Dataset Information

The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.

ABSTRACT:

Summary

Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity estimates from high-throughput phosphoproteomics datasets.

Availability and implementation

the KSEA App is a free online tool: casecpb.shinyapps.io/ksea/. The source code is on GitHub: github.com/casecpb/KSEA/. The application is also available as the R package "KSEAapp" on CRAN: CRAN.R-project.org/package=KSEAapp/.

Contact

mark.chance@case.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wiredja DD

PROVIDER: S-EPMC5860163 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.

Wiredja Danica D DD Koyutürk Mehmet M Chance Mark R MR

Bioinformatics (Oxford, England) 20170626 21

<h4>Summary</h4>Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to imple ...[more]

PMID: 28655153

Similar Datasets

Project description:BackgroundDespite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.MethodsWe developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4-11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.ResultsBiologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4-11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4-11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.ConclusionsWe developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.

Project description:Protein phosphorylation is a crucial regulatory mechanism that controls many aspects of cellular signaling. Casein kinase 2 (CK2), a constitutively expressed and active kinase, plays key roles in an array of cellular events including transcription and translation, ribosome biogenesis, cell cycle progression, and apoptosis. CK2 is implicated in cancerous transformation and is a therapeutic target in anti-cancer therapy. The specific and selective CK2 ATP competitive inhibitor, CX-4945 (silmitaseratib), is currently in phase 2 clinical trials. While many substrates and interactors of CK2 have been identified, less is known about CK2 substrates in mitosis. In the present work, we utilize CX-4945 and quantitative phosphoproteomics to inhibit CK2 activity in mitotically arrested HeLa cells and determine candidate CK2 substrates. We identify 330 phosphorylation sites on 202 proteins as significantly decreased in abundance upon inhibition of CK2 activity. Motif analysis of decreased sites reveals a linear kinase motif with aspartic and glutamic amino acids downstream of the phosphorylated residues, which is consistent with known substrate preferences for CK2. To validate specific candidate CK2 substrates, we perform in vitro kinase assays using purified components. Furthermore, we identified CK2 interacting proteins by affinity purification-mass spectrometry (AP-MS). To investigate the biological processes regulated by CK2 in mitosis, we perform network analysis and identify an enrichment of proteins involved in chromosome condensation, chromatin organization, and RNA processing. We demonstrate that overexpression of CK2 in HeLa cells affects proper chromosome condensation. Previously, we found that phosphoprotein phosphatase 6 (PP6), but not phosphoprotein phosphatase 2A (PP2A), opposes CK2 phosphorylation of the condensin I complex, which is essential for chromosome condensation. Here, we extend this observation and demonstrate that PP6 opposition of CK2 is a more general cellular regulatory mechanism.

Dataset Information

The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.

Summary

Availability and implementation

Contact

Supplementary information

Publications

The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets