NMR Spectroscopic Windows on the Systemic Effects of SARS-CoV-2 Infection on Plasma Lipoproteins and Metabolites in Relation to Circulating Cytokines.

Samantha Lodge,Elaine Holmes,Philipp Nitschke,Torben Kimhofer,Sze-How Bong,Sofina Begum,Edward Raby,Jerome D Coudert,John C Lindon,Dale Edgar,Ruey Leng Loo,Manfred Spraul,Toby Richards,Hartmut Schaefer,Jeremy K Nicholson

doi:10.1021/acs.jproteome.0c00876

Abstract

To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1–4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.

Highlights

COVID-19 is a complex respiratory and systemic disease caused by infection with the SARS-CoV-2 coronavirus
We have shown that a combination of in vitro diagnostic research (IVDr)-based lipoprotein measurements with mass spectrometry-based amino acid and biogenic amine analysis can discriminate between SARS-CoV-2 positive patients and healthy controls showing a distinctive pattern of underlying pathologies.[7]
Three groups of participants were recruited from the Fiona Stanley and Royal Perth Hospitals: (i) patients who presented COVID-19 disease symptoms and subsequently tested positive for SARS-CoV-2 infection from upper and/or lower respiratory tract swabs by reverse transcription polymerase chain reaction (PCR); (ii) healthy controls who had not exhibited COVID-19 disease symptoms (n = 34 participants with 39 specimens); and (iii) patients with COVID-19 disease symptoms and who tested negative (n = 35 participants with n = 35 specimens)

Summary

Introduction

COVID-19 is a complex respiratory and systemic disease caused by infection with the SARS-CoV-2 coronavirus. The term has previously been used to describe accurately, but more narrowly, the process of cellular metabolic activation by drugs to alter the biochemical response to subsequent drug treatments via enzyme induction.[8] The broader definition applied here extends the concept to cover disease processes and the resultant systemic metabolic state changes. This is appropriate for COVID-19 that expresses itself in multiple clinical subphenotypes involving several major organ systems. The exact pattern of phenoconversion may give insights into the underlying pathophysiological processes and their individual variations

Methods

Results

Conclusion