Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

Project description:The intestinal microflora and metabolites produced by these microbes serve as important regulators of the development of sepsis. Accordingly, this study was designed to systematically explore the relationships between the regulation of septicemia and both the intestinal flora and fecal metabolites by examining the functional roles of metabolites in the protection against sepsis-associated intestinal damage. To that end, fecal and peripheral blood mononuclear cell (PBMC) samples were collected from sepsis patients and healthy controls. A series of longitudinal multi-omics analyses were then used to assess the links between the intestinal flora or associated metabolites and PBMCs in sepsis patients, while animal model studies were further used to probe the protective effects of intestinal flora-derived metabolites on intestinal damage and immunity in the context of sepsis. These analyses revealed that intestinal dysbiosis was a common finding in sepsis patients, which commonly exhibited higher levels of deleterious bacteria and/or reductions in beneficial bacteria. A machine learning approach was used to identify samples from sepsis patients, revealing that at the genus level, sepsis samples could be distinguished by the presence of Bifidobacterium, Bacteroides, Porphyromonas, Prevotell, Enterococcus, Anaerococcus and Veillonella species. Metabolomics analyses indicated that there were significant differences in the levels of intestinal flora-derived metabolites including L-serine, L-valine and L-tyrosine when comparing samples from the sepsis and control groups, while corresponding transcriptomic analyses of PBMC samples using an ImmunecellAI analytical approach revealed a significant sepsis-related increase in the abundance of T cells and Th17 cells. Single-cell sequencing data from sepsis-associated PBMCs was also downloaded from the GEO database, confirming the observation that Th17 cell levels and those of other immune cells rose significantly in the context of septicemia. Animal model experiments revealed that intestinal microbiota-derived L-valine was able to alleviate inflammation and protest against sepsis-induced intestinal damage by inhibiting Th17 cell activation. Overall, these results thus highlight the successful application of machine learning to distinguish between sepsis and control samples based on the composition of the intestinal flora while demonstrating the potential therapeutic benefits of L-valine as an inhibitor of Th17 cell activity that may offer value as a means of alleviating or preventing intestinal damage in treated individuals.

Project description:OBJECTIVE: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM).METHOD: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve.RESULT: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism and protein translation. Through machine learning algorithms, 9 metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid and dihydrojasmonic acid.CONCLUSION: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

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.

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Integrative of Single-Cell RNA Sequencing and Metabolomics Decipher the Imbalanced Lipid-Metabolism in Maladaptive Immune Responses during Sepsis

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Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.

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