Evidence map›Paper›PMID 32040759›Full record

ArticlePlant molecular biology2020

Transcriptome analysis of sugar beet (Beta vulgaris L.) in response to alkaline stress.

Chunlei Zou, Dan Liu, Peiran Wu, Yubo Wang, Zhijia Gai, Lei Liu, Fangfang Yang, Caifeng Li, Guanghao Guo

Abstract read
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In one paragraph

Article in Plant molecular biology, 2020. 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
10.4field-weighted citation impact, top 2% 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, 40 citations in OpenAlex.

  1. Advances in Beet (Plants (Basel, Switzerland) · 2025
    Review
  2. Article
  3. Review
  4. Article
  5. Expression and Functional Identification ofInternational journal of molecular sciences · 2024
    Article
  6. Article
  7. Review
  8. Time-Course Transcriptome Analysis ofInternational journal of molecular sciences · 2023
    Article
  9. Article
  10. Article
  11. Review
  12. Identification of Potential Pathways ofPlants (Basel, Switzerland) · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. 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

9 authors at 2 institutions in 1 country.

Chunlei ZouCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Dan LiuCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Peiran WuCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Yubo WangCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Zhijia GaiJiamusi Branch, Heilongjiang Academy of Agricultural Sciences, Jiamusi, China.
Lei LiuCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Fangfang YangCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Caifeng LiCollege of Agronomy, Northeast Agricultural University, Harbin, China. licaifeng@neau.edu.cn.
Guanghao GuoCollege of Agronomy, Northeast Agricultural University, Harbin, China.
Northeast Agricultural University · CNHeilongjiang Academy of Agricultural Machinery Engineering · CN

Funding

National Natural Science Foundation of China 31671622Technical System of Sugar Industry of China CARS-170201
6 · The paper itself

Abstract

key messageRNA-seq was used to analyze the transcriptional changes in sugar beet (Beta vulgaris L.) triggered by alkaline solution to elucidate the molecular mechanism underlying alkaline tolerance in sugar beet. Several differentially expressed genes related to stress tolerance were identified. Our results provide a valuable resource for the breeding of new germplasms with high alkaline tolerance. Alkalinity is a highly stressful environmental factor that limits plant growth and production. Sugar beet own the ability to acclimate to various abiotic stresses, especially salt and alkaline stress. Although substantial previous studies on response of sugar beet to saline stress has been conducted, the expressions of alkali-responsive genes in sugar beet have not been comprehensively investigated. In this study, we conducted transcriptome analysis of leaves in sugar beet seedlings treated with alkaline solutions for 0 day (control, C), 3 days (short-term alkaline treatment, ST) and 7 days (long-term alkaline treatment, LT). The clean reads were obtained and assembled into 25,507 unigenes. Among them, 975 and 383 differentially expressed genes (DEGs) were identified in the comparison groups ST_vs_C and LT_vs_C, respectively. Gene ontology (GO) analysis revealed that oxidation-reduction process and lipid metabolic process were the most enriched GO term among the DEGs in ST_vs_C and LT_vs_C, respectively. According to Kyoto Encyclopedia of Genes and Genomes pathway, carbon fixation in photosynthetic organisms pathway were significantly enriched under alkaline stress. Besides, expression level of genes encoding D-3-phosphoglycerate dehydrogenase 1, glutamyl-tRNA reductase 1, fatty acid hydroperoxide lyase, ethylene-insensitive protein 2, metal tolerance protein 11 and magnesium-chelatase subunit ChlI, etc., were significantly altered under alkaline stress. Additionally, among the DEGs, 136 were non-annotated genes and 24 occurred with differential alternative splicing. Our results provide a valuable resource on alkali-responsive genes and should benefit the improvement of alkaline stress tolerance in sugar beet.

Indexed as

Gene Expression Regulation, PlantAcclimatizationAlkaliesBeta vulgarisCarbonGene Expression ProfilingGene OntologyGenes, PlantPlant LeavesSeedlingsSequence Analysis, RNASodium ChlorideStress, PhysiologicalTranscriptomeAlkaliesCarbonSodium ChlorideAlkaline stressAlternative splicingBeta vulgaris L.Non-annotated genesOxidation–reductionTranscriptome

Identifiers

PMID32040759
OpenAlexW3006067635

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Registered trials

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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.