• Children
• Proteomics
• Sepsis

• (2023JJ60101) The Natural Science Foundation of Hunan Province
• 20230508-1033 Financial Plan for the High-Level Health Personnel Program of Hunan Province

• Biomarkers
• Diagnosis
• Metabolomics
• Sepsis
• Septic shock

• artificial intelligence
• early detection
• personalized treatment
• real-time monitoring
• sepsis management

• biomarker
• critical care
• metabolomics
• sepsis
• septic shock

• Humans
• Shock, Septic/diagnosis
• Sepsis/diagnosis
• Sepsis/therapy
• Biomarkers
• Prognosis
• Cytokines

• Omics
• Presepsin
• Procalcitonin
• Sepsis
• Sepsis biomarkers

Sepsis is a deleterious systemic inflammatory response to infection, and despite advances in treatment, the mortality rate remains high. We hypothesized that plasma metabolism could clarify sepsis in patients complicated by organ dysfunction.

Plasma samples from 31 patients with sepsis and 23 healthy individuals of comparable age, gender, and body mass index (BMI) were collected. Plasma metabolites were detected through gas chromatography–mass spectrometry (GC–MS), and relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway database. Student’s t-test was employed for statistical analysis. In addition, to explore sepsis organ dysfunction, plasma samples of sepsis patients were further analyzed by metabolomics subgroup analysis according to organ dysfunction.

A total of 222 metabolites were detected, which included 124 metabolites with statistical significance between the sepsis and control groups. Among these, we found 26 were fatty acids, including 3 branched fatty acids, 10 were saturated fatty acids, and 13 were unsaturated fatty acids that were found in sepsis plasma samples but not in the controls. In addition, 158 metabolic pathways were predicted, 74 of which were significant. Further subgroup analysis identified seven metabolites in acute kidney injury (AKI), three metabolites in acute respiratory distress syndrome (ARDS), seven metabolites in sepsis-induced myocardial dysfunction (SIMD), and four metabolites in acute hepatic ischemia (AHI) that were significantly different. The results showed that the sepsis samples exhibited extensive changes in amino acids, fatty acids, and tricarboxylic acid (TCA)–cycle products. In addition, three metabolic pathways—namely, energy metabolism, amino acid metabolism, and lipid metabolism—were downregulated in sepsis patients.

The downregulated energy, amino acid, and lipid metabolism found in our study may serve as a novel clinical marker for the dysregulated internal environment, particularly involving energy metabolism, which results in sepsis.

• Plasma metabolic profiling
• gas chromatography–mass spectrometry (GC–MS)
• matching study
• multiple organ dysfunctions
• sepsis

• ARDS
• Biomarker
• MS
• Metabolomics
• NMR
• Sepsis

• Blood
• Death-related metabolic pathways
• Meta-analysis
• Metabolomics
• Outcome
• Prediction
• Sepsis

• APACHE
• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Biomarkers/blood
• Critical Illness/mortality
• Enzyme-Linked Immunosorbent Assay
• Female
• Humans
• Integrin-Binding Sialoprotein/blood
• Intensive Care Units/statistics & numerical data
• Male
• Middle Aged
• Prognosis
• Young Adult

• SFB/TRR57 Deutsche Forschungsgemeinschaft (German Research Foundation)
• DFG RO 4317/4-1 Deutsche Forschungsgemeinschaft (German Research Foundation)
• Project grant of the German Center for Cardiovascular Disease (DZHK, B18-005Ext)
• START grant from the medical faculty RWTH Aachen
• START grant from the medical faculty RWTH Aachen Project grant of the German Center for Cardiovascular Disease (DZHK, B18-005Ext)

• Commons arrangements
• Free/libre open source software
• Metabolomics
• Public health
• Sepsis

Biological interpretation of DNA microarray data may differ depending on underlying assumptions and statistical tests of bioinformatics tools used. We used Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) to analyze previously generated DNA microarray data from human monocytes stimulated with N. meningitidis and IL-10 (“the model system”), and with meningococcal sepsis plasma before and after immunodepletion of IL-10 (“the patient plasma system”). The objectives were to compare if the two bioinformatics methods resulted in similar biological interpretation of the datasets, and to identify whether GSEA provided additional insight compared with IPA about the monocyte host response to meningococcal activation.

In both experimental models, GSEA and IPA identified genes associated with pro-inflammatory innate immune activation, including TNF-signaling, Toll-like receptor signaling, JAK-STAT-signaling, and type I and type II interferon signaling. GSEA identified genes regulated by the presence of IL-10 with similar gene sets in both the model system and the patient plasma system. In the model system, GSEA and IPA in sum identified 170 genes associated with oxidative phosphorylation/mitochondrial function to be down-regulated in monocytes stimulated with meningococci. In the patient plasma system, GSEA and IPA in sum identified 122 genes associated with oxidative phosphorylation/mitochondrial dysfunction to be down-regulated by meningococcal sepsis plasma depleted for IL-10. Using IPA, we identified IL-10 to up-regulate 18 genes associated with oxidative phosphorylation/mitochondrial function that were down-regulated by N. meningitidis.

Biological processes associated with the gene expression changes in the model system of meningococcal sepsis were comparable with the results found in the patient plasma system. By combining GSEA with IPA, we discovered an inhibitory effect of N. meningitidis on genes associated with mitochondrial function and oxidative phosphorylation, and that IL-10 partially reverses this strong inhibitory effect, thereby identifying, to our knowledge, yet another group of genes where IL-10 regulates the effect of LPS. We suggest that relying on a single bioinformatics tool together with an arbitrarily chosen filtering criteria for data analysis may result in overlooking relevant biological processes and signaling pathways associated with genes differentially expressed between compared experimental conditions.

The online version of this article (10.1186/s12865-017-0229-5) contains supplementary material, which is available to authorized users.

• Bioinformatics
• Gene expression
• Gene set enrichment analysis
• Ingenuity pathway analysis
• Interleukin-10
• Meningococcal sepsis
• N. Meningitidis
• mRNA

Modern burn care has led to unprecedented survival rates in burn patients whose injuries were fatal a few decades ago. Along with improved survival, new challenges have emerged in the management of burn patients. Infections top the list of the most common complication after burns, and sepsis is the leading cause of death in both adult and pediatric burn patients. The diagnosis and management of sepsis in burns is complex as a tremendous hypermetabolic response secondary to burn injury can be superimposed on systemic infection, leading to organ dysfunction. The management of a septic burn patient represents a challenging scenario that is commonly encountered by providers caring for burn patients despite preventive efforts. Here, we discuss the current perspectives in the diagnosis and treatment of sepsis and septic shock in burn patients.

• antibiotics
• biomarkers
• burn injury
• burn sepsis
• cytokines
• procalcitonin
• thermal injury

Sepsis is an injurious systemic host response to infection, which can often lead to septic shock and death. Recently, the immune-pathogenesis and genomics of sepsis have become a research topic focusing on the establishment of diagnostic and prognostic biomarkers. As yet, none have been identified as having the necessary specificity to be used independently of other factors in this respect. However the accumulation of current evidence regarding genetic variations, especially the single nucleotide polymorphisms (SNPs) of cytokines and other innate immunity determinants, partially explains the susceptibility and individual differences of patients with regard to the evolution of sepsis. This article outlines the role of genetic variation of some serum proteins which have the potential to be used as biomarker values in evaluating sepsis susceptibility and the progression of the condition.

• biomarkers
• cell receptors
• cytokines
• genes
• sepsis

• Cancer
• Diagnostic evaluation
• Fever of unknown origin
• Infection
• Inflammation

Disease impacts important metabolic pathways and the alteration of metabolites may serve as a potential biomarker for early-stage diagnosis.

• N-acetylmuramoyl-l-alanine amidase
• biomarker
• inflammation
• proteomics
• sepsis

• Animals
• Biomarkers/analysis
• Critical Care
• Disease Models, Animal
• Humans
• Precision Medicine
• Sepsis/diagnosis
• Sepsis/physiopathology
• Sepsis/therapy

• UL1 TR000039 NCATS NIH HHS
• R01 GM112806 NIGMS NIH HHS
• R01 GM106419 NIGMS NIH HHS
• UL1-TR000039 NCATS NIH HHS
• F31 DK104533 NIDDK NIH HHS
• T32 GM106999 NIGMS NIH HHS

Sepsis-induced acute lung injury (ALI) remains a leading cause of death in intensive care units and early detection is very important. This work showed that metabolite phenotype profiling might be a useful tool for the effective diagnosis and further understanding of ALI.

Inflammatory, autoimmune and metabolic disorders have been associated with alterations in osteopontin (OPN) serum levels. Furthermore, elevated serum levels of OPN were reported from a small cohort of patients with sepsis. We therefore analyzed OPN serum concentrations in a large cohort of critically ill medical patients.

A total of 159 patients (114 with sepsis, 45 without sepsis) were studied prospectively upon admission to the medical intensive care unit (ICU) as well as after 3 days of ICU treatment and compared to 50 healthy controls. Clinical data, various laboratory parameters as well as investigational inflammatory cytokine profiles were assessed. Patients were followed for approximately 1 year.

We found significantly elevated serum levels of OPN at admission to the ICU and after 3 days of treatment in critically ill patients compared to healthy controls. OPN concentrations were related to disease severity and significantly correlated with established prognosis scores and classical as well as experimental markers of inflammation and multi-organ failure. In the total cohort, OPN levels decreased from admission to day 3 of ICU treatment. However, persistently elevated OPN levels at day 3 of ICU treatment were a strong independent predictor for an unfavorable prognosis, with similar or better diagnostic accuracy than routinely used markers of organ failure or prognostic scoring systems such as SAPS2 or APACHE II score.

Persistently elevated OPN serum concentrations are associated with an unfavourable outcome in patients with critical illness, independent of the presence of sepsis. Besides a possible pathogenic role of OPN in critical illness, our study indicates a potential value for OPN as a prognostic biomarker in critically ill patients during the early course of ICU treatment.

The online version of this article (doi:10.1186/s13054-015-0988-4) contains supplementary material, which is available to authorized users.

Non-targeted mass spectrometry was used to characterize peripheral biomarkers associated with the urine metabolome in severe sepsis (SS) patients. This is an efficient and convenient tool for diagnosing and screening of SS in a high-risk population.