Metabolic Snapshot of Plasma Samples Reveals New Pathways Implicated in SARS-CoV-2 Pathogenesis

Recent advances in nutritional metabolism studies on SARS-CoV-2 infection

In the context of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), metabolic research has become crucial for in-depth exploration of viral infection mechanisms and in searching for therapeutic strategies. This paper summarizes the interrelationships between carbohydrate, lipid, and amino acid metabolism and COVID-19 infection, discussing their roles in infection progression. SARS-CoV-2 infection leads to insulin resistance and increased glycolysis, reducing glucose utilization and shifting metabolism to use fat as an energy source. Fat is crucial for viral replication, and imbalances in amino acid metabolism may interfere with immune regulation. Consequently, metabolic changes such as hyperglycemia, hypolipidemia, and deficiency of certain amino acids following SARS-CoV-2 infection can contribute to progression toward severe conditions. These metabolic pathways not only have potential value in prediction and diagnosis but also provide new perspectives for the development of therapeutic strategies. By monitoring metabolic changes, infection severity can be predicted early, and modulating these metabolic pathways may help reduce inflammatory responses, improve immune responses, and reduce the risk of thrombosis. Research on the relationship between metabolism and SARS-CoV-2 infection provides an important scientific basis for addressing the global challenge posed by COVID-19, however, further studies are needed to validate these findings and provide more effective strategies for disease control.

Metabolomic characteristics and related pathways in patients with different severity of COVID-19: a systematic review and meta-analysis

Background

Despite advances in metabolomic research on COVID-19, existing studies have small sample sizes and few have comprehensively described the metabolic characteristics of patients with COVID-19 at each stage. In this systematic review, we aimed to summarise the similarities and differences of biomarkers in patients with COVID-19 of different severity and describe their metabolic characteristics at different stages.

Methods

We retrieved studies from PubMed, Embase, Web of Science, and the Cochrane Library published by October 2022. We performed a meta-analysis on untargeted and targeted metabolomics research data, using the ratio of means as the effect size. We compared changes in metabolite levels between patients with varying severity and controls and investigated sources of heterogeneity through subgroup analyses and meta-regression analysis.

Results

We included 22 cohorts from 21 studies, comprising 2421 participants, including COVID-19 patients of varying severity and healthy controls. We conducted meta-analysis and heterogeneity analysis on the 1058 metabolites included in the study. The results indicated that, compared to the healthy control group, 23 biomarkers were associated with mild cases (P < 0.05), 3 biomarkers with moderate cases (P < 0.05), and 37 biomarkers with severe cases (P < 0.05). Pathway enrichment analysis revealed significant disturbances in amino acid metabolism, aminoacyl-tRNA biosynthesis, primary bile acid biosynthesis, pantothenate and CoA biosynthesis, the tricarboxylic acid cycle, taurine and hypotaurine metabolism, and nitrogen metabolism in patients with mild, moderate, and severe disease. Additionally, we found that each severity stage exhibited unique metabolic patterns (all P < 0.05) and that the degree of metabolic dysregulation progressively worsened with increasing disease severity (P < 0.05).

Conclusions

The results of our meta-analysis indicate the similarities and differences of biomarkers and metabolic characteristics of patients with different severity in COVID-19, thereby providing new pathways for the study of pathogenesis, the development precise treatment, and the formulation of comprehensive strategies.

Registration

PROSPERO: CRD42022369937.

Reinforcing the Evidence of Mitochondrial Dysfunction in Long COVID Patients Using a Multiplatform Mass Spectrometry-Based Metabolomics Approach

Despite the recent and increasing knowledge surrounding COVID-19 infection, the underlying mechanisms of the persistence of symptoms for a long time after the acute infection are still not completely understood. Here, a multiplatform mass spectrometry-based approach was used for metabolomic and lipidomic profiling of human plasma samples from Long COVID patients (n = 40) to reveal mitochondrial dysfunction when compared with individuals fully recovered from acute mild COVID-19 (n = 40). Untargeted metabolomic analysis using CE-ESI(+/-)-TOF-MS and GC-Q-MS was performed. Additionally, a lipidomic analysis using LC-ESI(+/-)-QTOF-MS based on an in-house library revealed 447 lipid species identified with a high confidence annotation level. The integration of complementary analytical platforms has allowed a comprehensive metabolic and lipidomic characterization of plasma alterations in Long COVID disease that found 46 relevant metabolites which allowed to discriminate between Long COVID and fully recovered patients. We report specific metabolites altered in Long COVID, mainly related to a decrease in the amino acid metabolism and ceramide plasma levels and an increase in the tricarboxylic acid (TCA) cycle, reinforcing the evidence of an impaired mitochondrial function. The most relevant alterations shown in this study will help to better understand the insights of Long COVID syndrome by providing a deeper knowledge of the metabolomic basis of the pathology.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Increased Oxidative Stress and Decreased Citrulline in Blood Associated with Severe Novel Coronavirus Pneumonia in Adult Patients

This study investigated the correlation between oxidative stress and blood amino acids associated with nitric oxide metabolism in adult patients with coronavirus disease (COVID-19) pneumonia. Clinical data and serum samples were prospectively collected from 100 adult patients hospitalized for COVID-19 between July 2020 and August 2021. Patients with COVID-19 were categorized into three groups for analysis based on lung infiltrates, oxygen inhalation upon admission, and the initiation of oxygen therapy after admission. Blood data, oxidative stress-related biomarkers, and serum amino acid levels upon admission were compared in these groups. Patients with lung infiltrations requiring oxygen therapy upon admission or starting oxygen post-admission exhibited higher serum levels of hydroperoxides and lower levels of citrulline compared to the control group. No remarkable differences were observed in nitrite/nitrate, asymmetric dimethylarginine, and arginine levels. Serum citrulline levels correlated significantly with serum lactate dehydrogenase and C-reactive protein levels. A significant negative correlation was found between serum levels of citrulline and hydroperoxides. Levels of hydroperoxides decreased, and citrulline levels increased during the recovery period compared to admission. Patients with COVID-19 with extensive pneumonia or poor oxygenation showed increased oxidative stress and reduced citrulline levels in the blood compared to those with fewer pulmonary complications. These findings suggest that combined oxidative stress and abnormal citrulline metabolism may play a role in the pathogenesis of COVID-19 pneumonia.

A multicenter study of asymmetric and symmetric dimethylarginine as predictors of mortality risk in hospitalized COVID-19 patients

Mortality of patients hospitalized with COVID-19 has remained high during the consecutive SARS-CoV-2 pandemic waves. Early discrimination of patients at high mortality risk is crucial for optimal patient care. Symmetric (SDMA) and asymmetric dimethylarginine (ADMA) have been proposed as possible biomarkers to improve risk prediction of COVID-19 patients. We measured SDMA, ADMA, and other L-arginine-related metabolites in 180 patients admitted with COVID-19 in four German university hospitals as compared to 127 healthy controls. Patients were treated according to accepted clinical guidelines and followed-up until death or hospital discharge. Classical inflammatory markers (leukocytes, CRP, PCT), renal function (eGFR), and clinical scores (SOFA) were taken from hospital records. In a small subgroup of 23 COVID-19 patients, sequential blood samples were available and analyzed for biomarker trends over time until 14 days after admission. Patients had significantly elevated SDMA, ADMA, and L-ornithine and lower L-citrulline concentrations than controls. Within COVID-19 patients, SDMA and ADMA were significantly higher in non-survivors (n = 41, 22.8%) than in survivors. In ROC analysis, the optimal cut-off to discriminate non-survivors from survivors was 0.579 µmol/L for SDMA and 0.599 µmol/L for ADMA (both p < 0.001). High SDMA and ADMA were associated with odds ratios for death of 11.45 (3.37-38.87) and 5.95 (2.63-13.45), respectively. Analysis of SDMA and ADMA allowed discrimination of a high-risk (mortality, 43.7%), medium-risk (15.1%), and low-risk group (3.6%); risk prediction was significantly improved over classical laboratory markers. We conclude that analysis of ADMA and SDMA after hospital admission significantly improves risk prediction in COVID-19.

Proteomic snapshot of saliva samples predicts new pathways implicated in SARS-CoV-2 pathogenesis

BACKGROUND

Information on the microbiome's human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied a unique collection of samples harvested from April to June 2020 from unvaccinated patients.

METHODS

We compared 10 infected and hospitalized patients with severe (n = 5) and moderate (n = 5) coronavirus disease (COVID-19) with 10 uninfected individuals, including non-COVID-19 but susceptible individuals (n = 5) and non-COVID-19 and nonsusceptible healthcare workers with repeated high-risk exposures (n = 5).

RESULTS

By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤ 0.05) out of 3376 unambiguously identified proteins (false discovery rate ≤ 1%). Major differences were observed between the non-COVID-19 and nonsusceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with the saliva of SARS-CoV-2-infected patients (p-value ≤ 0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression levels of two human proteins related to protein transport in the cytoplasm, DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that were overrepresented in the infected group.

CONCLUSION

Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility, although further studies in larger cohorts will be necessary.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

Differential expression of biomarkers in saliva related to SARS-CoV-2 infection in patients with mild, moderate and severe COVID-19

BACKGROUND

Severe COVID-19 is a disease characterized by profound dysregulation of the innate immune system. There is a need to identify highly reliable prognostic biomarkers that can be rapidly assessed in body fluids for early identification of patients at higher risk for hospitalization and/or death. This study aimed to assess whether differential gene expression of immune response molecules and cellular enzymes, detected in saliva samples of COVID-19 patients, occurs according to disease severity staging.

METHODS

In this cross-sectional study, subjects with a COVID-19 diagnosis were classified as having mild, moderate, or severe disease based on clinical features. Transcripts of genes encoding 6 biomarkers, IL-1β, IL-6, IL-10, C-reactive protein, IDO1 and ACE2, were measured by RT‒qPCR in saliva samples of patients and COVID-19-free individuals.

RESULTS

The gene expression levels of all 6 biomarkers in saliva were significantly increased in severe disease patients compared to mild/moderate disease patients and healthy controls. A significant strong inverse relationship between oxemia and the level of expression of the 6 biomarkers (Spearman's correlation coefficient between -0.692 and -0.757; p < 0.001) was found.

CONCLUSIONS

Biomarker gene expression determined in saliva samples still needs to be validated as a potentially valuable predictor of severe clinical outcomes early at the onset of COVID-19 symptoms.

Differential abundance of lipids and metabolites related to SARS-CoV-2 infection and susceptibility

The mechanisms driving SARS-CoV-2 susceptibility remain poorly understood, especially the factors determining why unvaccinated individuals remain uninfected despite high-risk exposures. To understand lipid and metabolite profiles related with COVID-19 susceptibility and disease progression. We collected samples from an exceptional group of unvaccinated healthcare workers heavily exposed to SARS-CoV-2 but not infected ('non-susceptible') and subjects who became infected during the follow-up ('susceptible'), including non-hospitalized and hospitalized patients with different disease severity providing samples at early disease stages. Then, we analyzed their plasma metabolomic profiles using mass spectrometry coupled with liquid and gas chromatography. We show specific lipids profiles and metabolites that could explain SARS-CoV-2 susceptibility and COVID-19 severity. More importantly, non-susceptible individuals show a unique lipidomic pattern characterized by the upregulation of most lipids, especially ceramides and sphingomyelin, which could be interpreted as markers of low susceptibility to SARS-CoV-2 infection. This study strengthens the findings of other researchers about the importance of studying lipid profiles as relevant markers of SARS-CoV-2 pathogenesis.

The kynurenine pathway of tryptophan metabolism: a neglected therapeutic target of COVID-19 pathophysiology and immunotherapy

SARS-CoV-2 (COVID-19) exerts profound changes in the kynurenine (Kyn) pathway (KP) of tryptophan (Trp) metabolism that may underpin its pathophysiology. The KP is the main source of the vital cellular effector NAD+ and intermediate metabolites that modulate immune and neuronal functions. Trp metabolism is the top pathway influenced by COVID-19. Sixteen studies established virus-induced activation of the KP mediated mainly by induction of indoleamine 2,3-dioxygenase (IDO1) in most affected tissues and of IDO2 in lung by the increased release of proinflammatory cytokines but could additionally involve increased flux of plasma free Trp and induction of Trp 2,3-dioxygenase (TDO) by cortisol. The major Kyn metabolite targeted by COVID-19 is kynurenic acid (KA), the Kyn metabolite with the greatest affinity for the aryl hydrocarbon receptor (AhR), which is also activated by COVID-19. AhR activation initiates two important series of events: a vicious circle involving IDO1 induction, KA accumulation and further AhR activation, and activation of poly (ADP-ribose) polymerase (PARP) leading to NAD+ depletion and cell death. The virus further deprives the host of NAD+ by inhibiting its main biosynthetic pathway from quinolinic acid, while simultaneously acquiring NAD+ by promoting its synthesis from nicotinamide in the salvage pathway. Additionally, the protective effects of sirtuin 1 are minimised by the PARP activation. KP dysfunction may also underpin the mood and neurological disorders acutely and during 'long COVID'. More studies of potential effects of vaccination therapy on the KP are required and exploration of therapeutic strategies involving modulation of the KP changes are proposed.

The plasma metabolome of long COVID patients two years after infection

One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as "long COVID") which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC-MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Signaling metabolite β-aminoisobutyric acid as a metabolic regulator, biomarker, and potential exercise pill

Signaling metabolites can effectively regulate the biological functions of many tissues and organs. β-Aminoisobutyric acid (BAIBA), a product of valine and thymine catabolism in skeletal muscle, has been reported to participate in the regulation of lipid, glucose, and bone metabolism, as well as in inflammation and oxidative stress. BAIBA is produced during exercise and is involved in the exercise response. No side effect has been observed in human and rat studies, suggesting that BAIBA can be developed as a pill that confers the benefits of exercise to subjects who, for some reason, are unable to do so. Further, BAIBA has been confirmed to participate in the diagnosis and prevention of diseases as an important biological marker of disease. The current review aimed to discuss the roles of BAIBA in multiple physiological processes and the possible pathways of its action, and assess the progress toward the development of BAIBA as an exercise mimic and biomarker with relevance to multiple disease states, in order to provide new ideas and strategies for basic research and disease prevention in related fields.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

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.

Targeting ACLY efficiently inhibits SARS-CoV-2 replication

The Coronavirus Disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the biggest public health challenge the world has witnessed in the past decades. SARS-CoV-2 undergoes constant mutations and new variants of concerns (VOCs) with altered transmissibility, virulence, and/or susceptibility to vaccines and therapeutics continue to emerge. Detailed analysis of host factors involved in virus replication may help to identify novel treatment targets. In this study, we dissected the metabolome derived from COVID-19 patients to identify key host factors that are required for efficient SARS-CoV-2 replication. Through a series of metabolomic analyses, in vitro, and in vivo investigations, we identified ATP citrate lyase (ACLY) as a novel host factor required for efficient replication of SARS-CoV-2 wild-type and variants, including Omicron. ACLY should be further explored as a novel intervention target for COVID-19.