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1.
Anal Chem ; 94(19): 6919-6923, 2022 May 17.
Article in English | MEDLINE | ID: covidwho-1829921

ABSTRACT

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10-16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.


Subject(s)
COVID-19 , Humans , Magnetic Resonance Spectroscopy/methods , Metabolome , Metabolomics/methods , Proton Magnetic Resonance Spectroscopy
2.
Front Immunol ; 12: 809937, 2021.
Article in English | MEDLINE | ID: covidwho-1809383

ABSTRACT

Deep understanding of the SARS-CoV-2 effects on host molecular pathways is paramount for the discovery of early biomarkers of outcome of coronavirus disease 2019 (COVID-19) and the identification of novel therapeutic targets. In that light, we generated metabolomic data from COVID-19 patient blood using high-throughput targeted nuclear magnetic resonance (NMR) spectroscopy and high-dimensional flow cytometry. We find considerable changes in serum metabolome composition of COVID-19 patients associated with disease severity, and response to tocilizumab treatment. We built a clinically annotated, biologically-interpretable space for precise time-resolved disease monitoring and characterize the temporal dynamics of metabolomic change along the clinical course of COVID-19 patients and in response to therapy. Finally, we leverage joint immuno-metabolic measurements to provide a novel approach for patient stratification and early prediction of severe disease. Our results show that high-dimensional metabolomic and joint immune-metabolic readouts provide rich information content for elucidation of the host's response to infection and empower discovery of novel metabolic-driven therapies, as well as precise and efficient clinical action.


Subject(s)
Biomarkers/metabolism , COVID-19/immunology , COVID-19/metabolism , Metabolome/immunology , SARS-CoV-2/immunology , Adult , Aged , Biochemical Phenomena/immunology , Biomarkers/blood , COVID-19/blood , Female , Humans , Male , Metabolomics/methods , Middle Aged
4.
J Affect Disord ; 298(Pt A): 381-387, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-1717738

ABSTRACT

OBJECTIVES: To identify the prevalence, lifestyle factors, chronic disease status, and assessing the metabolic profile, comparing key differences in a cohort of subjects aged at least 50 years old among depression combined anxiety, depression and anxiety in a multi-ethnic population in west China. METHODS: A large multi-ethnic sample of 6838 participants aged 50 years old (mean age 62.4 ± 8.3 years) from West China Health and Aging Trend (WCHAT) study was analyzed. We categorized all participants into four groups: (a) comorbid anxiety and depression symptomology (CAD), (b) anxiety only, (c) depression only, or (d) neither depression nor anxiety. Different variables like anthropometry measures, life styles, chronic disease and blood test were collected. Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS-15). GDS-15 scores ≥5 indicate depression. Anxiety status was assessed using Generalized Anxiety Disorder (GAD-7) instrument and the scores ≥5 was considered as having anxiety. Different variables like anthropometry measures, life styles, cognitive function and chronic disease comorbidities were collected and serum parameters were tested. Multivariable logistic regression adjusted for age, sex, and ethnicity was done to compare between those with the mental outcomes and without. RESULTS: The proportions of CAD, anxiety and depression were 9.0%, 12.8% and 10.6% respectively with ethnic diversity. The 'comorbid' group shown greater frequency of being female, having a lower educational level, higher prevalence of being single/divorced/widowed, drinking alcohol and smoking, more chronic disease profile and cognitive decline compared with individuals with only one disorder. And the metabolic profile showed differences in albumin, total protein, creatinine, uric acid, thyroid hormones in comparing CAD symptomology and the 'neither symptomology'. CONCLUSIONS: Yi, Qiang and Uyghur ethnic groups have a higher prevalence of mental disease compared with Han in west China. And these mental disease had a distinct risk factor profile in age, sex, educational level, chronic disease and cognitive function. Vitamin D levels were lower among those with mental disease compared to those without.


Subject(s)
Depression , Aged , Anxiety/epidemiology , Anxiety Disorders/epidemiology , China/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Female , Humans , Metabolome , Middle Aged , Prevalence
5.
Eur J Immunol ; 52(3): 503-510, 2022 03.
Article in English | MEDLINE | ID: covidwho-1718287

ABSTRACT

Corona disease 2019 (COVID-19) affects multiple organ systems. Recent studies have indicated perturbations in the circulating metabolome linked to COVID-19 severity. However, several questions pertain with respect to the metabolome in COVID-19. We performed an in-depth assessment of 1129 unique metabolites in 27 hospitalized COVID-19 patients and integrated results with large-scale proteomic and immunology data to capture multiorgan system perturbations. More than half of the detected metabolic alterations in COVID-19 were driven by patient-specific confounding factors ranging from comorbidities to xenobiotic substances. Systematically adjusting for this, a COVID-19-specific metabolic imprint was defined which, over time, underwent a switch in response to severe acute respiratory syndrome coronavirus-2 seroconversion. Integration of the COVID-19 metabolome with clinical, cellular, molecular, and immunological severity scales further revealed a network of metabolic trajectories aligned with multiple pathways for immune activation, and organ damage including neurological inflammation and damage. Altogether, this resource refines our understanding of the multiorgan system perturbations in severe COVID-19 patients.


Subject(s)
COVID-19/immunology , COVID-19/metabolism , Metabolome/immunology , SARS-CoV-2 , Adolescent , Adult , Aged , COVID-19/complications , Case-Control Studies , Central Nervous System Diseases/etiology , Central Nervous System Diseases/immunology , Central Nervous System Diseases/metabolism , Cohort Studies , Female , Humans , Male , Metabolomics , Middle Aged , Organ Specificity , Pandemics , Phenotype , Proteomics , Severity of Illness Index , Young Adult
6.
J Proteome Res ; 21(3): 623-634, 2022 03 04.
Article in English | MEDLINE | ID: covidwho-1671479

ABSTRACT

Despite the scientific and human efforts to understand COVID-19, there are questions still unanswered. Variations in the metabolic reaction to SARS-CoV-2 infection could explain the striking differences in the susceptibility to infection and the risk of severe disease. Here, we used untargeted metabolomics to examine novel metabolic pathways related to SARS-CoV-2 susceptibility and COVID-19 clinical severity using capillary electrophoresis coupled to a time-of-flight mass spectrometer (CE-TOF-MS) in plasma samples. We included 27 patients with confirmed COVID-19 and 29 healthcare workers heavily exposed to SARS-CoV-2 but with low susceptibility to infection ("nonsusceptible"). We found a total of 42 metabolites of SARS-CoV-2 susceptibility or COVID-19 clinical severity. We report the discovery of new plasma biomarkers for COVID-19 that provide mechanistic explanations for the clinical consequences of SARS-CoV-2, including mitochondrial and liver dysfunction as a consequence of hypoxemia (citrulline, citric acid, and 3-aminoisobutyric acid (BAIBA)), energy production and amino acid catabolism (phenylalanine and histidine), and endothelial dysfunction and thrombosis (citrulline, asymmetric dimethylarginine (ADMA), and 2-aminobutyric acid (2-AB)), and we found interconnections between these pathways. In summary, in this first report several metabolic pathways implicated in SARS-CoV-2 susceptibility and COVID-19 clinical progression were found by CE-MS based metabolomics that could be developed as biomarkers of COVID-19.


Subject(s)
COVID-19 , SARS-CoV-2 , Biomarkers , Humans , Metabolome , Metabolomics/methods
7.
Sci Rep ; 12(1): 1650, 2022 01 31.
Article in English | MEDLINE | ID: covidwho-1661981

ABSTRACT

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


Subject(s)
Asymptomatic Infections/epidemiology , COVID-19/blood , COVID-19/diagnosis , Metabolome , Metabolomics/methods , Pandemics , SARS-CoV-2/genetics , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19/epidemiology , COVID-19/physiopathology , Case-Control Studies , Cohort Studies , Female , Follow-Up Studies , Humans , Male , Middle Aged , Patient Admission , Polymerase Chain Reaction/methods , Spain/epidemiology
8.
Signal Transduct Target Ther ; 7(1): 29, 2022 01 28.
Article in English | MEDLINE | ID: covidwho-1655546

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted on mink farms between minks and humans in many countries. However, the systemic pathological features of SARS-CoV-2-infected minks are mostly unknown. Here, we demonstrated that minks were largely permissive to SARS-CoV-2, characterized by severe and diffuse alveolar damage, and lasted at least 14 days post inoculation (dpi). We first reported that infected minks displayed multiple organ-system lesions accompanied by an increased inflammatory response and widespread viral distribution in the cardiovascular, hepatobiliary, urinary, endocrine, digestive, and immune systems. The viral protein partially co-localized with activated Mac-2+ macrophages throughout the body. Moreover, we first found that the alterations in lipids and metabolites were correlated with the histological lesions in infected minks, especially at 6 dpi, and were similar to that of patients with severe and fatal COVID-19. Particularly, altered metabolic pathways, abnormal digestion, and absorption of vitamins, lipids, cholesterol, steroids, amino acids, and proteins, consistent with hepatic dysfunction, highlight metabolic and immune dysregulation. Enriched kynurenine in infected minks contributed to significant activation of the kynurenine pathway and was related to macrophage activation. Melatonin, which has significant anti-inflammatory and immunomodulating effects, was significantly downregulated at 6 dpi and displayed potential as a targeted medicine. Our data first illustrate systematic analyses of infected minks to recapitulate those observations in severe and fetal COVID-19 patients, delineating a useful animal model to mimic SARS-CoV-2-induced systematic and severe pathophysiological features and provide a reliable tool for the development of effective and targeted treatment strategies, vaccine research, and potential biomarkers.


Subject(s)
COVID-19/metabolism , Lung/metabolism , Macrophages, Alveolar/metabolism , Metabolome , Mink/virology , SARS-CoV-2/metabolism , Amino Acids/metabolism , Animals , Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/genetics , COVID-19/pathology , Disease Models, Animal , Female , Humans , Lung/pathology , Lung/virology , Macrophages, Alveolar/pathology , Macrophages, Alveolar/virology , Melatonin/metabolism , Metabolic Networks and Pathways/genetics , Molecular Targeted Therapy/methods , SARS-CoV-2/drug effects , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Sterols/metabolism , Virulence , Virus Replication/genetics
9.
Anal Chim Acta ; 1196: 339405, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1632732

ABSTRACT

Metabolomics (both targeted and untargeted) has become the gold standard in biomarker discovery. Whereas targeted approaches only provide information for the selected markers, thus hampering the determination of out-of-the-box markers, the common bottleneck of untargeted metabolomics is the identification of detected biomarkers. In this study, we developed a strategy based on derivatization and LC-MS/MS detection in a precursor ion scan for the untargeted determination of a specific part of the metabolome (carbonyl-containing metabolites). The usefulness of this guided metabolomics approach has been demonstrated by elucidating carbonyl-containing biomarkers of COVID-19 severity. First, the LC-MS/MS behavior of 63 model compounds after O-benzylhydroxylamine derivatization was studied. A precursor ion scan of m/z 91 was selected as a suitable approach for the untargeted detection of carbonyl-containing metabolites. The method was able to detect ≈300 potential carbonyl-containing molecules in plasma, including mono-/di-/tricarbonylic compounds with satisfactory intra-day and inter-day repeatability and RSDs commonly <15%. Additionally, the semiquantitative nature of the precursor ion scan method was confirmed by comparison with a fully validated targeted method. The application of the guided metabolomics method to COVID-19 plasma samples revealed the presence of four potential COVID-19 severity biomarkers. Based on their LC-MS/MS behavior, these biomarkers were elucidated as 2-hydroxybutyrate, 2,3-dihydroxybutyrate, 2-oxobutyrate and 2-hydroxy-3-methylbutyrate. Their structures were confirmed by comparison with reference materials. The alterations of these biomarkers with COVID-19 severity were confirmed by a target analysis of a larger set of samples. Our results confirm that guided metabolomics is an alternative approach for the untargeted detection of selected families of metabolites; this approach can accelerate their elucidation and provide new perspectives for the establishment of health/disease biomarkers.


Subject(s)
COVID-19 , Tandem Mass Spectrometry , Biomarkers , Chromatography, Liquid , Humans , Metabolome , Metabolomics , SARS-CoV-2
10.
Int J Obes (Lond) ; 46(4): 817-824, 2022 04.
Article in English | MEDLINE | ID: covidwho-1607588

ABSTRACT

BACKGROUND: Different pathogens can cause community-acquired pneumonia (CAP); however, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has re-emphasized the vital role of respiratory viruses as a cause of CAP. The aim was to explore differences in metabolic profile, body composition, physical capacity, and inflammation between patients hospitalized with CAP caused by different etiology. METHODS: A prospective study of Danish patients hospitalized with CAP caused by SARS-CoV-2, influenza, or bacteria. Fat (FM) and fat-free mass (FFM) were assessed with bioelectrical impedance analysis. Physical activity and capacity were assessed using questionnaires and handgrip strength. Plasma (p)-glucose, p-lipids, hemoglobin A1c (HbA1c), p-adiponectin, and cytokines were measured. RESULTS: Among 164 patients with CAP, etiology did not affect admission levels of glucose, HbA1c, adiponectin, or lipids. Overall, 15.2% had known diabetes, 6.1% had undiagnosed diabetes, 51.3% had pre-diabetes, 81% had hyperglycemia, and 60% had low HDL-cholesterol, with no difference between groups. Body mass index, FM, and FFM were similar between groups, with 73% of the patients being characterized with abdominal obesity, although waist circumference was lower in patients with COVID-19. Physical capacity was similar between groups. More than 80% had low handgrip strength and low physical activity levels. Compared to patients with influenza, patients with COVID-19 had increased levels of interferon (IFN)-γ (mean difference (MD) 4.14; 95% CI 1.36-12.58; p = 0.008), interleukin (IL)-4 (MD 1.82; 95% CI 1.12-2.97; p = 0.012), IL-5 (MD 2.22; 95% CI 1.09-4.52; p = 0.024), and IL-6 (MD 2.41; 95% CI 1.02-5.68; p = 0.044) and increased IFN-γ (MD 6.10; 95% CI 2.53-14.71; p < 0.001) and IL-10 (MD 2.68; 95% CI 1.53-4.69; p < 0.001) compared to patients with bacterial CAP, but no difference in IL-1ß, tumor necrosis factor-α, IL-8, IL-18, IL-12p70, C-reactive protein, and adiponectin. CONCLUSION: Despite higher inflammatory response in patients with COVID-19, metabolic profile, body composition, and physical capacity were similar to patients with influenza and bacterial CAP.


Subject(s)
COVID-19 , Influenza, Human , Pneumonia , Bacteria , Body Composition , COVID-19/complications , COVID-19/epidemiology , Hand Strength , Humans , Influenza, Human/complications , Influenza, Human/epidemiology , Metabolome , Prospective Studies , SARS-CoV-2
11.
Cell Rep ; 38(3): 110271, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1588135

ABSTRACT

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.


Subject(s)
COVID-19/urine , Immunity , Metabolome , Proteome/analysis , SARS-CoV-2/immunology , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/blood , COVID-19/immunology , COVID-19/pathology , Case-Control Studies , Child , Child, Preschool , China , Cohort Studies , Female , Humans , Immunity/physiology , Male , Metabolome/immunology , Metabolomics , Middle Aged , Patient Acuity , Proteome/immunology , Proteome/metabolism , Proteomics , Urinalysis/methods , Young Adult
12.
STAR Protoc ; 3(1): 101051, 2022 03 18.
Article in English | MEDLINE | ID: covidwho-1575581

ABSTRACT

Here we describe a protocol for identifying metabolites in respiratory specimens of patients that are SARS-CoV-2 positive, SARS-CoV-2 negative, or H1N1 positive. This protocol provides step-by-step instructions on sample collection from patients, followed by metabolite extraction. We use ultra-high-pressure liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) for data acquisition and describe the steps for data analysis. The protocol was standardized with specific customization for SARS-CoV-2-containing respiratory specimens. For complete details on the use and execution of this protocol, please refer to Maras et al. (2021).


Subject(s)
COVID-19/diagnosis , Chromatography, High Pressure Liquid/methods , Metabolomics/methods , COVID-19/metabolism , Computational Biology , Diagnostic Tests, Routine , Gene Expression Profiling , Genetic Techniques , Humans , Influenza A Virus, H1N1 Subtype/metabolism , Influenza A Virus, H1N1 Subtype/pathogenicity , Mass Spectrometry/methods , Metabolome , Reference Standards , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Specimen Handling/methods
13.
Viruses ; 13(12)2021 12 11.
Article in English | MEDLINE | ID: covidwho-1572663

ABSTRACT

BACKGROUND: There is an urgent need for new antivirals with powerful therapeutic potential and tolerable side effects. METHODS: Here, we tested the antiviral properties of interferons (IFNs), alone and with other drugs in vitro. RESULTS: While IFNs alone were insufficient to completely abolish replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), IFNα, in combination with remdesivir, EIDD-2801, camostat, cycloheximide, or convalescent serum, proved to be more effective. Transcriptome and metabolomic analyses revealed that the IFNα-remdesivir combination suppressed SARS-CoV-2-mediated changes in Calu-3 cells and lung organoids, although it altered the homeostasis of uninfected cells and organoids. We also demonstrated that IFNα combinations with sofosbuvir, telaprevir, NITD008, ribavirin, pimodivir, or lamivudine were effective against HCV, HEV, FLuAV, or HIV at lower concentrations, compared to monotherapies. CONCLUSIONS: Altogether, our results indicated that IFNα can be combined with drugs that affect viral RNA transcription, protein synthesis, and processing to make synergistic combinations that can be attractive targets for further pre-clinical and clinical development against emerging and re-emerging viral infections.


Subject(s)
Antiviral Agents/pharmacology , Interferon-alpha/pharmacology , SARS-CoV-2/drug effects , Cell Line , Drug Synergism , Humans , Lung/drug effects , Lung/metabolism , Lung/virology , Metabolome/drug effects , Organoids , RNA, Viral/biosynthesis , RNA, Viral/drug effects , Signal Transduction/drug effects , Transcriptome/drug effects , Virus Replication/drug effects , Viruses/classification , Viruses/drug effects
14.
Microbiol Spectr ; 9(3): e0033821, 2021 12 22.
Article in English | MEDLINE | ID: covidwho-1562285

ABSTRACT

The heterogeneity in severity and outcome of COVID-19 cases points out the urgent need for early molecular characterization of patients followed by risk-stratified care. The main objective of this study was to evaluate the fluctuations of serum metabolomic profiles of COVID-19 patients with severe illness during the different disease stages in a longitudinal manner. We demonstrate a distinct metabolomic signature in serum samples of 32 hospitalized patients at the acute phase compared to the recovery period, suggesting the tryptophan (tryptophan, kynurenine, and 3-hydroxy-DL-kynurenine) and arginine (citrulline and ornithine) metabolism as contributing pathways in the immune response to SARS-CoV-2 with a potential link to the clinical severity of the disease. In addition, we suggest that glutamine deprivation may further result in inhibited M2 macrophage polarization as a complementary process, and highlight the contribution of phenylalanine and tyrosine in the molecular mechanisms underlying the severe course of the infection. In conclusion, our results provide several functional metabolic markers for disease progression and severe outcome with potential clinical application. IMPORTANCE Although the host defense mechanisms against SARS-CoV-2 infection are still poorly described, they are of central importance in shaping the course of the disease and the possible outcome. Metabolomic profiling may complement the lacking knowledge of the molecular mechanisms underlying clinical manifestations and pathogenesis of COVID-19. Moreover, early identification of metabolomics-based biomarker signatures is proved to serve as an effective approach for the prediction of disease outcome. Here we provide the list of metabolites describing the severe, acute phase of the infection and bring the evidence of crucial metabolic pathways linked to aggressive immune responses. Finally, we suggest metabolomic phenotyping as a promising method for developing personalized care strategies in COVID-19 patients.


Subject(s)
Amino Acids/metabolism , COVID-19/metabolism , Hospitals , Metabolome , Severity of Illness Index , Amino Acids/blood , Biomarkers/blood , Host Microbial Interactions , Humans , Kynurenine/analogs & derivatives , Metabolomics , SARS-CoV-2
15.
PLoS One ; 16(12): e0259909, 2021.
Article in English | MEDLINE | ID: covidwho-1546944

ABSTRACT

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.


Subject(s)
COVID-19/pathology , Metabolome , Metabolomics/methods , Adult , Aged , Area Under Curve , COVID-19/mortality , COVID-19/virology , Carnitine/metabolism , Citrulline/metabolism , Female , Glutamic Acid/metabolism , Humans , Kynurenine/metabolism , Male , Middle Aged , Ornithine/metabolism , ROC Curve , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Tryptophan/metabolism
16.
Cell Death Differ ; 28(12): 3199-3213, 2021 12.
Article in English | MEDLINE | ID: covidwho-1475289

ABSTRACT

SARS-CoV-2 vaccinations have greatly reduced COVID-19 cases, but we must continue to develop our understanding of the nature of the disease and its effects on human immunity. Previously, we suggested that a dysregulated STAT3 pathway following SARS-Co-2 infection ultimately leads to PAI-1 activation and cascades of pathologies. The major COVID-19-associated metabolic risks (old age, hypertension, cardiovascular diseases, diabetes, and obesity) share high PAI-1 levels and could predispose certain groups to severe COVID-19 complications. In this review article, we describe the common metabolic profile that is shared between all of these high-risk groups and COVID-19. This profile not only involves high levels of PAI-1 and STAT3 as previously described, but also includes low levels of glutamine and NAD+, coupled with overproduction of hyaluronan (HA). SARS-CoV-2 infection exacerbates this metabolic imbalance and predisposes these patients to the severe pathophysiologies of COVID-19, including the involvement of NETs (neutrophil extracellular traps) and HA overproduction in the lung. While hyperinflammation due to proinflammatory cytokine overproduction has been frequently documented, it is recently recognized that the immune response is markedly suppressed in some cases by the expansion and activity of MDSCs (myeloid-derived suppressor cells) and FoxP3+ Tregs (regulatory T cells). The metabolomics profiles of severe COVID-19 patients and patients with advanced cancer are similar, and in high-risk patients, SARS-CoV-2 infection leads to aberrant STAT3 activation, which promotes a cancer-like metabolism. We propose that glutamine deficiency and overproduced HA is the central metabolic characteristic of COVID-19 and its high-risk groups. We suggest the usage of glutamine supplementation and the repurposing of cancer drugs to prevent the development of severe COVID-19 pneumonia.


Subject(s)
COVID-19/physiopathology , Glutamine/deficiency , Animals , COVID-19/blood , COVID-19/epidemiology , Comorbidity , Glutamine/blood , Humans , Hyaluronic Acid/blood , Metabolome , Plasminogen Activator Inhibitor 1/blood , Risk Factors , Severity of Illness Index
17.
Biochim Biophys Acta Mol Basis Dis ; 1868(1): 166289, 2022 01 01.
Article in English | MEDLINE | ID: covidwho-1466061

ABSTRACT

To explore the recovery of renal function in severely ill coronavirus disease (COVID-19) survivors and determine the plasma metabolomic profile of patients with different renal outcomes 3 months after discharge, we included 89 severe COVID-19 survivors who had been discharged from Wuhan Union Hospital for 3 months. All patients had no underlying kidney disease before admission. At patient recruitment, renal function assessment, laboratory examination, chest computed tomography (CT) were performed. Liquid chromatography-mass spectrometry was used to detect metabolites in the plasma. We analyzed the longitudinally change in the estimated glomerular filtration rate (eGFR) based on serum creatinine and cystatin-c levels using the CKD-EPI equation and explored the metabolomic differences in patients with different eGFR change patterns from hospitalization to 3 months after discharge. Lung CT showed good recovery; however, the median eGFR significantly decreased at the 3-month follow-up. Among the 89 severely ill COVID-19 patients, 69 (77.5%) showed abnormal eGFR (<90 mL/min per 1.73 m2) at 3 months after discharge. Age (odds ratio [OR] = 1.26, 95% confidence interval [CI] = 1.08-1.47, p = 0.003), body mass index (OR = 1.97, 95% CI = 1.20-3.22, p = 0.007), and cystatin-c level (OR = 1.22, 95% CI = 1.07-1.39, p = 0.003) at discharge were independent risk factors for post-discharge abnormal eGFR. Plasma metabolomics at the 3-months follow-up revealed that ß-pseudouridine, uridine, and 2-(dimethylamino) guanosine levels gradually increased with an abnormal degree of eGFR. Moreover, the kynurenine pathway in tryptophan metabolism, vitamin B6 metabolism, cysteine and methionine metabolism, and arginine biosynthesis were also perturbed in survivors with abnormal eGFR.


Subject(s)
COVID-19/complications , COVID-19/virology , Energy Metabolism , Glomerular Filtration Rate , Kidney Diseases/etiology , Kidney Diseases/metabolism , SARS-CoV-2 , Aged , COVID-19/diagnosis , Comorbidity , Female , Humans , Kidney Diseases/diagnosis , Kidney Function Tests , Male , Metabolic Networks and Pathways , Metabolome , Metabolomics/methods , Middle Aged , Odds Ratio , Patient Discharge , Severity of Illness Index , Symptom Assessment
18.
Int J Mol Sci ; 22(19)2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1463710

ABSTRACT

The present Special Issue focuses on the latest approaches to health and public health microbiology using multiomics [...].


Subject(s)
Bacteria/growth & development , Holistic Health/standards , Metabolome , Metagenome , Microbiota , Proteome , Public Health/standards , Bacteria/classification , Bacteria/genetics , Bacteria/isolation & purification , Humans
19.
Int J Mol Sci ; 21(11)2020 May 29.
Article in English | MEDLINE | ID: covidwho-1456326

ABSTRACT

In the development of inflammatory bowel disease (IBD), the gut microbiota has been established as a key factor. Recently, metabolomics has become important for understanding the functional relevance of gut microbial changes in disease. Animal models for IBD enable the study of factors involved in disease development. However, results from animal studies may not represent the human situation. The aim of this study was to investigate whether results from metabolomics studies on animal models for IBD were similar to those from studies on IBD patients. Medline and Embase were searched for relevant studies up to May 2017. The Covidence systematic review software was used for study screening, and quality assessment was conducted for all included studies. Data showed a convergence of ~17% for metabolites differentiated between IBD and controls in human and animal studies with amino acids being the most differentiated metabolite subclass. The acute dextran sodium sulfate model appeared as a good model for analysis of systemic metabolites in IBD, but analytical platform, age, and biological sample type did not show clear correlations with any significant metabolites. In conclusion, this systematic review highlights the variation in metabolomics results, and emphasizes the importance of expanding the applied detection methods to ensure greater coverage and convergence between the various different patient phenotypes and animal models of inflammatory bowel disease.


Subject(s)
Inflammatory Bowel Diseases/metabolism , Metabolome , Metabolomics/methods , /methods , Animals , Disease Models, Animal , Humans , Inflammatory Bowel Diseases/etiology , Inflammatory Bowel Diseases/pathology , Mice , Sodium Dodecyl Sulfate/toxicity
20.
EBioMedicine ; 71: 103546, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1363149

ABSTRACT

BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection. METHODS: We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery. FINDINGS: A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations. INTERPRETATION: This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing. FUNDING: None.


Subject(s)
Influenza, Human/diagnosis , Machine Learning , Metabolome , Molecular Diagnostic Techniques/methods , Adolescent , Adult , Child , Child, Preschool , Female , Gas Chromatography-Mass Spectrometry/methods , Humans , Influenza, Human/metabolism , Influenza, Human/virology , Male , Metabolomics/methods , Nasal Mucosa/metabolism , Nasal Mucosa/virology , Orthomyxoviridae/pathogenicity , Pyrrolidonecarboxylic Acid/analysis
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