Dataset Information

Ly6Chigh Monocytes Protect against Kidney Damage during Sepsis via a CX3CR1-Dependent Adhesion Mechanism.

ABSTRACT: Monocytes have a crucial role in both proinflammatory and anti-inflammatory phenomena occurring during sepsis. Monocyte recruitment and activation are orchestrated by the chemokine receptors CX3CR1 and CCR2 and their cognate ligands. However, little is known about the roles of these cells and chemokines during the acute phase of inflammation in sepsis. Using intravital microscopy in a murine model of polymicrobial sepsis, we showed that inflammatory Ly6C(high) monocytes infiltrated kidneys, exhibited altered motility, and adhered strongly to the renal vascular wall in a chemokine receptor CX3CR1-dependent manner. Adoptive transfer of Cx3cr1-proficient monocyte-enriched bone marrow cells into septic Cx3cr1-depleted mice prevented kidney damage and promoted mouse survival. Modulation of CX3CR1 activation in septic mice controlled monocyte adhesion, regulated proinflammatory and anti-inflammatory cytokine expression, and was associated with the extent of kidney lesions such that the number of lesions decreased when CX3CR1 activity increased. Consistent with these results, the pro-adhesive I249 CX3CR1 allele in humans was associated with a lower incidence of AKI in patients with sepsis. These data show that inflammatory monocytes have a protective effect during sepsis via a CX3CR1-dependent adhesion mechanism. This receptor might be a new therapeutic target for kidney injury during sepsis.

SUBMITTER: Chousterman BG

PROVIDER: S-EPMC4769199 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Ly6Chigh Monocytes Protect against Kidney Damage during Sepsis via a CX3CR1-Dependent Adhesion Mechanism.

Chousterman Benjamin G BG Boissonnas Alexandre A Poupel Lucie L Baudesson de Chanville Camille C Adam Julien J Tabibzadeh Nahid N Licata Fabrice F Lukaszewicz Anne-Claire AC Lombès Amélie A Deterre Philippe P Payen Didier D Combadière Christophe C

Journal of the American Society of Nephrology : JASN 20150709 3

Monocytes have a crucial role in both proinflammatory and anti-inflammatory phenomena occurring during sepsis. Monocyte recruitment and activation are orchestrated by the chemokine receptors CX3CR1 and CCR2 and their cognate ligands. However, little is known about the roles of these cells and chemokines during the acute phase of inflammation in sepsis. Using intravital microscopy in a murine model of polymicrobial sepsis, we showed that inflammatory Ly6C(high) monocytes infiltrated kidneys, exhi ...[more]

PMID: 26160897

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Project description:IntroductionSepsis 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.MethodsSerum 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.ResultsThe 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.DiscussionThese findings highlighted the potential diagnostic value of the ML, and also provided new insight into enhanced therapy and/or preventative measures against sepsis.

Project description:<h4>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.</h4>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.

Dataset Information

Ly6Chigh Monocytes Protect against Kidney Damage during Sepsis via a CX3CR1-Dependent Adhesion Mechanism.

Publications

Ly6Chigh Monocytes Protect against Kidney Damage during Sepsis via a CX3CR1-Dependent Adhesion Mechanism.

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