Excessive degradation of adenine nucleotides by up-regulated AMP deaminase underlies afterload-induced diastolic dysfunction in the type 2 diabetic heart

Kouzu, Hidemichi; Miki, Takayuki; Tanno, Masaya; Kuno, Atsushi; Yano, Toshiyuki; Itoh, Takahito; Sato, Tatsuya; Sunaga, Daisuke; Murase, Hiromichi; Tobisawa, Toshiyuki; Ogasawara, M.; Ishikawa, Satoko; Miura, Tetsuji

doi:10.1016/j.yjmcc.2015.01.004

Cited by 27 publications

(45 citation statements)

References 49 publications

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“…Distinguishing the metabolic signatures of the type 2 diabetes from the amylin proteotoxicity component was what we next addressed using a published non-targeted metabolomics data set in a type 2 diabetes rat model recently published (Kouzu et al 2015). Using an ANOVA analysis of the cardiac HIP (and strain-matched wildtype) significant metabolites with the recently published T2D (and strain-matched wildtype) significant metabolites revealed that both models had similar significant decreases in glycine (HIP vs. T2D p>0.05; HIP vs. wildtype HIPTg+ p=0.001, T2D vs wildtype T2D p=0.028) and similar significant increases in leucine (Figure 6, indicated with boxes).…”

Section: Resultsmentioning

confidence: 99%

“…In order to differentiate amylin metabolic effect from diabetic metabolic signature, we compare our results with published non-targeted metabolomic datasets obtained from type 2 diabetic hearts (Kouzu et al 2015). PubMed was searched using the terms “Metabolomics” “heart” “type 2 diabetes” and “rat”, resulting in two articles.…”

Section: Methodsmentioning

confidence: 99%

“…PubMed was searched using the terms “Metabolomics” “heart” “type 2 diabetes” and “rat”, resulting in two articles. We chose the dataset from Kouzu, et al, 2015 (Kouzu et al 2015) for its similarity of T2D and baseline controls clinically. Metabolites identified in both our study and the Kouzu et al, 2015 (Kouzu et al 2015).…”

Section: Methodsmentioning

confidence: 99%

“…We chose the dataset from Kouzu, et al, 2015 (Kouzu et al 2015) for its similarity of T2D and baseline controls clinically. Metabolites identified in both our study and the Kouzu et al, 2015 (Kouzu et al 2015). The four groups compared were: (1) wildtypeHIP Tg+ ; (2) HIPTg+; (3) lean wildtype controls/Long-EvansTokushima-Otsuka/LETO; and (4) T2D/Otsuka Long-Evans Tokushima fatty/OLETF.…”

Section: Methodsmentioning

confidence: 99%

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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

et al. 2016

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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 3 or more of the 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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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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

et al. 2016

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“…Importantly, CE also gives complementary information to HILIC [13], as well as, significantly better peak shape in many cases. It is therefore not surprising that CE-MS has already been considered for multiple metabolomics studies covering several applications, from aging [19], amino acid analysis [20,21] [46][47][48][49][50][51][52][53], CVD [54][55][56][57][58], diabetes [59][60][61][62][63][64][65], lifestyle [61][62][63][64][65][66][67][68][69][70][71], kidney disease [72,73], liver disease [74][75][76][77][78][79][80][81][82], the microbiome [83][84]…”

Section: Current Advantages and Biotechnology Applicationsmentioning

confidence: 99%

Capillary electrophoresis mass spectrometry based metabolomics

Buko¹

2017

J Appl Bioanal

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IntroductionSince its first formal definition more than a decade ago, metabolomics, or, the comprehensive analysis of all metabolites present within a biological system, has attracted growing interest in clinical research by academia, industry and government labs. This is most prevalent in biomarker and drug development applications where a considerable change has been witnessed in how new diagnoses, prognoses, and therapeutic options are being discovered and developed using omic technologies. Moreover, many chronic diseases suggest a strong metabolic involvement or even a clear metabolic cause, including cancer. Together with the other omic disciplines, including genomics and proteomics, metabolomics plays a key role in the implementation of personalized medicine; evidence-based medicine designed for individually designed healthcare strategies. In turn, biomarker discovery and the understanding of biochemical pathways typically rely on a multimodal approach. Among these modalities, there continues to be a growing interest in CE-MS based development and implementation in clinical development. Formulating the problemDepending on how you classify the metabolome, there is a complex chemical space separated by hydrophilicity, polarity and size, excluding a broad range of metabolites classified as lipids, separately referred to as lipidomics. This class of metabolites also covers a large range of physical properties for which specifically designed platforms work the best. For example, for years liquid chromatography-mass spectrometry (LC-MS) has been used to capture a host of hydrophilic and hydrophobic metabolites, while gas chromatography-mass spectrometry (GC-MS) has been used to capture small molecular weight metabolites. For this review, the metabolome refers to endogenous molecules with a molecular weight typically less than 1000 Kd. To measure, catalogue, and compare the entirety of the metabolic space, the implementation of comprehensive mass spectral databases was needed (e.g., Human Metab- Because of this diversity in physical properties and size of the metabolome in biological systems, there has been a need for the development of several analytical protocols based on different chromatographic methods to select specific subgroups of metabolites based on these different chemical properties beyond the technical capabilities of LC-MS and GC-MS. These new methods have their own advantages for selecting specific types of molecules: lipids, nucleotides, aminoacids or steroids. Nonmass spectrometric methods such as nuclear magnetic resonance (NMR) and non-chromatographic methods such as matrix-assisted laser desorption-time of flight (MALDI-TOF) imaging are successfully being used for selected metabolomic analyses. The focus of this review is recent publications that use CE-MS to extend the polar metabolome beyond what is observed by LC-MS and GC-MS. Metabolomics -current methods

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