Project description:Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2-6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the "healthy" state faster than the lipoproteome.
Project description:Circulating microRNAs (miRNAs) have been shown to be excellent disease diagnostic or prognostic biomarkers in a wide range of chronic and acute inflammatory and infectious diseases including viral respiratory infection. Crucially, circulating miRNA levels are thought to reflect the state of the diseased tissue. Despite their proven value as mechanism-based clinical stratification indicators, miRNAs have only started being explored in the context of COVID-19. here, we aimed to explore whether integrating miRNA with other clinical and biological measurements would reveal more accurate correlates of COVID-19 severity and outcome, and to identify severity-specific correlations of miRNAs with COVID-19-associated inflammatory mediators, clinical parameters, and otucome.
Project description:Coronavirus disease 2019 (COVID-19) can be asymptomatic or lead to a wide spectrum of symptoms, ranging from mild upper respiratory system involvement to acute respiratory distress syndrome, multi-organ damage and death. In this study, we explored the potential of microRNAs (miRNA) in delineating patient condition and in predicting clinical outcome. Analysis of the circulating miRNA profile of COVID-19 patients, sampled at different hospitalization intervals after admission, allowed to identify miR-144-3p as a dynamically regulated miRNA in response to COVID-19.
Project description:Coronavirus disease 2019 (COVID-19) is an unprecedented global threat caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic is a global health crisis. Recent reports have exposed an astonishing case fatality rate of 61.5% for critical cases, increasing sharply with age and for patients with underlying comorbidities. Mass spectrometry (MS)-based proteomics has the potential to become an ideal technology to be applied in this urgent situations, because it can quickly deliver substantial amounts of clinical and biological information from blood plasma or serum in an untargeted fashion. Furthermore, these MS-based proteomic workflows for biomarker discovery and profiling are well established. However, only two studies have presently applied proteomics to serum of COVID-19 patients with moderate proteome depth. Therefore, it is necessary to gain a more detailed understanding with in-depth proteome of plasma or serum to develop prognostic or predictive protein markers. In this study, we performed in-depth proteome profiling of undepleted plasma samples using BoxCar acquisition method from an exploratory cohort comprising ten COVID-19 patients to identify candidate biomarkers for disease severity evaluation.