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Dietary Modulation of Gut Microbiota Contributes to Alleviation of Both Genetic and Simple Obesity in Children.

Zhang C, Yin A, Li H, Wang R, Wu G, Shen J, Zhang M, Wang L, Hou Y, Ouyang H, Zhang Y, Zheng Y, Wang J, Lv X, Wang Y, Zhang F, Zeng B, Li W, Yan F, Zhao Y, Pang X, Zhang X, Fu H, Chen F, Zhao N, Hamaker BR, Bridgewater LC, Weinkove D, Clement K, Dore J, Holmes E, Xiao H, Zhao G, Yang S, Bork P, Nicholson JK, Wei H, Tang H, Zhang X, Zhao L - EBioMedicine (2015)

Bottom Line: NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations.Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut.A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

ABSTRACT

Unlabelled: Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader-Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations. Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut. When transplanted into germ-free mice, the pre-intervention gut microbiota induced higher inflammation and larger adipocytes compared with the post-intervention microbiota from the same volunteer. Our multi-omics-based systems analysis indicates a significant etiological contribution of dysbiotic gut microbiota to both genetic and simple obesity in children, implicating a potentially effective target for alleviation.

Research in context: Poorly managed diet and genetic mutations are the two primary driving forces behind the devastating epidemic of obesity-related diseases. Lack of understanding of the molecular chain of causation between the driving forces and the disease endpoints retards progress in prevention and treatment of the diseases. We found that children genetically obese with Prader-Willi syndrome shared a similar dysbiosis in their gut microbiota with those having diet-related obesity. A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

No MeSH data available.


Related in: MedlinePlus

Altered profiles of urinary metabolites during the dietary intervention. (a) PCA score plot of urinary metabolite profiles obtained from SO and PWS groups during the dietary intervention (left) and the metabolic trajectories generated from the PCA score plot. (b) Validated OPLS-DA coefficient plots showing the alterations of metabolic profiles in the urine caused by the 30-day intervention. The plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of SO (top) groups (n = 17, r > 0.468, P < 0.05). Plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of the PWS groups (n = 17, r > 0.468, P < 0.05). See Table S14 for the metabolite identification key. (c) Heat map showing significantly changed metabolites after the 30-day intervention in the SO cohort. (d) Heat map showing the significantly changed metabolites after 30-, 60- and 90-day interventions in the PWS cohort. The significance of each statistical comparison is shown in Fig. S31.
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f0025: Altered profiles of urinary metabolites during the dietary intervention. (a) PCA score plot of urinary metabolite profiles obtained from SO and PWS groups during the dietary intervention (left) and the metabolic trajectories generated from the PCA score plot. (b) Validated OPLS-DA coefficient plots showing the alterations of metabolic profiles in the urine caused by the 30-day intervention. The plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of SO (top) groups (n = 17, r > 0.468, P < 0.05). Plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of the PWS groups (n = 17, r > 0.468, P < 0.05). See Table S14 for the metabolite identification key. (c) Heat map showing significantly changed metabolites after the 30-day intervention in the SO cohort. (d) Heat map showing the significantly changed metabolites after 30-, 60- and 90-day interventions in the PWS cohort. The significance of each statistical comparison is shown in Fig. S31.

Mentions: We used an NMR-based metabonomics approach in the current study to obtain unbiased profiles of urine metabolites before and after the intervention to identify those that were significantly changed by the intervention. Score plots of PCA and OPLS-DA of NMR-based metabolite profiling data of urine samples collected from the SO (Days 0 and 30) and the PWS cohorts (Day 0, 30, 60 and 90) showed significant metabolic shifts after the intervention in both cohorts (Fig. 5a). Among all the metabolites detected by NMR, only thirteen varied significantly between pre- and post-intervention samples in SO and in PWS cohorts, using OPLS-DA (Figs. 5b–d, S29–S31 and Table S14). Interestingly, 4 of the 13 significantly changed metabolites are co-metabolites between gut bacteria and host, which were all decreased after the intervention. They are trimethylamine N-oxide (TMAO), indoxyl sulfate, phenylacetylglutamine (PAG) and hippurate. TMAO is generated when TMA enters the bloodstream and is metabolized by the human liver (Wang et al., 2011). The precursors of the other three metabolites are produced by bacterial fermentation of aromatic amino acids in the gut, e.g. indole is produced by bacterial fermentation of tryptophan (Russell et al., 2011). TMAO, an independent marker for predicting clinical vascular events, has been mechanistically linked with the development of atherosclerosis in humans and mice (Wang et al., 2011, Wang et al., 2014). Likewise, indoxyl sulfate has been linked with hypertension and cardio-vascular disease in chronic kidney disease patients (Barreto et al., 2009). Importantly, the decrease in urine TMAO after the intervention was accompanied by a corresponding increase in urinary dimethylglycine (DMG) (Fig. 5c and d), an intermediate in human metabolism of dietary choline. This suggests that proportionally more of the fat-derived choline in the interventional diet was absorbed and metabolized by the human host instead of undergoing fermentation to TMA by bacteria in the gut (Dumas et al., 2006).


Dietary Modulation of Gut Microbiota Contributes to Alleviation of Both Genetic and Simple Obesity in Children.

Zhang C, Yin A, Li H, Wang R, Wu G, Shen J, Zhang M, Wang L, Hou Y, Ouyang H, Zhang Y, Zheng Y, Wang J, Lv X, Wang Y, Zhang F, Zeng B, Li W, Yan F, Zhao Y, Pang X, Zhang X, Fu H, Chen F, Zhao N, Hamaker BR, Bridgewater LC, Weinkove D, Clement K, Dore J, Holmes E, Xiao H, Zhao G, Yang S, Bork P, Nicholson JK, Wei H, Tang H, Zhang X, Zhao L - EBioMedicine (2015)

Altered profiles of urinary metabolites during the dietary intervention. (a) PCA score plot of urinary metabolite profiles obtained from SO and PWS groups during the dietary intervention (left) and the metabolic trajectories generated from the PCA score plot. (b) Validated OPLS-DA coefficient plots showing the alterations of metabolic profiles in the urine caused by the 30-day intervention. The plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of SO (top) groups (n = 17, r > 0.468, P < 0.05). Plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of the PWS groups (n = 17, r > 0.468, P < 0.05). See Table S14 for the metabolite identification key. (c) Heat map showing significantly changed metabolites after the 30-day intervention in the SO cohort. (d) Heat map showing the significantly changed metabolites after 30-, 60- and 90-day interventions in the PWS cohort. The significance of each statistical comparison is shown in Fig. S31.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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f0025: Altered profiles of urinary metabolites during the dietary intervention. (a) PCA score plot of urinary metabolite profiles obtained from SO and PWS groups during the dietary intervention (left) and the metabolic trajectories generated from the PCA score plot. (b) Validated OPLS-DA coefficient plots showing the alterations of metabolic profiles in the urine caused by the 30-day intervention. The plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of SO (top) groups (n = 17, r > 0.468, P < 0.05). Plot related to the discrimination between 1H NMR spectra of urine from Day 0 and Day 30 of the PWS groups (n = 17, r > 0.468, P < 0.05). See Table S14 for the metabolite identification key. (c) Heat map showing significantly changed metabolites after the 30-day intervention in the SO cohort. (d) Heat map showing the significantly changed metabolites after 30-, 60- and 90-day interventions in the PWS cohort. The significance of each statistical comparison is shown in Fig. S31.
Mentions: We used an NMR-based metabonomics approach in the current study to obtain unbiased profiles of urine metabolites before and after the intervention to identify those that were significantly changed by the intervention. Score plots of PCA and OPLS-DA of NMR-based metabolite profiling data of urine samples collected from the SO (Days 0 and 30) and the PWS cohorts (Day 0, 30, 60 and 90) showed significant metabolic shifts after the intervention in both cohorts (Fig. 5a). Among all the metabolites detected by NMR, only thirteen varied significantly between pre- and post-intervention samples in SO and in PWS cohorts, using OPLS-DA (Figs. 5b–d, S29–S31 and Table S14). Interestingly, 4 of the 13 significantly changed metabolites are co-metabolites between gut bacteria and host, which were all decreased after the intervention. They are trimethylamine N-oxide (TMAO), indoxyl sulfate, phenylacetylglutamine (PAG) and hippurate. TMAO is generated when TMA enters the bloodstream and is metabolized by the human liver (Wang et al., 2011). The precursors of the other three metabolites are produced by bacterial fermentation of aromatic amino acids in the gut, e.g. indole is produced by bacterial fermentation of tryptophan (Russell et al., 2011). TMAO, an independent marker for predicting clinical vascular events, has been mechanistically linked with the development of atherosclerosis in humans and mice (Wang et al., 2011, Wang et al., 2014). Likewise, indoxyl sulfate has been linked with hypertension and cardio-vascular disease in chronic kidney disease patients (Barreto et al., 2009). Importantly, the decrease in urine TMAO after the intervention was accompanied by a corresponding increase in urinary dimethylglycine (DMG) (Fig. 5c and d), an intermediate in human metabolism of dietary choline. This suggests that proportionally more of the fat-derived choline in the interventional diet was absorbed and metabolized by the human host instead of undergoing fermentation to TMA by bacteria in the gut (Dumas et al., 2006).

Bottom Line: NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations.Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut.A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

ABSTRACT

Unlabelled: Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader-Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations. Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut. When transplanted into germ-free mice, the pre-intervention gut microbiota induced higher inflammation and larger adipocytes compared with the post-intervention microbiota from the same volunteer. Our multi-omics-based systems analysis indicates a significant etiological contribution of dysbiotic gut microbiota to both genetic and simple obesity in children, implicating a potentially effective target for alleviation.

Research in context: Poorly managed diet and genetic mutations are the two primary driving forces behind the devastating epidemic of obesity-related diseases. Lack of understanding of the molecular chain of causation between the driving forces and the disease endpoints retards progress in prevention and treatment of the diseases. We found that children genetically obese with Prader-Willi syndrome shared a similar dysbiosis in their gut microbiota with those having diet-related obesity. A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

No MeSH data available.


Related in: MedlinePlus