NMRpQuant: an automated software for large scale urinary total protein quantification by one-dimensional 1H NMR profiles

Author:

Takis Panteleimon G12ORCID,Vuckovic Ivan3ORCID,Tan Tricia45ORCID,Denic Aleksandar6ORCID,Lieske John C6ORCID,Lewis Matthew R12ORCID,Macura Slobodan37ORCID

Affiliation:

1. Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London , London SW7 2AZ, UK

2. National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London , London W12 0NN, UK

3. Metabolomics Core, Mayo Clinic , Rochester, MN 55905, USA

4. Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London , London W12 0NN, UK

5. Clinical Biochemistry, Blood Sciences, North West London Pathology, Charing Cross Hospital , London W6 8RF, UK

6. Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic , Rochester, MN 55905, USA

7. Department of Biochemistry and Molecular Biology, Mayo Clinic , Rochester, MN 55905, USA

Abstract

Abstract Summary 1H nuclear magnetic resonance (NMR) spectroscopy is an established bioanalytical technology for metabolic profiling of biofluids in both clinical and large-scale population screening applications. Recently, urinary protein quantification has been demonstrated using the same 1D 1H NMR experimental data captured for metabolic profiling. Here, we introduce NMRpQuant, a freely available platform that builds on these findings with both novel and further optimized computational NMR approaches for rigorous, automated protein urine quantification. The results are validated by interlaboratory comparisons, demonstrating agreement with clinical/biochemical methodologies, pointing at a ready-to-use tool for routine protein urinalyses. Availability and implementation NMRpQuant was developed on MATLAB programming environment. Source code and Windows/macOS compiled applications are available at https://github.com/pantakis/NMRpQuant, and working examples are available at https://doi.org/10.6084/m9.figshare.18737189.v1. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Medical Research Council

National Institute for Health Research

MRC UK Consortium for MetAbolic Phenotyping

NIHR Imperial Biomedical Research Centre

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference6 articles.

1. The human urine metabolome;Bouatra;PLoS One,2013

2. Albuminuria and renal insufficiency prevalence guides population screening: results from the NHANES III;Garg;Kidney Int,2002

3. Analytical reproducibility in 1H NMR-based metabonomic urinalysis;Keun;Chem. Res. Toxicol,2002

4. Deconvoluting interrelationships between concentrations and chemical shifts in urine provides a powerful analysis tool;Takis;Nat. Commun,2017

5. SMolESY: an efficient and quantitative alternative to on-instrument macromolecular 1H-NMR signal suppression;Takis;Chem. Sci,2020

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