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

SARS-CoV2 variants differentially impact on the plasma metabolome

INTRODUCTION

Infection with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) leads to COVID19 disease and caused a worldwide pandemic in 2019. Since the first wave of infections, there has been significant antigenic shifts, leading to the emergence of new variants. Today, infections have shifted away from the severe, fatal infection seen in 2019.

OBJECTIVE

This study aimed to assess how the plasma metabolomes from patients varied with infection with different strains and could reflect disease severity.

METHODS

Patients with COVID19 not requiring intensive care were recruited between January 2021 and May 2022 from the Queen Elizabeth Hospital Birmingham; 33 patients with alpha, 13 delta and 14 omicron variants. These were compared to 26 age matched contemporaneously recruited controls. Plasma samples were extracted into chloroform/methanol/water (1:2.5/1 v/v) and assessed by flow injection electrospray mass spectrometry (FIE-MS) using an Exactive Orbitrap mass spectrometer. Derived data were assessed using the R based MetaboAnalyst platform.

RESULTS

Plasma metabolomes from COVID19 patients were clearly different from controls. Metabolite variation could be related to infection with different SARS-CoV2 variants. Variant showed different levels of some phospholipids, ganglioside GD1a and a dihydroxyvitamin D3 derivative. Correlations of the plasma metabolomes indicated negative correlations between selected phospholipids and the levels of C-reactive protein, creatinine, neutrophil and D-dimer.

CONCLUSION

The plasma metabolomes of COVID19 patients show changes, particularly in phospholipids, which could reflect disease severity and SARS-CoV2 variant infection.

Longitudinal study on immunologic, lipoproteomic, and inflammatory responses indicates the safety of sequential COVID-19 vaccination

COVID-19 vaccines are crucial in reducing SARS-CoV-2 transmission and severe health outcomes. Despite widespread administration, their long-term systemic effects on human metabolism remain inadequately understood. This longitudinal study aims to evaluate IgG responses, 34 cytokines, 112 lipoproteins, and 21 low-molecular-weight metabolites in 33 individuals receiving two to four COVID-19 vaccine doses. Changes in metabolic profiles for the first 16 days post each dose of vaccine, and up to 480 days post-initial dose, were compared to baseline (before vaccination). Additionally, metabolic profiles of vaccinated participants were compared to a reference cohort of unvaccinated individuals without prior exposure to SARS-CoV-2 infection (controls) and SARS-CoV-2 cases. Positive IgG responses were observed in 78.8% (N = 26) of participants after the first dose, reaching 100% with subsequent doses. The most common side effects were localized pain at the injection site and "flu-like" symptoms, reported by > 50% of participants. Systemic side effects, e.g., sore lymph nodes, fatigue, and brain fog, were reported but showed no significant correlations to IgG responses. Transient temporal changes were observed for cytokine IP10 (CXCL10) and glutamic acid around the third vaccine dose. Compared to the reference cohort, 497 vaccinated samples (95.0%) had profiles similar to the controls, while the remaining 26 samples with prior infection exposures were similar to mild cases of SARS-CooV-2 infection. In conclusion, COVID-19 vaccination did not induce lasting changes in inflammatory and metabolic responses, nor did it induce changes similar to mild cases of SARS-CoV-2 infection. This supports the metabolic safety of the vaccine and contributes to increased vaccine confidence. KEY MESSAGES: Minimal changes in inflammatory/metabolic markers up to 480 days post-vaccination. Transient increase in IP10 (CXCL10) and glutamic acid around the third dose. Post-vaccination IgG response did not alter metabolic profiles like SARS-CoV-2 cases. Our findings provide insights into the safety of repeated COVID-19 vaccinations.

Metabolomic and lipoproteomic differences and similarities between COVID-19 and other types of pneumonia

COVID-19 infection has revealed significant effects on the human blood metabolome and lipoproteome, which have been coherently observed in different cohorts worldwide and across the various waves of SARS-CoV-2 pandemic. As one of the main clinical manifestations of COVID-19 is a severe acute respiratory illness, it is pertinent to explore whether this metabolic/lipoproteomic disturbance is associated with the respiratory symptoms. To this purpose we are here reporting comparative1H NMR analyses of the plasma of 252 COVID-19 patients and of patients with non-COVID-19 interstitial (24 individuals) or lobar (21 individuals) pneumonia, all matched by age, gender and disease severity. The analysis is based on 24 metabolites and 114 lipoprotein parameters. Several common traits are observed among the three groups, albeit with some peculiar features characteristic of each group. The main differences were observed between the lobar cases and all the others.

Starve a cold or feed a fever? Identifying cellular metabolic changes following infection and exposure to SARS-CoV-2

Viral infections induce major shifts in cellular metabolism elicited by active viral replication and antiviral responses. For the virus, harnessing cellular metabolism and evading changes that limit replication are essential for productive viral replication. In contrast, the cellular response to infection disrupts metabolic pathways to prevent viral replication and promote an antiviral state in the host cell and neighboring bystander cells. This competition between the virus and cell results in measurable shifts in cellular metabolism that differ depending on the virus, cell type, and extracellular environment. The resulting metabolic shifts can be observed and analyzed using global metabolic profiling techniques to identify pathways that are critical for either viral replication or cellular defense. SARS-CoV-2 is a respiratory virus that can exhibit broad tissue tropism and diverse, yet inconsistent, symptomatology. While the factors that determine the presentation and severity of SARS-CoV-2 infection remain unclear, metabolic syndromes are associated with more severe manifestations of SARS-CoV-2 disease. Despite these observations a critical knowledge gap remains between cellular metabolic responses and SARS-CoV-2 infection. Using a well-established untargeted metabolomics analysis workflow, we compared SARS-CoV-2 infection of human lung carcinoma cells. We identified significant changes in metabolic pathways that correlate with either productive or non-productive viral infection. This information is critical for characterizing the factors that contribute to SARS-CoV-2 replication that could be targeted for therapeutic interventions to limit viral disease.

Quantitative Blood Serum IVDr NMR Spectroscopy in Clinical Metabolomics of Cancer, Neurodegeneration, and Internal Medicine

Despite more than two decades of metabolomics having joined the "omics" scenery, to date only a few novel blood metabolite biomarkers have found their way into the clinic. This is changing now by massive large-scale population metabolic phenotyping for both healthy and disease cohorts. Here, nuclear magnetic resonance (NMR) spectroscopy is a method of choice, as typical blood serum markers can be easily quantified and by knowledge of precise reference concentrations, more and more NMR-amenable biomarkers are established, moving NMR from research to clinical application. Besides customized approaches, to date two major commercial platforms have evolved based on either 600 MHz (14.1 Tesla) or 500 MHz (11.7 Tesla) high-field NMR systems. This chapter provides an introduction into the field of quantitative in vitro diagnostics research (IVDr) NMR at 600 MHz and its application within clinical research of cancer, neurodegeneration, and internal medicine.

Differential analysis of lipoprotein and glycoprotein profiles in bacterial infections and COVID-19 using proton nuclear magnetic resonance and machine learning

Background

We scrutinized variations in the proton nuclear magnetic resonance (1H NMR) lipoprotein and glycoprotein profiles among hospitalized individuals with infectious diseases.

Methods

We obtained sera from 124 patients with COVID-19, 50 patients with catheter-related bacterial infections, and 50 healthy volunteers. Results were interpreted using machine learning.

Results

COVID-19 patients had bigger and more abundant VLDL particles than the control group and higher VLDL-cholesterol and VLDL-triglyceride concentrations. Patients with bacterial infections showed similar trends, but differences often did not reach statistical significance. Both types of patients showed lower LDL-cholesterol concentrations than the controls. LDL were larger, and the number of particles was lower than that of the healthy individuals. HDL particles had decreased cholesterol and increased triglycerides. Small particles were reduced. Glycoproteins were increased in both groups of patients. All these alterations were more pronounced in COVID-19 patients than those with bacterial infections. The diagnostic accuracy of these profiles exceeded 90 % when distinguishing between healthy individuals and patients, and 85 % when differentiating between the two patient groups.

Conclusion

Our findings highlight the potential of 1H NMR analysis for lipoproteins and glycoproteins as infection biomarkers. Additionally, they reveal differences between viral and bacterial infections, shedding light on an area with promising clinical significance.

Metabolomic profiling of maternal plasma identifies inverse associations of acetate and urea with anti-SARS-CoV-2 antibody titers following COVID-19 vaccination during pregnancy

We conducted a comprehensive metabolomic analysis of plasma samples obtained from pregnant women who displayed varying post-vaccination antibody titers after receiving mRNA-1273-SARS-CoV-2 vaccines. The study involved 62 pregnant women, all of whom had been vaccinated after reaching 24 weeks of gestation. To quantify post-vaccination plasma antibody titers, we employed binding antibody units (BAU) in accordance with the World Health Organization International Standard. Subsequently, we classified the study participants into three distinct BAU/mL categories: those with high titers (above 2000), medium titers (ranging from 1000 to 2000), and low titers (below 1000). Plasma metabolomic profiling was conducted using 1H nuclear magnetic resonance spectroscopy, and the obtained data were correlated with the categorized antibody titers. Notably, in pregnant women exhibiting elevated anti-SARS-CoV-2 antibody titers, reduced plasma concentrations of acetate and urea were observed. A significant negative correlation between these compounds and antibody titers was also evident. An analysis of metabolomics pathways revealed significant inverse associations between antibody titers and four distinct amino acid metabolic pathways: (1) biosynthesis of phenylalanine, tyrosine, and tryptophan; (2) biosynthesis of valine, leucine, and isoleucine; (3) phenylalanine metabolism; and (4) degradation of valine, leucine, and isoleucine. Additionally, an association between the synthesis and degradation pathways of ketone bodies was evident. In conclusion, we identified different metabolic pathways that underlie the diverse humoral responses triggered by COVID-19 mRNA vaccines during pregnancy. Our data hold significant implications for refining COVID-19 vaccination approaches in expectant mothers. KEY MESSAGES : Anti-SARS-CoV-2 antibody titers decline as the number of days since COVID-19 vaccination increases. Anti-SARS-CoV-2 antibody titers are inversely associated with acetate, a microbial-derived metabolite, and urea. Amino acid metabolism is significantly associated with SARS-CoV-2 antibody titers.

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning

BACKGROUND

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.

METHODS

Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.

RESULTS

The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.

CONCLUSIONS

Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Starve a cold or feed a fever? Identifying cellular metabolic changes following infection and exposure to SARS-CoV-2

Viral infections induce major shifts in cellular metabolism elicited by active viral replication and antiviral responses. For the virus, harnessing cellular metabolism and evading changes that limit replication are essential for productive viral replication. In contrast, the cellular response to infection disrupts metabolic pathways to prevent viral replication and promote an antiviral state in the host cell and neighboring bystander cells. This competition between the virus and cell results in measurable shifts in cellular metabolism that differ depending on the virus, cell type, and extracellular environment. The resulting metabolic shifts can be observed and analyzed using global metabolic profiling techniques to identify pathways that are critical for either viral replication or cellular defense. SARS-CoV-2 is a respiratory virus that can exhibit broad tissue tropism and diverse, yet inconsistent, symptomatology. While the factors that determine the presentation and severity of SARS-CoV-2 infection remain unclear, metabolic syndromes are associated with more severe manifestations of SARS-CoV-2 disease. Despite these observations a critical knowledge gap remains between cellular metabolic responses and SARS-CoV-2 infection. Using a well-established untargeted metabolomics analysis workflow, we compared SARS-CoV-2 infection of human lung carcinoma cells. We identified significant changes in metabolic pathways that correlate with either productive or non-productive viral infection. This information is critical for characterizing the factors that contribute to SARS-CoV-2 replication that could be targeted for therapeutic interventions to limit viral disease.

Stratification of Sepsis Patients on Admission into the Intensive Care Unit According to Differential Plasma Metabolic Phenotypes

Delayed diagnosis of patients with sepsis or septic shock is associated with increased mortality and morbidity. UPLC-MS and NMR spectroscopy were used to measure panels of lipoproteins, lipids, biogenic amines, amino acids, and tryptophan pathway metabolites in blood plasma samples collected from 152 patients within 48 h of admission into the Intensive Care Unit (ICU) where 62 patients had no sepsis, 71 patients had sepsis, and 19 patients had septic shock. Patients with sepsis or septic shock had higher concentrations of neopterin and lower levels of HDL cholesterol and phospholipid particles in comparison to nonsepsis patients. Septic shock could be differentiated from sepsis patients based on different concentrations of 10 lipids, including significantly lower concentrations of five phosphatidylcholine species, three cholesterol esters, one dihydroceramide, and one phosphatidylethanolamine. The Supramolecular Phospholipid Composite (SPC) was reduced in all ICU patients, while the composite markers of acute phase glycoproteins were increased in the sepsis and septic shock patients within 48 h admission into ICU. We show that the plasma metabolic phenotype obtained within 48 h of ICU admission is diagnostic for the presence of sepsis and that septic shock can be differentiated from sepsis based on the lipid profile.

Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD+ pathway and SIRT1 activation

OBJECTIVES

The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata.

METHODS

The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data.

RESULTS

Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway.

CONCLUSIONS

Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.

Rapid and Self-Administrable Capillary Blood Microsampling Demonstrates Statistical Equivalence with Standard Venous Collections in NMR-Based Lipoprotein Analysis

We investigated plasma and serum blood derivatives from capillary blood microsamples (500 μL, MiniCollect tubes) and corresponding venous blood (10 mL vacutainers). Samples from 20 healthy participants were analyzed by 1H NMR, and 112 lipoprotein subfraction parameters; 3 supramolecular phospholipid composite (SPC) parameters from SPC1, SPC2, and SPC3 subfractions; 2 N-acetyl signals from α-1-acid glycoprotein (Glyc), GlycA, and GlycB; and 3 calculated parameters, SPC (total), SPC3/SPC2, and Glyc (total) were assessed. Using linear regression between capillary and venous collection sites, we explained that agreement (Adj. R2 ≥ 0.8, p < 0.001) was witnessed for 86% of plasma parameters (103/120) and 88% of serum parameters (106/120), indicating that capillary lipoprotein, SPC, and Glyc concentrations follow changes in venous concentrations. These results indicate that capillary blood microsamples are suitable for sampling in remote areas and for high-frequency longitudinal sampling of the majority of lipoproteins, SPCs, and Glycs.

Integrative Molecular Structure Elucidation and Construction of an Extended Metabolic Pathway Associated with an Ancient Innate Immune Response in COVID-19 Patients

We present compelling evidence for the existence of an extended innate viperin-dependent pathway, which provides crucial evidence for an adaptive response to viral agents, such as SARS-CoV-2. We show the in vivo biosynthesis of a family of novel endogenous cytosine metabolites with potential antiviral activities. Two-dimensional nuclear magnetic resonance (NMR) spectroscopy revealed a characteristic spin-system motif, indicating the presence of an extended panel of urinary metabolites during the acute viral replication phase. Mass spectrometry additionally enabled the characterization and quantification of the most abundant serum metabolites, showing the potential diagnostic value of the compounds for viral infections. In total, we unveiled ten nucleoside (cytosine- and uracil-based) analogue structures, eight of which were previously unknown in humans allowing us to propose a new extended viperin pathway for the innate production of antiviral compounds. The molecular structures of the nucleoside analogues and their correlation with an array of serum cytokines, including IFN-α2, IFN-γ, and IL-10, suggest an association with the viperin enzyme contributing to an ancient endogenous innate immune defense mechanism against viral infection.

Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants (n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m2) with (n = 57) and without diabetes (n = 58) or overweight/obese (BMI > 24.99 kg/m2) with (n = 102) and without diabetes (n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Gut microbiota and metabolomics profiles in patients with chronic stable angina and acute coronary syndrome

Cardiovascular disease (CVD) is the leading cause of death worldwide. The gut microbiota and its associated metabolites may be involved in the development and progression of CVD, although the mechanisms and impact on clinical outcomes are not fully understood. This study investigated the gut microbiome profile and associated metabolites in patients with chronic stable angina (CSA) and acute coronary syndrome (ACS) compared with healthy controls. Bacterial alpha diversity in stool from patients with ACS or CSA was comparable to healthy controls at both baseline and follow-up visits. Differential abundance analysis identified operational taxonomic units (OTUs) assigned to commensal taxa differentiating patients with ACS from healthy controls at both baseline and follow-up. Patients with CSA and ACS had significantly higher levels of trimethylamine N-oxide compared with healthy controls (CSA: 0.032 ± 0.023 mmol/L, P < 0.01 vs. healthy, and ACS: 0.032 ± 0.023 mmol/L, P = 0.02 vs. healthy, respectively). Patients with ACS had reduced levels of propionate and butyrate (119 ± 4 vs. 139 ± 5.1 µM, P = 0.001, and 14 ± 4.3 vs. 23.5 ± 8.1 µM, P < 0.001, respectively), as well as elevated serum sCD14 (2245 ± 75.1 vs. 1834 ± 45.8 ng/mL, P < 0.0001) and sCD163 levels (457.3 ± 31.8 vs. 326.8 ± 20.7 ng/mL, P = 0.001), compared with healthy controls at baseline. Furthermore, a modified small molecule metabolomic and lipidomic signature was observed in patients with CSA and ACS compared with healthy controls. These findings provide evidence of a link between gut microbiome composition and gut bacterial metabolites with CVD. Future time course studies in patients to observe temporal changes and subsequent associations with gut microbiome composition are required to provide insight into how these are affected by transient changes following an acute coronary event.NEW & NOTEWORTHY The study found discriminative microorganisms differentiating patients with acute coronary syndrome (ACS) from healthy controls. In addition, reduced levels of certain bacterial metabolites and elevated sCD14 and sCD163 were observed in patients with ACS compared with healthy controls. Furthermore, modified small molecule metabolomic and lipidomic signatures were found in both patient groups. Although it is not known whether these differences in profiles are associated with disease development and/or progression, the findings provide exciting options for potential new disease-related mechanism(s) and associated therapeutic target(s).

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

COVID-19: A complex disease with a unique metabolic signature

Plasma of COVID-19 patients contains a strong metabolomic/lipoproteomic signature, revealed by the NMR analysis of a cohort of >500 patients sampled during various waves of COVID-19 infection, corresponding to the spread of different variants, and having different vaccination status. This composite signature highlights common traits of the SARS-CoV-2 infection. The most dysregulated molecules display concentration trends that scale with disease severity and might serve as prognostic markers for fatal events. Metabolomics evidence is then used as input data for a sex-specific multi-organ metabolic model. This reconstruction provides a comprehensive view of the impact of COVID-19 on the entire human metabolism. The human (male and female) metabolic network is strongly impacted by the disease to an extent dictated by its severity. A marked metabolic reprogramming at the level of many organs indicates an increase in the generic energetic demand of the organism following infection. Sex-specific modulation of immune response is also suggested.

Stratification of hypertension and SARS-CoV-2 infection by quantitative NMR spectroscopy of human blood serum

BACKGROUND

Diagnostic approaches like the nuclear magnetic resonance spectroscopy (NMR) based quantification of metabolites, lipoproteins, and inflammation markers has helped to identify typical alterations in the blood serum of COVID-19 patients. However, confounders such as sex, and comorbidities, which strongly influence the metabolome, were often not considered. Therefore, the aim of this NMR study was to consider sex, as well as arterial hypertension (AHT), when investigating COVID-19-positive serum samples in a large age-and sex matched cohort.

METHODS

NMR serum data from 329 COVID-19 patients were compared with 305 healthy controls. 134 COVID-19 patients were affected by AHT. These were analyzed together with NMR data from 58 hypertensives without COVID-19. In addition to metabolite, lipoprotein, and glycoprotein data from NMR, common laboratory parameters were considered. Sex was considered in detail for all comparisons.

RESULTS

Here, we show that several differences emerge from previous NMR COVID-19 studies when AHT is considered. Especially, the previously described triglyceride-rich lipoprotein profile is no longer observed in COVID-19 patients, nor an increase in ketone bodies. Further alterations are a decrease in glutamine, leucine, isoleucine, and lysine, citric acid, HDL-4 particles, and total cholesterol. Additionally, hypertensive COVID-19 patients show higher inflammatory NMR parameters than normotensive patients.

CONCLUSIONS

We present a more precise picture of COVID-19 blood serum parameters. Accordingly, considering sex and comorbidities should be included in future metabolomics studies for improved and refined patient stratification. Due to metabolic similarities with other viral infections, these results can be applied to other respiratory diseases in the future.

Integrated NMR and MS Analysis of the Plasma Metabolome Reveals Major Changes in One-Carbon, Lipid, and Amino Acid Metabolism in Severe and Fatal Cases of COVID-19

Brazil has the second-highest COVID-19 death rate worldwide, and Rio de Janeiro is among the states with the highest rate in the country. Although vaccine coverage has been achieved, it is anticipated that COVID-19 will transition into an endemic disease. It is concerning that the molecular mechanisms underlying clinical evolution from mild to severe disease, as well as the mechanisms leading to long COVID-19, are not yet fully understood. NMR and MS-based metabolomics were used to identify metabolites associated with COVID-19 pathophysiology and disease outcome. Severe COVID-19 cases (n = 35) were enrolled in two reference centers in Rio de Janeiro within 72 h of ICU admission, alongside 12 non-infected control subjects. COVID-19 patients were grouped into survivors (n = 18) and non-survivors (n = 17). Choline-related metabolites, serine, glycine, and betaine, were reduced in severe COVID-19, indicating dysregulation in methyl donors. Non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid, and N-acetylserine, indicating liver and kidney dysfunction. Several changes were greater in women; thus, patients' sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. These metabolic alterations may be useful to monitor organ (dys) function and to understand the pathophysiology of acute and possibly post-acute COVID-19 syndromes.

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls (n = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

COVID-19-The Shift of Homeostasis into Oncopathology or Chronic Fibrosis in Terms of Female Reproductive System Involvement

The COVID-19 pandemic caused by the SARS-CoV-2 coronavirus remains a global public health concern due to the systemic nature of the infection and its long-term consequences, many of which remain to be elucidated. SARS-CoV-2 targets endothelial cells and blood vessels, altering the tissue microenvironment, its secretion, immune-cell subpopulations, the extracellular matrix, and the molecular composition and mechanical properties. The female reproductive system has high regenerative potential, but can accumulate damage, including due to SARS-CoV-2. COVID-19 is profibrotic and can change the tissue microenvironment toward an oncogenic niche. This makes COVID-19 and its consequences one of the potential regulators of a homeostasis shift toward oncopathology and fibrosis in the tissues of the female reproductive system. We are looking at SARS-CoV-2-induced changes at all levels in the female reproductive system.

Metabolic Adaptations Correlated with Antibody Response after Immunization with Inactivated SARS-CoV-2 in Brazilian Subjects

The adsorbed vaccine SARS-CoV-2 (inactivated) produced by Sinovac (SV) was the first vaccine against COVID-19 to be used in Brazil. To understand the metabolic effects of SV in Brazilian subjects, NMR-based metabolomics was used, and the immune response was studied in Brazilian subjects. Forty adults without (group^-, n = 23) and with previous COVID-19 infection (group⁺, n = 17) were followed-up for 90 days postcompletion of the vaccine regimen. After 90 days, our results showed that subjects had increased levels of lipoproteins, lipids, and N-acetylation of glycoproteins (NAG) as well as decreased levels of amino acids, lactate, citrate, and 3-hydroxypropionate. NAG and threonine were the highest correlated metabolites with N and S proteins, and neutralizing Ab levels. This study sheds light on the immunometabolism associated with the use of SV in Brazilian subjects from Rio de Janeiro and identifies potential metabolic markers associated with the immune status.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Background

Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods

This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results

Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion

The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Metabolomics as a powerful tool for diagnostic, pronostic and drug intervention analysis in COVID-19

COVID-19 currently represents one of the major health challenges worldwide. Albeit its infectious character, with onset affectation mainly at the respiratory track, it is clear that the pathophysiology of COVID-19 has a systemic character, ultimately affecting many organs. This feature enables the possibility of investigating SARS-CoV-2 infection using multi-omic techniques, including metabolomic studies by chromatography coupled to mass spectrometry or by nuclear magnetic resonance (NMR) spectroscopy. Here we review the extensive literature on metabolomics in COVID-19, that unraveled many aspects of the disease including: a characteristic metabotipic signature associated to COVID-19, discrimination of patients according to severity, effect of drugs and vaccination treatments and the characterization of the natural history of the metabolic evolution associated to the disease, from the infection onset to full recovery or long-term and long sequelae of COVID.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

NMR-Metabolomics in COVID-19 Research

COVID-19 stands for Corona Virus Disease 2019, which starts as a viral infection that provokes illness with different symptoms and severity. The infected individuals can be asymptomatic or present with mild, moderate, severe, and critical illness with acute respiratory distress syndrome (ARDS), acute cardiac injury, and multiorgan failure. When the virus enters the cells, it replicates and provokes responses. Most diseased individuals resolve the problems in a short time but unfortunately, some may die, and almost 3 years after the first reported cases, COVID-19 still kills thousands per day worldwide. One of the problems in not curing the viral infection is that the virus passes by undetected in cells. This can be caused by the lack of pathogen-associated molecular patterns (PAMPs) that start an orchestrated immune response, such as activation of type 1 interferons (IFNs), inflammatory cytokines, chemokines, and antiviral defenses. Before all of these events can happen, the virus uses the infected cells and numerous small molecules as sources of energy and building blocks for newly synthesized viral nanoparticles that travel to and infect other host cells. Therefore, studying the cell metabolome and metabolomic changes in biofluids might give insights into the state of the viral infection, viral loads, and defense response. NMR-metabolomics can help in solving the real-time host interactions by monitoring concentration changes in metabolites. This chapter addresses the state of the art of COVIDomics by NMR analyses and presents exemplified biomolecules identified in different world regions and gravities of illness as potential biomarkers.

Prospective Metabolomic Studies in Precision Medicine: The AKRIBEA Project

For a long time, conventional medicine has analysed biomolecules to diagnose diseases. Yet, this approach has proven valid only for a limited number of metabolites and often through a bijective relationship with the disease (i.e. glucose relationship with diabetes), ultimately offering incomplete diagnostic value. Nowadays, precision medicine emerges as an option to improve the prevention and/or treatment of numerous pathologies, focusing on the molecular mechanisms, acting in a patient-specific dimension, and leveraging multiple contributing factors such as genetic, environmental, or lifestyle. Metabolomics grasps the required subcellular complexity while being sensitive to all these factors, which results in a most suitable technique for precision medicine. The aim of this chapter is to describe how NMR-based metabolomics can be integrated in the design of a precision medicine strategy, using the Precision Medicine Initiative of the Basque Country (the AKRIBEA project) as a case study. To that end, we will illustrate the procedures to be followed when conducting an NMR-based metabolomics study with a large cohort of individuals, emphasizing the critical points. The chapter will conclude with the discussion of some relevant biomedical applications.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Quantitative Serum NMR Spectroscopy Stratifies COVID-19 Patients and Sheds Light on Interfaces of Host Metabolism and the Immune Response with Cytokines and Clinical Parameters

The complex manifestations of COVID-19 are still not fully decoded on the molecular level. We combined quantitative the nuclear magnetic resonance (NMR) spectroscopy serum analysis of metabolites, lipoproteins and inflammation markers with clinical parameters and a targeted cytokine panel to characterize COVID-19 in a large (534 patient samples, 305 controls) outpatient cohort of recently tested PCR-positive patients. The COVID-19 cohort consisted of patients who were predominantly in the initial phase of the disease and mostly exhibited a milder disease course. Concerning the metabolic profiles of SARS-CoV-2-infected patients, we identified markers of oxidative stress and a severe dysregulation of energy metabolism. NMR markers, such as phenylalanine, inflammatory glycoproteins (Glyc) and their ratio with the previously reported supramolecular phospholipid composite (Glyc/SPC), showed a predictive power comparable to laboratory parameters such as C-reactive protein (CRP) or ferritin. We demonstrated interfaces between the metabolism and the immune system, e.g., we could trace an interleukin (IL-6)-induced transformation of a high-density lipoprotein (HDL) to a pro-inflammatory actor. Finally, we showed that metadata such as age, sex and constitution (e.g., body mass index, BMI) need to be considered when exploring new biomarkers and that adding NMR parameters to existing diagnoses expands the diagnostic toolbox for patient stratification and personalized medicine.

Computational simulation of 1 H NMR profiles of complex biofluid analyte mixtures at differential operating frequencies: Applications to low-field benchtop spectra

Estimations of accurate and reliable NMR chemical shift values, coupling patterns and constants within a reasonable timeframe remain significantly challenging, and the unavailability of reliable software strategies for the prediction of low-field (e.g., 60 MHz) spectra from those acquired at higher operating frequencies hampers their direct comparison. Hence, this study explored the applications of accessible software options for predicting these parameters in the ¹ H NMR profiles of analytes as a function of magnetic field strength; this was performed for individual analytes and also for complex biofluid matrices featured in metabolomics investigations. For this purpose, results from the very first successful experimental acquisition and simulation of the ¹ H NMR profiles of intact human salivary supernatant samples on a 60 MHz benchtop spectrometer were evaluated. Using salivary metabolite concentrations determined at 400 MHz, it was demonstrated that simulation of the low-field spectra of five biomolecules with the most prominent ¹ H resonances detectable allowed multiple component fits to be applied to experimental spectra. Hence, these salivary ¹ H NMR profiles could be successfully predicted throughout the 45-600 MHz operating frequency range. With the exception of propionate resonance multiplets, which revealed more complex coupling patterns at low field and required more astute computational and fitting options, valuable quantitative metabolomics data on salivary acetate, formate, methanol and glycine could be attained from low-field spectrometres. These studies are both timely and pertinent in view of the recent advancement of low-field benchtop NMR facilities for diagnostically significant biomarker tracking in biofluids. Experiments performed with added ammonium chloride to facilitate the release of salivary metabolites from biopolymer binding sites provided evidence that a small but nevertheless significant proportion of propionate, but not lactate, was bound to such sites, an observation of much relevance to biomolecule quantification in salivary metabolomics investigations.

Congruence and Complementarity of Differential Mobility Spectrometry and NMR Spectroscopy for Plasma Lipidomics

The lipid composition of lipoprotein particles is determinative of their respective formation and function. In turn, the combination and correlation of nuclear magnetic resonance (NMR)-based lipoprotein measurements with mass spectrometry (MS)-based lipidomics is an appealing technological combination for a better understanding of lipid metabolism in health and disease. Here, we developed a combined workflow for subsequent NMR- and MS-based analysis on single sample aliquots of human plasma. We evaluated the quantitative agreement of the two platforms for lipid quantification and benchmarked our combined workflow. We investigated the congruence and complementarity between the platforms in order to facilitate a better understanding of patho-physiological lipoprotein and lipid alterations. We evaluated the correlation and agreement between the platforms. Next, we compared lipid class concentrations between healthy controls and rheumatoid arthritis patient samples to investigate the consensus among the platforms on differentiating the two groups. Finally, we performed correlation analysis between all measured lipoprotein particles and lipid species. We found excellent agreement and correlation (r > 0.8) between the platforms and their respective diagnostic performance. Additionally, we generated correlation maps detailing lipoprotein/lipid interactions and describe disease-relevant correlations.

Quantitative NMR-Based Lipoprotein Analysis Identifies Elevated HDL-4 and Triglycerides in the Serum of Alzheimer’s Disease Patients

Alzheimer’s disease (AD) is the most common form of dementia in the elderly and has been associated with changes in lipoprotein metabolism. We performed quantitative lipoprotein analysis in a local cohort of cognitively impaired elderly and control subjects using standardized nuclear magnetic resonance (NMR) spectroscopy. A commercially available quantitative NMR-based assay covering 112 lipoprotein main and subtype variables was used to investigate blood serum samples from a moderate cohort size of 161 persons (71 female, 90 male), including measures of quality control. Additionally, clinical metadata and cerebrospinal fluid AD biomarkers were collected and used for analysis. High-density lipoprotein (HDL) HDL-4 subfraction levels were mostly high in female individuals with mild cognitive impairment (MCI), followed by AD. Low-density lipoprotein (LDL) LDL-2 cholesterol was slightly elevated in male AD patients. HDL-2 apolipoprotein Apo-A1, HDL-2 phospholipids, and HDL-3 triglycerides were highly abundant in AD and MCI women compared to men. When considering clinical biomarkers (Aβ, tau), very low-density lipoprotein (VLDL) VLDL-1 and intermediate-density lipoprotein (IDL) triglycerides were substantially higher in AD compared to MCI. In addition, triglyceride levels correlated positively with dementia. Different lipoprotein serum patterns were identified for AD, MCI, and control subjects. Interestingly, HDL-4 and LDL-2 cholesterol parameters revealed strong gender-specific changes in the context of AD-driven dementia. As gender-based comparisons were based on smaller sub-groups with a low n-number, several statistical findings did not meet the significance threshold for multiple comparisons testing. Still, our finding suggests that serum HDL-4 parameters and various triglycerides correlate positively with AD pathology which could be a read-out of extended lipids traveling through the blood-brain barrier, supporting amyloid plaque formation processes. Thereof, we see herein a proof of concept that this quantitative NMR-based lipoprotein assay can generate important and highly interesting data for refined AD diagnosis and patient stratification, especially when larger cohorts are available.

Tools shaping drug discovery and development

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy)

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

Severe COVID-19 Is Characterised by Perturbations in Plasma Amines Correlated with Immune Response Markers, and Linked to Inflammation and Oxidative Stress

The COVID-19 pandemic raised a need to characterise the biochemical response to SARS-CoV-2 infection and find biological markers to identify therapeutic targets. In support of these aims, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The first publication in a series reports the results of quantitative LC-MS/MS profiling of 56 amino acids and derivatives. A comparison between samples taken from ICU and ward patients revealed a notable increase in ten post-translationally modified amino acids that correlated with markers indicative of an excessive immune response: TNF-alpha, neutrophils, markers for macrophage, and leukocyte activation. Severe patients also had increased kynurenine, positively correlated with CRP and cytokines that induce its production. ICU and ward patients with high IL-6 showed decreased levels of 22 immune-supporting and anti-oxidative amino acids and derivatives (e.g., glutathione, GABA). These negatively correlated with CRP and IL-6 and positively correlated with markers indicative of adaptive immune activation. Including corresponding alterations in convalescing ward patients, the overall metabolic picture of severe COVID-19 reflected enhanced metabolic demands to maintain cell proliferation and redox balance, alongside increased inflammation and oxidative stress.

1H qNMR-Based Metabolomics Discrimination of Covid-19 Severity

The coronavirus disease 2019 (Covid-19), which caused respiratory problems in many patients worldwide, led to more than 5 million deaths by the end of 2021. Experienced symptoms vary from mild to severe illness. Understanding the infection severity to reach a better prognosis could be useful to the clinics, and one study area to fulfill one piece of this biological puzzle is metabolomics. The metabolite profile and/or levels being monitored can help predict phenotype properties. Therefore, this study evaluated plasma metabolomes of 110 individual samples, 57 from control patients and 53 from recent positive cases of Covid-19 (IgM 98% reagent), representing mild to severe symptoms, before any clinical intervention. Polar metabolites from plasma samples were analyzed by quantitative 1H NMR. Glycerol, 3-aminoisobutyrate, formate, and glucuronate levels showed alterations in Covid-19 patients compared to those in the control group (Tukey's HSD p-value cutoff = 0.05), affecting the lactate, phenylalanine, tyrosine, and tryptophan biosynthesis and d-glutamine, d-glutamate, and glycerolipid metabolisms. These metabolic alterations show that SARS-CoV-2 infection led to disturbance in the energetic system, supporting the viral replication and corroborating with the severe clinical conditions of patients. Six polar metabolites (glycerol, acetate, 3-aminoisobutyrate, formate, glucuronate, and lactate) were revealed by PLS-DA and predicted by ROC curves and ANOVA to be potential prognostic metabolite panels for Covid-19 and considered clinically relevant for predicting infection severity due to their straight roles in the lipid and energy metabolism. Thus, metabolomics from samples of Covid-19 patients is a powerful tool for a better understanding of the disease mechanism of action and metabolic consequences of the infection in the human body and may corroborate allowing clinicians to intervene quickly according to the needs of Covid-19 patients.

The role of IL-1 family of cytokines and receptors in pathogenesis of COVID-19

A global pandemic has erupted as a result of the new brand coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This pandemic has been consociated with widespread mortality worldwide. The antiviral immune response is an imperative factor in confronting the recent coronavirus disease 2019 (COVID-19) infections. Meantime, cytokines recognize as crucial components in guiding the appropriate immune pathways in the restraining and eradication of the virus. Moreover, SARS-CoV-2 can induce uncontrolled inflammatory responses characterized by hyper-inflammatory cytokine production, which causes cytokine storm and acute respiratory distress syndrome (ARDS). As excessive inflammatory responses are contributed to the severe stage of the COVID-19 disease, therefore, the pro-inflammatory cytokines are regarded as the Achilles heel during COVID-19 infection. Among these cytokines, interleukin (IL-) 1 family cytokines (IL-1, IL-18, IL-33, IL-36, IL-37, and IL-38) appear to have a strong inflammatory role in severe COVID-19. Hence, understanding the underlying inflammatory mechanism of these cytokines during infection is critical for reducing the symptoms and severity of the disease. Here, the possible mechanisms and pathways involved in inflammatory immune responses are discussed.

Immunometabolic analysis shows a distinct cyto-metabotype in Covid-19 compared to sepsis from other causes

Background

In Covid-19, profound systemic inflammatory responses are accompanied by both metabolic risk factors for severity and, separately, metabolic mechanisms have been shown to underly disease progression. It is unknown whether this reflects similar situations in sepsis or is a unique characteristic of Covid-19.

Aims

Define the immunometabolic signature of Covid-19.

Methods

65 patients with Covid-19,19 patients with sepsis and 14 healthy controls were recruited and sampled for plasma, serum and peripheral blood mononuclear cells (PBMCs) through 10 days of critical illness. Metabotyping was performed using the Biocrates p180 kit and multiplex cytokine profiling undertaken. PBMCs underwent phenotyping by flow cytometry. Immune and metabolic readouts were integrated and underwent pathway analysis.

Results

Phopsphatidylcholines (PC) are reduced in Covid-19 but greater than in sepsis. Compared to controls, tryptophan is reduced in Covid-19 and inversely correlated with the severity of the disease and IFN-ɣ concentrations, conversely the kyneurine and kyneurine/tryptophan ratio increased in the most severe cases. These metabolic changes were consistent through 2 pandemic waves in our centre. PD-L1 expression in CD8+ T cells, Tregs and CD14+ monocytes was increased in Covid-19 compared to controls.

Conclusions

In our cohort, Covid-19 is associated with monocytopenia, increased CD14+ and Treg PD-L1 expression correlating with IFN-ɣ plasma concentration and disease severity (SOFA score). The latter is also associated with metabolic derangements of Tryptophan, LPC 16:0 and PCs. Lipid metabolism, in particular phosphatidylcholines and lysophosphatidylcolines, seems strictly linked to immune response in Covid-19. Our results support the hypothesis that IFN-ɣ -PD-L1 axis might be involved in the cytokine release syndrome typical of severe Covid-19 and the phenomenon persisted through multiple pandemic waves despite use of immunomodulation.

Serum NMR Profiling Reveals Differential Alterations in the Lipoproteome Induced by Pfizer-BioNTech Vaccine in COVID-19 Recovered Subjects and Naïve Subjects

¹H NMR spectra of sera have been used to define the changes induced by vaccination with Pfizer-BioNTech vaccine (2 shots, 21 days apart) in 10 COVID-19-recovered subjects and 10 COVID-19-naïve subjects at different time points, starting from before vaccination, then weekly until 7 days after second injection, and finally 1 month after the second dose. The data show that vaccination does not induce any significant variation in the metabolome, whereas it causes changes at the level of lipoproteins. The effects are different in the COVID-19-recovered subjects with respect to the naïve subjects, suggesting that a previous infection reduces the vaccine modulation of the lipoproteome composition.

Are the Follicular Fluid Characteristics of Recovered Coronavirus Disease 2019 Patients Different From Those of Vaccinated Women Approaching in vitro Fertilization?

The aim of this pilot study is to evaluate if SARS-CoV-2 infection or vaccination against SARS-CoV-2 infection induce observable metabolic effects in follicular fluid of women who are following in vitro fertilization (IVF) treatments. The possible impact of coronavirus disease 2019 (COVID-19) on fertility and IVF outcome is considered. We have selected for this study: six women vaccinated against SARS-CoV-2 infection, five recovered COVID-19 patients, and we used nine healthy women as the control group. At the time of oocytes retrieval from participants in the study, follicular fluids were collected and metabolomic analysis was performed by ¹H NMR spectroscopy in combination with multivariate analysis to interpret the spectral data. The search for antibody positivity in the follicular fluid aspirates was also carried out, together with the western blotting analysis of some inflammatory proteins, interleukin-6, tumor necrosis factor α (TNFα), and the free radical scavenger superoxide dismutase 2. Higher levels of Ala and Pro together with lower levels of lipids and trimethylamine N-oxide (TMAO) were found in follicular fluids (FFs) of vaccinated women while lower levels of many metabolites were detected in FFs of recovered COVID patients. Expression level of TNF-α was significantly lower both in recovered COVID-19 patients and vaccinated women in comparison to healthy controls.

Metabolomics study of COVID-19 patients in four different clinical stages

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is the coronavirus strain causing the respiratory pandemic COVID-19 (coronavirus disease 2019). To understand the pathobiology of SARS-CoV-2 in humans it is necessary to unravel the metabolic changes that are produced in the individuals once the infection has taken place. The goal of this work is to provide new information about the altered biomolecule profile and with that the altered biological pathways of patients in different clinical situations due to SARS-CoV-2 infection. This is done via metabolomics using HPLC-QTOF-MS analysis of plasma samples at COVID-diagnose from a total of 145 adult patients, divided into different clinical stages based on their subsequent clinical outcome (25 negative controls (non-COVID); 28 positive patients with asymptomatic disease not requiring hospitalization; 27 positive patients with mild disease defined by a total time in hospital lower than 10 days; 36 positive patients with severe disease defined by a total time in hospital over 20 days and/or admission at the ICU; and 29 positive patients with fatal outcome or deceased). Moreover, follow up samples between 2 and 3 months after hospital discharge were also obtained from the hospitalized patients with mild prognosis. The final goal of this work is to provide biomarkers that can help to better understand how the COVID-19 illness evolves and to predict how a patient could progress based on the metabolites profile of plasma obtained at an early stage of the infection. In the present work, several metabolites were found as potential biomarkers to distinguish between the end-stage and the early-stage (or non-COVID) disease groups. These metabolites are mainly involved in the metabolism of carnitines, ketone bodies, fatty acids, lysophosphatidylcholines/phosphatidylcholines, tryptophan, bile acids and purines, but also omeprazole. In addition, the levels of several of these metabolites decreased to "normal" values at hospital discharge, suggesting some of them as early prognosis biomarkers in COVID-19 at diagnose.

Solid-state 1H NMR-based metabolomics assessment of tributylin effects in zebrafish bone

Tributyltin (TBT), an endocrine disruptor is used globally in agribusiness and industries as biocides, heat stabilizers, and in chemical catalysis. It is known for its deleterious effects on bone by negatively impacting the functions of osteoblasts, osteoclasts and mesenchymal stem cells. However, the impact of TBT on the metabolomics profile in bone is not yet studied. Here, we demonstrate alterations in chemical metabolomics profiles measured by solid state ¹H nuclear magnetic resonance (¹H NMR) spectroscopy in zebrafish bone following tributyltin (TBT) treatment. TBT of 0, 100, 200, 300, 400 and 500 μg/L were exposed to zebrafish. From this, zebrafish bone has subjected for further metabolomics profiling. Samples were measured via one-dimensional (1D) solvent -suppressed and T₂- filtered methods with in vivo zebrafish metabolites. A dose dependent alteration in the metabolomics profile was observed and results indicated a disturbed aminoacid metabolism, TCA cycle, and glycolysis. We found a significant alteration in the levels of glutamate, glutamine, glutathione, trimethylamine N-oxide (TMAO), and other metabolites. This investigation hints us the deleterious effects of TBT on zebrafish bone enabling a comprehensive understanding of metabolomics profile and is expected to play a crucial role in understanding the deleterious effects of various endocrine disruptor on bone.

Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum

A JEDI NMR pulse experiment incorporating relaxation, diffusion and J-modulation peak editing was implemented at a low field (80 MHz) spectrometer system to quantify two recently discovered plasma markers of SARS-CoV-2 infection and general inflammation.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

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NMR analysis of various bio-samples can explore disease course and vaccine interaction.

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Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

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Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.

Chemometric analysis of the global pattern of volatile organic compounds in the exhaled breath of patients with COVID-19, post-COVID and healthy subjects. Proof of concept for post-COVID assessment

The objective of this research was to evaluate the application of an electronic nose and chemometric analysis to discriminate volatile organic compounds between patients with COVID-19, post-COVID syndrome and controls in exhaled breath samples. A cross-sectional study was performed on 102 exhaled breath samples, 42 with COVID-19, 30 with the post-COVID syndrome and 30 control subjects. Breath-print analysis was performed by the Cyranose 320 electronic nose with 32 sensors. Group data were evaluated by Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA), and Support Vector Machine (SVM), and the test's diagnostic power was evaluated through a Receiver Operaring Characteristic curve(ROC curve). The results of the chemometric analysis indicate in the PCA a 97.6% (PC₁ = 95.9%, PC₂ = 1.0%, PC₃ = 0.7%) of explanation of the variability between the groups by means of 3 PCs, the CDA presents a 100% of correct classification of the study groups, SVM a 99.4% of correct classification, finally the PLS-DA indicates an observable separation between the groups and the 12 sensors that were related. The sensitivity, specificity of post-COVID vs. controls value reached 97.6% (87.4%–99.9%) and 100% (88.4%–100%) respectively, according to the ROC curve. As a perspective, we consider that this technology, due to its simplicity, low cost and portability, can support strategies for the identification and follow-up of post-COVID patients. The proposed classification model provides the basis for evaluating post-COVID patients; therefore, further studies are required to enable the implementation of this technology to support clinical management and mitigation of effects.

Diabetes and COVID-19: What 2 Years of the Pandemic Has Taught Us

As the world enters its third year of the COVID-19 pandemic, individuals with diabetes have faced particular challenges from the virus. A deleterious bidirectional relationship exists between the two disorders, with heightened inflammatory, immunologic, and cellular mechanisms leading to a more severe illness and increased morbidity and mortality. Tight glucose control, though necessary, is hampered by physical restrictions and difficulty accessing health care. Novel glucose-lowering medications may provide unique benefits in this regard. It is imperative that multi-pronged efforts be prioritized in order to reduce adverse outcomes in patients with diabetes at risk for COVID-19.