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| Author(s): |
Priscila Marques Firmiano Dalle Piagge
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Química de São Carlos (IQSC/BT) |
| Defense date: | 2025-06-13 |
| Examining board members: |
Daniel Rodrigues Cardoso;
Luis Fernando Barbisan;
Adriano Mondini;
Tiago Venâncio
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| Advisor: | Daniel Rodrigues Cardoso; Luiz Alberto Colnago |
| Abstract | |
Infectious diseases are a cause of concern for health authorities, as they impact global health due to their contagious nature and the periodic emergence of new diseases, such as the COVID-19 pandemic caused by the novel coronavirus (SARS-CoV-2). It is well known that viruses are among the primary causative agents of such diseases, and as obligate intracellular parasites, they compete for nutrients and metabolites within the host cell, leading to alterations in its metabolome. In this context, it is crucial to seek analytical tools that enable easy sample preparation, are non-destructive and reproducible, and can effectively identify and/or quantify the key biomolecules affected during disease progression. Accordingly, the present study first aimed to identify metabolite markers of COVID-19 infection in human blood, as well as its severity levels, using NMR spectroscopy. For this purpose, quantitative ¹H NMR analysis was conducted on the plasma metabolomes of 110 individual samples, representing mild to severe symptoms, prior to any clinical intervention. In this phase, plasma metabolomes from 110 samples were analyzed. Five polar metabolites (glycerol, acetate, formate, glucuronate, and lactate) were identified through PLS-DA as potential prognostic metabolic biomarkers for COVID-19, and they were considered clinically relevant for predicting infection severity due to their direct roles in lipid and energy metabolism. Subsequently, blood plasma was analyzed using Raman spectroscopy, and pellets were examined through high- and low-field NMR spectroscopy from 26 samples, aiming to investigate correlations between these different data types and analytical methods. Upon individually analyzing the variables using PLS-DA, a strong correlation (>0.7) was observed between the signals corresponding to Nacetylglucosamine in glycoproteins as well as lipid-related signals, with the second peak obtained via low-field NMR. This finding suggests that the differences observed in the T2 decay profile between groups may be attributed to variations in glycoproteins present in blood plasma. Additionally, as expected, a strong correlation (>0.7) was observed between the Raman and low-field NMR signals associated with lipids. Thus, the metabolomics approach using highfield NMR proved to be the most effective for developing predictive models based on the analyzed blood plasma samples, contributing to a better understanding of the prognosis of SARS-CoV-2 and the metabolic consequences of the infection in the human body. Nonetheless, it is worth noting that high-field NMR analyses of glycoprotein-containing samples also revealed promising results. (AU) | |
| FAPESP's process: | 19/07309-3 - Development of a microfluidic device to evaluate protein digestibility and allergic response |
| Grantee: | Priscila Marques Firmiano Dalle Piagge |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |