Evidence map›Paper›PMID 39673776›Full record

ArticleJournal of experimental botany2025

Quantitative detection of the maize phytocytokine Zip1 utilizing ELISA.

Maurice Koenig, Zarah Sorger, Shania Pin Yin Keh, Gunther Doehlemann, Johana C Misas Villamil

Abstract read
In one paragraph

Article in Journal of experimental botany, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

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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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Processing and release of endogenous immunogenic peptide signals.The Plant journal : for cell and molecular biology · 2026
    Review
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

5 authors.

Maurice KoenigInstitute for Plant Sciences, University of Cologne, Cologne, Germany.
Zarah SorgerInstitute for Plant Sciences, University of Cologne, Cologne, Germany.
Shania Pin Yin KehInstitute for Plant Sciences, University of Cologne, Cologne, Germany.ORCID 0009-0007-0964-8611
Gunther DoehlemannInstitute for Plant Sciences, University of Cologne, Cologne, Germany.ORCID 0000-0002-7353-8456
Johana C Misas VillamilInstitute for Plant Sciences, University of Cologne, Cologne, Germany.ORCID 0000-0002-9623-0710

Funding

Deutsche Forschungsgemeinschaft
6 · The paper itself

Abstract

Plant signaling peptides, also known as phytocytokines, play a crucial role in cell-to-cell communication during plant development and immunity. The detection of small peptides in plant tissues is challenging and often relies on time-consuming and cost-intensive approaches. Here, we present an ELISA-based assay as a rapid and cost-effective method for the detection of naturally released peptides in plant tissues. Our ELISA-based method was developed to detect Zip1, a 17-amino-acid phytocytokine derived from Zea mays that elicits salicylic acid signaling in maize leaves. Using a custom peptide-antibody, we designed an experimental pipeline to achieve peptide specificity, selectivity, and sensitivity allowing the detection of the Zip1 peptide in complex biological samples. As a proof of concept, we first overexpressed the precursor molecule PROZIP1 in Nicotiana benthamiana and in transfected maize protoplasts and monitored the release of Zip1-containing peptides. In a second approach we treated maize leaves with salicylic acid to induce native PROZIP1 expression and processing. Using ELISA, we were able to quantify native Zip1 signals with a detection limit in the nanogram range, which allowed us to detect different Zip1-containing peptides in plant material. This method can be adapted for the detection and quantification of a variety of plant signaling peptides.

Indexed as

Enzyme-Linked Immunosorbent AssayPlant ProteinsZea maysNicotianaPlant LeavesSalicylic AcidPlant ProteinsSalicylic AcidELISApeptide detectionphytocytokinesplant immunityplant signaling peptidesZip1

Identifiers

PMID39673776
PMCPMC11714748

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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.