Evidence map›Paper›PMID 40041019›Full record

ArticleFrontiers in plant science2025

Dynamic changes in the transcriptome of tropical region-originated king grasses in response to cold stress.

Xianjun Lai, Junfeng Yan, Zihan Chen, Yizheng Zhang, Fan Luo, Guangze Cai, Lang Yan

Abstract read
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Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xianjun LaiPanxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agriculture Science, Xichang University, Liangshan, China.
Junfeng YanChengdu Ke'an Technology Co., Ltd., Chengdu, China.
Zihan ChenPanxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agriculture Science, Xichang University, Liangshan, China.
Yizheng ZhangSichuan Key Laboratory of Molecular Biology and Biotechnology, College of Life Sciences, Sichuan University, Chengdu, China.
Fan LuoPanxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agriculture Science, Xichang University, Liangshan, China.
Guangze CaiPanxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agriculture Science, Xichang University, Liangshan, China.
Lang YanPanxi Crops Research and Utilization Key Laboratory of Sichuan Province, College of Agriculture Science, Xichang University, Liangshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cold acclimatization in tropical region-originated plants involves complex gene expression reprogramming to adapt to fluctuating temperatures. However, the molecular mechanisms and gene networks regulating cold tolerance in king grass remain largely unknown. Methods: To address this, we established a full-length reference transcriptome of king grass to enhance assembly quality and performed multiple time-point transcriptomic analyses following cold treatment at 4°C. Differentially expressed genes (DEGs) and transcription factors (TFs) involved in cold stress response were identified and analyzed through clustering and co-expression network analysis. Results: A total of 13,056 DEGs were identified and classified into nine clusters via k-means analysis. The cold response exhibited three distinct phases: early (before 3 h), middle (6-24 h), and late (48-72 h). Early-responsive genes were enriched in glycolipid metabolism and photosynthesis, middle-stage genes in carbohydrate metabolism, and late-stage genes in cold stress, osmotic stress, and endogenous stimuli responses. Key regulators of the ICE-CBF-COR signaling module, including 13 positive and negative regulators, were identified. The co-expression network further revealed mutual regulatory interactions within this module, highlighting its role in cold stress adaptation. Discussion: Our findings provide insights into the cold tolerance mechanisms of king grass, offering a genetic basis for modifying cold stress regulators. This research contributes to the broader understanding of low-temperature adaptive mechanisms in tropical plants and supports future breeding strategies for improved cold tolerance.

Indexed as

cold treatmentdifferential gene expressiongene regulatory networkking grasstime-course RNA-seq

Identifiers

PMID40041019
PMCPMC11876387

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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.