KRSA: Network-based Prediction of Differential Kinase Activity from Kinome Array Data

Abstract

MotivationPhosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based approaches for measuring multiple kinases allow for the testing of differential phosphorylation between conditions for distinct sub-kinomes. While bioinformatics tools exist for processing and analyzing such kinome array data, current open-source tools lack the automated approach of upstream kinase prediction and network modeling. The presented tool, alongside other tools and methods designed for gene expression and protein-protein interaction network analyses, help the user better understand the complex regulation of gene and protein activities that forms biological systems and cellular signaling networks.ResultsWe present the Kinome Random Sampling Analyzer (KRSA), a web-application for kinome array analysis. While the underlying algorithm has been experimentally validated in previous publications, we tested the full KRSA application on dorsolateral prefrontal cortex (DLPFC) in male (n=3) and female (n=3) subjects to identify differential phosphorylation and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar patterns to the global phosphorylation signal. Additionally, kinase hits were compared to gene expression databases for in silico validation at the transcript level and showed differential gene expression of kinases.Availability and implementationKRSA as a web-based application can be found at http://bpg-n.utoledo.edu:3838/CDRL/KRSA/. The code and data are available at https://github.com/kalganem/KRSA.Supplementary informationSupplementary data are available online.

Kinome Array Profiling of Patient-Derived Pancreatic Ductal Adenocarcinoma Identifies Differentially Active Protein Tyrosine Kinases

“…Shared identification of upstream kinases responsible for the observed peptide phosphorylation patterns between pipelines could, therefore, be weighted and integrated into the final analysis. These pipelines include (1) Upstream Kinase Analysis (UKA) from PamGene, (2) Post-Translational Modification Signature Enrichment Analysis (PTM-SEA) from the Broad Institute of MIT and Harvard, (3) Kinase Enrichment Analysis Version 3 (KEA3) from the Ma’ayan laboratory, and (4) Kinome Random Sampling Analyzer (KRSA) developed by our own laboratory [ 12 , 13 , 14 , 15 , 16 , 17 ]. Meaningful differences between pipelines were compared in the Discussion section, while the methods by which we deployed each pipeline are presented below.…”

“…Our laboratory developed Kinome Random Sampling Analyzer (KRSA) (version 2.0, Toledo, OH, USA) to associate differentially phosphorylated peptide sequences with specific kinases [ 12 , 13 , 14 , 15 , 16 , 17 ]. To accomplish this, we mapped phosphorylation sites within the reporter peptides to individual protein kinases that phosphorylate these sites.…”

“…These technologies include the PamStation and the Protein Tyrosine Kinase PamChip. The bioinformatic pipelines include the Kinome Random Sampling Analyzer (KRSA) pipeline, developed by our own laboratory [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], and the Upstream Kinase Analysis (UKA) pipeline, which is part of the BioNavigator software tool developed by collaborators at PamGene [ 20 , 21 ]. To passively validate and further contextualize the results of this approach, two additional bioinformatic pipelines were utilized.…”

Alterations in protein kinase networks in astrocytes and neurons derived from patients with familial Alzheimer’s Disease

“…UKA reports the final score as a metric for ranking implicated kinases, which is calculated based on the specificity of the peptides mapped to the kinases and the significance of phosphorylation changes of the peptides between the compared groups. To analyze the kinome array profiles, we further deployed the Kinome Random Sampling Analyzer (KRSA) R package to pre-process, apply quality control checks, and select differentially phosphorylated peptides (33). KRSA was used to analyze the kinome profiles of our cell lines.…”

Microcystin-LR (MC-LR) Triggers Inflammatory Responses in Macrophages

“…Upstream kinase mapping was performed by utilizing the in silico phosphosite-substrate databases GPS 3.0, Kinexus Phosphonet (http://www.phosphonet.ca/, accessed on 15 November 2019), PhosphoELM (http://phospho.elm.eu.org/ (accessed on 15 November 2019)), and PhosphoSite Plus (www.phosphosite.org, accessed on 15 November 2019) [36,37]. To determine which kinases were most likely to be implicated in our experiment, a random sampling analysis was performed using the Kinome Random Sampling Analyzer (KRSA) [38]. This analysis randomly selected the same number of the set of differentially phosphorylated peptides from the chip 2000 times.…”