Bibliographic Details
| Title: |
Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites. |
| 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 |
| Source: |
PLoS Neglected Tropical Diseases; 7/24/2023, Vol. 17 Issue 7, p1-22, 22p |
| Subject Terms: |
ZIKA virus infections, MIDDLE-income countries, METABOLOMICS, LOW-income countries, SUPERVISED learning |
| Abstract: |
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. Author summary: Fever-associated infectious diseases are still a major cause of concern in low to middle income countries. Inappropriate treatment of misdiagnosed diseases can contribute to the selection of drug resistant microbes. Therefore, improved diagnostics of febrile patients and specific biomarker discovery to support new diagnostics is desirable. Metabolomics studies can provide the necessary information which leads to the discovery of biomarkers. In this study we have investigated three different metabolomics datasets; including those for malaria, leishmaniasis and Zika virus infection, all associated with fever. We aimed to integrate these metabolomics datasets to determine metabolites which behave in the same way in different infectious diseases. One of the challenges in integrating metabolomics datasets is a non-linear drift which occurs between them in terms of retention time. In this case, we proposed to correct this drift by using a supervised machine learning algorithm called Gaussian Process Regression. Following the integration or alignment of the datasets statistical analysis and annotation of the metabolites was performed. We identified several metabolites which acted in a similar manner across the datasets, specifically those found in the kynurenine pathway of tryptophan metabolism. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
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