Ontology highlight
ABSTRACT:
PROVIDER: PRJNA905077 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22404400_1.fastq.gz | Fastqsanger.gz | |||
| SRR22404400_2.fastq.gz | Fastqsanger.gz | |||
| SRR22404401_1.fastq.gz | Fastqsanger.gz | |||
| SRR22404401_2.fastq.gz | Fastqsanger.gz | |||
| SRR22404402_1.fastq.gz | Fastqsanger.gz |
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Project description:<p>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. </p>
2023-07-28 | MTBLS7866 | MetaboLights
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
Project description:<h4><strong>INTRODUCTION:</strong> 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><p><strong>METHODS:</strong> 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.</p><p><strong>RESULTS:</strong> 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 <em>in vitro</em> and <em>in vivo</em> examination, we revealed that sphinganine strongly protects against sepsis-induced intestinal barrier injury.</p><p><strong>DISCUSSION:</strong> These findings highlighted the potential diagnostic value of the ML, and also provided new insight into enhanced therapy and/or preventative measures against sepsis.</p>
2023-07-28 | MTBLS7878 | MetaboLights
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
2020-02-20 | GSE112099 | GEO
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
2020-02-20 | GSE112098 | GEO
Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168440 | GEO
Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168442 | GEO
Project description:Analysis of intestinal flora of Japanese black breeding cows and their calves using machine learning
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Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperative infection with (n=77) or without (n=49) sepsis, non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110). Machine learning classification models built on preoperative transcriptomic signatures predicted postoperative outcomes including sepsis.
2024-08-30 | GSE208581 | GEO