizationmolecular dynamics simulations

Identifiers

PMID35928227
PMCPMC9343951
OpenAlexW4285796706

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.