Abordagem metabolômica não direcionada em plasma de indivíduos diagnosticados com COVID-19
Data
2023-04-27
Autores
Lima, Vinicius Sousa [UNIFESP]
Orientadores
Assunção, Nilson Antônio de [UNIFESP]
Tipo
Dissertação de mestrado
Título da Revista
ISSN da Revista
Título de Volume
Resumo
Objetivo: Caracterizar indivíduos diagnosticados com CoViD-19 e seus três graus de gravidade da doença (leve, moderado e grave) utilizando abordagem metabolômica não direcionada. Métodos: A cromatografia líquida acoplada à espectrometria de massas de alta resolução foi utilizada para fornecer informações com base no perfil metabólico de amostras do plasma sanguíneo de indivíduos não vacinados diagnosticados com CoViD-19, divididos em três níveis de severidade dos sintomas (leve, moderado e grave) pela análise das razões massa/carga (m/z), posterior detecção de features pelo ProfileAnalisys vers. 4.2, normalização dos dados pelo software Lowess normalization tool vers. 1.1, seleção e interpretação de dados através de análises quimiométricas realizadas pelo MetaboAnalyst vers. 5.0 (p < 0.05), e anotação dos metabólitos (erro de massa exata < 10 ppm) e identificação molecular com emprego de bancos de dados metabolômicos específicos e motores de busca multiplataforma. Resultados: 77 metabólitos foram inicialmente adquiridos por pré-processamento de dados, com porcentagens de missing values < 25%. No entanto, após análise das concentrações relativas, detecção e seleção de outliers por modelos quimiométricos, alguns compostos apresentaram discriminação significativa na diferenciação dos grupos e subgrupos de indivíduos estudados. Esses possíveis biomarcadores relacionavam-se com o metabolismo do glutamato e da prolina entre os grupos controle e estudo; com o metabolismo da fenilalanina nas análises entre o grupo controle e os subgrupos de indivíduos (leve, moderado e grave), sendo observado entre os subgrupos leve e grave, e moderado e grave. Gama-butirobetaína e o pipecolato foram observados na via da degradação da lisina entre o grupo controle e os subgrupos leve e moderado. Conclusão: Diferentes compostos foram identificados como potenciais biomarcadores de gravidade durante o curso da doença, devido à sua capacidade preditiva e discriminatória entre os diferentes níveis de sintomas da CoViD-19. Adicionalmente, os perfis metabólicos mostraram-se discrepantes entre pessoas saudáveis e aquelas infectadas com o SARS-CoV-2, sendo as vias do metabolismo lipídico, de aminas e de ácidos carboxílicos e derivados, as rotas metabólicas mais afetadas.
Goal: To characterize and identify potential biomarkers in patients diagnosed with CoViD-19 and their different degrees of severity (mild, moderate, and severe) by applying an untargeted metabolomics approach. Methods: Liquid chromatography coupled with high-resolution mass spectrometry was used to provide information based on the metabolic profile of blood plasma samples from unvaccinated patients diagnosed with CoViD-19, divided into three levels of symptom severity (mild, moderate, and severe) by analysis of mass/charge (m/z) ratios, later detection of features by ProfileAnalisys vers. 4.2, normalization by the software Lowess normalization tool vers. 1.1, selection and interpretation of data through chemometric studies performed by MetaboAnalyst vers. 5.0 (p < 0.05), and annotation (mass error < 10 ppm) and identification through specific metabolomics databases and cross-platform search engines. Results: 77 metabolites were initially acquired by data pre-processing, with percentages of missing values < 25%. However, after analysis of relative concentrations, detection, and selection of outliers by chemometric models, some compounds showed significant discrimination in differentiating groups and subgroups of individuals studied. These possible biomarkers were related to glutamate and proline metabolism between control and patient groups; the metabolism of phenylalanine in the analysis between control and patient subgroups (mild, moderate, and severe), being observed between the mild and severe subgroups, and moderate and severe. Gamma-butyrobetaine and pipecolate were observed between the control and the mild and moderate subgroups in the lysine degradation pathway. Conclusion: Different compounds have been identified as potential biomarkers of severity during the course of the disease, due to their predictive and discriminatory ability between different levels of CoViD-19 symptoms. Additionally, the metabolic profiles were shown to be discrepant between healthy people and those infected with SARS-CoV-2, with the pathways of lipid metabolism, amines, and carboxylic acids and their derivatives being the most affected metabolic pathways.
Goal: To characterize and identify potential biomarkers in patients diagnosed with CoViD-19 and their different degrees of severity (mild, moderate, and severe) by applying an untargeted metabolomics approach. Methods: Liquid chromatography coupled with high-resolution mass spectrometry was used to provide information based on the metabolic profile of blood plasma samples from unvaccinated patients diagnosed with CoViD-19, divided into three levels of symptom severity (mild, moderate, and severe) by analysis of mass/charge (m/z) ratios, later detection of features by ProfileAnalisys vers. 4.2, normalization by the software Lowess normalization tool vers. 1.1, selection and interpretation of data through chemometric studies performed by MetaboAnalyst vers. 5.0 (p < 0.05), and annotation (mass error < 10 ppm) and identification through specific metabolomics databases and cross-platform search engines. Results: 77 metabolites were initially acquired by data pre-processing, with percentages of missing values < 25%. However, after analysis of relative concentrations, detection, and selection of outliers by chemometric models, some compounds showed significant discrimination in differentiating groups and subgroups of individuals studied. These possible biomarkers were related to glutamate and proline metabolism between control and patient groups; the metabolism of phenylalanine in the analysis between control and patient subgroups (mild, moderate, and severe), being observed between the mild and severe subgroups, and moderate and severe. Gamma-butyrobetaine and pipecolate were observed between the control and the mild and moderate subgroups in the lysine degradation pathway. Conclusion: Different compounds have been identified as potential biomarkers of severity during the course of the disease, due to their predictive and discriminatory ability between different levels of CoViD-19 symptoms. Additionally, the metabolic profiles were shown to be discrepant between healthy people and those infected with SARS-CoV-2, with the pathways of lipid metabolism, amines, and carboxylic acids and their derivatives being the most affected metabolic pathways.