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Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide

INTRODUCTION: Immunometabolism examines the links between immune cell function and metabolism. Dysregulation of immune cell metabolism is now an established feature of innate immune cell activation. Advances in liquid chromatography mass spectrometry (LC-MS) technologies have allowed discovery of un...

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Autores principales: Leacy, Emma, Batten, Isabella, Sanelli, Laetitia, McElheron, Matthew, Brady, Gareth, Little, Mark A., Khouri, Hania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077522/
https://www.ncbi.nlm.nih.gov/pubmed/37033938
http://dx.doi.org/10.3389/fimmu.2023.1116760
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author Leacy, Emma
Batten, Isabella
Sanelli, Laetitia
McElheron, Matthew
Brady, Gareth
Little, Mark A.
Khouri, Hania
author_facet Leacy, Emma
Batten, Isabella
Sanelli, Laetitia
McElheron, Matthew
Brady, Gareth
Little, Mark A.
Khouri, Hania
author_sort Leacy, Emma
collection PubMed
description INTRODUCTION: Immunometabolism examines the links between immune cell function and metabolism. Dysregulation of immune cell metabolism is now an established feature of innate immune cell activation. Advances in liquid chromatography mass spectrometry (LC-MS) technologies have allowed discovery of unique insights into cellular metabolomics. Here we have studied and compared different sample preparation techniques and data normalisation methods described in the literature when applied to metabolomic profiling of human monocytes. METHODS: Primary monocytes stimulated with lipopolysaccharide (LPS) for four hours was used as a study model. Monocytes (n=24) were freshly isolated from whole blood and stimulated for four hours with lipopolysaccharide (LPS). A methanol-based extraction protocol was developed and metabolomic profiling carried out using a Hydrophilic Interaction Liquid Chromatography (HILIC) LC-MS method. Data analysis pipelines used both targeted and untargeted approaches, and over 40 different data normalisation techniques to account for technical and biological variation were examined. Cytokine levels in supernatants were measured by ELISA. RESULTS: This method provided broad coverage of the monocyte metabolome. The most efficient and consistent normalisation method was measurement of residual protein in the metabolite fraction, which was further validated and optimised using a commercial kit. Alterations to the monocyte metabolome in response to LPS can be detected as early as four hours post stimulation. Broad and profound changes in monocyte metabolism were seen, in line with increased cytokine production. Elevated levels of amino acids and Krebs cycle metabolites were noted and decreases in aspartate and β-alanine are also reported for the first time. In the untargeted analysis, 154 metabolite entities were significantly altered compared to unstimulated cells. Pathway analysis revealed the most prominent changes occurred to (phospho-) inositol metabolism, glycolysis, and the pentose phosphate pathway. DISCUSSION: These data report the emergent changes to monocyte metabolism in response to LPS, in line with reports from later time points. A number of these metabolites are reported to alter inflammatory gene expression, which may facilitate the increases in cytokine production. Further validation is needed to confirm the link between metabolic activation and upregulation of inflammatory responses.
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spelling pubmed-100775222023-04-07 Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide Leacy, Emma Batten, Isabella Sanelli, Laetitia McElheron, Matthew Brady, Gareth Little, Mark A. Khouri, Hania Front Immunol Immunology INTRODUCTION: Immunometabolism examines the links between immune cell function and metabolism. Dysregulation of immune cell metabolism is now an established feature of innate immune cell activation. Advances in liquid chromatography mass spectrometry (LC-MS) technologies have allowed discovery of unique insights into cellular metabolomics. Here we have studied and compared different sample preparation techniques and data normalisation methods described in the literature when applied to metabolomic profiling of human monocytes. METHODS: Primary monocytes stimulated with lipopolysaccharide (LPS) for four hours was used as a study model. Monocytes (n=24) were freshly isolated from whole blood and stimulated for four hours with lipopolysaccharide (LPS). A methanol-based extraction protocol was developed and metabolomic profiling carried out using a Hydrophilic Interaction Liquid Chromatography (HILIC) LC-MS method. Data analysis pipelines used both targeted and untargeted approaches, and over 40 different data normalisation techniques to account for technical and biological variation were examined. Cytokine levels in supernatants were measured by ELISA. RESULTS: This method provided broad coverage of the monocyte metabolome. The most efficient and consistent normalisation method was measurement of residual protein in the metabolite fraction, which was further validated and optimised using a commercial kit. Alterations to the monocyte metabolome in response to LPS can be detected as early as four hours post stimulation. Broad and profound changes in monocyte metabolism were seen, in line with increased cytokine production. Elevated levels of amino acids and Krebs cycle metabolites were noted and decreases in aspartate and β-alanine are also reported for the first time. In the untargeted analysis, 154 metabolite entities were significantly altered compared to unstimulated cells. Pathway analysis revealed the most prominent changes occurred to (phospho-) inositol metabolism, glycolysis, and the pentose phosphate pathway. DISCUSSION: These data report the emergent changes to monocyte metabolism in response to LPS, in line with reports from later time points. A number of these metabolites are reported to alter inflammatory gene expression, which may facilitate the increases in cytokine production. Further validation is needed to confirm the link between metabolic activation and upregulation of inflammatory responses. Frontiers Media S.A. 2023-03-23 /pmc/articles/PMC10077522/ /pubmed/37033938 http://dx.doi.org/10.3389/fimmu.2023.1116760 Text en Copyright © 2023 Leacy, Batten, Sanelli, McElheron, Brady, Little and Khouri https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Leacy, Emma
Batten, Isabella
Sanelli, Laetitia
McElheron, Matthew
Brady, Gareth
Little, Mark A.
Khouri, Hania
Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title_full Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title_fullStr Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title_full_unstemmed Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title_short Optimal LC-MS metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
title_sort optimal lc-ms metabolomic profiling reveals emergent changes to monocyte metabolism in response to lipopolysaccharide
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077522/
https://www.ncbi.nlm.nih.gov/pubmed/37033938
http://dx.doi.org/10.3389/fimmu.2023.1116760
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