Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers

Patient subtyping analysis of baseline multi-omic data reveals distinct pre-immune states associated with antibody response to seasonal influenza vaccination

Understanding the molecular mechanisms underpinning diverse vaccination responses is critical for developing efficient vaccines. Molecular subtyping can offer insights into heterogeneous nature of responses and aid in vaccine design. We analyzed multi-omic data from 62 haemagglutinin seasonal influenza vaccine recipients (2019-2020), including transcriptomics, proteomics, glycomics, and metabolomics data collected pre-vaccination. We performed a subtyping analysis on the integrated data revealing five subtypes with distinct molecular signatures. These subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining the effectiveness of seasonal vaccines. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

New insights into healthy ageing, inflammageing and frailty using metabolomics

Human ageing is a normal process and does not necessarily result in the development of frailty. A mix of genetic, environmental, dietary, and lifestyle factors can have an impact on ageing, and whether an individual develops frailty. Frailty is defined as the loss of physiological reserve both at the physical and cellular levels, where systemic processes such as oxidative stress and inflammation contribute to physical decline. The newest "omics" technology and systems biology discipline, metabolomics, enables thorough characterisation of small-molecule metabolites in biological systems at a particular time and condition. In a biological system, metabolites-cellular intermediate products of metabolic reactions-reflect the system's final response to genomic, transcriptomic, proteomic, epigenetic, or environmental alterations. As a relatively newer technique to characterise metabolites and biomarkers in ageing and illness, metabolomics has gained popularity and has a wide range of applications. We will give a comprehensive summary of what is currently known about metabolomics in studies of ageing, with a focus on biomarkers for frailty. Metabolites related to amino acids, lipids, carbohydrates, and redox metabolism may function as biomarkers of ageing and/or frailty development, based on data obtained from human studies. However, there is a complexity that underpins biological ageing, due to both genetic and environmental factors that play a role in orchestrating the ageing process. Therefore, there is a critical need to identify pathways that contribute to functional decline in people with frailty.

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Cutting-edge biorecognition strategies to boost the detection performance of COVID-19 electrochemical biosensors: A review

Electrochemical biosensors are known for their high sensitivity, selectivity, and low cost. Recently, they have gained significant attention and became particularly important as promising tools for the detection of COVID-19 biomarkers, since they offer a rapid and accurate means of diagnosis. Biorecognition strategies are a crucial component of electrochemical biosensors and determine their specificity and sensitivity based on the interaction of biological molecules, such as antibodies, enzymes, and DNA, with target analytes (e.g., viral particles, proteins and genetic material) to create a measurable signal. Different biorecognition strategies have been developed to enhance the performance of electrochemical biosensors, including direct, competitive, and sandwich binding, alongside nucleic acid hybridization mechanisms and gene editing systems. In this review article, we present the different strategies used in electrochemical biosensors to target SARS-CoV-2 and other COVID-19 biomarkers, as well as explore the advantages and disadvantages of each strategy and highlight recent progress in this field. Additionally, we discuss the challenges associated with developing electrochemical biosensors for clinical COVID-19 diagnosis and their widespread commercialization.

Metabolomics-driven discovery of therapeutic targets for cancer cachexia

Cancer cachexia (CC) is a devastating metabolic syndrome characterized by skeletal muscle wasting and body weight loss, posing a significant burden on the health and survival of cancer patients. Despite ongoing efforts, effective treatments for CC are still lacking. Metabolomics, an advanced omics technique, offers a comprehensive analysis of small-molecule metabolites involved in cellular metabolism. In CC research, metabolomics has emerged as a valuable tool for identifying diagnostic biomarkers, unravelling molecular mechanisms and discovering potential therapeutic targets. A comprehensive search strategy was implemented to retrieve relevant articles from primary databases, including Web of Science, Google Scholar, Scopus and PubMed, for CC and metabolomics. Recent advancements in metabolomics have deepened our understanding of CC by uncovering key metabolic signatures and elucidating underlying mechanisms. By targeting crucial metabolic pathways including glucose metabolism, amino acid metabolism, fatty acid metabolism, bile acid metabolism, ketone body metabolism, steroid metabolism and mitochondrial energy metabolism, it becomes possible to restore metabolic balance and alleviate CC symptoms. This review provides a comprehensive summary of metabolomics studies in CC, focusing on the discovery of potential therapeutic targets and the evaluation of modulating specific metabolic pathways for CC treatment. By harnessing the insights derived from metabolomics, novel interventions for CC can be developed, leading to improved patient outcomes and enhanced quality of life.

The roles of the kynurenine pathway in COVID-19 neuropathogenesis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the highly contagious respiratory disease Corona Virus Disease 2019 (COVID-19) that may lead to various neurological and psychological disorders that can be acute, lasting days to weeks or months and possibly longer. The latter is known as long-COVID or more recently post-acute sequelae of COVID (PASC). During acute COVID-19 infection, a strong inflammatory response, known as the cytokine storm, occurs in some patients. The levels of interferon-γ (IFN-γ), interferon-β (IFN-β), interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) are particularly increased. These cytokines are known to activate the enzyme indoleamine 2,3-dioxygenase 1 (IDO-1), catalysing the first step of tryptophan (Trp) catabolism through the kynurenine pathway (KP) leading to the production of several neurotoxic and immunosuppressive metabolites. There is already data showing elevation in KP metabolites both acutely and in PASC, especially regarding cognitive impairment. Thus, it is likely that KP involvement is significant in SARS-CoV-2 pathogenesis especially neurologically.

SARS-CoV-2 infection unevenly impacts metabolism in the coronal periphery of the lungs

COVID-19 significantly decreases amino acids, fatty acids, and most eicosanoidsSARS-CoV-2 preferentially localizes to central lung tissueMetabolic disturbance is highest in peripheral tissue, not central like viral loadSpatial metabolomics allows detection of metabolites not altered overallSARS-CoV-2, the virus responsible for COVID-19, is a highly contagious virus that can lead to hospitalization and death. COVID-19 is characterized by its involvement in the lungs, particularly the lower lobes. To improve patient outcomes and treatment options, a better understanding of how SARS-CoV-2 impacts the body, particularly the lower respiratory system, is required. In this study, we sought to understand the spatial impact of COVID-19 on the lungs of mice infected with mouse-adapted SARS2-N501Y MA30 . Overall, infection caused a decrease in fatty acids, amino acids, and most eicosanoids. When analyzed by segment, viral loads were highest in central lung tissue, while metabolic disturbance was highest in peripheral tissue. Infected peripheral lung tissue was characterized by lower levels of fatty acids and amino acids when compared to central lung tissue. This study highlights the spatial impacts of SARS-CoV-2 and helps explain why peripheral lung tissue is most damaged by COVID-19.

Dysregulated Leukotriene Metabolism in Patients with COVID-19

This study aimed to examine the leukotriene metabolism during COVID-19. In total, 180 participants were included in this study, of which 60 were healthy controls, 60 required intensive care units (ICU), and 60 did not require intensive care (non-ICU). The serum levels of 5-lipoxygenase (5-LO), 5-LO activating protein (ALOX5AP), and cysteinyl leukotriene (CYSLT) were measured, and the mRNA expression levels of 5-LO, ALOX5AP, and cysteinyl leukotriene receptor 1 (CYSLTR1) were investigated. Compared with the control group, both the non-ICU and ICU groups had lower levels of 5-LO and mRNA expression. ICU patients had lower levels of 5-LO and mRNA expression than non-ICU patients. CYSLTR1 mRNA expression was highest in the ICU group, followed by the non-ICU group, and healthy controls had the lowest mRNA expression levels. CYSLT levels were higher in the control group than in the non-ICU and ICU groups. CYSLTR1 expression was higher in patients than in controls; therefore, selective leukotriene receptor blockers can be used as treatment options. CYSLTR1 expression was higher in the ICU group than in the non-ICU group. Furthermore, CYSLTR1 mRNA expression may be a promising biomarker of COVID-19 severity.

Metabolic predictors of COVID-19 mortality and severity: a survival analysis

Introduction

The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.

Methods

In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes.

Results

A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.

Conclusions

Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.

A supervised Bayesian factor model for the identification of multi-omics signatures

MOTIVATION

Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful.

RESULTS

We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes.

AVAILABILITY AND IMPLEMENTATION

SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

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.

Anthranilic Acid, a GPR109A Agonist, and Schizophrenia

Introduction

Limited clinical efficiency of current medications warrants search for new antipsychotic agents. Deorphanized G-protein coupled receptor (GPR)109A has not attracted much of attention of schizophrenia researchers. We analyzed literature and our data on endogenous agonists of GPR109A, beta-hydroxybutyrate (BHB), anthranilic (AA), butyric (BA), and nicotinic (NA) acids, in individuals with schizophrenia.

Data

Sex specific differences: plasma AA levels were 27% higher in female than in male patients and correlated with PANSS before 6 weeks of antipsychotics treatment (r = .625, P < .019, Spearman's test). There was no sex specific differences of plasma AA levels after treatment. AA plasma levels inversely correlated (-.58, P < .005) with PANSS scores in responders to treatment (at least, 50% improvement) but not in nonresponders. Preclinical studies suggested antipsychotic effect of BHB and BA. Clinical studies observed antipsychotic effect of NA; benzoate sodium, an AA precursor; and interventions associated with BHB upregulation (eg, fasting and ketogenic diets).

Discussion

Upregulation of GPR109A, an anti-inflammatory and neuroprotective receptor, inhibits cytosolic phospholipase A2 (cPLA2), an enzyme that breakdown myelin, lipid-based insulating axonal sheath that protects and promotes nerve conduction. Brain cPLA2 is upregulated in individuals with schizophrenia and subjects at high-risk for development of psychosis. Lower myelin content is associated with cognitive decline in individuals with schizophrenia. Therefore, GPR109A might exert antipsychotic effect via suppression of cPLA2, and, consequently, preservation of myelin integrity. Future research might explore antipsychotic effects of (1) human pegylated kynureninase, an enzyme that catalyzes formation of AA from kynurenine (Kyn); (2) inhibitors of Kyn conversion into kynurenic acid, for example, KYN5356, to patients with already impaired Kyn conversion into 3-hydroxykynurenine; (3) synthetic GPR 109A agonists, for example, MK-1903 and SCH900271 and GSK256073, that underwent clinical trials as anti-dyslipidemia agents. GPR109A expression, that might be a new endophenotype of schizophrenia, especially associated with cognitive impairment, needs thorough assessment.

The Accumulation of Phenyllactic Acid Impairs Host Glutamine Metabolism and Inhibits African Swine Fever Virus Replication: A Novel Target for the Development of Anti-ASFV Drugs

African swine fever (ASF) is a highly contagious and hemorrhagic disease caused by infection with the African swine fever virus (ASFV), resulting in a mortality rate of up to 100%. Currently, there are no effective treatments and commercially available vaccines for ASF. Therefore, it is crucial to identify biochemicals derived from host cells that can impede ASFV replication, with the aim of preventing and controlling ASF. The ASFV is an acellular organism that promotes self-replication by hijacking the metabolic machinery and biochemical resources of host cells. ASFV specifically alters the utilization of glucose and glutamine, which are the primary metabolic sources in mammalian cells. This study aimed to investigate the impact of glucose and glutamine metabolic dynamics on the rate of ASFV replication. Our findings demonstrate that ASFV infection favors using glutamine as a metabolic fuel to facilitate self-replication. ASFV replication can be substantially inhibited by blocking glutamine metabolism. The metabolomics analysis of the host cell after late-stage ASFV infection revealed a significant disruption of normal glutamine metabolic pathways due to the abundant expression of PLA (phenyllactic acid). Pretreatment with PLA also inhibited ASFV proliferation and glutamine consumption following infection. The metabolomic analysis also showed that PLA pretreatment greatly slowed down the metabolism of amino acids and nucleotides that depend on glutamine. The depletion of these building blocks directly hindered the replication of ASFV by decreasing the biosynthetic precursors produced during the replication of ASFV's progeny virus. These findings provide valuable insight into the possibility of pursuing the development of antiviral drugs against ASFV that selectively target metabolic pathways.

Diabetes Mellitus, Energy Metabolism, and COVID-19

Obesity, diabetes mellitus (mostly type 2), and COVID-19 show mutual interactions because they are not only risk factors for both acute and chronic COVID-19 manifestations, but also because COVID-19 alters energy metabolism. Such metabolic alterations can lead to dysglycemia and long-lasting effects. Thus, the COVID-19 pandemic has the potential for a further rise of the diabetes pandemic. This review outlines how preexisting metabolic alterations spanning from excess visceral adipose tissue to hyperglycemia and overt diabetes may exacerbate COVID-19 severity. We also summarize the different effects of SARS-CoV-2 infection on the key organs and tissues orchestrating energy metabolism, including adipose tissue, liver, skeletal muscle, and pancreas. Last, we provide an integrative view of the metabolic derangements that occur during COVID-19. Altogether, this review allows for better understanding of the metabolic derangements occurring when a fire starts from a small flame, and thereby help reducing the impact of the COVID-19 pandemic.

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Nucleotide, Phospholipid, and Kynurenine Metabolites Are Robustly Associated with COVID-19 Severity and Time of Plasma Sample Collection in a Prospective Cohort Study

Understanding the molecular underpinnings of disease severity and progression in human studies is necessary to develop metabolism-related preventative strategies for severe COVID-19. Metabolites and metabolic pathways that predispose individuals to severe disease are not well understood. In this study, we generated comprehensive plasma metabolomic profiles in >550 patients from the Longitudinal EMR and Omics COVID-19 Cohort. Samples were collected before (n = 441), during (n = 86), and after (n = 82) COVID-19 diagnosis, representing 555 distinct patients, most of which had single timepoints. Regression models adjusted for demographics, risk factors, and comorbidities, were used to determine metabolites associated with predisposition to and/or persistent effects of COVID-19 severity, and metabolite changes that were transient/lingering over the disease course. Sphingolipids/phospholipids were negatively associated with severity and exhibited lingering elevations after disease, while modified nucleotides were positively associated with severity and had lingering decreases after disease. Cytidine and uridine metabolites, which were positively and negatively associated with COVID-19 severity, respectively, were acutely elevated, reflecting the particular importance of pyrimidine metabolism in active COVID-19. This is the first large metabolomics study using COVID-19 plasma samples before, during, and/or after disease. Our results lay the groundwork for identifying putative biomarkers and preventive strategies for severe COVID-19.

Amino acid catabolite markers for early prognostication of pneumonia in patients with COVID-19

Effective early-stage markers for predicting which patients are at risk of developing SARS-CoV-2 infection have not been fully investigated. Here, we performed comprehensive serum metabolome analysis of a total of 83 patients from two cohorts to determine that the acceleration of amino acid catabolism within 5 days from disease onset correlated with future disease severity. Increased levels of de-aminated amino acid catabolites involved in the de novo nucleotide synthesis pathway were identified as early prognostic markers that correlated with the initial viral load. We further employed mice models of SARS-CoV2-MA10 and influenza infection to demonstrate that such de-amination of amino acids and de novo synthesis of nucleotides were associated with the abnormal proliferation of airway and vascular tissue cells in the lungs during the early stages of infection. Consequently, it can be concluded that lung parenchymal tissue remodeling in the early stages of respiratory viral infections induces systemic metabolic remodeling and that the associated key amino acid catabolites are valid predictors for excessive inflammatory response in later disease stages.

MetaboliteCOVID: A manually curated database of metabolite markers for COVID-19

In the realm of unraveling COVID-19's intricacies, numerous metabolomic investigations have been conducted to discern the unique metabolic traits exhibited within infected patients. These endeavors have yielded a substantial reservoir of potential data pertaining to metabolic biomarkers linked to the virus. Despite these strides, a comprehensive and meticulously structured database housing these crucial biomarkers remains absent. In this study, we developed MetaboliteCOVID, a manually curated database of COVID-19-related metabolite markers. The database currently comprises 665 manually selected entries of significantly altered metabolites associated with early diagnosis, disease severity, prognosis, and drug response in COVID-19, encompassing 337 metabolites. Additionally, the database offers a user-friendly interface, containing abundant information for querying, browsing, and analyzing COVID-19-related abnormal metabolites in different body fluids. In summary, we believe that this database will effectively facilitate research on the functions and mechanisms of COVID-19-related metabolic biomarkers, thereby advancing both basic and clinical research on COVID-19. MetaboliteCOVID is free available at: https://cellknowledge.com.cn/MetaboliteCOVID.

Serotonin reduction in post-acute sequelae of viral infection

Post-acute sequelae of COVID-19 (PASC, "Long COVID") pose a significant global health challenge. The pathophysiology is unknown, and no effective treatments have been found to date. Several hypotheses have been formulated to explain the etiology of PASC, including viral persistence, chronic inflammation, hypercoagulability, and autonomic dysfunction. Here, we propose a mechanism that links all four hypotheses in a single pathway and provides actionable insights for therapeutic interventions. We find that PASC are associated with serotonin reduction. Viral infection and type I interferon-driven inflammation reduce serotonin through three mechanisms: diminished intestinal absorption of the serotonin precursor tryptophan; platelet hyperactivation and thrombocytopenia, which impacts serotonin storage; and enhanced MAO-mediated serotonin turnover. Peripheral serotonin reduction, in turn, impedes the activity of the vagus nerve and thereby impairs hippocampal responses and memory. These findings provide a possible explanation for neurocognitive symptoms associated with viral persistence in Long COVID, which may extend to other post-viral syndromes.

Emerging Landscape of Preclinical Models for Studying COVID-19 Neurologic Diseases

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and has globally infected 768 million people and caused over 6 million deaths. COVID-19 primarily affects the respiratory system but increasing reports of neurologic symptoms associated with COVID-19 have been reported in the literature. The exact mechanism behind COVID-19 neurologic pathophysiology remains poorly understood due to difficulty quantifying clinical neurologic symptoms in humans and correlating them to findings in human post-mortem samples and animal models. Thus, robust preclinical experimental models for COVID-19 neurologic manifestations are urgently needed. Here, we review recent advances in in vitro, in vivo, and other models and technologies for studying COVID-19 including primary cell cultures, pluripotent stem cell-derived neurons and organoids, rodents, nonhuman primates, 3D bioprinting, artificial intelligence, and multiomics. We specifically focus our discussion on the contribution, recent advancements, and limitations these preclinical models have on furthering our understanding of COVID-19's neuropathic physiology. We also discuss these models' roles in the screening and development of therapeutics, vaccines, antiviral drugs, and herbal medicine, and on future opportunities for COVID-19 neurologic research and clinical management.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Metabolomics-directed nanotechnology in viral diseases management: COVID-19 a case study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently regarded as the twenty-first century's plague accounting for coronavirus disease 2019 (COVID-19). Besides its reported symptoms affecting the respiratory tract, it was found to alter several metabolic pathways inside the body. Nanoparticles proved to combat viral infections including COVID-19 to demonstrate great success in developing vaccines based on mRNA technology. However, various types of nanoparticles can affect the host metabolome. Considering the increasing proportion of nano-based vaccines, this review compiles and analyses how COVID-19 and nanoparticles affect lipids, amino acids, and carbohydrates metabolism. A search was conducted on PubMed, ScienceDirect, Web of Science for available information on the interrelationship between metabolomics and immunity in the context of SARS-CoV-2 infection and the effect of nanoparticles on metabolite levels. It was clear that SARS-CoV-2 disrupted several pathways to ensure a sufficient supply of its building blocks to facilitate its replication. Such information can help in developing treatment strategies against viral infections and COVID-19 based on interventions that overcome these metabolic changes. Furthermore, it showed that even drug-free nanoparticles can exert an influence on biological systems as evidenced by metabolomics.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Comprehensive proteomics and meta-analysis of COVID-19 host response

COVID-19 is characterised by systemic immunological perturbations in the human body, which can lead to multi-organ damage. Many of these processes are considered to be mediated by the blood. Therefore, to better understand the systemic host response to SARS-CoV-2 infection, we performed systematic analyses of the circulating, soluble proteins in the blood through global proteomics by mass-spectrometry (MS) proteomics. Here, we show that a large part of the soluble blood proteome is altered in COVID-19, among them elevated levels of interferon-induced and proteasomal proteins. Some proteins that have alternating levels in human cells after a SARS-CoV-2 infection in vitro and in different organs of COVID-19 patients are deregulated in the blood, suggesting shared infection-related changes.The availability of different public proteomic resources on soluble blood proteome alterations leaves uncertainty about the change of a given protein during COVID-19. Hence, we performed a systematic review and meta-analysis of MS global proteomics studies of soluble blood proteomes, including up to 1706 individuals (1039 COVID-19 patients), to provide concluding estimates for the alteration of 1517 soluble blood proteins in COVID-19. Finally, based on the meta-analysis we developed CoViMAPP, an open-access resource for effect sizes of alterations and diagnostic potential of soluble blood proteins in COVID-19, which is publicly available for the research, clinical, and academic community.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Transcriptomic and proteomic assessment of tocilizumab response in a randomized controlled trial of patients hospitalized with COVID-19

High interleukin (IL)-6 levels are associated with greater COVID-19 severity. IL-6 receptor blockade by tocilizumab (anti-IL6R; Actemra) is used globally for the treatment of severe COVID-19, yet a molecular understanding of the therapeutic benefit remains unclear. We characterized the immune profile and identified cellular and molecular pathways modified by tocilizumab in peripheral blood samples from patients enrolled in the COVACTA study, a phase 3, randomized, double-blind, placebo-controlled trial of the efficacy and safety of tocilizumab in hospitalized patients with severe COVID-19. We identified markers of inflammation, lymphopenia, myeloid dysregulation, and organ injury that predict disease severity and clinical outcomes. Proteomic analysis confirmed a pharmacodynamic effect for tocilizumab and identified novel pharmacodynamic biomarkers. Transcriptomic analysis revealed that tocilizumab treatment leads to faster resolution of lymphopenia and myeloid dysregulation associated with severe COVID-19, indicating greater anti-inflammatory activity relative to placebo and potentially leading to faster recovery in patients hospitalized with COVID-19.

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.

Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy

Oncogenesis is associated with intestinal dysbiosis, and stool shotgun metagenomic sequencing in individuals with this condition might constitute a non-invasive approach for the early diagnosis of several cancer types. The prognostic relevance of antibiotic intake and gut microbiota composition urged investigators to develop tools for the detection of intestinal dysbiosis to enable patient stratification and microbiota-centred clinical interventions. Moreover, since the advent of immune-checkpoint inhibitors (ICIs) in oncology, the identification of biomarkers for predicting their efficacy before starting treatment has been an unmet medical need. Many previous studies addressing this question, including a meta-analysis described herein, have led to the description of Gut OncoMicrobiome Signatures (GOMS). In this Review, we discuss how patients with cancer across various subtypes share several GOMS with individuals with seemingly unrelated chronic inflammatory disorders who, in turn, tend to have GOMS different from those of healthy individuals. We discuss findings from the aforementioned meta-analysis of GOMS patterns associated with clinical benefit from or resistance to ICIs across different cancer types (in 808 patients), with a focus on metabolic and immunological surrogate markers of intestinal dysbiosis, and propose practical guidelines to incorporate GOMS in decision-making for prospective clinical trials in immuno-oncology.

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite-metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity.

COVID-19 Metabolomic-Guided Amino Acid Therapy Protects from Inflammation and Disease Sequelae

The outbreak of coronavirus disease 2019 (COVID-19) has caused a worldwide pandemic since 2019. A metabolic disorder is a contributing factor to deaths from COVID-19. However, the underlying mechanism of metabolic dysfunction in COVID-19 patients and the potential interventions are not elucidated. Here targeted plasma metabolomic is performed, and the metabolite profiles among healthy controls, and asymptomatic, moderate, and severe COVID-19 patients are compared. Among the altered metabolites, arachidonic acid and linolenic acid pathway metabolites are profoundly up-regulated in COVID-19 patients. Arginine biosynthesis, alanine, aspartate, and glutamate metabolism pathways are significantly disturbed in asymptomatic patients. In the comparison of metabolite variances among the groups, higher levels of l-citrulline and l-glutamine are found in asymptomatic carriers and moderate or severe patients at the remission stage. Furthermore, l-citrulline and l-glutamine combination therapy is demonstrated to effectively protect mice from coronavirus infection and endotoxin-induced sepsis, and is observed to efficiently prevent the occurrence of pulmonary fibrosis and central nervous system damage. Collectively, the data reveal the metabolite profile of asymptomatic COVID-19 patients and propose a potential strategy for COVID-19 treatment.

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.

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.

Neutrophil metabolomics in severe COVID-19 reveal GAPDH as a suppressor of neutrophil extracellular trap formation

Severe COVID-19 is characterized by an increase in the number and changes in the function of innate immune cells including neutrophils. However, it is not known how the metabolome of immune cells changes in patients with COVID-19. To address these questions, we analyzed the metabolome of neutrophils from patients with severe or mild COVID-19 and healthy controls. We identified widespread dysregulation of neutrophil metabolism with disease progression including in amino acid, redox, and central carbon metabolism. Metabolic changes in neutrophils from patients with severe COVID-19 were consistent with reduced activity of the glycolytic enzyme GAPDH. Inhibition of GAPDH blocked glycolysis and promoted pentose phosphate pathway activity but blunted the neutrophil respiratory burst. Inhibition of GAPDH was sufficient to cause neutrophil extracellular trap (NET) formation which required neutrophil elastase activity. GAPDH inhibition increased neutrophil pH, and blocking this increase prevented cell death and NET formation. These findings indicate that neutrophils in severe COVID-19 have an aberrant metabolism which can contribute to their dysfunction. Our work also shows that NET formation, a pathogenic feature of many inflammatory diseases, is actively suppressed in neutrophils by a cell-intrinsic mechanism controlled by GAPDH.

Plasma metabolomic characterization of SARS-CoV-2 Omicron infection

Omicron variants of SARS-CoV-2 have spread rapidly worldwide; however, most infected patients have mild or no symptoms. This study aimed to understand the host response to Omicron infections by performing metabolomic profiling of plasma. We observed that Omicron infections triggered an inflammatory response and innate immune, and adaptive immunity was suppressed, including reduced T-cell response and immunoglobulin antibody production. Similar to the original SARS-CoV-2 strain circulating in 2019, the host developed an anti-inflammatory response and accelerated energy metabolism in response to Omicron infection. However, differential regulation of macrophage polarization and reduced neutrophil function has been observed in Omicron infections. Interferon-induced antiviral immunity was not as strong in Omicron infections as in the original SARS-CoV-2 infections. The host response to Omicron infections increased antioxidant capacity and liver detoxification more than in the original strain. Hence, these findings suggest that Omicron infections cause weaker inflammatory alterations and immune responses than the original SARS-CoV-2 strain.

Cancer and COVID-19: unravelling the immunological interplay with a review of promising therapies against severe SARS-CoV-2 for cancer patients

Cancer patients, due to their immunocompromised status, are at an increased risk for severe SARS-CoV-2 infection. Since severe SARS-CoV-2 infection causes multiple organ damage through IL-6-mediated inflammation while stimulating hypoxia, and malignancy promotes hypoxia-induced cellular metabolic alterations leading to cell death, we propose a mechanistic interplay between both conditions that results in an upregulation of IL-6 secretion resulting in enhanced cytokine production and systemic injury. Hypoxia mediated by both conditions results in cell necrosis, dysregulation of oxidative phosphorylation, and mitochondrial dysfunction. This produces free radicals and cytokines that result in systemic inflammatory injury. Hypoxia also catalyzes the breakdown of COX-1 and 2 resulting in bronchoconstriction and pulmonary edema, which further exacerbates tissue hypoxia. Given this disease model, therapeutic options are currently being studied against severe SARS-COV-2. In this study, we review several promising therapies against severe disease supported by clinical trial evidence-including Allocetra, monoclonal antibodies (Tixagevimab-Cilgavimab), peginterferon lambda, Baricitinib, Remdesivir, Sarilumab, Tocilizumab, Anakinra, Bevacizumab, exosomes, and mesenchymal stem cells. Due to the virus's rapid adaptive evolution and diverse symptomatic manifestation, the use of combination therapies offers a promising approach to decrease systemic injury. By investing in such targeted interventions, cases of severe SARS-CoV-2 should decrease along with its associated long-term sequelae and thereby allow cancer patients to resume their treatments.

Multiplatform analyses reveal distinct drivers of systemic pathogenesis in adult versus pediatric severe acute COVID-19

The pathogenesis of multi-organ dysfunction associated with severe acute SARS-CoV-2 infection remains poorly understood. Endothelial damage and microvascular thrombosis have been identified as drivers of COVID-19 severity, yet the mechanisms underlying these processes remain elusive. Here we show alterations in fluid shear stress-responsive pathways in critically ill COVID-19 adults as compared to non-COVID critically ill adults using a multiomics approach. Mechanistic in-vitro studies, using microvasculature-on-chip devices, reveal that plasma from critically ill COVID-19 adults induces fibrinogen-dependent red blood cell aggregation that mechanically damages the microvascular glycocalyx. This mechanism appears unique to COVID-19, as plasma from non-COVID sepsis patients demonstrates greater red blood cell membrane stiffness but induces less significant alterations in overall blood rheology. Multiomics analyses in pediatric patients with acute COVID-19 or the post-infectious multi-inflammatory syndrome in children (MIS-C) demonstrate little overlap in plasma cytokine and metabolite changes compared to adult COVID-19 patients. Instead, pediatric acute COVID-19 and MIS-C patients show alterations strongly associated with cytokine upregulation. These findings link high fibrinogen and red blood cell aggregation with endotheliopathy in adult COVID-19 patients and highlight differences in the key mediators of pathogenesis between adult and pediatric populations.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Integrated Metabolic and Inflammatory Signatures Associated with Severity of, Fatality of, and Recovery from COVID-19

Severe manifestations of coronavirus disease 2019 (COVID-19) and mortality have been associated with physiological alterations that provide insights into the pathogenesis of the disease. Moreover, factors that drive recovery from COVID-19 can be explored to identify correlates of protection. The cellular metabolism represents a potential target to improve survival upon severe disease, but the associations between the metabolism and the inflammatory response during COVID-19 are not well defined. We analyzed blood laboratorial parameters, cytokines, and metabolomes of 150 individuals with mild to severe disease, of which 33 progressed to a fatal outcome. A subset of 20 individuals was followed up after hospital discharge and recovery from acute disease. We used hierarchical community networks to integrate metabolomics profiles with cytokines and markers of inflammation, coagulation, and tissue damage. Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) promotes significant alterations in the plasma metabolome, whose activity varies according to disease severity and correlates with oxygen saturation. Differential metabolism underlying death was marked by amino acids and related metabolites, such as glutamate, glutamyl-glutamate, and oxoproline, and lipids, including progesterone, phosphocholine, and lysophosphatidylcholines (lysoPCs). Individuals who recovered from severe disease displayed persistent alterations enriched for metabolism of purines and phosphatidylinositol phosphate and glycolysis. Recovery of mild disease was associated with vitamin E metabolism. Data integration shows that the metabolic response is a hub connecting other biological features during disease and recovery. Infection by SARS-CoV-2 induces concerted activity of metabolic and inflammatory responses that depend on disease severity and collectively predict clinical outcomes of COVID-19. IMPORTANCE COVID-19 is characterized by diverse clinical outcomes that include asymptomatic to mild manifestations or severe disease and death. Infection by SARS-CoV-2 activates inflammatory and metabolic responses that drive protection or pathology. How inflammation and metabolism communicate during COVID-19 is not well defined. We used high-resolution mass spectrometry to investigate small biochemical compounds (<1,500 Da) in plasma of individuals with COVID-19 and controls. Age, sex, and comorbidities have a profound effect on the plasma metabolites of individuals with COVID-19, but we identified significant activity of pathways and metabolites related to amino acids, lipids, nucleotides, and vitamins determined by disease severity, survival outcome, and recovery. Furthermore, we identified metabolites associated with acute-phase proteins and coagulation factors, which collectively identify individuals with severe disease or individuals who died of severe COVID-19. Our study suggests that manipulating specific metabolic pathways can be explored to prevent hyperinflammation, organ dysfunction, and death.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Salivary Metabolomic Analysis Reveals Amino Acid Metabolism Shift in SARS-CoV-2 Virus Activity and Post-Infection Condition

The SARS-CoV-2 virus primarily infects salivary glands suggesting a change in the saliva metabolite profile; this shift may be used as a monitoring instrument during SARS-CoV-2 infection. The present study aims to determine the salivary metabolomic profile of patients with and post-SARS-CoV-19 infection. Patients were without (PCR-), with SARS-CoV-2 (PCR+), or post-SARS-CoV-2 infection. Unstimulated whole saliva was collected, and the ¹H spectra were acquired in a 500 MHz Bruker nuclear magnetic resonance spectrometer at 25 °C. They were subjected to multivariate analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), as well as univariate analysis through t-tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05. A distinction was found when comparing PCR- subjects to those with SARS-CoV-2 infection. When comparing the three groups, the PLS-DA cross-validation presented satisfactory accuracy (ACC = 0.69, R2 = 0.39, Q2 = 0.08). Seventeen metabolites were found in different proportions among the groups. The results suggested the downregulation of major amino acid levels, such as alanine, glutamine, histidine, leucine, lysine, phenylalanine, and proline in the PCR+ group compared to the PCR- ones. In addition, acetate, valerate, and capronic acid were higher in PCR- patients than in PCR+. Sucrose and butyrate were higher in post-SARS-CoV-2 infection compared to PCR-. In general, a reduction in amino acids was observed in subjects with and post-SARS-CoV-2 disease. The salivary metabolomic strategy NMR-based was able to differentiate between non-infected individuals and those with acute and post-SARS-CoV-19 infection.

Continuous diagnosis and prognosis by controlling the update process of deep neural networks

Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.

Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults

COVID-19 represents a multi-system infectious disease with broad-spectrum manifestations, including changes in host metabolic processes connected to the disease pathogenesis. Understanding biochemical dysregulation patterns as a consequence of COVID-19 illness promises to be crucial for tracking disease course and clinical outcomes. Surface-enhanced Raman scattering (SERS) has attracted considerable interest in biomedical diagnostics for the sensitive detection of intrinsic profiles of unique fingerprints of serum biomolecules indicative of SARS-CoV-2 infection in a label-free format. Here, we applied label-free SERS and chemometrics for rapid interrogation of temporal metabolic dynamics in longitudinal sera of mildly infected non-hospitalized patients (n = 22), at 4 and 16 weeks post PCR-positive diagnosis, and compared them with negative controls (n = 8). SERS spectral markers revealed distinct metabolic profiles in patient sera that significantly deviated from the healthy metabolic state at the two sampling time intervals. Multivariate and univariate analyses of the spectral data identified abundance dynamics in amino acids, lipids, and protein vibrations as the key spectral features underlying the metabolic differences detected in convalescent samples and perhaps associated with patient recovery progression. A validation study performed using spontaneous Raman spectroscopy yielded spectral data results that corroborated SERS spectral findings and confirmed the detected disease-specific molecular phenotypes in clinical samples. Label-free SERS promises to be a valuable analytical technique for rapid screening of the metabolic phenotype induced by SARS-CoV-2 infection to allow appropriate healthcare intervention.

Serum Metabolome Adaptations Following 12 Weeks of High-Intensity Interval Training or Moderate-Intensity Continuous Training in Obese Older Adults

Physical activity can be effective in preventing some of the adverse effects of aging on health. High-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are beneficial interventions for the quality of life of obese older individuals. The understanding of all possible metabolic mechanisms underlying these beneficial changes has not yet been established. The aim of this study was to analyze changes in the serum metabolome after 12 weeks of HIIT and MICT in obese older adults. Thirty-eight participants performed either HIIT (n = 26) or MICT (n = 12) three times per week for 12 weeks. Serum metabolites as well as clinical and biological parameters were assessed before and after the 12-week intervention. Among the 364 metabolites and ratio of metabolites identified, 51 metabolites changed significantly following the 12-week intervention. Out of them, 21 significantly changed following HIIT intervention and 18 significantly changed following MICT. Associations with clinical and biological adaptations revealed that changes in acyl-alkyl-phosphatidylcholine (PCae) (22:1) correlated positively with changes in handgrip strength in the HIIT group (r = 0.52, p < 0.01). A negative correlation was also observed between 2-oxoglutaric acid and HOMA-IR (r = -0.44, p < 0.01) when considering both groups together (HIIT and MICT). This metabolite also correlated positively with quantitative insulin-sensitivity check index (QUICKI) in both groups together (r = 0.46, p < 0.01) and the HIIT group (r = 0.51, p < 0.01). Additionally, in the MICT group, fumaric acid was positively correlated with triglyceride levels (r = 0.73, p < 0.01) and acetylcarnitine correlated positively with low-density lipoprotein (LDL) cholesterol (r = 0.81, p < 0.01). These four metabolites might represent potential metabolites of interest concerning muscle strength, glycemic parameters, as well as lipid profile parameters, and hence, for a potential healthy aging. Future studies are needed to confirm the association between these metabolites and a healthy aging.

Association between Circulating Amino Acids and COVID-19 Severity

The severity of the symptoms associated with COVID-19 is highly variable, and has been associated with circulating amino acids as a group of analytes in metabolomic studies. However, for each individual amino acid, there are discordant results among studies. The aims of the present study were: (i) to investigate the association between COVID-19-symptom severity and circulating amino-acid concentrations; and (ii) to assess the ability of circulating amino-acid levels to predict adverse outcomes (intensive-care-unit admission or hospital death). We studied a sample of 736 participants from the Biobanque Québécoise COVID-19. All participants tested positive for COVID-19, and the severity of symptoms was determined using the World-Health-Organization criteria. Circulating amino acids were measured by HPLC-MS/MS. We used logistic models to assess the association between circulating amino acids concentrations and the odds of presenting mild vs. severe or mild vs. moderate symptoms, as well as their accuracy in predicting adverse outcomes. Patients with severe COVID-19 symptoms were older on average, and they had a higher prevalence of obesity and type 2 diabetes. Out of 20 amino acids tested, 16 were significantly associated with disease severity, with phenylalanine (positively) and cysteine (inversely) showing the strongest associations. These associations remained significant after adjustment for age, sex and body mass index. Phenylalanine had a fair ability to predict the occurrence of adverse outcomes, similar to traditionally measured laboratory variables. A multivariate model including both circulating amino acids and clinical variables had a 90% accuracy at predicting adverse outcomes in this sample. In conclusion, patients presenting severe COVID-19 symptoms have an altered amino-acid profile, compared to those with mild or moderate symptoms.

An untargeted metabolomic approach to investigate antiviral defence mechanisms in memory leukocytes secreting anti-SARS-CoV-2 IgG in vitro

Evidence shows that individuals infected by SARS-CoV-2 experience an altered metabolic state in multiple organs. Metabolic activities are directly involved in modulating immune responses against infectious diseases, yet our understanding of how host metabolism relates to inflammatory responses remains limited. To better elucidate the underlying biochemistry of the leukocyte response, we focused our analysis on possible relationships between SARS-CoV-2 post-infection stages and distinct metabolic pathways. Indeed, we observed a significant altered metabolism of tryptophan and urea cycle pathways in cultures of peripheral blood mononuclear cells obtained 60-90 days after infection and showing in vitro IgG antibody memory for spike-S1 antigen (n = 17). This work, for the first time, identifies metabolic routes in cell metabolism possibly related to later stages of immune defence against SARS-CoV-2 infection, namely, when circulating antibodies may be absent but an antibody memory is present. The results suggest reprogramming of leukocyte metabolism after viral pathogenesis through activation of specific amino acid pathways possibly related to protective immunity against SARS-CoV-2.

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.

The Cytokines CXCL10 and CCL2 and the Kynurenine Metabolite Anthranilic Acid Accurately Predict Patients at Risk of Developing Dengue With Warning Signs

BACKGROUND

The resolution or aggravation of dengue infection depends on the patient's immune response during the critical phase. Cytokines released by immune cells increase with the worsening severity of dengue infections. Cytokines activate the kynurenine pathway (KP) and the extent of KP activation then influences disease severity.

METHODS

KP metabolites and cytokines in plasma samples of patients with dengue infection (dengue without warning signs [DWS-], dengue with warning signs [DWS+], or severe dengue) were analyzed. Cytokines (interferon gamma [IFN-ɣ], tumor necrosis factor, interleukin 6, CXCL10/interferon-inducile protein 10 [IP-10], interleukin 18 [IL-18], CCL2/monocyte chemoattractant protein-1 [MCP-1], and CCL4/macrophage inflammatory protein-1beta [MIP-1β] were assessed by a Human Luminex Screening Assay, while KP metabolites (tryptophan, kynurenine, anthranilic acid [AA], picolinic acid, and quinolinic acid) were assessed by ultra-high-performance liquid chromatography and Gas Chromatography Mass Spectrophotometry [GCMS] assays.

RESULTS

Patients with DWS+ had increased activation of the KP where kynurenine-tryptophan ratio, anthranilic acid, and picolinic acid were elevated. These patients also had higher levels of the cytokines IFN-ɣ, CXCL10, CCL4, and IL-18 than those with DWS-. Further receiver operating characteristic analysis identified 3 prognostic biomarker candidates, CXCL10, CCL2, and AA, which predicted patients with higher risks of developing DWS+ with an accuracy of 97%.

CONCLUSIONS

The data suggest a unique biochemical signature in patients with DWS+. CXCL10 and CCL2 together with AA are potential prognostic biomarkers that discern patients with higher risk of developing DWS+ at earlier stages of infection.

Tryptophan Metabolism 'Hub' Gene Expression Associates with Increased Inflammation and Severe Disease Outcomes in COVID-19 Infection and Inflammatory Bowel Disease

The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Both COVID-19 and Inflammatory Bowel Disease (IBD) show commonalities in symptoms and treatment with sensitization of the epithelial barrier inviting an immune response. In this study we use a multi-omics strategy to identify a common signature of immune disease that may be able to predict for more severe patient outcomes. Global proteomic approaches were applied to transcriptome and proteome. Further semi- and relative- quantitative targeted mass spectrometry methods were developed to substantiate the proteomic and metabolomics changes in nasal swabs from healthy, COVID-19 (24 h and 3 weeks post infection); serums from Crohn's disease patients (scored for epithelial leak), terminal ileum tissue biopsies (patient matched inflamed and non-inflamed regions, and controls). We found that the tryptophan/kynurenine metabolism pathway is a 'hub' regulator of canonical and non-canonical transcription, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course. Significantly modified pathways include stress response regulator EIF2 signaling (p = 1 × 10^-3); energy metabolism, KYNU (p = 4 × 10^-4), WARS (p = 1 × 10^-7); inflammation, and IDO activity (p = 1 × 10^-6). Heightened levels of PARP1, WARS and KYNU are predictive at the acute stage of infection for resilience, while in contrast, levels remained high and are predictive of persistent and more severe outcomes in COVID disease. Generation of a targeted marker profile showed these changes in immune disease underlay resolution of epithelial barrier function and have the potential to define disease trajectory and more severe patient outcomes.

Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients

Metabolomic analysis of blood plasma samples from COVID-19 patients is a promising approach allowing for the evaluation of disease progression. We performed the metabolomic analysis of plasma samples of 30 COVID-19 patients and the 19 controls using the high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometric detection (LC-MS/MS). In our analysis, we identified 103 metabolites enriched in KEGG metabolic pathways such as amino acid metabolism and the biosynthesis of aminoacyl-tRNAs, which differed significantly between the COVID-19 patients and the controls. Using ANDSystem software, we performed the reconstruction of gene networks describing the potential genetic regulation of metabolic pathways perturbed in COVID-19 patients by SARS-CoV-2 proteins. The nonstructural proteins of SARS-CoV-2 (orf8 and nsp5) and structural protein E were involved in the greater number of regulatory pathways. The reconstructed gene networks suggest the hypotheses on the molecular mechanisms of virus-host interactions in COVID-19 pathology and provide a basis for the further experimental and computer studies of the regulation of metabolic pathways by SARS-CoV-2 proteins. Our metabolomic analysis suggests the need for nonstructural protein-based vaccines and the control strategy to reduce the disease progression of COVID-19.

Alterations in the Kynurenine-Tryptophan Pathway and Lipid Dysregulation Are Preserved Features of COVID-19 in Hemodialysis

Coronavirus disease 2019 (COVID-19)-induced metabolic alterations have been proposed as a source for prognostic biomarkers and may harbor potential for therapeutic exploitation. However, the metabolic impact of COVID-19 in hemodialysis (HD), a setting of profound a priori alterations, remains unstudied. To evaluate potential COVID-19 biomarkers in end-stage kidney disease (CKD G5), we analyzed the plasma metabolites in different COVID-19 stages in patients with or without HD. We recruited 18 and 9 asymptomatic and mild, 11 and 11 moderate, 2 and 13 severely affected, and 10 and 6 uninfected HD and non-HD patients, respectively. Plasma samples were taken at the time of diagnosis and/or upon admission to the hospital and analyzed by targeted metabolomics and cytokine/chemokine profiling. Targeted metabolomics confirmed stage-dependent alterations of the metabolome in non-HD patients with COVID-19, which were less pronounced in HD patients. Elevated kynurenine levels and lipid dysregulation, shown by an increase in circulating free fatty acids and a decrease in lysophospholipids, could distinguish patients with moderate COVID-19 from non-infected individuals in both groups. Kynurenine and lipid alterations were also associated with ICAM-1 and IL-15 levels in HD and non-HD patients. Our findings support the kynurenine pathway and plasma lipids as universal biomarkers of moderate and severe COVID-19 independent of kidney function.