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FXR is a molecular target for the effects of vertical sleeve gastrectomy

Nature volume 509, pages 183–188 (2014)Cite this article

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Abstract

Bariatric surgical procedures, such as vertical sleeve gastrectomy (VSG), are at present the most effective therapy for the treatment of obesity, and are associated with considerable improvements in co-morbidities, including type-2 diabetes mellitus. The underlying molecular mechanisms contributing to these benefits remain largely undetermined, despite offering the potential to reveal new targets for therapeutic intervention. Substantial changes in circulating total bile acids are known to occur after VSG. Moreover, bile acids are known to regulate metabolism by binding to the nuclear receptor FXR (farsenoid-X receptor, also known as NR1H4). We therefore examined the results of VSG surgery applied to mice with diet-induced obesity and targeted genetic disruption of FXR. Here we demonstrate that the therapeutic value of VSG does not result from mechanical restriction imposed by a smaller stomach. Rather, VSG is associated with increased circulating bile acids, and associated changes to gut microbial communities. Moreover, in the absence of FXR, the ability of VSG to reduce body weight and improve glucose tolerance is substantially reduced. These results point to bile acids and FXR signalling as an important molecular underpinning for the beneficial effects of this weight-loss surgery.

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Figure 1: Unbiased pathway analysis in VSG ileum.
Figure 2: FXR contributes to the maintenance of weight loss following VSG.
Figure 3: FXR alters feeding behaviour after VSG.
Figure 4: FXR contributes to the improvement in glucose tolerance observed following VSG.
Figure 5: VSG and FXR alter the composition of caecal microbial communities.

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Primary accessions

Gene Expression Omnibus

Data deposits

Raw and normalized RNA-seq data have been deposited in the NCBI Gene Expression Omnibus database under accession number GSE53782.

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Acknowledgements

We thank J. Berger, A. Haller, B. Li, E. Orr and M. Toure for technical assistance. This work was supported by grants from the UNIK Food Fitness and Pharma for Health and Disease research programme (C.C.), the Torsten Söderberg and NovoNordisk foundations (F.B.), Ethicon Endo-Surgery (R.K., D.A.S., R.J.S.) and the NIH (DK082173, HL111319 to K.K.R., DK093848 to R.J.S. and the Bioinformatics Core of the Digestive Disease Research Core Center in Cincinnati DK078392).

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Authors and Affiliations

  1. Department of Internal Medicine, Division of Endocrinology, Diabetes and Metabolism, University of Cincinnati, Cincinnati, Ohio, 45237, USA

    Karen K. Ryan, Christoffer Clemmensen, Hilary E. Wilson-Pérez, Darleen A. Sandoval & Randy J. Seeley

  2. Department of Molecular and Clinical Medicine and Sahlgrenska Center for Cardiovascular and Metabolic Research, Wallenberg Laboratory, University of Gothenburg, S-413 45 Gothenburg, Sweden,

    Valentina Tremaroli, Petia Kovatcheva-Datchary & Fredrik Bäckhed

  3. Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark,

    Christoffer Clemmensen

  4. Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, 45229, Ohio, USA

    Andriy Myronovych & Rohit Kohli

  5. Divison of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, 45229, Ohio, USA

    Rebekah Karns

  6. Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, DK-2200, Denmark ,

    Fredrik Bäckhed

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Contributions

K.K.R. conceptualized, designed, performed and analysed the experiments and wrote the manuscript. C.C., A.M., H.E.W.-P., D.A.S. and R. Kohli performed experiments and edited the manuscript. R. Karns performed the bioinformatics analysis of the RNA-seq data. V.T. and F.B. designed and performed the microbiota analysis and edited the manuscript. P.K.-D. and F.B. designed and performed the analysis of caecal metabolites and edited the manuscript. R.J.S. conceptualized, designed and analysed the experiments and wrote the manuscript.

Corresponding author

Correspondence to Randy J. Seeley.

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Competing interests

D.A.S. receives research support from Ethicon Endo-Surgery, Novo Nordisk, and Boehringer-Ingelheim, is a consultant for Givaudan and is on the scientific advisory board for Ethicon Endo-Surgery. R. Kohli receives research support from Ethicon Endo-Surgery. F.B. is a founder of and owns equity in Metabogen AB. R.J.S. has received research support from Ethicon Surgical Care, Novo Nordisk, Ablaris, Roche, Boehringer-Ingelheim and Zealand. He has served as a consultant or paid speaker for Ethicon Surgical Care, Eissai, Forrest and Givaudan. He has a small equity position in Zafgen. The other authors have nothing to declare.

Extended data figures and tables

Extended Data Figure 1 Body weight and body fat in a weight-matched subset of WT and FXR KO mice.

These post-hoc analyses include the lightest 9 WT and heaviest 8 FXR-KO mice before surgery, creating 4 well-matched groups. a–d, In this subset, WT-VSG lose weight relative to WT-sham controls, and maintain this weight loss for 14 weeks (a), whereas KO-VSG mice lose weight initially, but recover to match the weight of KO-sham controls within 4–5 weeks (b). Consequently, by week 8 KO-VSG mice were heavier than WT-VSG mice (c). Likewise, these groups were well-matched for pre-surgical body fat (d). At 11 weeks after surgery, WT-VSG mice had significantly less body fat compared to both WT-sham controls and KO-VSG mice. KO-sham and KO-VSG mice had equivalent adiposity (e). Data are shown as mean ± s.e. *P < 0.05, **P < 0.01, ***P < 0.001. For all panels n = 5 WT-sham, 4 WT-VSG, 4 KO-sham, 4 KO-VSG.

Extended Data Figure 2 Glucose tolerance in WT-VSG and KO-VSG mice.

When the glucose excursion of WT-VSG and KO-VSG mice are compared directly, KO-VSG mice exhibit significantly impaired glucose clearance at both 30 and 60 min. Data are shown as mean ± s.e. *P < 0.05. n = 10 per group.

Extended Data Figure 3 Effect of genotype and VSG on distribution along PC1.

Among WT mice, sham and VSG mice separate significantly along PC1. In contrast, among KO mice there is no significant difference between sham and VSG. Data are shown as mean ± s.e. ***P < 0.001. n = 12 WT-sham, 7 WT-VSG, 9 KO-sham, 8 KO-VSG.

Extended Data Figure 4 Relative abundance of Bacteroides, an uncharacterized genus in Porphyromonadaceae, and Roseburia correlated with metabolic parameters.

a–d, The relative abundance of Bacteroides was significantly correlated with change in body weight (a), change in body fat (b), and the area under the curve (AUC) in the glucose tolerance test (d), but not with fasting blood glucose (c). e–h, The relative abundance of an uncharacterized genus in Porphyromonadaceae was significantly correlated with change in body weight (e), fasting blood glucose (g) and AUC in the glucose tolerance test (h), but not with change in body fat (f). i–l, The relative abundance of Roseburia was significantly correlated with change in body weight (i), change in body fat (j), fasting blood glucose (k) and AUC in the glucose tolerance test (l). n = 36.

Extended Data Figure 5 Effect of genotype and VSG on the relative abundance of Lactobacillus, Lactococcus and Escherichia.

a–c, VSG was associated with a significant increase in the relative abundance of Lactobacillus (a), Lactococcus (b) and Escherichia/Shigella (c) that did not vary according to genotype. Data are presented as Tukey box-plots. n = 12 WT-sham, 7 WT-VSG, 9 KO-sham, 8 KO-VSG.

Extended Data Figure 6 VSG alters the abundance of caecal SCFAs.

a–c, The relative concentration of butyrate (a) and propionate (b), but not acetate (c), was altered by VSG, and this did not differ depending on genotype. d, The acetate:butyrate ratio is increased following VSG. Data are presented as Tukey box-plots. *P < 0.05, **P < 0.01. Also see Extended Data Table 1. n = 12 WT-sham, 7 WT-VSG, 9 KO-sham, 8 KO-VSG.

Extended Data Table 1 The effect of VSG on caecal organic acids
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Ryan, K., Tremaroli, V., Clemmensen, C. et al. FXR is a molecular target for the effects of vertical sleeve gastrectomy. Nature 509, 183–188 (2014). https://doi.org/10.1038/nature13135

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