Evidence map›Paper›PMID 42359401›Full record

ReviewFrontiers in plant science2026

Cis-regulatory elements in CAMTA-mediated stress signalling: mechanisms and prospects for CRISPR-based crop improvement.

Hena Gain, Joydeep Banerjee

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 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. 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

2 authors.

Hena GainAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India.
Joydeep BanerjeeAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhancements in crop resilience strategies that maintain production are essential to address the challenges posed by climate change and increasing food consumption. Calcium-dependent signaling networks are essential for plant responses to abiotic and biotic stressors, with calmodulin-binding transcription activator (CAMTA) transcription factors serving as crucial regulators within this framework, as these factors govern gene expression through specific cis-regulatory elements located in promoter regions. Recent investigations have expanded to include CAMTA-binding motifs as the stress-responsive cis-regulatory modules across several plant species under examination. These findings indicate that CAMTA-associated cis-elements, comprising CGCG motifs and ABA-responsive regions, facilitate the integration of environmental signals that influence transcription. Cis-regulatory elements (CREs), such as promoters, enhancers, silencers, and insulators, control the exact timing and location of stress-responsive gene expression in plants. Recent breakthroughs in genome editing have enabled the direct manipulation of these cis-regulatory areas, facilitating precise control over gene expression. This work presents an overview of CAMTA structures, their interaction with promoter cis-regulatory regions, and the potential for promoter cis-element engineering to enhance agricultural performance under diverse settings. It emphasizes CRISPR-based strategies for precise CRE modifications and highlights the role of CAMTA in identifying stress-responsive regions. This establishes the foundation for the advancement of next-generation stress-resilient crops, which will ensure food security.

Indexed as

calcium signallingCAMTACGCG motifcis-regulatory elementsCRiSPR/Cascrop engineeringgenome editingstress tolerance

Identifiers

PMID42359401
PMCPMC13290587

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Registered trials

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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.