Identifying Novel Proteins for Chronic Pain: Integration of Human Brain Proteomes and Genome-wide Association Data

被引:0
|
作者
Huang, Haoquan [1 ,2 ]
Ji, Fengtao [1 ]
Hu, Chuwen [1 ]
Huang, Jingxuan [1 ]
Liu, Fan [2 ]
Han, Zhixiao [1 ]
Liu, Ling [1 ]
Cao, Minghui [1 ,2 ]
Fu, Ganglan [1 ,2 ]
机构
[1] Sun Yat sen Univ, Sun Yat sen Mem Hosp, Dept Anesthesiol, Guangzhou, Peoples R China
[2] Sun Yat Sen Mem Hosp, Med Res Ctr, Shenshan Med Ctr, Shanwei, Peoples R China
来源
JOURNAL OF PAIN | 2024年 / 25卷 / 10期
基金
中国国家自然科学基金;
关键词
Chronic pain; proteomes; proteome-wide association study; transcriptome-wide association study; DORSOLATERAL PREFRONTAL CORTEX; EXPRESSION;
D O I
10.1016/j.jpain.2024.104610
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Numerous genome-wide association studies have identified risk genes for chronic pain, yet the mechanisms by which genetic variants modify susceptibility have remained elusive. We sought to identify key genes modulating chronic pain risk by regulating brain protein expression. We integrated brain proteomic data with the largest genome-wide dataset for multisite chronic pain (N = 387,649) in a proteome-wide association study (PWAS) using discovery and confirmatory proteomic datasets (N = 376 and 152) from the dorsolateral prefrontal cortex. Leveraging summary data- based Mendelian randomization and Bayesian colocalization analysis, we pinpointed potential causal genes, while a transcriptome-wide association study integrating 452 human brain transcriptomes investigated whether cis-effects on protein abundance extended to the transcriptome. Single-cell RNA-sequencing data and single-nucleus transcriptomic data revealed cell-type-specific expression patterns for identified causal genes in the dorsolateral prefrontal cortex and dorsal root ganglia (DRG), complemented by RNA microarray analysis of expression profiles in other pain-related brain regions. Of the 22 genes cis-regulating protein abundance identified by the discovery PWAS, 18 (82%) were deemed causal by summary data-based Mendelian randomization or Bayesian colocalization analysis analyses, with 7 of these 18 genes (39%) replicating in the confirmatory PWAS, including guanosine diphosphate-mannose pyrophosphorylase B, which also associated at the transcriptome level. Several causal genes exhibited selective expression in excitatory and inhibitory neurons, oligodendrocytes, and astrocytes, while most identified genes were expressed across additional pain-related brain regions. This integrative proteogenomic approach identified 18 high- confidence causal genes for chronic pain, regulated by cis-effects on brain protein levels, suggesting promising avenues for treatment research and indicating a contributory role for the DRG. Perspective: The current post genome-wide association study analyses identified 18 high-confidence causal genes regulating chronic pain risk via cis-modulation of brain protein abundance, suggesting promising avenues for future chronic pain therapies. Additionally, the significant expression of these genes in the DRG indicated a potential contributory role, warranting further investigation. (c) 2024 (c) Published by Elsevier Inc. on behalf of United States Association for the Study of Pain, Inc All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Identifying disease associations via genome-wide association studies
    Wenhui Huang
    Pengyuan Wang
    Zhen Liu
    Liqing Zhang
    [J]. BMC Bioinformatics, 10
  • [42] Human genetics - Genome-wide association for HIV
    Goymer, Patrick
    [J]. NATURE REVIEWS GENETICS, 2007, 8 (09) : 655 - 655
  • [43] Identifying disease associations via genome-wide association studies
    Huang, Wenhui
    Wang, Pengyuan
    Liu, Zhen
    Zhang, Liqing
    [J]. BMC BIOINFORMATICS, 2009, 10
  • [44] Efficiently Identifying Significant Associations in Genome-wide Association Studies
    Kostem, Emrah
    Eskin, Eleazar
    [J]. JOURNAL OF COMPUTATIONAL BIOLOGY, 2013, 20 (10) : 817 - 830
  • [45] Parallel Integration of Heterogeneous Genome-Wide Data Sources
    Greene, Derek
    Bryan, Kenneth
    Cunningham, Padraig
    [J]. 8TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING, VOLS 1 AND 2, 2008, : 368 - 374
  • [46] Identification of novel therapeutics for complex diseases from genome-wide association data
    Grover, Mani P.
    Ballouz, Sara
    Mohanasundaram, Kaavya A.
    George, Richard A.
    Sherman, Craig D. H.
    Crowley, Tamsyn M.
    Wouters, Merridee A.
    [J]. BMC MEDICAL GENOMICS, 2014, 7
  • [47] Identification of novel therapeutics for complex diseases from genome-wide association data
    Mani P Grover
    Sara Ballouz
    Kaavya A Mohanasundaram
    Richard A George
    Craig D H Sherman
    Tamsyn M Crowley
    Merridee A Wouters
    [J]. BMC Medical Genomics, 7
  • [48] A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data
    Davies, Joanna L.
    Cazier, Jean-Baptiste
    Dunlop, Malcolm G.
    Houlston, Richard S.
    Tomlinson, Ian P.
    Holmes, Chris C.
    [J]. PLOS ONE, 2012, 7 (12):
  • [49] Genome-wide Association Study of Chronic Kidney Disease
    Li, Changwei
    Kelly, Tanika N.
    Zhang, Dingding
    Shen, Luqi
    Chen, Jing
    He, Jiang
    [J]. CIRCULATION, 2017, 135
  • [50] A genome-wide approach to identifying novel-imprinted genes
    Pollard, Katherine S.
    Serre, David
    Wang, Xu
    Tao, Heng
    Grundberg, Elin
    Hudson, Thomas J.
    Clark, Andrew G.
    Frazer, Kelly
    [J]. HUMAN GENETICS, 2008, 122 (06) : 625 - 634