Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

Andrew D. Patterson, Jessica A. Bonzo, Fei Li, Kristopher W. Krausz, Gabriel S. Eichler, Sadaf Aslam, Xenia Tigno, John N. Weinstein, Barbara C. Hansen, Jeffrey R. Idle, Frank J. Gonzalez

Research output: Contribution to journalArticle

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Abstract

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na +-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

Original languageEnglish (US)
Pages (from-to)19511-19522
Number of pages12
JournalJournal of Biological Chemistry
Volume286
Issue number22
DOIs
StatePublished - Jun 3 2011

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Metabolomics
Medical problems
Type 2 Diabetes Mellitus
Primates
Kidney
Metabolites
Haplorhini
Betaine
Mass spectrometry
Urine
Kynurenic Acid
Proximal Kidney Tubule
Metabolome
Liquid chromatography
Tandem Mass Spectrometry
Macaca mulatta
Proline
Citric Acid
Liquid Chromatography
Learning algorithms

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Molecular Biology
  • Cell Biology

Cite this

Patterson, Andrew D. ; Bonzo, Jessica A. ; Li, Fei ; Krausz, Kristopher W. ; Eichler, Gabriel S. ; Aslam, Sadaf ; Tigno, Xenia ; Weinstein, John N. ; Hansen, Barbara C. ; Idle, Jeffrey R. ; Gonzalez, Frank J. / Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus. In: Journal of Biological Chemistry. 2011 ; Vol. 286, No. 22. pp. 19511-19522.
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abstract = "To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5{\%}. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na +-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.",
author = "Patterson, {Andrew D.} and Bonzo, {Jessica A.} and Fei Li and Krausz, {Kristopher W.} and Eichler, {Gabriel S.} and Sadaf Aslam and Xenia Tigno and Weinstein, {John N.} and Hansen, {Barbara C.} and Idle, {Jeffrey R.} and Gonzalez, {Frank J.}",
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Patterson, AD, Bonzo, JA, Li, F, Krausz, KW, Eichler, GS, Aslam, S, Tigno, X, Weinstein, JN, Hansen, BC, Idle, JR & Gonzalez, FJ 2011, 'Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus', Journal of Biological Chemistry, vol. 286, no. 22, pp. 19511-19522. https://doi.org/10.1074/jbc.M111.221739

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus. / Patterson, Andrew D.; Bonzo, Jessica A.; Li, Fei; Krausz, Kristopher W.; Eichler, Gabriel S.; Aslam, Sadaf; Tigno, Xenia; Weinstein, John N.; Hansen, Barbara C.; Idle, Jeffrey R.; Gonzalez, Frank J.

In: Journal of Biological Chemistry, Vol. 286, No. 22, 03.06.2011, p. 19511-19522.

Research output: Contribution to journalArticle

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T1 - Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

AU - Patterson, Andrew D.

AU - Bonzo, Jessica A.

AU - Li, Fei

AU - Krausz, Kristopher W.

AU - Eichler, Gabriel S.

AU - Aslam, Sadaf

AU - Tigno, Xenia

AU - Weinstein, John N.

AU - Hansen, Barbara C.

AU - Idle, Jeffrey R.

AU - Gonzalez, Frank J.

PY - 2011/6/3

Y1 - 2011/6/3

N2 - To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na +-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.

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