A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D <sup>1</sup>H NMR

Takis, Panteleimon G.; Jiménez, Beatriz; Al-Saffar, Nada M.S.; Harvey, Nikita; Chekmeneva, Elena; Misra, Shivani; Lewis, Matthew R.

doi:10.1021/acs.analchem.1c00113

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…One of the main analytical methods for metabolic profiling is proton nuclear magnetic resonance (NMR) spectroscopy, which is high throughput, has high reproducibility, and requires minor sample preparation. − Low-molecular metabolites from a wide range of chemical classes may be quantified. ,, In addition, NMR provides detailed information on lipoproteins. − Lipoproteins are lipid carriers consisting of a hydrophobic core made up of triglycerides and cholesterol, surrounded by a monolayer membrane . Different lipoprotein classes have been defined, based on their density, size, lipid composition, and apolipoproteins, and NMR provides information on lipid and apolipoprotein concentrations, particle numbers, and sizes of several lipoprotein subfractions. ,, …”

Section: Introductionmentioning

confidence: 99%

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Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

et al. 2022

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Metabolic profiling is widely used for large-scale association studies, based on biobank material. The main obstacle to the translation of metabolomic findings into clinical application is the lack of standardization, making validation in independent cohorts challenging. One reason for this is sensitivity of metabolites to preanalytical conditions. We present a systematic investigation of the effect of delayed centrifugation on the levels of NMR-measured metabolites and lipoproteins in serum and plasma samples. Blood was collected from 20 anonymous donors, of which 10 were recruited from an obesity clinic. Samples were stored at room temperature until centrifugation after 30 min, 1, 2, 4, or 8 h, which is within a realistic time scenario in clinical practice. The effect of delaying centrifugation on plasma and serum metabolic concentrations, and on concentrations of lipoprotein subfractions, was investigated. Our results show that lipoproteins are only minimally affected by a delay in centrifugation while metabolite levels are more sensitive to a delay. Metabolites significantly increased or decreased in concentration depending on delay duration. Further, we describe differences in the stability of serum and plasma, showing that plasma is more stable for metabolites, while lipoprotein subfractions are equally stable for both types of matrices.

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Section: Introductionmentioning

confidence: 99%

“… 15 − 17 Low-molecular metabolites from a wide range of chemical classes may be quantified. 16 , 18 , 19 In addition, NMR provides detailed information on lipoproteins. 19 − 21 Lipoproteins are lipid carriers consisting of a hydrophobic core made up of triglycerides and cholesterol, surrounded by a monolayer membrane.…”

Section: Introductionmentioning

confidence: 99%

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

et al. 2022

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“…Serum NMR spectra (n = 940) were downloaded from the Metabolights repository (https://www.ebi.ac.uk/ metabolights) (MTBLS395), 22 and the plasma-EDTA 1 H NMR spectra (n = 1,052) were collected from internal databases (see Takis et al 18,23 ). For both multicenter/ independently collected cohorts, solution 1 H NMR spectra were acquired using a Bruker 600 MHz spectrometer (Bruker BioSpin).…”

Section: Nmr Spectra Acquisition/ Processingmentioning

confidence: 99%

Mapping of ¹H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

Takis,

Aggelidou,

Sands

et al. 2023

Magnetic Reson in Chemistry

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One‐dimensional (1D) proton‐nuclear magnetic resonance (1H‐NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large‐scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma‐EDTA spectra, were able to successfully predict the 1H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv1/2) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of 1H NMR chemical shifts based solely on chemically defined constraints.

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“…Both on-instrument and computational methods have been developed, which mitigate those endogenous interferences and their effect on the smallmolecule profile. 3,4 In an analogous manner, the broad signal produced by derivatized urea in the GC-MS analysis of urine is specifically problematic for metabolic profiling applications, requiring urease treatment to prevent metabolite masking. 5,6 LC-MS has been proved as a powerful tool for the analysis of biofluids, providing analytically sensitive and specific measurements of small-molecule profiles with direct compatibility to liquid samples.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the presence of macromolecules (e.g., proteins) in biofluids produces broad NMR signals, which overlap the desired small-molecule signals and confound their interpretation. Both on-instrument and computational methods have been developed, which mitigate those endogenous interferences and their effect on the small-molecule profile. , In an analogous manner, the broad signal produced by derivatized urea in the GC-MS analysis of urine is specifically problematic for metabolic profiling applications, requiring urease treatment to prevent metabolite masking. , …”

Section: Introductionmentioning

confidence: 99%

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

et al. 2022

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Liquid chromatography−mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple "dilute-and-shoot" approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences that lack consideration in the relevant metabolomics literature. In this work, we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate prolinecontaining dipeptides: N,N,N-trimethyl-L-alanine-L-proline betaine (L,L-TMAP) and N,N-dimethyl-L-proline-L-proline betaine (L,L-DMPP), the latter reported here for the first time. Offline LC-MS/MS, magnetic resonance mass spectrometry (MRMS), and nuclear magnetic resonance (NMR) spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the coexistence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of column temperature and mobile-phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

Cited by 6 publications

References 17 publications

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

Mapping of ¹H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR

Cited by 6 publications

References 17 publications

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

Effect of Delayed Centrifugation on the Levels of NMR-Measured Lipoproteins and Metabolites in Plasma and Serum Samples

Mapping of 1H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

Mapping of ¹H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products