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. 2018 Jan 30;8(9):5042-5057.
doi: 10.1039/c7ra12067b. eCollection 2018 Jan 24.

Metabonomic strategy for the detection of metabolic effects of probiotics combined with prebiotic supplementation in weaned rats

Mengxia Wang  1 , Xiaojun Zhang  1 , Yuanyuan Wang  2 , Yuan Li  1 , Yongxiong Chen  1 , Haihui Zheng  3 , Fangli Ma  2 , Chung Wah Ma  2 , Biyu Lu  1 , Zhiyong Xie  3 , Qiongfeng Liao  1
Affiliations

Metabonomic strategy for the detection of metabolic effects of probiotics combined with prebiotic supplementation in weaned rats

Mengxia Wang et al. RSC Adv. .

Abstract

The purpose of this study is to investigate the effects of probiotics combined with prebiotics (PP) supplementation on weaned rat metabolism. A metabonomic strategy employing 1H-NMR spectroscopy and multivariate data analysis was used to examine weaned rat biological responses to PP supplementation. Male Sprague-Dawley rats (post-natal day 21, PD 21) received probiotics (Lactobacillus acidophilus NCFM (L-NCFM) and Bifidobacterium lactis Bi-07 (B-LBi07), 1 : 1, 1.0 × 1011 cfu kg-1) and prebiotics (Lycium barbarum polysaccharides (LBP), Poria cocos polysaccharides (PCPs) and Lentinan, 1 : 1 : 1, 24 g kg-1) via intragastric administration for 28 consecutive days. Urine and feces were collected for analysis. Significant topographical metabolic variations were present in urine and feces. Urinary metabolites upregulated by PP treatment included alanine, N-acetylglycine, glutamine, dimethylamine, phosphorylcholine, ethylene glycol, mannitol, phenylacetylglycine and glycoate, which were related to alanine, aspartate and glutamate metabolism, and choline metabolism. Feces-derived metabolites, including caproate, valerate, butyrate, propionate, lactate, acetate, succinate, methanol, threonine and methionine, were significantly increased, which were related to short-chain fatty acid (SCFA) metabolism and TCA cycle metabolism. These results indicate that dietary PP supplementation can regulate common systemic metabolic processes, including energy metabolism, amino acid metabolism, lipid metabolism, nucleic acid metabolism, and gut microbiota-related metabolism. This study also illuminates the vital role of PP supplementation in regulating the metabolism of weaned rats.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1. Representative 600 MHz 1H-NMR spectra obtained from urine extracts (A) and feces extracts (B) at day 28 post treatment. Metabolite keys are shown in Tables S1 and S2.
Fig. 2
Fig. 2. 1H-NMR-based metabonomic analysis of urine samples. PCA (A) and PLS-DA (B) score plots derived from the 1H-NMR spectra of urine extracts obtained from CON and PP groups, and cross validation (C) by permutation test at day 28. OPLS-DA (D) score plot and coefficient plot (E) derived from the 1H-NMR spectra of urine extracts showing the discrimination between CON and PP groups at day 28. Keys to metabolites assignment are given in Table 1.
Fig. 3
Fig. 3. Relative changes for metabolite concentrations in urine induced by PP supplementation at day 14 (A) and day 28 (B). Solid bars indicate significant changes with red for increase and blue for decrease, whereas hollow bars mean no significant changes. Keys to metabolites assignment are given in Table 1.
Fig. 4
Fig. 4. 1H-NMR-based metabonomic analysis of feces samples. PCA (A) and PLS-DA (B) score plots derived from the 1H-NMR spectra of feces extracts obtained from CON and PP groups, and cross validation (C) by permutation test at day 28. OPLS-DA (D) score plot and coefficient plot (E) derived from the 1H-NMR spectra of feces extracts showing the discrimination between CON and PP groups at day 28. Keys to metabolites assignment are given in Table 2.
Fig. 5
Fig. 5. Relative changes for metabolite concentrations in feces induced by PP supplementation at day 14 (A) and day 28 (B). Solid bars indicate significant changes with red for increase and blue for decrease, whereas hollow bars mean no significant changes. Keys to metabolites assignment are given in Table 2.
Fig. 6
Fig. 6. Correlation of urinary metabolite NMR peak areas (A) and feces metabolite NMR peak areas (B). Red denotes a positive correlation and blue a negative correlation. Network analysis of urinary metabolites (C) and feces metabolites (D) with |r| ≥ 0.9. Nodes colored red for up-regulation, green for down-regulation. Red lines correspond to positive correlations, whereas bluelines correspond to negative correlations.
Fig. 7
Fig. 7. Potential pathways for PP supplementation in urine extracts identified by using MSEA pathway analysis. (A) (a) Alanine, aspartate and glutamate metabolism, (b) valine, leucine and isoleucine biosynthesis, (c) glyoxylate and dicarboxylate metabolism. (B) Alanine, aspartate and glutamate metabolism. (C) Valine, leucine and isoleucine biosynthesis. (D) Glyoxylate and dicarboxylate metabolism.
Fig. 8
Fig. 8. Potential pathways for PP supplementation in feces extracts identified by using MSEA pathway analysis. (A) (a) Glycine, serine and threonine metabolism, (b) pyruvate metabolism, (c) valine, leucine and isoleucine biosynthesis. (B) Glycine, serine and threonine metabolism. (C) Pyruvate metabolism. (D) Valine, leucine and isoleucine biosynthesis.
Fig. 9
Fig. 9. Shotgun sequencing validates the predicted microbial metabolic trends in a small subset of feces samples from the different two groups (CON group and PP group).
Fig. 10
Fig. 10. Metabolic pathways altered by PP supplementation. (↑) Up-regulated; (↓) down-regulated; red colour, feces; blue colour, urine; green, feces and urine.
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