Evidence map›Paper›PMID 32138663›Full record

ArticleBMC plant biology2020

Identification of tissue-specific and cold-responsive lncRNAs in Medicago truncatula by high-throughput RNA sequencing.

Mingui Zhao, Tianzuo Wang, Tianyang Sun, Xiaoxi Yu, Rui Tian, Wen-Hao Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC plant biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
5.9field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 56 citations in OpenAlex.

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  11. Exploring the Regulatory Dynamics ofInternational journal of molecular sciences · 2024
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  19. Non-Coding RNAs in Response to Drought Stress.International journal of molecular sciences · 2021
    Review
  20. 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

6 authors at 1 institution in 1 country.

Mingui ZhaoState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China.
Tianzuo WangState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China.
Tianyang SunState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China.
Xiaoxi YuState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China.
Rui TianState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China.
Wen-Hao ZhangState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, People's Republic of China. whzhang@ibcas.ac.cn.ORCID http://orcid.org/0000-0003-2708-2221
Chinese Academy of Sciences · CN

Funding

Chinese Academy of Sciences KFJ-STS-ZDTP-056Natural Science Foundation of China 31671270
6 · The paper itself

Abstract

backgroundLong non-coding RNAs (lncRNAs) play important roles in the regulation of plant responses to environmental stress by acting as essential regulators of gene expression. However, whether and how lncRNAs are involved in cold acclimation-dependent freezing tolerance in plants remains largely unknown. Medicago truncatula is a prominent model for studies of legume genomics, and distinguished by its cold-acclimation characteristics. To determine the roles of lncRNAs in plant cold stress response, we conducted genome-wide high-throughput sequencing in the legume model plant M. truncatula.

resultsRNA-seq data were generated from twelve samples for the four treatments, i.e., non-cold treated leaves and roots, cold-treated leaves and roots of M. truncatula Jemalong A17 seedlings. A total of 1204 million raw reads were generated. Of them, 1150 million filtered reads after quality control (QC) were subjected to downstream analysis. A large number of 24,368 unique lncRNAs were identified from the twelve samples. Among these lncRNAs, 983 and 1288 were responsive to cold treatment in the leaves and roots, respectively. We further found that the intronic-lncRNAs were most sensitive to the cold treatment. The cold-responsive lncRNAs were unevenly distributed across the eight chromosomes in M. truncatula seedlings with obvious preferences for locations. Further analyses revealed that the cold-responsive lncRNAs differed between leaves and roots. The putative target genes of the lncRNAs were predicted to mainly involve the processes of protein translation, transport, metabolism and nucleic acid transcription. Furthermore, the networks of a tandem array of CBF/DREB1 genes that were reported to be located in a major freezing tolerance QTL region on chromosome 6 and their related lncRNAs were dissected based on their gene expression and chromosome location.

conclusionsWe identified a comprehensive set of lncRNAs that were responsive to cold treatment in M. truncatula seedlings, and discovered tissue-specific cold-responsive lncRNAs in leaves and roots. We further dissected potential regulatory networks of CBF Intergenic RNA (MtCIR1) and MtCBFs that play critical roles in response and adaptation of M. truncatula to cold stress.

Indexed as

AcclimatizationCold TemperatureGene Expression ProfilingHigh-Throughput Nucleotide SequencingMedicago truncatulaRNA, Long NoncodingRNA, PlantRNA, Long NoncodingRNA, PlantCBFsCold stressLong non-coding RNAsMedicago truncatulaMtCIR1

Identifiers

PMID32138663
PMCPMC7059299
OpenAlexW3010943742

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.