Por favor, use este identificador para citar o enlazar este Item:http://hdl.handle.net/20.500.12105/16332
Título
Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites
Autor(es)
Năstase, Ana-Maria | Barrett, Michael P | Cárdenas, Washington B | Cordeiro, Fernanda Bertuccez | Zambrano, Mildred | Andrade, Joyce | Chang, Juan | Regato, Mary | Carrillo, Eugenia ISCIII | Botana, Laura ISCIII | Moreno, Javier ISCIII | Regnault, Clément | Milne, Kathryn | Spence, Philip J | Rowe, J Alexandra | Rogers, Simon
Fecha de publicación
2023-07
Cita
PLoS Negl Trop Dis. 2023 Jul 24;17(7):e0011133.
Idioma
Inglés
Tipo de documento
research article
Resumen
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.
MESH
Zika Virus | Zika Virus Infection | Humans | Chromatography, Liquid | Tandem Mass Spectrometry | Algorithms | Metabolomics
Versión en línea
DOI
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