Molecular studies of the proteome and metabolome in readily available body fluids such as urine and blood performed in a comprehensive qualitative and quantitative way are a valuable source of information for kidney disease research. They provide potential biomarkers of disease progression, markers of efficacy of interventions, as well as information on the underlying pathophysiology. Identified proteins and metabolites may point to dysregulated biological pathways and this knowledge may be useful in the identification of new treatment targets.
Many studies, focusing on chronic kidney disease as well as diabetic nephropathy, demonstrate that peptidome and metabolome analysis can substantially contribute to early detection and prediction of disease progression, but also stratification of kidney disease in clinical practice. An innovative, well-explored application of urinary peptidome analysis is the back-translation of results obtained in humans to animals, for animal model validation and improvement of the preclinical readouts. In this chapter, we provide an overview of urinary proteomic analysis with the CE-MS analytical platform, a strategy that has been successfully employed in several studies for the identification and validation of biomarkers in kidney diseases. We describe how to obtain the orthology between the animal model and humans. We also deliver an overview of the analysis of the metabolome with the GC×GC-TOF-MS and UHPLC-Q-TOF-MS analytical platforms for blood and serum as new methods being applied in kidney disease.
It is expected that a systems medicine approach to kidney disease including multiple omics methods will provide us with the best way to understand and treat diabetic kidney disease in the future.
Capillary electrophoresis Mass spectrometry Urine Rodent Clinical proteomics Peptidomics
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van der Kloet FM et al (2012) Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study). Metabolomics 8(1):109–119PubMedCrossRefPubMedCentralGoogle Scholar
Liu X et al (2015) A systematic review of metabolite profiling in diabetic nephropathy. J Endocrinol Diab 2(3):1–11Google Scholar
Pena MJ et al (2014) Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with type 2 diabetes mellitus. Diabet Med 31(9):1138–1147PubMedCrossRefPubMedCentralGoogle Scholar
Lacroix C et al (2014) Label-free quantitative urinary proteomics identifies the arginase pathway as a new player in congenital obstructive nephropathy. Mol Cell Proteomics 13(12):3421–3434PubMedPubMedCentralCrossRefGoogle Scholar
Sana TR et al (2008) Molecular formula and METLIN personal metabolite database matching applied to the identification of compounds generated by LC/TOF-MS. J Biomol Tech 19(4):258–266PubMedPubMedCentralGoogle Scholar
Draper J et al (2009) Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour rules. BMC Bioinformatics 10(1):227PubMedPubMedCentralCrossRefGoogle Scholar
Kind T, Fiehn O (2006) Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics 7(1):234PubMedPubMedCentralCrossRefGoogle Scholar
Chen C et al (2008) Identification of novel toxicity-associated metabolites by metabolomics and mass isotopomer analysis of acetaminophen metabolism in wild-type and Cyp2e1-null mice. J Biol Chem 283(8):4543–4559PubMedCrossRefPubMedCentralGoogle Scholar
Viant MR, Rosenblum ES, Tjeerdema RS (2003) NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. Environ Sci Technol 37(21):4982–4989PubMedCrossRefPubMedCentralGoogle Scholar
Ciborowski M et al (2012) Metabolomics with LC-QTOF-MS permits the prediction of disease stage in aortic abdominal aneurysm based on plasma metabolic fingerprint. PLoS One 7(2):e31982PubMedPubMedCentralCrossRefGoogle Scholar
O’Gorman A et al (2017) Identification of a plasma signature of psychotic disorder in children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Transl Psychiatry 7:e1240PubMedPubMedCentralCrossRefGoogle Scholar
Rodrigues CM et al (2007) Metabolic fingerprinting using direct flow injection electrospray ionization tandem mass spectrometry for the characterization of proanthocyanidins from the barks of Hancornia speciosa. Rapid Commun Mass Spectrom 21(12):1907–1914PubMedCrossRefPubMedCentralGoogle Scholar
Sarafian MH et al (2014) Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography–mass spectrometry. Anal Chem 86(12):5766–5774PubMedCrossRefPubMedCentralGoogle Scholar
Want EJ et al (2006) Solvent-dependent metabolite distribution, clustering, and protein extraction for serum profiling with mass spectrometry. Anal Chem 78(3):743–752PubMedCrossRefPubMedCentralGoogle Scholar
Bruce SJ et al (2008) Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: from extraction to data analysis. Anal Biochem 372(2):237–249PubMedCrossRefPubMedCentralGoogle Scholar
Tulipani S et al (2013) Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more. Anal Chem 85(1):341–348PubMedCrossRefPubMedCentralGoogle Scholar
Bruce SJ et al (2009) Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Anal Chem 81(9):3285–3296PubMedCrossRefPubMedCentralGoogle Scholar
Neuhoff N et al (2004) Mass spectrometry for the detection of differentially expressed proteins: a comparison of surface-enhanced laser desorption/ionization and capillary electrophoresis/mass spectrometry. Rapid Commun Mass Spectrom 18(2):149–156PubMedCrossRefPubMedCentralGoogle Scholar
Rossing K et al (2016) Urinary proteomics pilot study for biomarker discovery and diagnosis in heart failure with reduced ejection fraction. PLoS One 11(6):e0157167PubMedPubMedCentralCrossRefGoogle Scholar
Klein J et al (2014) Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine. Electrophoresis 35(7):1060–1064PubMedCrossRefPubMedCentralGoogle Scholar
Mischak H et al (2009) Capillary electrophoresis-mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: an update of recent developments. Mass Spectrom Rev 28(5):703–724PubMedPubMedCentralCrossRefGoogle Scholar