Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Interpreting the dynamic pathogenesis of Parkinson’s disease by longitudinal blood transcriptome analysis

Gang Xue, Gang Wang, Qianqian Shi, Hui Wang, Bo-Min Lv, Min Gao, Xiaohui Niu, Hong-Yu Zhang
doi: https://doi.org/10.1101/2020.10.26.356204
Gang Xue
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gang Wang
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
2Department of Basic Medical Laboratory, General Hospital of the Central Command Theater of PLA, Wuhan, 430015, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qianqian Shi
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hui Wang
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bo-Min Lv
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Min Gao
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
3Lab of Epigenetics and Advanced Health Technology, Space Science and Technology Institute (Shenzhen), Shenzhen, 518117, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaohui Niu
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhy630@mail.hzau.edu.cn niuxiaoh@mail.hzau.edu.cn
Hong-Yu Zhang
1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhy630@mail.hzau.edu.cn niuxiaoh@mail.hzau.edu.cn
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Achieving an improved understanding of the temporal sequence of factors involved in Parkinson’s disease (PD) pathogenesis may accelerate drug discovery. In this study, we performed a longitudinal transcriptome analysis to identify associated genes underlying the pathogenesis of PD at three temporal phases. We firstly found that multiple initiator genes, which are related to processes of olfactory transduction and stem cell pluripotency, indicate PD risk to those subjects at the prodromal phase. And many facilitator genes involved in calcium signaling and stem cell pluripotency contribute to PD onset. We next identified 325 aggravator genes whose expression could lead to disease progression through damage to dopaminergic synapses and ferroptosis via an integrative analysis with DNA methylation. Last, we made a systematic comparison of gene expression patterns across PD development and accordingly provided candidate drugs at different phases in an attempt to prevent the neurodegeneration process.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted October 28, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Interpreting the dynamic pathogenesis of Parkinson’s disease by longitudinal blood transcriptome analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Interpreting the dynamic pathogenesis of Parkinson’s disease by longitudinal blood transcriptome analysis
Gang Xue, Gang Wang, Qianqian Shi, Hui Wang, Bo-Min Lv, Min Gao, Xiaohui Niu, Hong-Yu Zhang
bioRxiv 2020.10.26.356204; doi: https://doi.org/10.1101/2020.10.26.356204
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Interpreting the dynamic pathogenesis of Parkinson’s disease by longitudinal blood transcriptome analysis
Gang Xue, Gang Wang, Qianqian Shi, Hui Wang, Bo-Min Lv, Min Gao, Xiaohui Niu, Hong-Yu Zhang
bioRxiv 2020.10.26.356204; doi: https://doi.org/10.1101/2020.10.26.356204

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4688)
  • Biochemistry (10379)
  • Bioengineering (7695)
  • Bioinformatics (26372)
  • Biophysics (13547)
  • Cancer Biology (10722)
  • Cell Biology (15460)
  • Clinical Trials (138)
  • Developmental Biology (8509)
  • Ecology (12843)
  • Epidemiology (2067)
  • Evolutionary Biology (16886)
  • Genetics (11416)
  • Genomics (15493)
  • Immunology (10638)
  • Microbiology (25254)
  • Molecular Biology (10240)
  • Neuroscience (54593)
  • Paleontology (402)
  • Pathology (1671)
  • Pharmacology and Toxicology (2899)
  • Physiology (4355)
  • Plant Biology (9263)
  • Scientific Communication and Education (1588)
  • Synthetic Biology (2561)
  • Systems Biology (6789)
  • Zoology (1470)