Evidence map›Paper›PMID 24894044›Full record

ArticleThe Plant cell2014

Meta-Analysis of Arabidopsis thaliana Phospho-Proteomics Data Reveals Compartmentalization of Phosphorylation Motifs.

Klaas J van Wijk, Giulia Friso, Dirk Walther, Waltraud X Schulze

Abstract read
In one paragraph

Article in The Plant cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

90 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Article
  5. Searching for Sulfotyrosines (sY) in a HA(pY)STACK.Journal of proteome research · 2025
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  10. DecodingPlants (Basel, Switzerland) · 2024
    Article
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  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
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30 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Klaas J van WijkDepartment of Plant Biology, Cornell University, Ithaca, New York 14850 kv35@cornell.edu.ORCID http://orcid.org/0000-0001-9536-0487
Giulia FrisoDepartment of Plant Biology, Cornell University, Ithaca, New York 14850.
Dirk WaltherMax Planck Institute of Molecular Plant Physiology, 14476 Golm, Germany.
Waltraud X SchulzeDepartment of Plant Systems Biology, University of Hohenheim, 70593 Stuttgart, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein (de)phosphorylation plays an important role in plants. To provide a robust foundation for subcellular phosphorylation signaling network analysis and kinase-substrate relationships, we performed a meta-analysis of 27 published and unpublished in-house mass spectrometry-based phospho-proteome data sets for Arabidopsis thaliana covering a range of processes, (non)photosynthetic tissue types, and cell cultures. This resulted in an assembly of 60,366 phospho-peptides matching to 8141 nonredundant proteins. Filtering the data for quality and consistency generated a set of medium and a set of high confidence phospho-proteins and their assigned phospho-sites. The relation between single and multiphosphorylated peptides is discussed. The distribution of p-proteins across cellular functions and subcellular compartments was determined and showed overrepresentation of protein kinases. Extensive differences in frequency of pY were found between individual studies due to proteomics and mass spectrometry workflows. Interestingly, pY was underrepresented in peroxisomes but overrepresented in mitochondria. Using motif-finding algorithms motif-x and MMFPh at high stringency, we identified compartmentalization of phosphorylation motifs likely reflecting localized kinase activity. The filtering of the data assembly improved signal/noise ratio for such motifs. Identified motifs were linked to kinases through (bioinformatic) enrichment analysis. This study also provides insight into the challenges/pitfalls of using large-scale phospho-proteomic data sets to nonexperts.

Identifiers

PMID24894044
PMCPMC4114939

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.