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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Bibliographic Details
Authors: 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
Format: article
Publication Date:2023
Country:España
Institution:Instituto de Salud Carlos III (ISCIII)
Repository:Repisalud
Language:English
OAI Identifier:oai:repisalud.isciii.es:20.500.12105/16332
Online Access:http://hdl.handle.net/20.500.12105/16332
Access Level:Open access
Keyword:Zika Virus
Zika Virus Infection
Humans
Chromatography, Liquid
Tandem Mass Spectrometry
Algorithms
Metabolomics
Description
Summary: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.

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