Multi-omics analysis identifies drivers of protein phosphorylation

被引:4
|
作者
Zhang, Tian [1 ]
Keele, Gregory R. [2 ]
Gyuricza, Isabela Gerdes [2 ]
Vincent, Matthew [2 ]
Brunton, Catherine [2 ]
Bell, Timothy A. [3 ]
Hock, Pablo [3 ]
Shaw, Ginger D. [3 ]
Munger, Steven C. [2 ]
de Villena, Fernando Pardo-Manuel [3 ,4 ]
Ferris, Martin T. [3 ]
Paulo, Joao A. [1 ]
Gygi, Steven P. [1 ]
Churchill, Gary A. [2 ]
机构
[1] Harvard Med Sch, Boston, MA 02115 USA
[2] Jackson Lab, Bar Harbor, ME 04609 USA
[3] Univ N Carolina, Dept Genet, Chapel Hill, NC 27599 USA
[4] Univ N Carolina, Lineberger Comprehens Canc Ctr, Chapel Hill, NC 27599 USA
基金
美国国家卫生研究院;
关键词
Collaborative Cross; Phosphorylation; Quantitative trait loci (QTL); Multi-omics; Medation analysis; Phosphorylation regulation; PROPIONYL-COA CARBOXYLASE; TRANSCRIPTIONAL REGULATION; COLLABORATIVE CROSS; SUBSPECIFIC ORIGIN; GENETIC-VARIATION; LUNG-CANCER; CELL-CYCLE; KINASE; MODEL; RECEPTOR;
D O I
10.1186/s13059-023-02892-2
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
BackgroundPhosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.ResultsWe quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped similar to 700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.ConclusionsTogether, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.
引用
收藏
页数:29
相关论文
共 50 条
  • [1] Multi-omics analysis identifies drivers of protein phosphorylation
    Tian Zhang
    Gregory R. Keele
    Isabela Gerdes Gyuricza
    Matthew Vincent
    Catherine Brunton
    Timothy A. Bell
    Pablo Hock
    Ginger D. Shaw
    Steven C. Munger
    Fernando Pardo-Manuel de Villena
    Martin T. Ferris
    Joao A. Paulo
    Steven P. Gygi
    Gary A. Churchill
    Genome Biology, 24
  • [2] Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application
    Shahrajabian, Mohamad Hesam
    Sun, Wenli
    CURRENT PHARMACEUTICAL ANALYSIS, 2023, 19 (04) : 267 - 281
  • [3] Multi-omics analysis identifies OSGEPL1 as an oncogene in hepatocellular carcinoma
    Mui, Sintim
    Shi, Juanyi
    Wen, Kai
    Yan, Yongcong
    Li, Huoming
    Wang, Weidong
    Zhou, Zhenyu
    Xiao, Zhiyu
    DISCOVER ONCOLOGY, 2025, 16 (01)
  • [4] Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets
    Argelaguet, Ricard
    Velten, Britta
    Arnol, Damien
    Dietrich, Sascha
    Zenz, Thorsten
    Marioni, John C.
    Buettner, Florian
    Huber, Wolfgang
    Stegle, Oliver
    MOLECULAR SYSTEMS BIOLOGY, 2018, 14 (06)
  • [5] Visual analysis of multi-omics data
    Swart, Austin
    Caspi, Ron
    Paley, Suzanne
    Karp, Peter D.
    FRONTIERS IN BIOINFORMATICS, 2024, 4
  • [6] Comprehensive analysis of multi-omics data of recurrent gliomas identifies a recurrence-related signature as a novel prognostic marker
    Wang, Qiang-Wei
    Zhao, Zheng
    Bao, Zhao-Shi
    Jiang, Tao
    Zhu, Yong-Jian
    AMERICAN JOURNAL OF CANCER RESEARCH, 2021, 11 (04): : 1226 - +
  • [7] Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies
    Mantini, Giulia
    Pham, Thang, V
    Piersma, Sander R.
    Jimenez, Connie R.
    PROTEOMICS, 2021, 21 (3-4)
  • [8] Connecting 'multi-omics' approaches to endogenous protein complexes
    Wu, Di
    Robinson, Carol V.
    TRENDS IN CHEMISTRY, 2021, 3 (06): : 445 - 455
  • [9] A Customizable Analysis Flow in Integrative Multi-Omics
    Lancaster, Samuel M.
    Sanghi, Akshay
    Wu, Si
    Snyder, Michael P.
    BIOMOLECULES, 2020, 10 (12) : 1 - 15
  • [10] Panomicon: A web-based environment for interactive, visual analysis of multi-omics data
    Osorio, Rodolfo S. Allendes
    Nystrom-Persson, Johan T.
    Nojima, Yosui
    Kosugi, Yuji
    Mizuguchi, Kenji
    Natsume-Kitatani, Yayoi
    HELIYON, 2020, 6 (08)