Evidence map›Paper›PMID 41718086›Full record

ArticleProteomes2026

Cross-Species Analysis of ABA-Induced Phosphosignaling Landscapes in Rice, Soybean, and Arabidopsis.

Hinano Takase, Sotaro Katagiri, Takuma Ide, Aina Nagano, Haruki Sakurai, Hana Kokubo, Taiki Yanagisawa, Masanori Okamoto, Taishi Umezawa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proteomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Hinano TakaseGraduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.ORCID 0009-0004-6428-3668
Sotaro KatagiriRIKEN Center for Sustainable Resource Science (CSRS), Yokohama 230-0045, Japan.ORCID 0000-0001-5741-4835
Takuma IdeGraduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
Aina NaganoGraduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.
Haruki SakuraiGraduate School of Advanced Interdisciplinary Science, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan.
Hana KokuboFaculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan.
Taiki YanagisawaFaculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan.
Masanori OkamotoRIKEN Center for Sustainable Resource Science (CSRS), Yokohama 230-0045, Japan.ORCID 0000-0002-1743-0408
Taishi UmezawaGraduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan.ORCID 0000-0003-3750-0503

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAbscisic acid (ABA) is a key phytohormone that regulates plant growth and stress responses through protein phosphorylation. While ABA-induced phosphosignaling has been extensively studied in

methodsHere, we performed phosphoproteomic analysis using LC-MS/MS in Arabidopsis, rice (

resultsABA treatments on Arabidopsis, rice, and soybean seedlings yielded approximately 24,604, 18,865, and 24,930 phosphopeptides, respectively. Comparative analyses revealed both conserved and species-specific ABA-responsive phosphoproteins.

conclusionsThis work provides insights into the evolutionary diversification of ABA signaling and its potential applications in improving crop stress tolerance.

Indexed as

abscisic acidArabidopsisphosphoproteomeprotein kinasericesoybean

Identifiers

PMID41718086
PMCPMC12922155

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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.