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Metabolomic Analysis of Human Astrocytes in Lipotoxic Condition: Potential Biomarker Identification by Machine Learning Modeling.

dc.contributor.authorCastellanos, Daniel Báez
dc.contributor.authorMartín-Jiménez, Cynthia A
dc.contributor.authorPinzón, Andrés
dc.contributor.authorBarreto, George E
dc.contributor.authorPadilla-González, Guillermo Federico
dc.contributor.authorAristizábal, Andrés
dc.contributor.authorZuluaga, Martha
dc.contributor.authorGonzález Santos, Janneth
dc.date.accessioned2024-07-16T08:22:37Z
dc.date.available2024-07-16T08:22:37Z
dc.date.issued2022-07-15
dc.identifier.pmid35883542
dc.identifier.doi10.3390/biom12070986
dc.identifier.urihttp://hdl.handle.net/10147/642210
dc.descriptionThe association between neurodegenerative diseases (NDs) and obesity has been well studied in recent years. Obesity is a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. An excess of FA generates a metabolic condition known as lipotoxicity, which triggers pathological cellular and molecular responses, causing dysregulation of homeostasis and a decrease in cell viability. This condition is a hallmark of NDs, and astrocytes are particularly sensitive to it, given their crucial role in energy production and oxidative stress management in the brain. However, analyzing cellular mechanisms associated with these conditions represents a challenge. In this regard, metabolomics is an approach that allows biochemical analysis from the comprehensive perspective of cell physiology. This technique allows cellular metabolic profiles to be determined in different biological contexts, such as those of NDs and specific metabolic insults, including lipotoxicity. Since data provided by metabolomics can be complex and difficult to interpret, alternative data analysis techniques such as machine learning (ML) have grown exponentially in areas related to omics data. Here, we developed an ML model yielding a 93% area under the receiving operating characteristic (ROC) curve, with sensibility and specificity values of 80% and 93%, respectively. This study aimed to analyze the metabolomic profiles of human astrocytes under lipotoxic conditions to provide powerful insights, such as potential biomarkers for scenarios of lipotoxicity induced by palmitic acid (PA). In this work, we propose that dysregulation in seleno-amino acid metabolism, urea cycle, and glutamate metabolism pathways are major triggers in astrocyte lipotoxic scenarios, while increased metabolites such as alanine, adenosine, and glutamate are suggested as potential biomarkers, which, to our knowledge, have not been identified in human astrocytes and are proposed as candidates for further research and validation.en_US
dc.language.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAstrocytesen_US
dc.subjectLipotoxicityen_US
dc.subjectmetabolomicsen_US
dc.subjectNEURODEGENERATIVE DISEASESen_US
dc.subjectOBESITYen_US
dc.titleMetabolomic Analysis of Human Astrocytes in Lipotoxic Condition: Potential Biomarker Identification by Machine Learning Modeling.en_US
dc.typeArticleen_US
dc.typeOtheren_US
dc.identifier.eissn2218-273X
dc.identifier.journalBiomoleculesen_US
dc.source.journaltitleBiomolecules
dc.source.volume12
dc.source.issue7
refterms.dateFOA2024-07-16T08:22:39Z
dc.source.countrySwitzerland


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International