PhosR enables processing and functional analysis of phosphoproteomic data

Hani Jieun Kim; Taiyun Kim; Nolan J Hoffman; Di Xiao; David E James; Sean J Humphrey; Pengyi Yang

doi:10.1016/j.celrep.2021.108771

PhosR enables processing and functional analysis of phosphoproteomic data

Cell Rep. 2021 Feb 23;34(8):108771. doi: 10.1016/j.celrep.2021.108771.

Authors

Hani Jieun Kim¹, Taiyun Kim¹, Nolan J Hoffman², Di Xiao³, David E James⁴, Sean J Humphrey², Pengyi Yang⁵

Affiliations

¹ School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
² Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Environmental and Life Sciences, The University of Sydney, Sydney, NSW, Australia.
³ Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.
⁴ Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Environmental and Life Sciences, The University of Sydney, Sydney, NSW, Australia; Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
⁵ School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Computational Systems Biology Group, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia. Electronic address: pengyi.yang@sydney.edu.au.

PMID: 33626354
DOI: 10.1016/j.celrep.2021.108771

Abstract

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

Keywords: batch correction; imputation; kinase-substrate prediction; mass spectrometry; normalisation; phosphoproteomics; signalling networks; signalomes; stably phosphorylated sites.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

AMP-Activated Protein Kinases / metabolism
Aminoimidazole Carboxamide / analogs & derivatives
Aminoimidazole Carboxamide / pharmacology
Animals
Cell Line, Tumor
Enzyme Activation
Insulin / pharmacology
Liver / drug effects
Liver / metabolism*
Mass Spectrometry*
Mice
Muscle Fibers, Skeletal / drug effects
Muscle Fibers, Skeletal / metabolism*
Phosphorylation
Proteome* / drug effects
Proteomics*
Rats
Ribonucleotides / pharmacology
Signal Transduction* / drug effects
Software Design*

Substances

Insulin
Proteome
Ribonucleotides
Aminoimidazole Carboxamide
AMP-Activated Protein Kinases
AICA ribonucleotide