2026-06-14T03:31:58Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/163322025-05-13T11:18:19Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609

00925njm 22002777a 4500 dc Năstase, Ana-Maria author Barrett, Michael P author Cárdenas, Washington B author Cordeiro, Fernanda Bertuccez author Zambrano, Mildred author Andrade, Joyce author Chang, Juan author Regato, Mary author Carrillo, Eugenia author Botana, Laura author Moreno, Javier author Regnault, Clément author Milne, Kathryn author Spence, Philip J author Rowe, J Alexandra author Rogers, Simon author 2024 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. Năstase AM, Barrett MP, Cárdenas WB, Cordeiro FB, Zambrano M, Andrade J, Chang J, Regato M, Carrillo E, Botana L, Moreno J, Regnault C, Milne K, Spence PJ, Rowe JA, Rogers S. Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites. PLoS Negl Trop Dis. 2023 Jul 24;17(7):e0011133. 10.1371/journal.pntd.0011133 1935-2735 PLoS neglected tropical diseases 37486920 http://hdl.handle.net/20.500.12105/16332 Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites