Evidence map›Paper›PMID 32140098›Full record

ArticleFrontiers in cellular neuroscience2020

Depletion of LncRNA NEAT1 Rescues Mitochondrial Dysfunction Through NEDD4L-Dependent PINK1 Degradation in Animal Models of Alzheimer's Disease.

Zhonghua Huang, Jing Zhao, Wei Wang, Jun Zhou, Jie Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cellular neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 pooled it
4.1field-weighted citation impact, top 4% 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

58 citing papers in PubMed, 3 syntheses or guidelines pooled it, 77 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

5 authors at 2 institutions in 1 country.

Zhonghua HuangDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.
Jing ZhaoDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.
Wei WangDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.
Jun ZhouMedical Science Research Center, Xiangya Hospital, Central South University, Changsha, China.
Jie ZhangDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNSecond Xiangya Hospital of Central South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disorder and the main cause of dementia among the elderly worldwide. Unfortunately, the mechanism of AD remains unclear, and no effective therapies are available yet. An increasing amount of studies have demonstrated that long non-coding RNAs (LncRNAs) play a notable role in the pathogenesis of plenty of human diseases, and they have served as biomarkers and potential therapeutic targets. However, the function of LncRNAs in AD remains unclear. This study aimed to explore the potential role of LncRNA nuclear enriched abundant transcript 1 (NEAT1) in AD. We found that LncRNA NEAT1 was upregulated in the AD animal models. Furthermore, we demonstrated that NEAT1 could interact with NEDD4L and promote PTEN-induced putative kinase 1 (PINK1)'s ubiquitination and degradation and then impaired PINK1-dependent autophagy. Collectively, the lncRNA NEAT1 promotes the pathogenesis of AD and serves as a promising novel target for pharmacological intervention.

Indexed as

Alzheimer’s diseaseautophagyNEAT1NEDD4LPINK1

Identifiers

PMID32140098
PMCPMC7043103
OpenAlexW3007703151

What OpenQuestion holds

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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.