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Diagnostics Analytical science, COVID-19, Omics

After the Storm

Manfred Spraul

The stages of COVID-19 and its pathology are well characterized, from the contagious phase and symptom onset through to either recovery or, in some cases, death. Briefly, the first one or two days of infection are usually asymptomatic, but the disease is already infectious. The SARS-CoV-2 virus then migrates down the airways, triggering the immune response. For most people, this disease stage is relatively mild and restricted to the upper and conducting airways. However, some individuals experience an abnormal immune response and progress to acute respiratory distress syndrome – a life-threatening condition in which the lungs cannot provide enough oxygen to the body’s vital organs. This exaggerated immune response is associated with the overproduction of cytokines (sometimes called a “cytokine storm”), resulting in dangerous systemic inflammation.

Metabolomic studies can provide insights into these pathobiological processes and, in combination with other cellular and biochemical analyses, can measure the immune response to SARS-CoV-2 infection. Researchers from the Nuclear Magnetic Resonance (NMR) International COVID-19 Research Network have used NMR spectroscopy to establish whether the disease has a metabolic signature at the molecular level by analyzing COVID-19 patients’ plasma metabolic profiles. NMR is ideally suited to this type of research because it enables comprehensive and quantitative investigation of the metabolism and because multiple metabolites can be analyzed in one measurement.

Key research from the Australian National Phenome Center (ANPC) shows a complex pattern of dysregulation of the systemic metabolism caused by SARS-CoV-2 infection (1). The group, led by Jeremy Nicholson, linked this metabolic pattern with multiple organ-specific changes that went beyond primary respiratory symptoms. Similar findings from the Precision Medicine and Metabolism Laboratory at CIC bioGUNE in Spain show considerable changes in the lipoprotein and metabolic profiles of COVID-19 patients – consistent with systemic disease (2). The group described a complex lipoprotein profile in COVID-19 patients and elevated signals for inflammation marker GlycA in the NMR spectra. Using nonlinear statistical models, they separated COVID-19 and non-COVID-19 patients based on these profiles and established a link to increased risk of cardiovascular disease and liver damage in the infected group.

The metabolic truth about long COVID-19

NMR-based metabolomics is also emerging as a groundbreaking research tool to map the course of COVID-19 and predict clinical outcomes in severely affected patients. A growing body of research indicates that a subset of individuals who do not succumb to SARS-CoV-2 infection do not fully recover either. This disease continuation has been described as post-acute COVID-19 syndrome (PACS), or “long COVID-19.” Patients report ongoing symptoms such as extreme fatigue, shortness of breath, chest pain and tightness, and cognitive problems.

Researchers from the COVID-19 NMR Consortium have identified a series of systemic metabolic and lipidomic markers that remain detectable weeks to months after infection and are now translating these biomarkers into a tool that can monitor patients’ risk of PACS, facilitating early and effective treatment. Based on the strong foundations of the Consortium’s work, Nicholson’s group is developing new metrics for measuring systemic metabolic COVID-19 markers throughout PACS patient journey, from the conversion of a healthy state to a disease state (phenoconversion) and back to a healthy state (phenoreversion).

The group assessed the long-term effects of COVID-19 in non-hospitalized patients, analyzed plasma samples using NMR and mass spectrometry (MS) at three months following the acute phase of the disease, and recorded persistent symptoms during the acute phase and at six months following infection (3). The results showed distinct metabolic patterns associated with persistent inflammatory and metabolic instability in the majority of non-hospitalized three-month PACS patients, with one or more symptoms persisting in 57 percent of individuals up to six months following the acute phase. 

Figure 1 shows 10 metabolic parameter comparisons between plasma samples of healthy individuals, SARS-CoV-2 positive individuals, and PACS patients. Some parameters – such as neopterin, glucose, and ABA1 – reverted back to a baseline level in follow-up PACS patients, whereas others – such as taurine and glutamine-to-glutamate ratio – remained at levels characteristic of acute infection.

Figure 1. Univariate plasma metabolic parameters in healthy, SARS-CoV-2-positive, and follow-up patients showing a range of individual and group phenoreversion and non-recovery patterns. A) neopterin, B) kynurenine/tryptophan ratio, C) taurine, D) glutamine/glutamate ratio, E) glucose, F) lipoprotein ABA1 fraction, G) glycoprotein A integral, H) glycoprotein B integral, I) SPC total/glycoprotein A ratio, and J) lipoprotein total plasma cholesterol. Reproduced from (3) in accordance with CC-BY-NC-ND license.

Glutamate plays a major role in cellular energy generation in multiple organ systems, including the kidney, heart, and brain. It is also a key excitatory amino acid in the central nervous system (CNS); elevated levels can induce acute injury. The relationship between glutamate and glutamine is important in immune cell homeostasis, especially for T cells and astrocytes within the CNS, where it acts as an immunomodulator. Additional research is needed to determine the clinical significance of a low glutamate-to-glutamine ratio, but the persistence of a reduced ratio in most PACS patients implies ongoing post-COVID-19 immunometabolic dysregulation.

These results demonstrate variable patterns of functional recovery from COVID-19, with many patients exhibiting residual biomarker signatures months after acute infection. For the first time, researchers have used NMR and MS to measure phenoreversion of some metabolic parameters and report significant metabolic differences between asymptomatic and symptomatic follow-up patients. The Nicholson group’s unique approach in statistically integrating data from NMR and MS into a single dataset using mathematical modeling and machine learning provides a powerful tool for PACS biomarker identification and patient stratification.

Moving forward

Studying PACS requires analytical methods that use highly standardized workflows and can provide reproducible results from large clinical cohorts in longitudinal studies. Given its reproducibility, transferability, and ability to quantitatively measure the concentration and dynamics of a molecule continuously over time, NMR is an increasingly popular tool in these investigations. The COVID-19 NMR Consortium has collected many samples worldwide – and, by collaborating on a global scale using a standardized method, they can share their data to obtain translatable results.

For instance, researchers from the ANPC, CIC bioGUNE, Imperial College London, the University of Valle (Colombia), and Bruker BioSpin conducted an inter-laboratory cross-validation study of two independent patient cohorts from Spain and Australia (4). The results demonstrated the robustness of the NMR-based metabolomics approach and established integrated cross-population biomarkers for acute-phase SARS-CoV-2 infection. Studies like this help us understand how metabolic phenotypes influence disease risk not only in individuals, but also in entire populations.

These findings, together with the ANPC’s PACS research and ongoing research from other members of the COVID-19 NMR Consortium, are important for the potential development of a quantitative metabolic phenotyping approach that can be used to assess individual COVID-19 patient’s risk of long-term systemic disease. Not only that but, in the future, it’s our hope that these approaches can be translated to other diseases as well.

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  1. VR Anicetti et al., “Immunoassay for the detection of E. coli proteins in recombinant DNA derived human growth hormone,” J Immunol Meth, 91, 213 (1986). PMID: 3525680.
  2. T Kimhofer et al., “Integrative modeling of quantitative plasma lipoprotein, metabolic, and amino acid data reveals a multiorgan pathological signature of SARS-CoV-2 infection,” J Proteome Res, 19, 4442 (2020). PMID: 32806897.
  3. C Bruzzone et al., “SARS-CoV-2 infection dysregulates the metabolomic and lipidomic profiles of serum,” iScience, 23, 101645 (2020). PMID: 33043283.
  4. E Holmes et al., “Incomplete systemic recovery and metabolic phenoreversion in post-acute-phase nonhospitalized COVID-19 patients: implications for assessment of post-acute COVID-19 syndrome,” J Proteome Res, 20, 3315 (2021). PMID: 34009992.
  5. R Masuda et al., “Integrative modeling of plasma metabolic and lipoprotein biomarkers of SARS-CoV‑2 infection in Spanish and Australian COVID-19 patient cohorts,” J Proteome Res, 20, 4139 (2021). PMID: 34251833.
About the Authors
Claire Cannet

Clinical Market Manager, Bruker BioSpin, Ettlingen, Germany.

Manfred Spraul

CTO AIC Division, Bruker BioSpin, Ettlingen, Germany.

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