Metabolomics

, 12:95 | Cite as

Human amylin proteotoxicity impairs protein biosynthesis, and alters major cellular signaling pathways in the heart, brain and liver of humanized diabetic rat model in vivo

  • Amro Ilaiwy
  • Miao Liu
  • Traci L. Parry
  • James R. Bain
  • Christopher B. Newgard
  • Jonathan C. Schisler
  • Michael J. Muehlbauer
  • Florin Despa
  • Monte S. Willis
Original Article

Abstract

Introduction

Chronic hypersecretion of the 37 amino acid amylin is common in type 2 diabetics (T2D). Recent studies implicate human amylin aggregates cause proteotoxicity (cell death induced by misfolded proteins) in both the brain and the heart.

Objectives

Identify systemic mechanisms/markers by which human amylin associated with cardiac and brain defects might be identified.

Methods

We investigated the metabolic consequences of amyloidogenic and cytotoxic amylin oligomers in heart, brain, liver, and plasma using non-targeted metabolomics analysis in a rat model expressing pancreatic human amylin (HIP model).

Results

Four metabolites were significantly different in three or more of the four compartments (heart, brain, liver, and plasma) in HIP rats. When compared to a T2D rat model, HIP hearts uniquely had significant DECREASES in five amino acids (lysine, alanine, tyrosine, phenylalanine, serine), with phenylalanine decreased across all four tissues investigated, including plasma. In contrast, significantly INCREASED circulating phenylalanine is reported in diabetics in multiple recent studies.

Conclusion

DECREASED phenylalanine may serve as a unique marker of cardiac and brain dysfunction due to hyperamylinemia that can be differentiated from alterations in T2D in the plasma. While the deficiency in phenylalanine was seen across tissues including plasma and could be monitored, reduced tyrosine was seen only in the brain. The 50 % reduction in phenylalanine and tyrosine in HIP brains is significant given their role in supporting brain chemistry as a precursor for catecholamines (dopamine, norepinephrine, epinephrine), which may contribute to the increased morbidity and mortality in diabetics at a multi-system level beyond the effects on glucose metabolism.

Keywords

Amylin Metabolomics Neurodegenerative disease Cardiomyopathy Diabetes Proteotoxicity 

Abbreviations

AbetaPP

Amyloid beta protein precursor

GABA

Gamma-aminobutyric acid

HIP

Rat model expressing pancreatic human amylin

hIAPP

Human islet amyloid polypeptide

PCA

Principal component analysis

PLS-DA

Partial least squares discriminant analysis

VIP

Variable importance in projection

Supplementary material

11306_2016_1022_MOESM1_ESM.pdf (3.2 mb)
Supplemental Fig. 1. Non-targeted cardiac metabolomics of HIP heart tissue at one year of age. A. Enrichment by pathway-associated metabolite sets. B. Location-Based Metabolite sets determined from t-test significant metabolites identified. Supplementary material 1 (PDF 3304 kb)
11306_2016_1022_MOESM2_ESM.pdf (4.2 mb)
Supplemental Fig. 2. Non-targeted cardiac metabolomics of HIP brain tissue at one year of age Enrichment by A. Pathway-associated metabolite sets, B. Location-Based Metabolite sets, and C. Disease-associated metabolite sets in the brain determined from from t-test significant metabolites identified. Supplementary material 2 (PDF 4314 kb)
11306_2016_1022_MOESM3_ESM.pdf (3.1 mb)
Supplemental Fig. 3. Non-targeted cardiac metabolomics of HIP liver tissue at one year of age. Enrichment by A. Pathway-associated metabolite sets and B. Location-Based Metabolite sets determined from from t-test significant metabolites identified. Supplementary material 3 (PDF 3213 kb)
11306_2016_1022_MOESM4_ESM.pdf (4.5 mb)
Supplemental Fig. 4. Non-targeted cardiac metabolomics of HIP plasma at one year of age. Enrichment by A. Pathway-associated metabolite sets, B. Location-Based Metabolite sets, and C. Disease-associated metabolite sets in the blood determined from from t-test significant metabolites identified. Supplementary material 4 (PDF 4564 kb)
11306_2016_1022_MOESM5_ESM.pdf (3.8 mb)
Supplemental Fig. 5. Pathway enrichment analysis of significant metabolite sets identified by non-targeted cardiac metabolomics. A. Heart, B. Brain, C. Liver, and D. Plasma. A-D indicates top pathways identified, along with the specific significant metabolites found that placed it in this category. Supplementary material 5 (PDF 3869 kb)
11306_2016_1022_MOESM6_ESM.pdf (724 kb)
Supplemental Fig. 6. Significantly altered HIP-unique metabolites found by non-targeted metabolomics analysis of the heart using a recently published type 2 diabetes model (Kouzu et al. 2015). Data was normalized to the strain-matched wildtype control, followed by an ANOVA analysis of all four groups and included significantly altered metabolites found in both studies (see supplemental materials and methods for details, Supplemental Table 2 for data used in analysis). A. HIP significant cardiac metabolites, not altered in T2D model (indicated by *). B. HIP significant cardiac amino acids, not altered in T2D model (indicated by *). C. Comparison of HIP transgenic model (current study) and T2D model used in this analysis recently published (Kouzu et al. 2015). Boxes indicate similarly altered metabolites in both HIP and T2D models. A One-Way ANOVA was performed, with Holm-Sidak post hoc comparisons made to the wildtypeHIPTg+. Significance was defined as p < 0.05. Data is mean ± SEM. Supplementary material 6 (PDF 723 kb)
11306_2016_1022_MOESM7_ESM.xlsx (456 kb)
Supplementary material 7 (XLSX 456 kb)
11306_2016_1022_MOESM8_ESM.xlsx (33 kb)
Supplementary material 8 (XLSX 33 kb)
11306_2016_1022_MOESM9_ESM.pptx (50 kb)
Supplementary material 9 (PPTX 50 kb)

References

  1. Ashford, A. J., & Pain, V. M. (1986). Effect of diabetes on the rates of synthesis and degradation of ribosomes in rat muscle and liver in vivo. Journal of Biological Chemistry, 261, 4059–4065.PubMedGoogle Scholar
  2. Aung, M. H., et al. (2014). Dopamine deficiency contributes to early visual dysfunction in a rodent model of type 1 diabetes. Journal of Neuroscience, 34, 726–736. doi:10.1523/JNEUROSCI.3483-13.2014.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Banerjee, R., et al. (2015). Non-targeted metabolomics of double-mutant cardiomyocytes reveals a novel role for SWI/SNF complexes in metabolic homeostasis. Metabolomics, 11, 1287–1301. doi:10.1007/s11306-015-0786-7.CrossRefPubMedGoogle Scholar
  4. Boirie, Y., Short, K. R., Ahlman, B., Charlton, M., & Nair, K. S. (2001). Tissue-specific regulation of mitochondrial and cytoplasmic protein synthesis rates by insulin. Diabetes, 50, 2652–2658.CrossRefPubMedGoogle Scholar
  5. Castellani, R. J., et al. (2001). Active glycation in neurofibrillary pathology of Alzheimer disease: N(epsilon)-(carboxymethyl) lysine and hexitol-lysine. Free Radical Biology and Medicine, 31, 175–180.CrossRefPubMedGoogle Scholar
  6. Despa, S., et al. (2012). Hyperamylinemia contributes to cardiac dysfunction in obesity and diabetes: A study in humans and rats. Circulation Research, 110, 598–608. doi:10.1161/CIRCRESAHA.111.258285.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Despa, S., et al. (2014). Cardioprotection by controlling hyperamylinemia in a “humanized” diabetic rat model. Journal of the American Heart Association, 3, e001015. doi:10.1161/JAHA.114.001015.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Dunn, L., et al. (2014). Dysregulation of glucose metabolism is an early event in sporadic Parkinson’s disease. Neurobiology of Aging, 35, 1111–1115. doi:10.1016/j.neurobiolaging.2013.11.001.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Erickson, J. R., et al. (2013). Diabetic hyperglycaemia activates CaMKII and arrhythmias by O-linked glycosylation. Nature, 502, 372–376. doi:10.1038/nature12537.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fernstrom, J. D., & Fernstrom, M. H. (2007). Tyrosine, phenylalanine, and catecholamine synthesis and function in the brain. The Journal of Nutrition, 137(6), 1539S–1547S. discussion 1548S.PubMedGoogle Scholar
  11. Gonzalez-Dominguez, R., Garcia-Barrera, T., & Gomez-Ariza, J. L. (2015). Metabolite profiling for the identification of altered metabolic pathways in Alzheimer’s disease. Journal of Pharmaceutical and Biomedical Analysis, 107, 75–81. doi:10.1016/j.jpba.2014.10.010.CrossRefPubMedGoogle Scholar
  12. Hay, D. L., Chen, S., Lutz, T. A., Parkes, D. G., & Roth, J. D. (2015). Amylin: Pharmacology, physiology, and clinical potential. Pharmacological Reviews, 67, 564–600. doi:10.1124/pr.115.010629.CrossRefPubMedGoogle Scholar
  13. Jackson, K., Barisone, G. A., Diaz, E., Jin, L. W., DeCarli, C., & Despa, F. (2013). Amylin deposition in the brain: A second amyloid in Alzheimer disease? Annals of Neurology, 74, 517–526. doi:10.1002/ana.23956.CrossRefPubMedGoogle Scholar
  14. Jorda, A., Gomez, M., Cabo, J., & Grisolia, S. (1982). Effect of streptozotocin diabetes on some urea cycle enzymes. Biochemical and Biophysical Research Communications, 106, 37–43.CrossRefPubMedGoogle Scholar
  15. Kouzu, H., et al. (2015). Excessive degradation of adenine nucleotides by up-regulated AMP deaminase underlies afterload-induced diastolic dysfunction in the type 2 diabetic heart. Journal of Molecular and Cellular Cardiology, 80, 136–145. doi:10.1016/j.yjmcc.2015.01.004.CrossRefPubMedGoogle Scholar
  16. Lambert, R., Srodulski, S., Peng, X., Margulies, K. B., Despa, F., & Despa, S. (2015). Intracellular Na+ concentration ([Na+]i) is elevated in diabetic hearts due to enhanced Na+—Glucose cotransport. Journal of the American Heart Association, 4, e002183. doi:10.1161/JAHA.115.002183.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Lapidot, A., & Haber, S. (2001). Effect of endogenous β-hydroxybutyrate on glucose metabolism in the diabetic rabbit brain: A (13)C-magnetic resonance spectroscopy study of [U-(13)C]glucose metabolites. Journal of Neuroscience Research, 64, 207–216.CrossRefPubMedGoogle Scholar
  18. Li, L. O., Hu, Y. F., Wang, L., Mitchell, M., Berger, A., & Coleman, R. A. (2010). Early hepatic insulin resistance in mice: A metabolomics analysis. Molecular Endocrinology, 24, 657–666. doi:10.1210/me.2009-0152.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Lutz, T. A., & Meyer, U. (2015). Amylin at the interface between metabolic and neurodegenerative disorders. Frontiers in Neuroscience, 9, 216. doi:10.3389/fnins.2015.00216.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Makinen, K. K. (2000). Can the pentitol-hexitol theory explain the clinical observations made with xylitol? Medical Hypotheses, 54, 603–613. doi:10.1054/mehy.1999.0904.CrossRefPubMedGoogle Scholar
  21. Mans, A. M., DeJoseph, M. R., Davis, D. W., & Hawkins, R. A. (1987). Regional amino acid transport into brain during diabetes: Effect of plasma amino acids. American Journal of Physiology, 253, E575–E583.PubMedGoogle Scholar
  22. Pasini, E., Aquilani, R., Corsetti, G., & Dioguardi, F. S. (2014). Biomarkers to identify protein metabolism impairment in chronic/acute diseases. Journal of Molecular Biomarkers & Diagnosis, 5, 176–179.Google Scholar
  23. Pasini, E., Aquilani, R., & Dioguardi, F. S. (2013). The enemy within. How to identify chronic diseases induced-protein metabolism impairment and its possible pharmacological treatment. Pharmacological Research, 76, 28–33. doi:10.1016/j.phrs.2013.06.006.CrossRefPubMedGoogle Scholar
  24. Pietzsch, J., Bergmann, R., & Kopprasch, S. (2004). Analysis of non-protein amino acids as specific markers of low density lipoprotein apolipoprotein B-100 oxidation in human atherosclerotic lesions: The use of N(O)-ethoxycarbonyl trifluoroethyl ester derivatives and GC-MS. Spectroscopy, 18, 177–183.CrossRefGoogle Scholar
  25. Rampersad, O. R., & Wool, I. G. (1965). Protein synthesis by ribosomes from heart muscle: Effect of insulin and diabetes. Science, 149, 1102–1103.CrossRefPubMedGoogle Scholar
  26. Reusch, R. N., Bryant, E. M., & Henry, D. N. (2003). Increased poly-(R)-3-hydroxybutyrate concentrations in streptozotocin (STZ) diabetic rats. Acta Diabetologica, 40, 91–94. doi:10.1007/s005920300011.CrossRefPubMedGoogle Scholar
  27. Scholtens, D. M., et al. (2014). Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy. Diabetes Care, 37, 158–166. doi:10.2337/dc13-0989.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Schutz, Y. (2011). Protein turnover, ureagenesis and gluconeogenesis. International Journal for Vitamin and Nutrition Research, 81, 101–107. doi:10.1024/0300-9831/a000064.CrossRefPubMedGoogle Scholar
  29. Srodulski, S., et al. (2014). Neuroinflammation and neurologic deficits in diabetes linked to brain accumulation of amylin. Mol Neurodegener, 9, 30. doi:10.1186/1750-1326-9-30.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Tillin, T., et al. (2015). Diabetes risk and amino acid profiles: Cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia, 58, 968–979. doi:10.1007/s00125-015-3517-8.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Tiwari, V., & Patel, A. B. (2014). Pyruvate carboxylase and pentose phosphate fluxes are reduced in AbetaPP-PS1 mouse model of Alzheimer’s disease: A (1)(3)C NMR study. Journal of Alzheimer’s Disease, 41, 387–399. doi:10.3233/JAD-122449.PubMedGoogle Scholar
  32. Verdile, G., Fuller, S. J., & Martins, R. N. (2015). The role of type 2 diabetes in neurodegeneration. Neurobiology of Diseases. doi:10.1016/j.nbd.2015.04.008.Google Scholar
  33. Wootton-Gorges, S. L., et al. (2005). Detection of cerebral β-hydroxy butyrate, acetoacetate, and lactate on proton MR spectroscopy in children with diabetic ketoacidosis. American Journal of Neuroradiology, 26, 1286–1291.PubMedGoogle Scholar
  34. Wurtz, P., et al. (2015). Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts. Circulation, 131, 774–785. doi:10.1161/CIRCULATIONAHA.114.013116.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009). MetaboAnalyst: A web server for metabolomic data analysis and interpretation. Nucleic Acids Research, 37, W652–W660. doi:10.1093/nar/gkp356.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0—Making metabolomics more meaningful. Nucleic Acids Research, 43, W251–W257. doi:10.1093/nar/gkv380.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Faculty of MedicineUniversity of DamascusDamascusSyria
  2. 2.Department of Pharmacology and Nutritional SciencesUniversity of KentuckyLexingtonUSA
  3. 3.Department of PharmacologyUniversity of North CarolinaChapel HillUSA
  4. 4.McAllister Heart InstituteUniversity of North CarolinaChapel HillUSA
  5. 5.Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology InstituteDuke University Medical CenterDurhamUSA
  6. 6.Division of Endocrinology, Metabolism, and Nutrition, Department of MedicineDuke University Medical CenterDurhamUSA
  7. 7.Department of Pathology & Laboratory MedicineUniversity of North CarolinaChapel HillUSA

Personalised recommendations