Profiling the metabolome changes caused by cranberry procyanidins in plasma of female rats using (1) H NMR and UHPLC-Q-Orbitrap-HRMS global metabolomics approaches

Abstract

Scope: The objective was to investigate the metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) using (1) H NMR and UHPLC-Q-Orbitrap-HRMS metabolomics approaches, and to identify the contributing metabolites.

Methods and results: Twenty-four female Sprague-Dawley rats were randomly separated into two groups and administered PPCP or partially purified apple procyanidins (PPAP) for three times using a 250 mg extracts/kg body weight dose. Plasma was collected 6 h after the last gavage and analyzed using (1) H NMR and UHPLC-Q-Orbitrap-HRMS. No metabolome difference was observed using (1) H NMR metabolomics approach. However, LC-HRMS metabolomics data show that metabolome in the plasma of female rats administered PPCP differed from those gavaged with PPAP. Eleven metabolites were tentatively identified from a total of 36 discriminant metabolic features based on accurate masses and/or product ion spectra. PPCP caused a greater increase of exogenous metabolites including p-hydroxybenzoic acid, phenol, phenol-sulphate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, and 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide in rat plasma. Furthermore, the plasma level of O-methyl-(-)-epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(hydroxyphenyl)-ϒ-valerolactone-O-sulphate, 4-hydroxydiphenylamine, and peonidin-3-O-hexose were higher in female rats administered with PPAP.

Conclusion: The metabolome changes caused by cranberry procyanidins were revealed using an UHPLC-Q-Orbitrap-HRMS global metabolomics approach. Exogenous and microbial metabolites were the major identified discriminate biomarkers.

Keywords: 1H NMR; Cranberries; Metabolomics; Procyanidins; UHPLC-Q-Orbitrap-HRMS.

Conflict of interest statement

The authors have declared no conflict of interest.

© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Figures

Figure 1

The PLS-DA (A), OPLS-DA (B) score plots, PLS-DA (C) and OPLS-DA (D) cross-validated score plots derived from 1H NMR metabolomics. Triangle: rat plasma after administering PPCP. Filled box: rat plasma after administering PPAP. Each triangle or filled box represents an individual rat.

Figure 2

The PLS-DA (A), OPLS-DA (B) score plots, PLS-DA (C) and OPLS-DA (D) cross-validated score plots derived from LC-HRMS metabolomics. Triangle: rat plasma after administering PPCP. Filled box: rat plasma after administering PPAP. Each triangle or filled box represents an individual rat.

Figure 3

S-plots associated with the OPLS-DA score plot of data derived from LC-HRMS of rat plasma after administering PPCP or PPAP. p[1] is the loading vector of covariance in the first principal component. p(corr)[1] is loading vector of correlation in the first principal component. Variables with |p| ≥0.05 and |p(corr)| ≥0.5 are considered statistically significant. Significant variables marked in circles were identified and numbered in Table 2. Unidentified significant variables marked in triangles were listed in Table S2. Non-significant variables were marked in squares.