Evidence map›Paper›PMID 38628705›Full record

ArticleHeliyon2024

Functional prediction of the potential NGLY1 mutations associated with rare disease CDG.

Shuying Yuan, Yanwen Chen, Lin Zou, Xinrong Lu, Ruijie Liu, Shaoxing Zhang, Yuxin Zhang, Cuiying Chen, Dongqing Cheng, Li Chen and 1 more

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
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

11 authors at 3 institutions in 1 country.

Shuying YuanSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Yanwen ChenSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Lin ZouDepartment of Medical Microbiology and Parasitology, Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Xinrong LuDepartment of Medical Microbiology and Parasitology, Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Ruijie LiuSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Shaoxing ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Yuxin ZhangSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Cuiying ChenDepartment of Research and Development, SysDiagno Biotech, Nanjing, 211800, Jiangsu Province, China.
Dongqing ChengSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Li ChenDepartment of Medical Microbiology and Parasitology, Key Laboratory of Medical Molecular Virology of Ministries of Education and Health, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Guiqin SunSchool of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang Province, China.
Zhejiang Chinese Medical University · CNShanghai Medical College of Fudan University · CNViva Biotech (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic diseases are currently diagnosed by functional mutations. However, only some mutations are associated with disease. It is necessary to establish a quick prediction model for clinical screening. Pathogenic mutations in NGLY1 cause a rare autosomal recessive disease known as congenital disorder of deglycosylation (NGLY1-CDDG). Although NGLY1-CDDG can be diagnosed through gene sequencing, clinical relevance of a detected mutation in NGLY1 needs to be further confirmed. In this study, taken NGLY1-CDDG as an example, a comprehensive and practical predictive model for pathogenic mutations on NGLY1 through an NGLY1/Glycopeptide complex model was constructed, the binding sites of NGLY1 and glycopeptides were simulated, and an in vitro enzymatic assay system was established to facilitate quick clinical decisions for NGLY1-CDDG patients. The docking model covers 42 % of reported NGLY1-CDDG missense mutations (5/12). All reported mutations were subjected to in vitro enzymatic assay in which 18 mutations were dysfunctional (18/30). In addition, a full spectrum of functional R328 mutations was assayed and 11 mutations were dysfunctional (11/19). In this study, a model of NGLY1 and glycopeptides was built for potential functional mutations in NGLY1. In addition, the effect of potential regulatory compounds, including N-acetyl-l-cysteine and dithiothreitol, on NGLY1 was examined. The established in vitro assay may serve as a standard protocol to facilitate rapid diagnosis of all mutations in NGLY1-CDDG. This method could also be applied as a comprehensive and practical predictive model for the other rare genetic diseases.

Indexed as

DeglycosylationEnzymatic activityGain of function mutationPNGaseRare disease

Identifiers

PMID38628705
PMCPMC11016977
OpenAlexW4394008616

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.