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      Diffusion and Relaxation Edited Proton NMR Spectroscopy of Plasma Reveals a High-Fidelity Supramolecular Biomarker Signature of SARS-CoV-2 Infection

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          Abstract

          We have applied nuclear magnetic resonance spectroscopy based plasma phenotyping to reveal diagnostic molecular signatures of SARS-CoV-2 infection via combined diffusional and relaxation editing (DIRE). We compared plasma from healthy age-matched controls ( n = 26) with SARS-CoV-2 negative non-hospitalized respiratory patients and hospitalized respiratory patients ( n = 23 and 11 respectively) with SARS-CoV-2 rRT-PCR positive respiratory patients ( n = 17, with longitudinal sampling time-points). DIRE data were modelled using principal component analysis and orthogonal projections to latent structures discriminant analysis (O-PLS-DA), with statistical cross-validation indices indicating excellent model generalization for the classification of SARS-CoV-2 positivity for all comparator groups (area under the receiver operator characteristic curve = 1). DIRE spectra show biomarker signal combinations conferred by differential concentrations of metabolites with selected molecular mobility properties. These comprise the following: (a) composite N-acetyl signals from α-1-acid glycoprotein and other glycoproteins (designated GlycA and GlycB) that were elevated in SARS-CoV-2 positive patients [ p = 2.52 × 10 –10 (GlycA) and 1.25 × 10 –9 (GlycB) vs controls], (b) two diagnostic supramolecular phospholipid composite signals that were identified (SPC-A and SPC-B) from the – +N–(CH 3) 3 choline headgroups of lysophosphatidylcholines carried on plasma glycoproteins and from phospholipids in high-density lipoprotein subfractions (SPC-A) together with a phospholipid component of low-density lipoprotein (SPC–B). The integrals of the summed SPC signals (SPC total) were reduced in SARS-CoV-2 positive patients relative to both controls ( p = 1.40 × 10 –7) and SARS-CoV-2 negative patients ( p = 4.52 × 10 –8) but were not significantly different between controls and SARS-CoV-2 negative patients. The identity of the SPC signal components was determined using one and two dimensional diffusional, relaxation, and statistical spectroscopic experiments. The SPC total/GlycA ratios were also significantly different for control versus SARS-CoV-2 positive patients ( p = 1.23 × 10 –10) and for SARS-CoV-2 negatives versus positives ( p = 1.60 × 10 –9). Thus, plasma SPC total and SPC total/GlycA are proposed as sensitive molecular markers for SARS-CoV-2 positivity that could effectively augment current COVID-19 diagnostics and may have value in functional assessment of the disease recovery process in patients with long-term symptoms.

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          Most cited references45

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          Metabonomics: a platform for studying drug toxicity and gene function.

          The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.
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            750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma.

            High-resolution 750 MHz 1H NMR spectra of control human blood plasma have been measured and assigned by the concerted use of a range of spin-echo, two-dimensional J-resolved, and homonuclear and heteronuclear (1H-13C) correlation methods. The increased spectral dispersion and sensitivity at 750 MHz enable the assignment of numerous 1H and 13C resonances from many molecular species that cannot be detected at lower frequencies. This work presents the most comprehensive assignment of the 1H NMR spectra of blood plasma yet achieved and includes the assignment of signals from 43 low M(r) metabolites, including many with complex or strongly coupled spin systems. New assignments are also provided from the 1H and 13C NMR signals from several important macromolecular species in whole blood plasma, i.e., very-low-density, low-density, and high-density lipoproteins, albumin, and alpha 1-acid glycoprotein. The temperature dependence of the one-dimensional and spin-echo 750 MHz 1H NMR spectra of plasma was investigated over the range 292-310 K. The 1H NMR signals from the fatty acyl side chains of the lipoproteins increased substantially with temperature (hence also molecular mobility), with a disproportionate increase from lipids in low-density lipoprotein. Two-dimensional 1H-13C heteronuclear multiple quantum coherence spectroscopy at 292 and 310 K allowed both the direct detection of cholesterol and choline species bound in high-density lipoprotein and the assignment of their signals and confirmed the assignment of most of the lipoprotein resonances.
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              Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.

              We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
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                Author and article information

                Journal
                Anal Chem
                Anal Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                12 February 2021
                02 March 2021
                : 93
                : 8
                : 3976-3986
                Affiliations
                []Australian National Phenome Center, and Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University , Harry Perkins Building, Perth WA6150, Australia
                []Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London , Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, U.K.
                [§ ]Division of Surgery, Medical School, Faculty of Health and Medical Sciences, University of Western Australia , Harry Perkins Building, Murdoch, Perth WA6150, Australia
                []Bruker Biospin GmbH , Silberstreifen, Ettlingen 76275, Germany
                []Institute of Global Health Innovation, Faculty of Medicine, Imperial College London , Level 1, Faculty Building, South Kensington Campus, London SW7 2NA, U.K.
                [# ]Chemistry Department, Universidad del Valle , Cali 76001, Colombia
                Author notes
                Article
                10.1021/acs.analchem.0c04952
                7908063
                33577736
                7dcfdf98-3e84-47e2-8e77-bbefa3a0c6a6
                © 2021 The Authors. Published by American Chemical Society

                This article is made available via the PMC Open Access Subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 25 November 2020
                : 28 January 2021
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                ac0c04952
                ac0c04952

                Analytical chemistry
                Analytical chemistry

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