Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity (Part 2)
Ontology highlight
ABSTRACT:
ORGANISM(S): Mesocricetus Auratus
TISSUE(S): Blood
DISEASE(S): Covid-19
PROVIDER: ST001853 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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Project description: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.
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Project description:There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce 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 performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.
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Project description:Limited knowledge exists on immune markers associated with disease severity or recovery in patients with coronavirus disease 2019 (COVID-19). Here, we elucidated longitudinal evolution of SARS-CoV-2 antibody repertoire in patients with acute COVID-19. Differential kinetics was observed for immunoglobulin M (IgM)/IgG/IgA epitope diversity, antibody binding, and affinity maturation in "severe" versus "mild" COVID-19 patients. IgG profile demonstrated immunodominant antigenic sequences encompassing fusion peptide and receptor binding domain (RBD) in patients with mild COVID-19 who recovered early compared with "fatal" COVID-19 patients. In patients with severe COVID-19, high-titer IgA were observed, primarily against RBD, especially in patients who succumbed to SARS-CoV-2 infection. The patients with mild COVID-19 showed marked increase in antibody affinity maturation to prefusion SARS-CoV-2 spike that associated with faster recovery from COVID-19. This study revealed antibody markers associated with disease severity and resolution of clinical disease that could inform development and evaluation of effective immune-based countermeasures against COVID-19.
| S-EPMC7935365 | biostudies-literature
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
2022-03-01 | E-MTAB-10970 | biostudies-arrayexpress
Project description:The severity, disabilities, and lethality caused by the coronavirus 2019 (COVID-19) disease have dumbfounded the entire world on an unprecedented scale. The multifactorial aspect of the infection has generated interest in understanding the clinical history of COVID-19, particularly the classification of severity and early prediction on prognosis. Metabolomics is a powerful tool for identifying metabolite signatures when profiling parasitic, metabolic, and microbial diseases. This study undertook a metabolomic approach to identify potential metabolic signatures to discriminate severe COVID-19 from non-severe COVID-19. The secondary aim was to determine whether the clinical and laboratory data from the severe and non-severe COVID-19 patients were compatible with the metabolomic findings. Metabolomic analysis of samples revealed that 43 metabolites from 9 classes indicated COVID-19 severity: 29 metabolites for non-severe and 14 metabolites for severe disease. The metabolites from porphyrin and purine pathways were significantly elevated in the severe disease group, suggesting that they could be potential prognostic biomarkers. Elevated levels of the cholesteryl ester CE (18:3) in non-severe patients matched the significantly different blood cholesterol components (total cholesterol and HDL, both p < 0.001) that were detected. Pathway analysis identified 8 metabolomic pathways associated with the 43 discriminating metabolites. Metabolomic pathway analysis revealed that COVID-19 affected glycerophospholipid and porphyrin metabolism but significantly affected the glycerophospholipid and linoleic acid metabolism pathways (p = 0.025 and p = 0.035, respectively). Our results indicate that these metabolomics-based markers could have prognostic and diagnostic potential when managing and understanding the evolution of COVID-19.
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Project description:To trace immune responses in COVID-19 patients with severity, we performed in-depth, longitudinal single-cell multiomics involving T-cell receptor (TCR)/B-cell receptor (BCR) sequencing, feature barcoded antibody (Ab) panel detection (i.e., cellular indexing of transcriptomes and epitopes by sequencing, CITE-seq) followed by RNA sequencing in a single-cell resolution.
2025-01-27 | GSE196198 | GEO
Project description:BackgroundAlthough there are several severity predictors for COVID-19, none are specific. Serum levels of phenylalanine were recently associated with increased inflammation, higher SOFA scores, ICU admission, and mortality rates among non-COVID-19 patients. Here, we investigated the relationship between phenylalanine and inflammatory markers in adults with COVID-19.MethodsWe assessed adults with COVID-19 at hospital admission for clinical and laboratory data. Nuclear magnetic resonance spectroscopy measured serum levels of phenylalanine and other amino acids of its metabolomic pathway. Flow Cytometry measured serum levels of IL-2, IL-4, IL-6, Il-10, TNF-α, and IFN-γ. Linear regression models adjusted for potential confounders assessed the relationship between serum levels of phenylalanine and inflammatory cytokines.ResultsPhenylalanine and tyrosine were significantly lower in mild disease as compared to moderate and severe groups. Linear regression models showed that phenylalanine is independently and positively associated with disease severity regardless of the cytokine analyzed and after adjustment for potential confounders. In addition, mild cases showed consistently lower serum phenylalanine levels within the first ten days from disease onset to hospital admission.ConclusionsPhenylalanine is a marker of disease severity. This association is independent of the time between the onset of symptoms and the magnitude of the inflammatory state.
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Project description:End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n = 256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. Two hundred and three proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3), and epithelial injury (e.g. KRT19). Machine-learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets.
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2025-05-06 | PXD044386 | Pride