Evidence map›Paper›PMID 31726968›Full record

ArticleBMC genomics2019

A transcriptomic profile of topping responsive non-coding RNAs in tobacco roots (Nicotiana tabacum).

Xi Chen, Shuo Sun, Fangjie Liu, Enhui Shen, Lu Liu, Chuyu Ye, Bingguang Xiao, Michael P Timko, Qian-Hao Zhu, Longjiang Fan and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
5.2field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 35 citations in OpenAlex.

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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

11 authors at 6 institutions in 3 countries.

Xi ChenInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Shuo SunInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Fangjie LiuInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Enhui ShenInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Lu LiuInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Chuyu YeInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China.
Bingguang XiaoKey Laboratory of Tobacco Biotechnological Breeding, Yunnan Academy of Tobacco Agricultural Sciences, Kunming, 650021, China.
Michael P TimkoDepartment of Biology, University of Virginia, Charlottesville, VA, 22904, USA.
Qian-Hao ZhuCSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT, 2601, Australia.
Longjiang FanInstitute of Crop Science, Zhejiang University, Hangzhou, 310058, China. fanlj@zju.edu.cn.
Peijian CaoZhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China. peijiancao@163.com.ORCID http://orcid.org/0000-0001-9991-423X
Zhejiang University · CNCommonwealth Scientific and Industrial Research Organisation · AUTobacco Research Institute · CNUniversity of Virginia · USYunnan Academy of Agricultural Sciences · CNZhejiang University of Technology · CN

Funding

111 project of China B17039Fundamental Research Funds for the Central Universities of China 2017QNA6013
6 · The paper itself

Abstract

backgroundNon-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs) and circular RNAs (circRNAs), accomplish remarkable variety of biological functions. However, the composition of ncRNAs and their interactions with coding RNAs in modulating and controlling of cellular process in plants is largely unknown. Using a diverse group of high-throughput sequencing strategies, the mRNA, miRNA, lncRNA and circRNA compositions of tobacco (Nicotiana tabacum) roots determined and their alteration and potential biological functions in response to topping treatment analyzed.

resultsA total of 688 miRNAs, 7423 non-redundant lncRNAs and 12,414 circRNAs were identified, among which, some selected differentially expressed RNAs were verified by quantitative real-time PCR. Using the differentially expressed RNAs, a co-expression network was established that included all four types of RNAs. The number of circRNAs identified were higher than that of miRNAs and lncRNAs, but only two circRNAs were present in the co-expression network. LncRNAs appear to be the most active ncRNAs based on their numbers presented in the co-expression network, but none of them seems to be an eTM (endogenous Target Mimicry) of miRNAs. Integrated with analyses of sequence interaction, several mRNA-circRNA-miRNA interaction networks with a potential role in the regulation of nicotine biosynthesis were uncovered, including a QS-circQS-miR6024 interaction network. In this network miR6024 was significantly down-regulated, while the expression levels of its two targets, circQS and its host gene QS, were sharply increased following the topping treatment.

conclusionsThese results illustrated the transcriptomic profiles of tobacco roots, the organ responsible for nicotine biosynthesis. mRNAs always play the most important roles, while ncRNAs are also expressed extensively for topping treatment response, especially circRNAs are the most activated in the ncRNA pool. These studies also provided insights on the coordinated regulation module of coding and non-coding RNAs in a single plant biological sample. The findings reported here indicate that ncRNAs appear to form interaction complex for the regulation of stress response forming regulation networks with transcripts involved in nicotine biosynthesis in tobacco.

Indexed as

Gene Expression ProfilingTranscriptomeBiosynthetic PathwaysComputational BiologyGene OntologyGene Regulatory NetworksHigh-Throughput Nucleotide SequencingNicotianaNicotinePlant RootsRNA, Long NoncodingRNA, PlantNicotineRNA, Long NoncodingRNA, PlantCircular RNACo-expression networkLong non-coding RNAmicroRNANicotiana tabacumToppingTranscriptomic profile

Identifiers

PMID31726968
PMCPMC6854694
OpenAlexW2985264173

What OpenQuestion holds

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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.