Principal component and hierarchical clustering analysis of metabolites in destructive weeds; polygonaceous plants

被引:0
|
作者
Atsuko Miyagi
Hideyuki Takahashi
Kentaro Takahara
Takayuki Hirabayashi
Yoshiki Nishimura
Takafumi Tezuka
Maki Kawai-Yamada
Hirofumi Uchimiya
机构
[1] The University of Tokyo,Institute of Molecular and Cellular Biosciences
[2] Iwate Biotechnology Research Center,Graduate School of Science
[3] Kyoto University,School of Health and Human Life
[4] Nagoya Bunri University,Department of Environmental Science and Human Engineering
[5] Saitama University,Core Research for Evolutional Science and Technology (CREST)
[6] Japan Science and Technology Agency (JST),undefined
来源
Metabolomics | 2010年 / 6卷
关键词
Polygonaceae; Metabolite profile; Capillary electrophoresis–mass spectrometry; Oxalate; Principal component analysis; Hierarchical clustering analysis;
D O I
暂无
中图分类号
学科分类号
摘要
Comprehensive analysis of metabolites using capillary electrophoresis–mass spectrometry was carried out in harmful weeds belonging to Polygonaceae. A principal component analysis revealed clear distinctions among eight Rumex species and Fallopia japonica. Hierarchical clustering data showed that respective metabolites can be grouped due to species differences. There was a positive relationship between oxalate and citrate, oxalate and ascorbate, and oxalate and glutamine. The amount of oxalate per leaf fresh weight was not affected by increased concentrations of exogenously supplied nutrients from Hoagland’s formulation in one of the most destructive weeds R. obtusifolius. The oxalate accumulation in this plant is independent of external nutrient level, where nutrient-rich environments apparently stimulate internal constituents such as amino acids and other metabolites.
引用
收藏
页码:146 / 155
页数:9
相关论文
共 50 条
  • [1] Principal component and hierarchical clustering analysis of metabolites in destructive weeds; polygonaceous plants
    Miyagi, Atsuko
    Takahashi, Hideyuki
    Takahara, Kentaro
    Hirabayashi, Takayuki
    Nishimura, Yoshiki
    Tezuka, Takafumi
    Kawai-Yamada, Maki
    Uchimiya, Hirofumi
    METABOLOMICS, 2010, 6 (01) : 146 - 155
  • [2] Use of principal component analysis and hierarchical clustering analysis to evaluate fingerprint residues
    Thomas, Robert
    Kuhns, Teresa
    Zentz, Stephanie
    Egolf, Debra
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2018, 255
  • [3] Improving Hierarchical Clustering of Genotypic Data via Principal Component Analysis
    Odong, T. L.
    van Heerwaarden, J.
    van Hintum, T. J. L.
    van Eeuwijk, F. A.
    Jansen, J.
    CROP SCIENCE, 2013, 53 (04) : 1546 - 1554
  • [4] Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis
    Darand, Mohammad
    Daneshvar, Mohammad Reza Mansouri
    ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL, 2014, 1 (04): : 517 - 532
  • [5] Regionalization of Precipitation Regimes in Iran Using Principal Component Analysis and Hierarchical Clustering Analysis
    Mohammad Darand
    Mohammad Reza Mansouri Daneshvar
    Environmental Processes, 2014, 1 (4) : 517 - 532
  • [6] Application of Hierarchical Clustering Based on Principal Component Analysis to Railway Station Classification
    Xu, Chang'an
    Li, Junjie
    Zou, Congcong
    Ni, Shaoquan
    PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON TRANSPORTATION ENGINEERING (ICTE 2019), 2019, : 162 - 171
  • [7] Hierarchical disjoint principal component analysis
    Cavicchia, Carlo
    Vichi, Maurizio
    Zaccaria, Giorgia
    ASTA-ADVANCES IN STATISTICAL ANALYSIS, 2023, 107 (03) : 537 - 574
  • [8] Hierarchical disjoint principal component analysis
    Carlo Cavicchia
    Maurizio Vichi
    Giorgia Zaccaria
    AStA Advances in Statistical Analysis, 2023, 107 : 537 - 574
  • [9] Clustering and disjoint principal component analysis
    Vichi, Maurizio
    Saporta, Gilbert
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2009, 53 (08) : 3194 - 3208
  • [10] XML clustering by principal component analysis
    Liu, JH
    Wang, JTL
    Hsu, W
    Herbert, KG
    ICTAI 2004: 16TH IEEE INTERNATIONALCONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2004, : 658 - 662