Systemic Perturbations in Amine and Kynurenine Metabolism Associated with Acute SARS-CoV-2 Infection and Inflammatory Cytokine Responses

Lawler, Nathan G.; Gray, Nicola; Kimhofer, Torben; Boughton, Berin A.; Gay, Melvin; Yang, Rongchang; Morillon, Aude-Claire; Chin, Sung‐Tong; Ryan, Monique; Begum, Sofina; Bong, Sze-How; Coudert, Jérôme D.; Edgar, Dale W.; Raby, Edward; Pettersson, Sven; Richards, Toby; Holmes, Elaine; Whiley, Luke; Nicholson, Jeremy K.

doi:10.1021/acs.jproteome.1c00052

Cited by 84 publications

(131 citation statements)

References 77 publications

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“…We adopted a targeted approach in which validated analytical methods were used to focus on metabolic parameters that had previously been described as biomarkers ( Tables S1–S4 ) in the active phase of COVID-19. 23 , 26 , 54 This approach enables quantitative phenoreversion to be assessed and patients with incomplete functional recovery to be identified. We examined these variables from both univariate and multivariate modeling perspectives to investigate individual and group recovery behaviors.…”

Section: Introductionmentioning

confidence: 99%

Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome

et al. 2021

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We present a multivariate metabotyping approach to assess the functional recovery of nonhospitalized COVID-19 patients and the possible biochemical sequelae of “Post-Acute COVID-19 Syndrome”, colloquially known as long-COVID. Blood samples were taken from patients ca. 3 months after acute COVID-19 infection with further assessment of symptoms at 6 months. Some 57% of the patients had one or more persistent symptoms including respiratory-related symptoms like cough, dyspnea, and rhinorrhea or other nonrespiratory symptoms including chronic fatigue, anosmia, myalgia, or joint pain. Plasma samples were quantitatively analyzed for lipoproteins, glycoproteins, amino acids, biogenic amines, and tryptophan pathway intermediates using Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. Metabolic data for the follow-up patients ( n = 27) were compared with controls ( n = 41) and hospitalized severe acute respiratory syndrome SARS-CoV-2 positive patients ( n = 18, with multiple time-points). Univariate and multivariate statistics revealed variable patterns of functional recovery with many patients exhibiting residual COVID-19 biomarker signatures. Several parameters were persistently perturbed, e.g., elevated taurine ( p = 3.6 × 10 –3 versus controls) and reduced glutamine/glutamate ratio ( p = 6.95 × 10 –8 versus controls), indicative of possible liver and muscle damage and a high energy demand linked to more generalized tissue repair or immune function. Some parameters showed near-complete normalization, e.g., the plasma apolipoprotein B100/A1 ratio was similar to that of healthy controls but significantly lower ( p = 4.2 × 10 –3 ) than post-acute COVID-19 patients, reflecting partial reversion of the metabolic phenotype (phenoreversion) toward the healthy metabolic state. Plasma neopterin was normalized in all follow-up patients, indicative of a reduction in the adaptive immune activity that has been previously detected in active SARS-CoV-2 infection. Other systemic inflammatory biomarkers such as GlycA and the kynurenine/tryptophan ratio remained elevated in some, but not all, patients. Correlation analysis, principal component analysis (PCA), and orthogonal-partial least-squares discriminant analysis (O-PLS-DA) showed that the follow-up patients were, as a group, metabolically distinct from controls and partially comapped with the acute-phase patients. Significant systematic metabolic differences between asymptomatic and symptomatic follow-up patients were also observed for multiple metabolites. The overall metabolic variance of the symptomatic patients was significantly greater than that of nonsymptomatic patients for multiple parameters (χ 2 p = 0.014). Thus, asymptomatic follow-up patients including those with post-acute COVID-19 Syndrome displayed a ...

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Section: Introductionmentioning

confidence: 99%

Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome

et al. 2021

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“…The concept of metabolomic phenotyping and lipidomic approaches as diagnostic tools using ultra-high-pressure liquid chromatography coupled with mass spectrometry (UHPLC–MS) technologies is well established in the field of inborn errors of metabolism [ 2 , 3 , 4 ], and has more recently been demonstrated in large-scale biomedical research to uncover biomarkers associated with infectious disease and discriminate specific infectious pathogens [ 5 ]. With respect to SARS-CoV-2, early studies have indicated that infection results in changes in an individual’s phenotype, leading to phenoconversion (the change from a normal or healthy state to a disordered pathophysiological state or overt pathology [ 6 ]), which is reflected in perturbations in specific metabolic pathways that are associated with infection [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

et al. 2021

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Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

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“…Up-regulation of the kynurenine pathway, due to in ammation, is inversely related to indole metabolism Despite widespread decreases in most amino acids, circulating levels of kynurenine, a tryptophan catabolite (and other pathway intermediates) were con rmed 1,4,16,20,22 to be signi cantly increased in COVID-19 patients as a function of IL-6 levels (Fig. 2.A-B).…”

Section: Resultsmentioning

confidence: 95%

“…40 Indole metabolites of microbial origin 68 were also signi cantly decreased in COVID-19 patients, especially in those with the poorest outcomes. These decreases may result from tryptophan depletion as a function of kynurenine pathway activation in COVID-19, 1,16,22,23 especially in older males. We con rmed that kynurenine levels correlated with SARS-CoV-2 infection, disease severity, and mortality.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, some of the reported observations could result from in ammation or infection, in general, rather than COVID-19, in particular. For example, activation of the kynurenine pathway 1,16,20,22,23 could result from in ammatory or interferon-activated responses to viral infection, 24 and from downstream activation of indole 2,3dioxygenase. Based on these observational reports, it was correctly 25 predicted that subjects with basal activation of these pathways (e.g., Down syndrome) may be more susceptible to life-threatening COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Biological and Clinical Factors contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19

D’Alessandro

Akpan

Thomas

et al. 2021

Preprint

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The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. Exploratory studies evaluating the impact of COVID-19 infection on the plasma metabolome have been performed, often with small numbers of patients, and with or without relevant control data; however, determining the impact of biological and clinical variables remains critical to understanding potential markers of disease severity and progression. The present large study, including relevant controls, sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831), testing positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on 831 plasma samples from acutely ill patients while in the emergency department, at admission, and during hospitalization. We then performed additional lipidomics analyses of the 60 subjects with the lowest and highest body mass index, either COVID-19 positive or negative. Omics data were correlated to detailed data on patient characteristics and clinical laboratory assays measuring coagulation, hematology and chemistry analytes. Significant changes in arginine/proline/citrulline, tryptophan/indole/kynurenine, fatty acid and acyl-carnitine metabolism emerged as highly relevant markers of disease severity, progression and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half yielding ~ 78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for follow-up mechanistic studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.

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Systemic Perturbations in Amine and Kynurenine Metabolism Associated with Acute SARS-CoV-2 Infection and Inflammatory Cytokine Responses

Cited by 84 publications

References 77 publications

Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome

Incomplete Systemic Recovery and Metabolic Phenoreversion in Post-Acute-Phase Nonhospitalized COVID-19 Patients: Implications for Assessment of Post-Acute COVID-19 Syndrome

Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

Biological and Clinical Factors contributing to the Metabolic Heterogeneity of Hospitalized Patients with and without COVID-19

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