Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography...

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Detalles Bibliográficos
Autores: Năstase, Ana-Maria, Barrett, Michael P, Cárdenas, Washington B, Cordeiro, Fernanda Bertuccez, Zambrano, Mildred, Andrade, Joyce, Chang, Juan, Regato, Mary, Carrillo, Eugenia, Botana, Laura, Moreno, Javier, Regnault, Clément, Milne, Kathryn, Spence, Philip J, Rowe, J Alexandra, Rogers, Simon
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Instituto de Salud Carlos III (ISCIII)
Repositorio:Repisalud
Idioma:inglés
OAI Identifier:oai:repisalud.isciii.es:20.500.12105/16332
Acceso en línea:http://hdl.handle.net/20.500.12105/16332
Access Level:acceso abierto
Palabra clave:Zika Virus
Zika Virus Infection
Humans
Chromatography, Liquid
Tandem Mass Spectrometry
Algorithms
Metabolomics
Descripción
Sumario:Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.