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Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis
INTRODUCTION: Sepsis is intricately linked to intestinal damage and barrier dysfunction. At present times, there is a growing interest in a metabolite-based therapy for multiple diseases. METHODS: Serum samples from septic patients and healthy individuals were collected and their metabonomics profil...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245321/ https://www.ncbi.nlm.nih.gov/pubmed/37292192 http://dx.doi.org/10.3389/fimmu.2023.1151728 |
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author | Wang, Zetian Qi, Yue Wang, Fei Zhang, Baiyin Jianguo, Tang |
author_facet | Wang, Zetian Qi, Yue Wang, Fei Zhang, Baiyin Jianguo, Tang |
author_sort | Wang, Zetian |
collection | PubMed |
description | INTRODUCTION: Sepsis is intricately linked to intestinal damage and barrier dysfunction. At present times, there is a growing interest in a metabolite-based therapy for multiple diseases. METHODS: Serum samples from septic patients and healthy individuals were collected and their metabonomics profiling assessed using Ultra-Performance Liquid Chromatography-Time of Flight Mass Spectrometry (UPLC-TOFMS). The eXtreme Gradient Boosting algorithms (XGBOOST) method was used to screen essential metabolites associated with sepsis, and five machine learning models, including Logistic Regression, XGBoost, GaussianNB(GNB), upport vector machines(SVM) and RandomForest were constructed to distinguish sepsis including a training set (75%) and validation set(25%). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. Pearson analysis was used to analysis the relationship between the metabolites and the severity of sepsis. Both cellular and animal models were used to HYPERLINK "javascript:;" assess the function of the metabolites. RESULTS: The occurrence of sepsis involve metabolite dysregulation. The metabolites mannose-6-phosphate and sphinganine as the optimal sepsis-related variables screened by XGBOOST algorithm. The XGBoost model (AUROC=0.956) has the most stable performance to establish diagnostic model among the five machine learning methods. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Pearson analysis reinforced the expression of Sphinganine, Mannose 6-phosphate were positively associated with the APACHE-II, PCT, WBC, CRP, and IL-6. We also demonstrated that sphinganine strongly diminished the LDH content in LPS-treated Caco-2 cells. In addition, using both in vitro and in vivo examination, we revealed that sphinganine strongly protects against sepsis-induced intestinal barrier injury. DISCUSSION: These findings highlighted the potential diagnostic value of the ML, and also provided new insight into enhanced therapy and/or preventative measures against sepsis. |
format | Online Article Text |
id | pubmed-10245321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102453212023-06-08 Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis Wang, Zetian Qi, Yue Wang, Fei Zhang, Baiyin Jianguo, Tang Front Immunol Immunology INTRODUCTION: Sepsis is intricately linked to intestinal damage and barrier dysfunction. At present times, there is a growing interest in a metabolite-based therapy for multiple diseases. METHODS: Serum samples from septic patients and healthy individuals were collected and their metabonomics profiling assessed using Ultra-Performance Liquid Chromatography-Time of Flight Mass Spectrometry (UPLC-TOFMS). The eXtreme Gradient Boosting algorithms (XGBOOST) method was used to screen essential metabolites associated with sepsis, and five machine learning models, including Logistic Regression, XGBoost, GaussianNB(GNB), upport vector machines(SVM) and RandomForest were constructed to distinguish sepsis including a training set (75%) and validation set(25%). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. Pearson analysis was used to analysis the relationship between the metabolites and the severity of sepsis. Both cellular and animal models were used to HYPERLINK "javascript:;" assess the function of the metabolites. RESULTS: The occurrence of sepsis involve metabolite dysregulation. The metabolites mannose-6-phosphate and sphinganine as the optimal sepsis-related variables screened by XGBOOST algorithm. The XGBoost model (AUROC=0.956) has the most stable performance to establish diagnostic model among the five machine learning methods. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Pearson analysis reinforced the expression of Sphinganine, Mannose 6-phosphate were positively associated with the APACHE-II, PCT, WBC, CRP, and IL-6. We also demonstrated that sphinganine strongly diminished the LDH content in LPS-treated Caco-2 cells. In addition, using both in vitro and in vivo examination, we revealed that sphinganine strongly protects against sepsis-induced intestinal barrier injury. DISCUSSION: These findings highlighted the potential diagnostic value of the ML, and also provided new insight into enhanced therapy and/or preventative measures against sepsis. Frontiers Media S.A. 2023-05-24 /pmc/articles/PMC10245321/ /pubmed/37292192 http://dx.doi.org/10.3389/fimmu.2023.1151728 Text en Copyright © 2023 Wang, Qi, Wang, Zhang and Jianguo 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 Wang, Zetian Qi, Yue Wang, Fei Zhang, Baiyin Jianguo, Tang Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title | Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title_full | Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title_fullStr | Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title_full_unstemmed | Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title_short | Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
title_sort | circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245321/ https://www.ncbi.nlm.nih.gov/pubmed/37292192 http://dx.doi.org/10.3389/fimmu.2023.1151728 |
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