Evidence map›Paper›PMID 37735671›Full record

ArticleJournal of translational medicine2023

Integrating single-nucleus sequence profiling to reveal the transcriptional dynamics of Alzheimer's disease, Parkinson's disease, and multiple sclerosis.

Li-Yuan Fan, Jing Yang, Ruo-Yu Liu, Ying Kong, Guang-Yu Guo, Yu-Ming Xu

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
4.3field-weighted citation impact, top 5% 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

27 citing papers in PubMed, 35 citations in OpenAlex.

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  5. Is there a link between multiple sclerosis and Alzheimer disease? A critical note.Journal of neural transmission (Vienna, Austria : 1996) · 2026
    Review
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  7. Co-occurrence of parkinson disease and multiple sclerosis - a critical note.Journal of neural transmission (Vienna, Austria : 1996) · 2026
    Review
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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

6 authors at 2 institutions in 1 country.

Li-Yuan Fan *Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Jing YangDepartment of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Ruo-Yu LiuDepartment of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Ying KongDepartment of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China.
Guang-Yu Guo *Department of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China. guangyuguoer@163.com.
Yu-Ming XuDepartment of Neurology, First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China. xuyuming@zzu.edu.cn.ORCID http://orcid.org/0000-0003-2689-9897
First Affiliated Hospital of Zhengzhou University · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS) are three nervous system diseases that partially overlap clinically and genetically. However, bulk RNA-sequencing did not accurately detect the core pathogenic molecules in them. The availability of high-quality single cell RNA-sequencing data of post-mortem brain collections permits the generation of a large-scale gene expression in different cells in human brain, focusing on the molecular features and relationships between diseases and genes. We integrated single-nucleus RNA-sequencing (snRNA-seq) datasets of human brains with AD, PD, and MS to identify transcriptomic commonalities and distinctions among them.

methodsThe snRNA-seq datasets were downloaded from Gene Expression Omnibus (GEO) database. The Seurat package was used for snRNA-seq data processing. The uniform manifold approximation and projection (UMAP) were utilized for cluster identification. The FindMarker function in Seurat was used to identify the differently expressed genes. Functional enrichment analysis was carried out using the Gene Set Enrichment Analysis (GSEA) and Gene ontology (GO). The protein-protein interaction (PPI) analysis of differentially expressed genes (DEGs) was analyzed using STRING database ( http://string-db.org ). SCENIC analysis was performed using utilizing pySCENIC (v0.10.0) based on the hg19-tss-centered-10 kb-10species databases. The analysis of potential therapeutic drugs was analyzed on Connectivity Map ( https://clue.io ).

resultsThe gene regulatory network analysis identified several hub genes regulated in AD, PD, and MS, in which HSPB1 and HSPA1A were key molecules. These upregulated HSP family genes interact with ribosome genes in AD and MS, and with immunomodulatory genes in PD. We further identified several transcriptional regulators (SPI1, CEBPA, TFE3, GRHPR, and TP53) of the hub genes, which has important implications for uncovering the molecular crosstalk among AD, PD, and MS. Arctigenin was identified as a potential therapeutic drug for AD, PD, and MS.

conclusionsTogether, the integrated snRNA-seq data and findings have significant implications for unraveling the shared and unique molecular crosstalk among AD, PD, and MS. HSPB1 and HSPA1A as promising targets involved in the pathological mechanisms of neurodegenerative diseases. Additionally, the identification of arctigenin as a potential therapeutic drug for AD, PD, and MS further highlights its potential in treating these neurological disorders. These discoveries lay the groundwork for future research and interventions to enhance our understanding and treatment of AD, PD, and MS.

Indexed as

Alzheimer DiseaseMultiple SclerosisParkinson DiseaseFuransHumansLignansRNAarctigeninFuransLignansRNAAlzheimer’s diseasesArctigeninCrosstalkHSPA1AHSPB1Multiple sclerosisParkinson’s diseaseRibosomal proteinsSingle-cell sequence

Identifiers

PMID37735671
PMCPMC10515258
OpenAlexW4386941877

What OpenQuestion holds

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LicenceCC BY
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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.