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A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance
Open accessAbstract
Epidemiological and experimental data implicate branched-chain amino acids (BCAAs) in the development of insulin resistance, but the mechanisms that underlie this link remain unclear. Insulin resistance in skeletal muscle stems from the excess accumulation of lipid species, a process that requires blood-borne lipids to initially traverse the blood vessel wall. How this trans-endothelial transport occurs and how it is regulated are not well understood. Here we leveraged PPARGC1a (also known as PGC-1α; encoded by Ppargc1a), a transcriptional coactivator that regulates broad programs of fatty acid consumption, to identify 3-hydroxyisobutyrate (3-HIB), a catabolic intermediate of the BCAA valine, as a new paracrine regulator of trans-endothelial fatty acid transport. We found that 3-HIB is secreted from muscle cells, activates endothelial fatty acid transport, stimulates muscle fatty acid uptake in vivo and promotes lipid accumulation in muscle, leading to insulin resistance in mice. Conversely, inhibiting the synthesis of 3-HIB in muscle cells blocks the ability of PGC-1α to promote endothelial fatty acid uptake. 3-HIB levels are elevated in muscle from db/db mice with diabetes and from human subjects with diabetes, as compared to those without diabetes. These data unveil a mechanism in which the metabolite 3-HIB, by regulating the trans-endothelial flux of fatty acids, links the regulation of fatty acid flux to BCAA catabolism, providing a mechanistic explanation for how increased BCAA catabolic flux can cause diabetes.
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Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., Haqq, A. M., Shah, S. H., Arlotto, M., Slentz, C. A., et al. (2009) · Cell Metabolism · doi:10.1016/j.cmet.2009.02.002 · Cited by 3,165
Transcriptional co-activator PGC-1α drives the formation of slow-twitch muscle fibres
Lin, J., Wu, H., Tarr, P. T., Zhang, C.-Y., Wu, Z., Boss, O., Michael, L. F., Puigserver, P., Isotani, E., Olson, E. N., et al. (2002) · Nature · doi:10.1038/nature00904 · Cited by 2,546
Ectopic Fat in Insulin Resistance, Dyslipidemia, and Cardiometabolic Disease
Shulman, G. I. (2014) · New England Journal of Medicine · doi:10.1056/nejmra1011035 · Cited by 1,126
Interplay between lipids and branched-chain amino acids in development of insulin resistance
Newgard, C. B. (2012) · Cell Metabolism · doi:10.1016/j.cmet.2012.01.024 · Cited by 1,080
HIF-independent regulation of VEGF and angiogenesis by the transcriptional coactivator PGC-1α
Arany, Z., Foo, S.-Y., Ma, Y., Ruas, J. L., Bommi-Reddy, A., Girnun, G., Cooper, M., Laznik, D., Chinsomboon, J., Rangwala, S. M., et al. (2008) · Nature · doi:10.1038/nature06613 · Cited by 1,054
Self-citationRoberts, L. D., Boström, P., O’Sullivan, J. F., Schinzel, R. T., Lewis, G. D., Dejam, A., Lee, Y.-K., Palma, M. J., Calhoun, S., Georgiadi, A., et al. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2013.12.003 · Cited by 643
Hepatic insulin signalling is dispensable for suppression of glucose output by insulin in vivo
Titchenell, P. M., Chu, Q., Monks, B. R., & Birnbaum, M. J. (2015) · Nature Communications · doi:10.1038/ncomms8078 · Cited by 169
Self-citation
Research works printed but not linked: 18
- Wang, T.J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011). · DOI · Paper page
- Chan, M.C. & Arany, Z. The many roles of PGC-1α in muscle—recent developments. Metabolism 63, 441–451 (2014). · DOI
- Handschin, C. & Spiegelman, B.M. Peroxisome proliferator-activated receptor gamma coactivator 1 coactivators, energy homeostasis, and metabolism. Endocr. Rev. 27, 728–735 (2006). · DOI · Paper page
- Hagberg, C.E. et al. Vascular endothelial growth factor B controls endothelial fatty acid uptake. Nature 464, 917–921 (2010). · DOI · Paper page
- Arany, Z. et al. The transcriptional coactivator PGC-1β drives the formation of oxidative type IIX fibers in skeletal muscle. Cell Metab. 5, 35–46 (2007). · DOI · Paper page
- Hatazawa, Y. et al. Metabolomic analysis of the skeletal muscle of mice overexpressing PGC-1α. PLoS One 10, e0129084 (2015). · DOI
- Henkin, A.H. et al. Real-time noninvasive imaging of fatty acid uptake in vivo. ACS Chem. Biol. 7, 1884–1891 (2012). · DOI
- Choi, C.S. et al. Paradoxical effects of increased expression of PGC-1α on muscle mitochondrial function and insulin-stimulated muscle glucose metabolism. Proc. Natl. Acad. Sci. USA 105, 19926–19931 (2008). · DOI · Paper page
- Avogaro, A. & Bier, D.M. Contribution of 3-hydroxyisobutyrate to the measurement of 3-hydroxybutyrate in human plasma: comparison of enzymatic and gas-liquid chromatography-mass spectrometry assays in normal and in diabetic subjects. J. Lipid Res. 30, 1811–1817 (1989). · DOI
- Giesbertz, P. et al. Metabolite profiling in plasma and tissues of ob/ob and db/db mice identifies novel markers of obesity and type 2 diabetes. Diabetologia 58, 2133–2143 (2015). · DOI · Paper page
- Mullen, E. & Ohlendieck, K. Proteomic profiling of non-obese type 2 diabetic skeletal muscle. Int. J. Mol. Med. 25, 445–458 (2010).
- Sasaki, M. et al. A severely brain-damaged case of 3-hydroxyisobutyric aciduria. Brain Dev. 23, 243–245 (2001). · doi:10.1016/s0387-7604(01)00196-6
- Abbott, N.J., Hughes, C.C., Revest, P.A. & Greenwood, J. Development and characterisation of a rat brain capillary endothelial culture: towards an in vitro blood-brain barrier. J. Cell Sci. 103, 23–37 (1992). · DOI · Paper page
- Xie, Z. et al. Vascular endothelial hyperpermeability induces the clinical symptoms of Clarkson disease (the systemic capillary leak syndrome). Blood 119, 4321–4332 (2012). · DOI · Paper page
- Rowe, G.C. et al. Disconnecting mitochondrial content from respiratory chain capacity in PGC-1-deficient skeletal muscle. Cell Reports 3, 1449–1456 (2013). · DOI
- Sawada, N. et al. Endothelial PGC-1α mediates vascular dysfunction in diabetes. Cell Metab. 19, 246–258 (2014). · DOI · Paper page
- Darland, D.C. & D'Amore, P.A. TGFβ is required for the formation of capillary-like structures in three-dimensional cocultures of 10T1/2 and endothelial cells. Angiogenesis 4, 11–20 (2001). · DOI · Paper page
- Forman, D.E. et al. Analysis of skeletal muscle gene expression patterns and the impact of functional capacity in patients with systolic heart failure. J. Card. Fail. 20, 422–430 (2014). · DOI
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Metadata
- Authors
- Jang, C., Oh, S. F., Wada, S., Rowe, G. C., Liu, L., Chan, M. C., Rhee, J., Hoshino, A., Kim, B., Ibrahim, A., Baca, L. G., Kim, E., Ghosh, C. C., Parikh, S. M., Jiang, A., Chu, Q., Forman, D. E., Lecker, S. H., Krishnaiah, S., Rabinowitz, J. D., Weljie, A. M., Baur, J. A., Kasper, D. L., & Arany, Z.
- Venue
- Nature Medicine
- Published
- 2016-03-07
- Volume
- 22
- Issue
- 4
- Pages
- 421-426
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2294856235
- Cited by
- 583
- References
- 7
- FWCI
- 47.63
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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