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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

Concordance of structural shifts of gut microbiota and the improvement of the host metabolic health. (a) PCoA based on Bray–Curtis distance of all the 376 bacterial CAGs during the dietary intervention. (b) Clustering of gut microbiota based on distances between different groups calculated with MANOVA test of first 23 PCs (accounting for 80% of total variations) of PCoA based on Bray–Curtis distance of all bacterial CAGs. (c) Genome interaction groups interaction network. Network plot highlights correlation relationships between 18 GIGs of 161 prevalent bacterial CAGs at all time points from the two cohorts. Node size indicates the average abundance of the species/strains. Lines between nodes represent correlations between the nodes they connect, with line width indicating the correlation magnitude, and red and blue colors indicating positive and negative correlations, respectively. For clarity, only lines corresponding to correlations whose magnitude is greater than 0.5 are drawn, and unconnected nodes are omitted. (d) Procrustes analysis combining PCoA of GIGs (end of lines with solid symbols) with PCA of bioclinical variables presented in Fig. 1 (end of lines without solid symbols). For PWS, n = 17 at Day 0, 30, 60, and 90; For SO, n = 21 at Day 0 and n = 20 at Day 30. (e) Group level abundance shifts of GIGs that changed significantly during dietary intervention. Data are mean ± s.e.m. Wilcoxon matched-pairs signed rank test (two-tailed) was used to analyze variation between each two-time points in PWS or SO children. *P < 0.05, **P < 0.01.
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f0015: Concordance of structural shifts of gut microbiota and the improvement of the host metabolic health. (a) PCoA based on Bray–Curtis distance of all the 376 bacterial CAGs during the dietary intervention. (b) Clustering of gut microbiota based on distances between different groups calculated with MANOVA test of first 23 PCs (accounting for 80% of total variations) of PCoA based on Bray–Curtis distance of all bacterial CAGs. (c) Genome interaction groups interaction network. Network plot highlights correlation relationships between 18 GIGs of 161 prevalent bacterial CAGs at all time points from the two cohorts. Node size indicates the average abundance of the species/strains. Lines between nodes represent correlations between the nodes they connect, with line width indicating the correlation magnitude, and red and blue colors indicating positive and negative correlations, respectively. For clarity, only lines corresponding to correlations whose magnitude is greater than 0.5 are drawn, and unconnected nodes are omitted. (d) Procrustes analysis combining PCoA of GIGs (end of lines with solid symbols) with PCA of bioclinical variables presented in Fig. 1 (end of lines without solid symbols). For PWS, n = 17 at Day 0, 30, 60, and 90; For SO, n = 21 at Day 0 and n = 20 at Day 30. (e) Group level abundance shifts of GIGs that changed significantly during dietary intervention. Data are mean ± s.e.m. Wilcoxon matched-pairs signed rank test (two-tailed) was used to analyze variation between each two-time points in PWS or SO children. *P < 0.05, **P < 0.01.

Mentions: The composition of the gut microbiota showed a significant shift after 30 days of the intervention in both cohorts as indicated by principal coordinates analysis (PCoA, multivariate analysis of variance (MANOVA) test, P = 2.17e − 6) based on Bray–Curtis dissimilarity of the 376 bacterial CAGs (Fig. 3a and b). There was no significant difference in gut microbiota between PWS and SO either before (P = 0.99) or after the intervention (P = 0.8), suggesting that the PWS and SO gut microbiota were similarly dysbiotic prior to the intervention and that the intervention had the same effect on both (Fig. 3b). Analyses based on other β-diversity metrics and on pyrosequencing of the V1–V3 region of 16S rRNA genes confirmed these findings (Figs. S2 and S3). For both cohorts the gene richness and diversity of the gut microbiota significantly decreased after the intervention (Figs. S4 and S5). Delineation of low gene count/high gene count groups among our volunteers before the intervention was not evident (Fig. S6). Genus-level data from 16S rRNA gene sequencing gave two enterotypes represented by Bacteroides and Prevotella (Fig. S7). OTU-level data yielded three enterotypes, represented by: Bacteroides spp. uclustout#1111, Prevotella spp. uclustotu#3124 and Streptococcus spp. uclustotu#2404 (Fig. S8). CAG-based metagenomic data also differentiate the samples into three enterotypes represented by Bacteroides, Prevotella and Bifidobacterium respectively (Fig. S9). Some individuals changed their enterotypes after one month, indicating that changing enterotypes via long-term dietary intervention is possible (Tables S11–S13). Enterotypes and their changes responding to dietary intervention showed no correlation with any of the bio-clinical parameters.


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)

Concordance of structural shifts of gut microbiota and the improvement of the host metabolic health. (a) PCoA based on Bray–Curtis distance of all the 376 bacterial CAGs during the dietary intervention. (b) Clustering of gut microbiota based on distances between different groups calculated with MANOVA test of first 23 PCs (accounting for 80% of total variations) of PCoA based on Bray–Curtis distance of all bacterial CAGs. (c) Genome interaction groups interaction network. Network plot highlights correlation relationships between 18 GIGs of 161 prevalent bacterial CAGs at all time points from the two cohorts. Node size indicates the average abundance of the species/strains. Lines between nodes represent correlations between the nodes they connect, with line width indicating the correlation magnitude, and red and blue colors indicating positive and negative correlations, respectively. For clarity, only lines corresponding to correlations whose magnitude is greater than 0.5 are drawn, and unconnected nodes are omitted. (d) Procrustes analysis combining PCoA of GIGs (end of lines with solid symbols) with PCA of bioclinical variables presented in Fig. 1 (end of lines without solid symbols). For PWS, n = 17 at Day 0, 30, 60, and 90; For SO, n = 21 at Day 0 and n = 20 at Day 30. (e) Group level abundance shifts of GIGs that changed significantly during dietary intervention. Data are mean ± s.e.m. Wilcoxon matched-pairs signed rank test (two-tailed) was used to analyze variation between each two-time points in PWS or SO children. *P < 0.05, **P < 0.01.
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Related In: Results  -  Collection

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f0015: Concordance of structural shifts of gut microbiota and the improvement of the host metabolic health. (a) PCoA based on Bray–Curtis distance of all the 376 bacterial CAGs during the dietary intervention. (b) Clustering of gut microbiota based on distances between different groups calculated with MANOVA test of first 23 PCs (accounting for 80% of total variations) of PCoA based on Bray–Curtis distance of all bacterial CAGs. (c) Genome interaction groups interaction network. Network plot highlights correlation relationships between 18 GIGs of 161 prevalent bacterial CAGs at all time points from the two cohorts. Node size indicates the average abundance of the species/strains. Lines between nodes represent correlations between the nodes they connect, with line width indicating the correlation magnitude, and red and blue colors indicating positive and negative correlations, respectively. For clarity, only lines corresponding to correlations whose magnitude is greater than 0.5 are drawn, and unconnected nodes are omitted. (d) Procrustes analysis combining PCoA of GIGs (end of lines with solid symbols) with PCA of bioclinical variables presented in Fig. 1 (end of lines without solid symbols). For PWS, n = 17 at Day 0, 30, 60, and 90; For SO, n = 21 at Day 0 and n = 20 at Day 30. (e) Group level abundance shifts of GIGs that changed significantly during dietary intervention. Data are mean ± s.e.m. Wilcoxon matched-pairs signed rank test (two-tailed) was used to analyze variation between each two-time points in PWS or SO children. *P < 0.05, **P < 0.01.
Mentions: The composition of the gut microbiota showed a significant shift after 30 days of the intervention in both cohorts as indicated by principal coordinates analysis (PCoA, multivariate analysis of variance (MANOVA) test, P = 2.17e − 6) based on Bray–Curtis dissimilarity of the 376 bacterial CAGs (Fig. 3a and b). There was no significant difference in gut microbiota between PWS and SO either before (P = 0.99) or after the intervention (P = 0.8), suggesting that the PWS and SO gut microbiota were similarly dysbiotic prior to the intervention and that the intervention had the same effect on both (Fig. 3b). Analyses based on other β-diversity metrics and on pyrosequencing of the V1–V3 region of 16S rRNA genes confirmed these findings (Figs. S2 and S3). For both cohorts the gene richness and diversity of the gut microbiota significantly decreased after the intervention (Figs. S4 and S5). Delineation of low gene count/high gene count groups among our volunteers before the intervention was not evident (Fig. S6). Genus-level data from 16S rRNA gene sequencing gave two enterotypes represented by Bacteroides and Prevotella (Fig. S7). OTU-level data yielded three enterotypes, represented by: Bacteroides spp. uclustout#1111, Prevotella spp. uclustotu#3124 and Streptococcus spp. uclustotu#2404 (Fig. S8). CAG-based metagenomic data also differentiate the samples into three enterotypes represented by Bacteroides, Prevotella and Bifidobacterium respectively (Fig. S9). Some individuals changed their enterotypes after one month, indicating that changing enterotypes via long-term dietary intervention is possible (Tables S11–S13). Enterotypes and their changes responding to dietary intervention showed no correlation with any of the bio-clinical parameters.

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