Evidence map›Paper›PMID 41665762›Full record

ReviewPlanta2026

Advances in CRISPR/Cas systems for engineering abiotic stress tolerance in plants: mechanisms and future prospects.

Muhammad Farooq, Asma Khan, Amjad Hassan, Mohammad Maroof Shah

Abstract readReview
PubMed Publisher
In one paragraph

Review in Planta, 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

4 authors.

Muhammad FarooqDepartment of Biotechnology, Abbottabad Campus, COMSATS University Islamabad, University Road, Abbottabad, 22060, Pakistan. 9argus8@gmail.com.ORCID http://orcid.org/0000-0001-9438-4188
Asma KhanDepartment of Biotechnology, Abbottabad Campus, COMSATS University Islamabad, University Road, Abbottabad, 22060, Pakistan.
Amjad HassanDepartment of Biotechnology, Abbottabad Campus, COMSATS University Islamabad, University Road, Abbottabad, 22060, Pakistan.
Mohammad Maroof ShahDepartment of Biotechnology, Abbottabad Campus, COMSATS University Islamabad, University Road, Abbottabad, 22060, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abiotic stress factors such as drought, salinity, extreme temperatures, and oxidative stress significantly limit crop productivity and threaten global food security. Traditional breeding and transgenic approaches have been employed to enhance stress tolerance, but they are often time-consuming and face regulatory hurdles. The advent of CRISPR/Cas genome editing technology has revolutionized plant genetic engineering by enabling precise modifications to stress-responsive genes. This review explores recent advancements in CRISPR/Cas-based genome editing for improving abiotic stress resilience in crops. We discuss the mechanisms of CRISPR/Cas systems, their applications in stress tolerance, and emerging approaches such as multiplex genome editing, base editing, and AI-assisted CRISPR. Furthermore, we highlight challenges, ethical considerations, and future directions for integrating CRISPR into agricultural biotechnology. This review underscores the potential of CRISPR-based strategies in developing climate-resilient crops to ensure sustainable food production in the face of global climate change.

Indexed as

CRISPR-Cas SystemsCrops, AgriculturalGene EditingStress, PhysiologicalClimate ChangeDrought ResistanceGenetic EngineeringPlants, Genetically ModifiedAbiotic stress toleranceBase editingClimate-resilient cropsCRISPR/CasMultiplex genome editingSynthetic biology

Identifiers

PMID41665762

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.