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1.
Cell Death Dis ; 13(3): 235, 2022 03 14.
Article in English | MEDLINE | ID: covidwho-1740434

ABSTRACT

Coronavirus disease 2019 (COVID-19) has gained prominence as a global pandemic. Studies have suggested that systemic alterations persist in a considerable proportion of COVID-19 patients after hospital discharge. We used proteomic and metabolomic approaches to analyze plasma samples obtained from 30 healthy subjects and 54 COVID-19 survivors 6 months after discharge from the hospital, including 30 non-severe and 24 severe patients. Through this analysis, we identified 1019 proteins and 1091 metabolites. The differentially expressed proteins and metabolites were then subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Among the patients evaluated, 41% of COVID-19 survivors reported at least one clinical symptom and 26.5% showed lung imaging abnormalities at 6 months after discharge. Plasma proteomics and metabolomics analysis showed that COVID-19 survivors differed from healthy control subjects in terms of the extracellular matrix, immune response, and hemostasis pathways. COVID-19 survivors also exhibited abnormal lipid metabolism, disordered immune response, and changes in pulmonary fibrosis-related proteins. COVID-19 survivors show persistent proteomic and metabolomic abnormalities 6 months after discharge from the hospital. Hence, the recovery period for COVID-19 survivors may be longer.


Subject(s)
COVID-19/mortality , Metabolomics/methods , Patient Discharge/statistics & numerical data , Proteomics/methods , SARS Virus/pathogenicity , Female , Humans , Male , Middle Aged , Survivors , Time Factors
2.
Int J Mol Sci ; 23(5)2022 Feb 22.
Article in English | MEDLINE | ID: covidwho-1707649

ABSTRACT

Omics-based technologies have been largely adopted during this unprecedented global COVID-19 pandemic, allowing the scientific community to perform research on a large scale to understand the pathobiology of the SARS-CoV-2 infection and its replication into human cells. The application of omics techniques has been addressed to every level of application, from the detection of mutations, methods of diagnosis or monitoring, drug target discovery, and vaccine generation, to the basic definition of the pathophysiological processes and the biochemical mechanisms behind the infection and spread of SARS-CoV-2. Thus, the term COVIDomics wants to include those efforts provided by omics-scale investigations with application to the current COVID-19 research. This review summarizes the diverse pieces of knowledge acquired with the application of COVIDomics techniques, with the main focus on proteomics and metabolomics studies, in order to capture a common signature in terms of proteins, metabolites, and pathways dysregulated in COVID-19 disease. Exploring the multiomics perspective and the concurrent data integration may provide new suitable therapeutic solutions to combat the COVID-19 pandemic.


Subject(s)
COVID-19/metabolism , Metabolomics/methods , Proteome/metabolism , Proteomics/methods , COVID-19/epidemiology , COVID-19/virology , Chromatography, Liquid/methods , Host-Pathogen Interactions , Humans , Pandemics , SARS-CoV-2/physiology , Tandem Mass Spectrometry/methods
3.
Immunohorizons ; 6(2): 144-155, 2022 02 16.
Article in English | MEDLINE | ID: covidwho-1690086

ABSTRACT

Due to the severity of COVID-19 disease, the U.S. Centers for Disease Control and Prevention and World Health Organization recommend that manipulation of active viral cultures of SARS-CoV-2 and respiratory secretions from COVID-19 patients be performed in biosafety level (BSL)3 laboratories. Therefore, it is imperative to develop viral inactivation procedures that permit samples to be transferred to lower containment levels (BSL2), while maintaining the fidelity of complex downstream assays to expedite the development of medical countermeasures. In this study, we demonstrate optimal conditions for complete viral inactivation following fixation of infected cells with commonly used reagents for flow cytometry, UVC inactivation in sera and respiratory secretions for protein and Ab detection, heat inactivation following cDNA amplification for droplet-based single-cell mRNA sequencing, and extraction with an organic solvent for metabolomic studies. Thus, we provide a suite of viral inactivation protocols for downstream contemporary assays that facilitate sample transfer to BSL2, providing a conceptual framework for rapid initiation of high-fidelity research as the COVID-19 pandemic continues.


Subject(s)
COVID-19/prevention & control , Specimen Handling/methods , Virus Inactivation , Hot Temperature , Humans , Metabolomics/methods , Pandemics/prevention & control , SARS-CoV-2 , Ultraviolet Rays
4.
Ann N Y Acad Sci ; 1507(1): 70-83, 2022 01.
Article in English | MEDLINE | ID: covidwho-1673249

ABSTRACT

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.


Subject(s)
Aging/genetics , Aging/metabolism , Congresses as Topic/trends , Longevity/physiology , Research Report , Autophagy/physiology , COVID-19/genetics , COVID-19/metabolism , COVID-19/mortality , Cardiovascular Diseases/genetics , Cardiovascular Diseases/metabolism , Cardiovascular Diseases/therapy , Humans , Metabolomics/methods , Metabolomics/trends , Nervous System Diseases/genetics , Nervous System Diseases/metabolism , Nervous System Diseases/therapy , Stem Cell Transplantation/methods , Stem Cell Transplantation/trends
5.
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
6.
Metabolomics ; 18(1): 6, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1669925

ABSTRACT

INTRODUCTION: The diagnosis of COVID-19 is normally based on the qualitative detection of viral nucleic acid sequences. Properties of the host response are not measured but are key in determining outcome. Although metabolic profiles are well suited to capture host state, most metabolomics studies are either underpowered, measure only a restricted subset of metabolites, compare infected individuals against uninfected control cohorts that are not suitably matched, or do not provide a compact predictive model. OBJECTIVES: Here we provide a well-powered, untargeted metabolomics assessment of 120 COVID-19 patient samples acquired at hospital admission. The study aims to predict the patient's infection severity (i.e., mild or severe) and potential outcome (i.e., discharged or deceased). METHODS: High resolution untargeted UHPLC-MS/MS analysis was performed on patient serum using both positive and negative ionization modes. A subset of 20 intermediary metabolites predictive of severity or outcome were selected based on univariate statistical significance and a multiple predictor Bayesian logistic regression model was created. RESULTS: The predictors were selected for their relevant biological function and include deoxycytidine and ureidopropionate (indirectly reflecting viral load), kynurenine (reflecting host inflammatory response), and multiple short chain acylcarnitines (energy metabolism) among others. Currently, this approach predicts outcome and severity with a Monte Carlo cross validated area under the ROC curve of 0.792 (SD 0.09) and 0.793 (SD 0.08), respectively. A blind validation study on an additional 90 patients predicted outcome and severity at ROC AUC of 0.83 (CI 0.74-0.91) and 0.76 (CI 0.67-0.86). CONCLUSION: Prognostic tests based on the markers discussed in this paper could allow improvement in the planning of COVID-19 patient treatment.


Subject(s)
COVID-19/blood , Chromatography, Liquid/methods , Metabolomics/methods , Tandem Mass Spectrometry/methods , Aged , Biomarkers/blood , Female , Humans , Male , Middle Aged , Prognosis , SARS-CoV-2 , Severity of Illness Index
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.
Front Immunol ; 12: 825358, 2021.
Article in English | MEDLINE | ID: covidwho-1662589

ABSTRACT

Coronavirus disease 2019 (COVID-19) raises the issue of how hypoxia destroys normal physiological function and host immunity against pathogens. However, there are few or no comprehensive omics studies on this effect. From an evolutionary perspective, animals living in complex and changeable marine environments might develop signaling pathways to address bacterial threats under hypoxia. In this study, the ancient genomic model animal Takifugu obscurus and widespread Vibrio parahaemolyticus were utilized to study the effect. T. obscurus was challenged by V. parahaemolyticus or (and) exposed to hypoxia. The effects of hypoxia and infection were identified, and a theoretical model of the host critical signaling pathway in response to hypoxia and infection was defined by methods of comparative metabolomics and proteomics on the entire liver. The changing trends of some differential metabolites and proteins under hypoxia, infection or double stressors were consistent. The model includes transforming growth factor-ß1 (TGF-ß1), hypoxia-inducible factor-1α (HIF-1α), and epidermal growth factor (EGF) signaling pathways, and the consistent changing trends indicated that the host liver tended toward cell proliferation. Hypoxia and infection caused tissue damage and fibrosis in the portal area of the liver, which may be related to TGF-ß1 signal transduction. We propose that LRG (leucine-rich alpha-2-glycoprotein) is widely involved in the transition of the TGF-ß1/Smad signaling pathway in response to hypoxia and pathogenic infection in vertebrates as a conserved molecule.


Subject(s)
Hypoxia/metabolism , Signal Transduction/physiology , Takifugu/metabolism , Takifugu/microbiology , Vibrio Infections/metabolism , Vibrio parahaemolyticus/pathogenicity , Animals , Epidermal Growth Factor/metabolism , Intracellular Signaling Peptides and Proteins/metabolism , Metabolomics/methods , Proteomics/methods , Transforming Growth Factor beta1/metabolism , Vibrio Infections/microbiology
9.
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
10.
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
11.
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
12.
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
13.
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
14.
Int J Mol Sci ; 22(17)2021 Sep 02.
Article in English | MEDLINE | ID: covidwho-1390657

ABSTRACT

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.


Subject(s)
Biomarkers/blood , Carbon/metabolism , Liver/metabolism , Metabolome , Nitrogen/metabolism , Amino Acids/metabolism , COVID-19/blood , COVID-19/pathology , COVID-19/virology , Cytokines/blood , Discriminant Analysis , Humans , Least-Squares Analysis , Metabolic Networks and Pathways/genetics , Metabolomics/methods , SARS-CoV-2/isolation & purification , Severity of Illness Index
15.
J Proteome Res ; 20(2): 1415-1423, 2021 02 05.
Article in English | MEDLINE | ID: covidwho-1387126

ABSTRACT

The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance (1H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 µL plasma) and 5 mm SampleJet NMR tubes (300 µL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.


Subject(s)
COVID-19/diagnosis , Lipoproteins/metabolism , Metabolomics/methods , Proteomics/methods , Proton Magnetic Resonance Spectroscopy/methods , SARS-CoV-2/metabolism , Adult , Aged , Biomarkers/blood , Biomarkers/metabolism , COVID-19/blood , COVID-19/virology , Female , Humans , Lipoproteins/blood , Male , Middle Aged , Protein Interaction Maps , SARS-CoV-2/physiology
16.
Sci Rep ; 10(1): 16824, 2020 10 08.
Article in English | MEDLINE | ID: covidwho-1387453

ABSTRACT

The biological mechanisms involved in SARS-CoV-2 infection are only partially understood. Thus we explored the plasma metabolome of patients infected with SARS-CoV-2 to search for diagnostic and/or prognostic biomarkers and to improve the knowledge of metabolic disturbance in this infection. We analyzed the plasma metabolome of 55 patients infected with SARS-CoV-2 and 45 controls by LC-HRMS at the time of viral diagnosis (D0). We first evaluated the ability to predict the diagnosis from the metabotype at D0 in an independent population. Next, we assessed the feasibility of predicting the disease evolution at the 7th and 15th day. Plasma metabolome allowed us to generate a discriminant multivariate model to predict the diagnosis of SARS-CoV-2 in an independent population (accuracy > 74%, sensitivity, specificity > 75%). We identified the role of the cytosine and tryptophan-nicotinamide pathways in this discrimination. However, metabolomic exploration modestly explained the disease evolution. Here, we present the first metabolomic study in SARS-CoV-2 patients which showed a high reliable prediction of early diagnosis. We have highlighted the role of the tryptophan-nicotinamide pathway clearly linked to inflammatory signals and microbiota, and the involvement of cytosine, previously described as a coordinator of cell metabolism in SARS-CoV-2. These findings could open new therapeutic perspectives as indirect targets.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/epidemiology , Coronavirus Infections/metabolism , Cytosine/blood , Metabolome , Metabolomics/methods , Niacinamide/blood , Pneumonia, Viral/epidemiology , Pneumonia, Viral/metabolism , Tryptophan/blood , Aged , Aged, 80 and over , Biomarkers/blood , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Early Diagnosis , Female , France/epidemiology , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , Prognosis , Reproducibility of Results , SARS-CoV-2 , Sensitivity and Specificity , Severity of Illness Index
17.
Theranostics ; 11(16): 8008-8026, 2021.
Article in English | MEDLINE | ID: covidwho-1337803

ABSTRACT

Rationale: Children usually develop less severe symptoms responding to Coronavirus Disease 2019 (COVID-19) than adults. However, little is known about the molecular alterations and pathogenesis of COVID-19 in children. Methods: We conducted plasma proteomic and metabolomic profilings of the blood samples of a cohort containing 18 COVID-19-children with mild symptoms and 12 healthy children, which were enrolled from hospital admissions and outpatients, respectively. Statistical analyses were performed to identify molecules specifically altered in COVID-19-children. We also developed a machine learning-based pipeline named inference of biomolecular combinations with minimal bias (iBM) to prioritize proteins and metabolites strongly altered in COVID-19-children, and experimentally validated the predictions. Results: By comparing to the multi-omic data in adults, we identified 44 proteins and 249 metabolites differentially altered in COVID-19-children against healthy children or COVID-19-adults. Further analyses demonstrated that both deteriorative immune response/inflammation processes and protective antioxidant or anti-inflammatory processes were markedly induced in COVID-19-children. Using iBM, we prioritized two combinations that contained 5 proteins and 5 metabolites, respectively, each exhibiting a total area under curve (AUC) value of 100% to accurately distinguish COVID-19-children from healthy children or COVID-19-adults. Further experiments validated that all the 5 proteins were up-regulated upon coronavirus infection. Interestingly, we found that the prioritized metabolites inhibited the expression of pro-inflammatory factors, and two of them, methylmalonic acid (MMA) and mannitol, also suppressed coronaviral replication, implying a protective role of these metabolites in COVID-19-children. Conclusion: The finding of a strong antagonism of deteriorative and protective effects provided new insights on the mechanism and pathogenesis of COVID-19 in children that mostly underwent mild symptoms. The identified metabolites strongly altered in COVID-19-children could serve as potential therapeutic agents of COVID-19.


Subject(s)
COVID-19/blood , COVID-19/virology , Adult , COVID-19/epidemiology , COVID-19/immunology , Child , Child, Preschool , China/epidemiology , Female , Hospitalization , Humans , Male , Metabolomics/methods , Middle Aged , Proteomics/methods , SARS-CoV-2/isolation & purification
18.
Clin Chem Lab Med ; 59(12): 1891-1905, 2021 11 25.
Article in English | MEDLINE | ID: covidwho-1334799

ABSTRACT

Human Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection activates a complex interaction host/virus, leading to the reprogramming of the host metabolism aimed at the energy supply for viral replication. Alterations of the host metabolic homeostasis strongly influence the immune response to SARS-CoV-2, forming the basis of a wide range of outcomes, from the asymptomatic infection to the onset of COVID-19 and up to life-threatening acute respiratory distress syndrome, vascular dysfunction, multiple organ failure, and death. Deciphering the molecular mechanisms associated with the individual susceptibility to SARS-CoV-2 infection calls for a system biology approach; this strategy can address multiple goals, including which patients will respond effectively to the therapeutic treatment. The power of metabolomics lies in the ability to recognize endogenous and exogenous metabolites within a biological sample, measuring their concentration, and identifying perturbations of biochemical pathways associated with qualitative and quantitative metabolic changes. Over the last year, a limited number of metabolomics- and lipidomics-based clinical studies in COVID-19 patients have been published and are discussed in this review. Remarkable alterations in the lipid and amino acid metabolism depict the molecular phenotype of subjects infected by SARS-CoV-2; notably, structural and functional data on the lipids-virus interaction may open new perspectives on targeted therapeutic interventions. Several limitations affect most metabolomics-based studies, slowing the routine application of metabolomics. However, moving metabolomics from bench to bedside cannot imply the mere determination of a given metabolite panel; rather, slotting metabolomics into clinical practice requires the conversion of metabolic patient-specific data into actionable clinical applications.


Subject(s)
COVID-19/pathology , Metabolomics/methods , Amino Acids/analysis , Amino Acids/metabolism , COVID-19/epidemiology , COVID-19/virology , Cytokines/analysis , Eicosanoids/blood , Humans , Lipids/blood , Pandemics , Phenylalanine/analysis , Phenylalanine/metabolism , SARS-CoV-2/isolation & purification
19.
Nat Commun ; 12(1): 4543, 2021 07 27.
Article in English | MEDLINE | ID: covidwho-1328844

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.


Subject(s)
COVID-19/genetics , COVID-19/metabolism , Critical Illness , Genomics/methods , Humans , Lipidomics/methods , Metabolomics/methods , Neutrophils/metabolism , Transcriptome/genetics
20.
Cell Rep Med ; 2(8): 100369, 2021 08 17.
Article in English | MEDLINE | ID: covidwho-1322391

ABSTRACT

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.


Subject(s)
COVID-19/metabolism , Plasma/metabolism , SARS-CoV-2/metabolism , Adult , Biomarkers/blood , Female , Humans , Longitudinal Studies , Machine Learning , Male , Metabolome , Metabolomics/methods , Middle Aged , Patient Acuity , Plasma/chemistry , Prognosis , Severity of Illness Index
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