Evidence map›Paper›PMID 38625916›Full record

ArticlePloS one2024

Widely targeted metabolomics reveals differences in metabolites of Paeonia lactiflora cultivars.

Yonghui Li, Yingying Tian, Xiaojun Zhou, Xiangmeng Guo, Huiyuan Ya, Shipeng Li, Xiangli Yu, Congying Yuan, Kai Gao

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
7.9field-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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. 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.

Yonghui LiSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.ORCID 0000-0002-5444-8631
Yingying TianSchool of Life Sciences, Shaanxi Normal University, Xi'an, Shanxi, China.
Xiaojun ZhouSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.
Xiangmeng GuoSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.
Huiyuan YaSchool of Food and Drug, Luoyang Normal University, Luoyang, Henan, China.
Shipeng LiSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.
Xiangli YuSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.
Congying YuanSchool of Life Sciences, Luoyang Normal University, Luoyang, Henan, China.
Kai GaoLuoyang Academy of Agricultural and Forestry Sciences, Luoyang, Henan, China.
Luoyang Normal University · CNShaanxi Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPaeonia lactiflora contains diverse active constituents and exhibits various pharmacological activities. However, only partial identification of biologically active substances from P. lactiflora has been achieved using low-throughput techniques. Here, the roots of P. lactiflora, namely, Fenyunu (CK), Dafugui (DFG), and Red Charm (HSML), were studied. The primary and secondary metabolites were investigated using ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESIMS/MS).

methodsThe chemical compounds and categories were detected using broadly targeted UPLC-MS/MS. Principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and hierarchical clustering analysis (HCA) were carried out for metabolites of different varieties of P. lactiflora.

resultsA total of 1237 compounds were detected and classified into 11 categories. HCA, PCA, and OPLS-DA of these metabolites indicated that each variety of P. lactiflora was clearly separated from the other groups. Differential accumulated metabolite analysis revealed that the three P. lactiflora varieties contained 116 differentially activated metabolites (DAMs) involved in flavonoid, flavone, and flavonol metabolism. KEGG pathway analysis revealed that, in 65 pathways, 336 differentially abundant metabolites (DMs) were enriched in the CK and DFG groups; moreover, the type and content of terpenoids were greater in the CK group than in the DFG group. The CK and HSML groups contained 457 DMs enriched in 61 pathways; the type and amount of flavonoids, terpenoids, and tannins were greater in the CK group than in the HSML group. The DFG and HSML groups contained 497 DMs enriched in 65 pathways; terpenoids and alkaloids were more abundant in the HSML variety than in the DFG variety.

conclusionsA total of 1237 compounds were detected, and the results revealed significant differences among the three P. lactiflora varieties. Among the three P. lactiflora varieties, phenolic acids and flavonoids composed the largest and most diverse category of metabolites, and their contents varied greatly. Therefore, CK is suitable for medicinal plant varieties, and DFG and HSML are suitable for ornamental plant varieties. Twelve proanthocyanidin metabolites likely determined the differences in color among the three varieties.

Indexed as

PaeoniaChromatography, High Pressure LiquidChromatography, LiquidFlavonoidsMetabolomicsTandem Mass SpectrometryTerpenesFlavonoidsTerpenes

Identifiers

PMID38625916
PMCPMC11020836
OpenAlexW4394856498

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.