Dataset Information

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling.

ABSTRACT: INTRODUCTION:The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products. OBJECTIVE:Create a multivariate model to identify antiplasmodial metabolites from 1H NMR data of two African medicinal plants, Keetia leucantha and K. venosa. METHODS:The extracts of twigs and leaves of Keetia species were measured by 1H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation. RESULTS:Unsupervised 1H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples. CONCLUSION:NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.

SUBMITTER: Freire RT

PROVIDER: S-EPMC6394458 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling.

Freire Rafael Teixeira RT Bero Joanne J Beaufay Claire C Selegato Denise Medeiros DM Coqueiro Aline A Choi Young Hae YH Quetin-Leclercq Joëlle J

Metabolomics : Official journal of the Metabolomic Society 20190221 3

<h4>Introduction</h4>The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products.<h4>Obje ...[more]

PMID: 30830464

Similar Datasets

Project description:BackgroundRectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.MethodsHere, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the 1H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.ResultsExcellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.ConclusionOur findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

Project description:The commercial use of Panax ginseng berries is increasing as P. ginseng berries are known to contain large amounts of ginsenosides, and many pharmacological activities have been reported for the various ginsenosides. For the proper use of P.ginseng berries, it is necessary to study efficient and accurate quality control and the profiling of the overall composition of each cultivar. Ginseng berry samples from seven cultivars (Eumseung, Chung-buk Province, Republic of Korea) were analyzed using ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-QTOF/MS) for profiling of the ginsenosides, and high-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy for profiling of the primary metabolites. Comparing twenty-six ginsenoside profiles between the variant representatives and between the violet-stem variant, Kumpoong and Sunwon were classified. In the case of primary metabolites, the cultivars Kumpoong and Gopoong were classified. As a result of correlation analyses of the primary and secondary metabolites, in the Gopoong cultivar, the metabolism was found to lean toward energy metabolism rather than ginsenoside synthesis, and accumulation of osmolytes was low. The Gopoong cultivar had higher levels of most of the amino acids, such as arginine, phenylalanine, isoleucine, threonine, and valine, and it contained the highest level of choline and the lowest level of myo-inositol. Except for these, there were no significant differences of primary metabolites. In the Kumpoong cultivar, the protopanaxatriol (PPT)-type ginsenosides, ginsenoside Re and ginsenoside Rg2, were much lower than in the other cultivars, while the other PPT-type ginsenosides were inversely found in much higher amounts than in other cultivars. The Sunwon cultivar showed that variations of PPT-type ginsenosides were significantly different between samples. However, the median values of PPT-type ginsenosides of Sunwon showed similar levels to those of Kumpoong. The difference in primary metabolites used for metabolism for survival was found to be small in our results. Our data demonstrated the characteristics of each cultivar using profiling data of the primary and secondary metabolites, especially for Gopoong, Kumpoong, and Sunwon. These profiling data provided important information for further research and commercial use.

Project description:BackgroundAmbiguity in malignant transformation of glioma has made prognostic diagnosis very challenging. Tumor malignant transformation is closely correlated with specific alterations of the metabolic profile. Exploration of the underlying metabolic alterations in glioma cells of different malignant degree is therefore vital to develop metabolic biomarkers for prognosis monitoring.MethodsWe conducted (1)H nuclear magnetic resonance (NMR)-based metabolic analysis on cell lines (CHG5, SHG44, U87, U118, U251) developed from gliomas of different malignant grades (WHO II and WHO IV). Several methods were applied to analyze the (1)H-NMR spectral data of polar extracts of cell lines and to identify characteristic metabolites, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), fuzzy c-means clustering (FCM) analysis and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). The expression analyses of glial fibrillary acidic protein (GFAP) and matrix metal proteinases (MMP-9) were used to assess malignant behaviors of cell lines. GeneGo pathway analysis was used to associate characteristic metabolites with malignant behavior protein markers GFAP and MMP-9.ResultsStable and distinct metabolic profiles of the five cell lines were obtained. The metabolic profiles of the low malignancy grade group (CHG5, SHG44) were clearly distinguished from those of the high malignancy grade group (U87, U118, U251). Seventeen characteristic metabolites were identified that could distinguish the metabolic profiles of the two groups, nine of which were mapped to processes related to GFAP and MMP-9. Furthermore, the results from both quantitative comparison and metabolic correlation analysis indicated that the significantly altered metabolites were primarily involved in perturbation of metabolic pathways of tricarboxylic acid (TCA) cycle anaplerotic flux, amino acid metabolism, anti-oxidant mechanism and choline metabolism, which could be correlated with the changes in the glioma cells' malignant behaviors.ConclusionsOur results reveal the metabolic heterogeneity of glioma cell lines with different degrees of malignancy. The obtained metabolic profiles and characteristic metabolites are closely associated with the malignant features of glioma cells, which may lay the basis for both determining the molecular mechanisms underlying glioma malignant transformation and exploiting non-invasive biomarkers for prognosis monitoring.

Dataset Information

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling.

Publications

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets