Dataset Information

Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome

ABSTRACT:

INTRODUCTION: Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.

OBJECTIVES: In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.

METHODS: A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3’,4’-didehydro-3’-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients’ levels.

RESULTS & CONCLUSION: Finally, we demonstrate the added value of the kynurenic acid / tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.

INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Ivayla Roberts

PROVIDER: MTBLS7429 | MetaboLights | 2024-04-23

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS7429	Other
	FILES	Other
	a_MTBLS7429_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS7429_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsv	Tabular

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Publications

Quantitative LC-MS study of compounds found predictive of COVID-19 severity and outcome.

Roberts Ivayla I Wright Muelas Marina M Taylor Joseph M JM Davison Andrew S AS Winder Catherine L CL Goodacre Royston R Kell Douglas B DB

Metabolomics : Official journal of the Metabolomic Society 20231018 11

<h4>Introduction</h4>Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.<h4>Objectives</h4>In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.<h4>Methods</h4>A targeted LC-MS method was used in 4 ...[more]

PMID: 37853293

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