Evidence map›Paper›PMID 33198630›Full record

ArticleBMC plant biology2020

Natural variation of physiological traits, molecular markers, and chlorophyll catabolic genes associated with heat tolerance in perennial ryegrass accessions.

Jing Zhang, Hui Li, Yiwei Jiang, Huibin Li, Zhipeng Zhang, Zhipeng Xu, Bin Xu, Bingru Huang

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 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

13 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Alleviation of Shade Stress in Chinese Yew (Plants (Basel, Switzerland) · 2023
    Article
  9. Review
  10. Article
  11. Article
  12. The Alkali Tolerance of Broomcorn Millet (Frontiers in plant science · 2021
    Article
  13. 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 5 institutions in 2 countries.

Jing ZhangCollege of Agro-grassland Science, Nanjing Agricultural University, Nanjing, 210095, P.R. China.
Hui LiCollege of Agro-grassland Science, Nanjing Agricultural University, Nanjing, 210095, P.R. China.
Yiwei JiangDepartment of Agronomy, Purdue University, West Lafayette, IN, 47907, USA.
Huibin LiCollege of Agronomy, Hebei Agricultural University/State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province, Baoding, 071001, Hebei, China.
Zhipeng ZhangShanghai Biotechnology Corporation, Zhangjiang Hi-tech Park, Shanghai, 201203, P.R. China.
Zhipeng XuCollege of Agro-grassland Science, Nanjing Agricultural University, Nanjing, 210095, P.R. China.
Bin XuCollege of Agro-grassland Science, Nanjing Agricultural University, Nanjing, 210095, P.R. China. binxu@njau.edu.cn.ORCID http://orcid.org/0000-0002-9701-7583
Bingru HuangDepartment of Plant Biology and Pathology, Rutgers, the State University of New Jersey, New Brunswick, NJ, 08901, USA.
Nanjing Agricultural University · CNHebei Agricultural University · CNPurdue University West Lafayette · USRutgers, The State University of New Jersey · USShanghai CASB Biotechnology (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of genetic diversity in heat tolerance and associated traits is of great importance for improving heat tolerance in cool-season grass species. The objectives of this study were to determine genetic variations in heat tolerance associated with phenotypic and physiological traits and to identify molecular markers associated with heat tolerance in a diverse collection of perennial ryegrass (Lolium perenne L.).

resultsPlants of 98 accessions were subjected to heat stress (35/30 °C, day/night) or optimal growth temperature (25/20 °C) for 24 d in growth chambers. Overall heat tolerance of those accessions was ranked by principal component analysis (PCA) based on eight phenotypic and physiological traits. Among these traits, electrolyte leakage (EL), chlorophyll content (Chl), relative water content (RWC) had high correlation coefficients (- 0.858, 0.769, and 0.764, respectively) with the PCA ranking of heat tolerance. We also found expression levels of four Chl catabolic genes (CCGs), including LpNYC1, LpNOL, LpSGR, and LpPPH, were significant higher in heat sensitive ryegrass accessions then heat tolerant ones under heat stress. Furthermore, 66 pairs of simple sequence repeat (SSR) markers were used to perform association analysis based on the PCA result. The population structure of ryegrass can be grouped into three clusters, and accessions in cluster C were relatively more heat tolerant than those in cluster A and B. SSR markers significantly associated with above-mentioned traits were identified (R

conclusionThe result not only identified useful physiological parameters, including EL, Chl content, and RWC, and their associated SSR markers for heat-tolerance breeding of perennial ryegrass, but also highlighted the involvement of Chl catabolism in ryegrass heat tolerance. Such knowledge is of significance for heat-tolerance breeding and heat tolerance mechanisms in perennial ryegrass as well as in other cool-season grass species.

Indexed as

Cellular SenescenceChlorophyllGene Expression Regulation, PlantGenes, PlantGenetic MarkersGenetic VariationHeat-Shock ResponseLoliumPhenotypePlant LeavesThermotoleranceChlorophyllGenetic MarkersGenetic diversityHeat toleranceLeaf senescencePerennial ryegrassSSR

Identifiers

PMID33198630
PMCPMC7667755
OpenAlexW3099820620

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.