Evidence map›Paper›PMID 34979912›Full record

ArticleBMC plant biology2022

Novel insights into water-deficit-responsive mRNAs and lncRNAs during fiber development in Gossypium hirsutum.

Nan Wu, Jun Yang, Guoning Wang, Huifeng Ke, Yan Zhang, Zhengwen Liu, Zhiying Ma, Xingfen Wang

Open access · goldAbstract read
In one paragraph

Article in BMC plant biology, 2022. 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
2.1field-weighted citation impact, top 13% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Dual functionality ofFrontiers in plant science · 2022
    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

8 authors at 1 institution in 1 country.

Nan Wu *State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Jun Yang *State Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Guoning WangState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Huifeng KeState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Yan ZhangState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Zhengwen LiuState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China.
Zhiying MaState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China. mzhy@hebau.edu.cn.
Xingfen WangState Key Laboratory of North China Crop Improvement and Regulation, North China Key Laboratory for Crop Germplasm Resources of Education Ministry, Hebei Agricultural University, Baoding, 071001, China. cotton@hebau.edu.cn.
Hebei Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe fiber yield and quality of cotton are greatly and periodically affected by water deficit. However, the molecular mechanism of the water deficit response in cotton fiber cells has not been fully elucidated.

resultsIn this study, water deficit caused a significant reduction in fiber length, strength, and elongation rate but a dramatic increase in micronaire value. To explore genome-wide transcriptional changes, fibers from cotton plants subjected to water deficit (WD) and normal irrigation (NI) during fiber development were analyzed by transcriptome sequencing. Analysis showed that 3427 mRNAs and 1021 long noncoding RNAs (lncRNAs) from fibers were differentially expressed between WD and NI plants. The maximum number of differentially expressed genes (DEGs) and lncRNAs (DERs) was identified in fibers at the secondary cell wall biosynthesis stage, suggesting that this is a critical period in response to water deficit. Twelve genes in cotton fiber were differentially and persistently expressed at ≥ five time points, suggesting that these genes are involved in both fiber development and the water-deficit response and could potentially be used in breeding to improve cotton resistance to drought stress. A total of 540 DEGs were predicted to be potentially regulated by DERs by analysis of coexpression and genomic colocation, accounting for approximately 15.76% of all DEGs. Four DERs, potentially acting as target mimics for microRNAs (miRNAs), indirectly regulated their corresponding DEGs in response to water deficit.

conclusionsThis work provides a comprehensive transcriptome analysis of fiber cells and a set of protein-coding genes and lncRNAs implicated in the cotton response to water deficit, significantly affecting fiber quality during the fiber development stage.

Indexed as

Cotton FiberGossypiumRNA, Long NoncodingRNA, MessengerRNA, PlantWaterRNA, Long NoncodingRNA, MessengerRNA, PlantWaterFiberGossypium hirsutumLong noncoding RNA (lncRNA)mRNAWater deficit

Identifiers

PMID34979912
PMCPMC8722198
OpenAlexW4206015822

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.