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1.
Metabolomics ; 18(1): 6, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1588749

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
2.
OMICS ; 25(11): 681-692, 2021 11.
Article in English | MEDLINE | ID: covidwho-1541502

ABSTRACT

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.


Subject(s)
Computational Biology/trends , Genomics/trends , Metabolomics/trends , Proteomics/trends , Animals , COVID-19 , Computer Graphics , Epigenomics/trends , Gene Expression Profiling/trends , Humans , Pharmacogenetics/trends , Publications , SARS-CoV-2 , Terminology as Topic
4.
Sci Rep ; 11(1): 20866, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1479816

ABSTRACT

A causal relationship between plasma ceramide concentration and respiratory distress symptoms in COVID-19 patients is inferred. In this study, plasma samples of 52 individuals infected with COVID-19 were utilized in a lipidomic analysis. Lipids belonging to the ceramide class exhibited a 400-fold increase in total plasma concentration in infected patients. Further analysis led to the demonstration of concentration dependency for severe COVID-19 respiratory symptoms in a subclass of ceramides. The subclasses Cer(d18:0/24:1), Cer(d18:1/24:1), and Cer(d18:1/22:0) were shown to be increased by 48-, 40-, and 33-fold, respectively, in infected plasma samples and to 116-, 91- and 50-fold, respectively, in plasma samples with respiratory distress. Hence, monitoring plasma ceramide concentration, can be a valuable tool for measuring effects of therapies on COVID-19 respiratory distress patients.


Subject(s)
COVID-19/blood , COVID-19/complications , Ceramides/blood , Respiratory Distress Syndrome/blood , Respiratory Distress Syndrome/complications , Adult , Aged , Aged, 80 and over , Chromatography, Liquid , Drug Design , Female , Humans , Ions , Lipids/chemistry , Male , Metabolomics , Middle Aged , Principal Component Analysis , Software , Tandem Mass Spectrometry , Virus Diseases , Young Adult
5.
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
6.
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
7.
Int J Mol Sci ; 22(19)2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1444229

ABSTRACT

Extracellular vesicles (EVs) carry important biomolecules, including metabolites, and contribute to the spread and pathogenesis of some viruses. However, to date, limited data are available on EV metabolite content that might play a crucial role during infection with the SARS-CoV-2 virus. Therefore, this study aimed to perform untargeted metabolomics to identify key metabolites and associated pathways that are present in EVs, isolated from the serum of COVID-19 patients. The results showed the presence of antivirals and antibiotics such as Foscarnet, Indinavir, and lymecycline in EVs from patients treated with these drugs. Moreover, increased levels of anti-inflammatory metabolites such as LysoPS, 7-α,25-Dihydroxycholesterol, and 15-d-PGJ2 were detected in EVs from COVID-19 patients when compared with controls. Further, we found decreased levels of metabolites associated with coagulation, such as thromboxane and elaidic acid, in EVs from COVID-19 patients. These findings suggest that EVs not only carry active drug molecules but also anti-inflammatory metabolites, clearly suggesting that exosomes might play a crucial role in negotiating with heightened inflammation during COVID-19 infection. These preliminary results could also pave the way for the identification of novel metabolites that might act as critical regulators of inflammatory pathways during viral infections.


Subject(s)
COVID-19/metabolism , Extracellular Vesicles/metabolism , Metabolome , SARS-CoV-2/physiology , Adult , Anti-Inflammatory Agents/metabolism , COVID-19/pathology , Extracellular Vesicles/pathology , Female , Humans , Male , Metabolomics , Middle Aged
8.
Cell Mol Immunol ; 18(10): 2313-2324, 2021 10.
Article in English | MEDLINE | ID: covidwho-1392820

ABSTRACT

In response to emerging infectious diseases, such as the recent pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), it is critical to quickly identify and understand responsible pathogens, risk factors, host immune responses, and pathogenic mechanisms at both the molecular and cellular levels. The recent development of multiomic technologies, including genomics, proteomics, metabolomics, and single-cell transcriptomics, has enabled a fast and panoramic grasp of the pathogen and the disease. Here, we systematically reviewed the major advances in the virology, immunology, and pathogenic mechanisms of SARS-CoV-2 infection that have been achieved via multiomic technologies. Based on well-established cohorts, omics-based methods can greatly enhance the mechanistic understanding of diseases, contributing to the development of new diagnostics, drugs, and vaccines for emerging infectious diseases, such as COVID-19.


Subject(s)
COVID-19/genetics , COVID-19/metabolism , Genomics , Metabolomics , COVID-19/immunology , COVID-19/pathology , Host-Pathogen Interactions , Humans , Pandemics , SARS-CoV-2/physiology , Single-Cell Analysis , Transcriptome
9.
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
10.
Cells ; 10(9)2021 09 02.
Article in English | MEDLINE | ID: covidwho-1390543

ABSTRACT

The Corona Virus Disease 2019 (COVID-19) pandemic represents an ongoing worldwide challenge. The present large study sought to understand independent and overlapping metabolic features of samples from acutely ill patients (n = 831) that tested positive (n = 543) or negative (n = 288) for COVID-19. High-throughput metabolomics analyses were complemented with antigen and enzymatic activity assays on plasma from acutely ill patients collected while in the emergency department, at admission, or during hospitalization. Lipidomics analyses were also performed on COVID-19-positive or -negative subjects with the lowest and highest body mass index (n = 60/group). Significant changes in amino acid and fatty acid/acylcarnitine metabolism emerged as highly relevant markers of disease severity, progression, and prognosis as a function of biological and clinical variables in these patients. Further, machine learning models were trained by entering all metabolomics and clinical data from half of the COVID-19 patient cohort and then tested on the other half, yielding ~78% prediction accuracy. Finally, the extensive amount of information accumulated in this large, prospective, observational study provides a foundation for mechanistic follow-up studies and data sharing opportunities, which will advance our understanding of the characteristics of the plasma metabolism in COVID-19 and other acute critical illnesses.


Subject(s)
COVID-19/metabolism , Prognosis , Acute Disease , Adult , Amino Acids/blood , Body Mass Index , Carnitine/analogs & derivatives , Carnitine/blood , Cohort Studies , Fatty Acids/blood , Female , Humans , Kynurenine/blood , Machine Learning , Metabolomics , Middle Aged , Prospective Studies , SARS-CoV-2/isolation & purification , Severity of Illness Index , Tryptophan/blood
11.
Sci Rep ; 11(1): 10629, 2021 05 20.
Article in English | MEDLINE | ID: covidwho-1387475

ABSTRACT

Delirium is an acute change in attention and cognition occurring in ~ 65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer's disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein-protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation.


Subject(s)
COVID-19/metabolism , Delirium/metabolism , Monoamine Oxidase/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Aged , Aged, 80 and over , Female , Humans , Male , Metabolomics
12.
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
13.
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
14.
PLoS One ; 16(8): e0256784, 2021.
Article in English | MEDLINE | ID: covidwho-1378138

ABSTRACT

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).


Subject(s)
COVID-19/pathology , Metabolomics , Sepsis/diagnosis , Adult , Area Under Curve , COVID-19/complications , COVID-19/virology , Chemokines/blood , Cytokines/blood , Female , Humans , Kynurenine/blood , Lymphocytes/cytology , Male , Middle Aged , Neutrophils/cytology , ROC Curve , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification , Sepsis/etiology , Severity of Illness Index , Tryptophan/blood
15.
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
16.
Int J Mol Sci ; 22(15)2021 Jul 28.
Article in English | MEDLINE | ID: covidwho-1335099

ABSTRACT

Indigenous communities across the globe, especially in rural areas, consume locally available plants known as Traditional Food Plants (TFPs) for their nutritional and health-related needs. Recent research shows that many TFPs are highly nutritious as they contain health beneficial metabolites, vitamins, mineral elements and other nutrients. Excessive reliance on the mainstream staple crops has its own disadvantages. Traditional food plants are nowadays considered important crops of the future and can act as supplementary foods for the burgeoning global population. They can also act as emergency foods in situations such as COVID-19 and in times of other pandemics. The current situation necessitates locally available alternative nutritious TFPs for sustainable food production. To increase the cultivation or improve the traits in TFPs, it is essential to understand the molecular basis of the genes that regulate some important traits such as nutritional components and resilience to biotic and abiotic stresses. The integrated use of modern omics and gene editing technologies provide great opportunities to better understand the genetic and molecular basis of superior nutrient content, climate-resilient traits and adaptation to local agroclimatic zones. Recently, realizing the importance and benefits of TFPs, scientists have shown interest in the prospection and sequencing of TFPs for their improvements, cultivation and mainstreaming. Integrated omics such as genomics, transcriptomics, proteomics, metabolomics and ionomics are successfully used in plants and have provided a comprehensive understanding of gene-protein-metabolite networks. Combined use of omics and editing tools has led to successful editing of beneficial traits in several TFPs. This suggests that there is ample scope for improvement of TFPs for sustainable food production. In this article, we highlight the importance, scope and progress towards improvement of TFPs for valuable traits by integrated use of omics and gene editing techniques.


Subject(s)
Food Security/methods , Plants, Edible/genetics , Plants, Edible/metabolism , Gene Editing , Genomics/methods , Humans , Metabolomics , Plants, Edible/chemistry , Proteomics
17.
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
18.
Cell Rep ; 36(7): 109527, 2021 08 17.
Article in English | MEDLINE | ID: covidwho-1330685

ABSTRACT

COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. To expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients, including matched analysis of the whole-blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate herein the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.


Subject(s)
COVID-19/genetics , COVID-19/metabolism , Databases, Genetic , Metabolome , Proteome , Transcriptome , Access to Information , Adult , COVID-19/immunology , Case-Control Studies , Data Mining , Datasets as Topic , Female , Gene Expression Profiling , Humans , Male , Metabolomics , Middle Aged , Proteomics , Young Adult
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.
Sci Rep ; 11(1): 15107, 2021 07 23.
Article in English | MEDLINE | ID: covidwho-1322506

ABSTRACT

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.


Subject(s)
COVID-19/diagnosis , COVID-19/metabolism , Immunity, Innate/immunology , Machine Learning , SARS-CoV-2/metabolism , Biomarkers/blood , COVID-19/genetics , COVID-19/pathology , Computer Simulation , Databases, Factual , Databases, Genetic , Databases, Protein , Gene Expression Profiling , Humans , Immunity, Innate/genetics , Interferon-gamma/blood , Metabolomics , Prognosis , Proteomics , ROC Curve , SARS-CoV-2/genetics , Severity of Illness Index , Signal Transduction/genetics , Signal Transduction/immunology , Software
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