Amadori rearrangement products as potential biomarkers for inborn errors of amino-acid metabolism

Rianne E Van Outersterp,Tessa M A Peters,Clara D M Van Karnebeek,Giel Berden,Marleen C D G Huigen,Sam J Moons,Herman Bentlage,Jos Oomens,Siebolt De Boer,Leo A J Kluijtmans,Udo F H Engelke,Ron A Wevers,Ed Van Der Heeft,Karlien L M Coene,Thomas J Boltje,Jonathan Martens,Arno Van Rooij

doi:10.1038/s42003-021-01909-5

Abstract

The identification of disease biomarkers plays a crucial role in developing diagnostic strategies for inborn errors of metabolism and understanding their pathophysiology. A primary metabolite that accumulates in the inborn error phenylketonuria is phenylalanine, however its levels do not always directly correlate with clinical outcomes. Here we combine infrared ion spectroscopy and NMR spectroscopy to identify the Phe-glucose Amadori rearrangement product as a biomarker for phenylketonuria. Additionally, we find analogous amino acid-glucose metabolites formed in the body fluids of patients accumulating methionine, lysine, proline and citrulline. Amadori rearrangement products are well-known intermediates in the formation of advanced glycation end-products and have been associated with the pathophysiology of diabetes mellitus and ageing, but are now shown to also form under conditions of aminoacidemia. They represent a general class of metabolites for inborn errors of amino acid metabolism that show potential as biomarkers and may provide further insight in disease pathophysiology.

Highlights

These findings suggest that Amadori rearrangement product (ARP) form a general class of metabolites for inborn errors in amino-acid metabolism, which may function as biomarkers and contribute to insights into disease pathophysiology
Infrared ion spectroscopy (IRIS) can bridge the gap between both techniques, providing the direct information on molecular structure that has been lacking in metabolomics studies
As the formation of ARPs is a process that can occur spontaneously, though slowly, under certain chemical conditions, we evaluated the extent of their formation under conditions of sample storage and preparation to evaluate their utility as reliable disease biomarkers

Summary

Introduction

2. Algorithm for generation of unique SMILES notation. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules. Molecular geometries and vibrational frequencies for MMFF94. I. Basis, form, scope, parameterization, and performance of MMFF94. Bringing the MMFF force field to the RDKit: implementation and validation.

Methods

Results

Conclusion