Interleukin-1 Receptor 2: A New Biomarker for Sepsis Diagnosis and Gram-Negative/Gram-Positive Bacterial Differentiation

Regulation of plasma soluble receptors of TNF and IL-1 in patients with COVID-19 differs from that observed in sepsis

OBJECTIVES

IL-1α/β and TNF are closely linked to the pathology of severe COVID-19 and sepsis. The soluble forms of their receptors, functioning as decoy receptors, exhibit inhibitory effects. However, little is known about their regulation in severe bacterial and viral infections, which we aimed to investigate in this study.

METHODS

The circulating soluble receptors of TNF (sTNFR1 and sTNFR2) and IL-1α/β (sIL-1R1, sIL-1R2) were evaluated in the plasma of patients with COVID-19, severe bacterial infections, and sepsis and compared with healthy controls. Additionally, IL1R1, IL1R2, TNFRSF1A, and TNFRSF1B expression was evaluated at the single cell level in PBMCs derived from COVID-19 or sepsis patients.

RESULTS

Plasma concentrations of sIL-1R1, sTNFR1, and sTNFR2 were significantly higher in COVID-19 patients compared to healthy subjects. Notably, sIL-1R1 levels were particularly elevated in ICU COVID-19 patients, and transcriptome analysis indicated heightened IL1R1 expression in PBMCs from severe COVID-19 patients. In severe bacterial infections, only sTNFR1 and sTNFR2 exhibited increased levels compared to healthy controls. Sepsis patients had decreased sIL-1R1 plasma concentrations but elevated sIL-1R2, sTNFR1, and sTNFR2 levels compared to healthy individuals, reflecting the heightened expression due to the increased numbers of monocytes present in sepsis. Finally, elevated concentrations of sIL-1R2, sTNFR1, and sTNFR2 were moderately associated with reduced 28-day survival in sepsis patients.

CONCLUSION

Our study reveals distinct regulation of plasma concentrations of soluble IL-1 receptors in COVID-19 and sepsis. Moreover, soluble TNF receptors 1 and 2 consistently rise in all conditions and show a positive correlation with disease severity in sepsis.

Development and validation of a machine learning-based interpretable model for predicting sepsis by complete blood cell parameters

Background

Sepsis, a severe infectious disease, carries a high mortality rate. Early detection and prompt treatment are crucial for reducing mortality and improving prognosis. The aim of this research is to develop a clinical prediction model using machine learning algorithms, leveraging complete blood cell (CBC) parameters, to detect sepsis at an early stage.

Methods

The study involved 572 patients admitted to West China Hospital of Sichuan University between July 2020 and September 2021. Among them, 215 were diagnosed with sepsis, while 357 had local infections. Demographic information was collected, and 57 CBC parameters were analyzed to identify potential predictors using techniques such as the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The prediction model was built using Logistic Regression and evaluated for diagnostic specificity, discrimination, and clinical applicability including metrics such as the area under the curve (AUC), calibration curve, clinical impact curve, and clinical decision curve. Additionally, the model's diagnostic performance was assessed on a separate validation cohort. Shapley's additive explanations (SHAP), and breakdown (BD) profiles were used to explain the contribution of each variable in predicting the outcome.

Results

Among all the machine learning methods' prediction models, the LASSO-based model (λ = min) demonstrated the highest diagnostic performance in both the discovery cohort (AUC = 0.9446, P < 0.001) and the validation cohort (AUC = 0.9001, P < 0.001). Furthermore, upon local analysis and interpretation of the model, we demonstrated that LY-Z, MO-Z, and PLT-I had the most significant impact on the outcome.

Conclusions

The predictive model based on CBC parameters can be utilized as an effective approach for the early detection of sepsis.

Putative mRNA Biomarkers for the Eradication of Infection in an Equine Experimental Model of Septic Arthritis

Septic arthritis (SA) in horses has long-term health implications. The success of its resolution hinges on the implementation of early, aggressive treatment, which is often sustained over a prolonged period. Common diagnostic methods do not allow for the reliable detection of the eradication of joint infection. A potential alternative is the discovery and characterization of mRNA biomarkers. The purpose of this study was to identify potential mRNA biomarkers for the eradication of joint infection in equine SA and to compare their expression with our previously published proteomics data. In addition, the transcriptomics data were compared to the mRNA biomarker panel, SeptiCyte Lab, used to distinguish sepsis from non-septic shock in humans. A comparative transcriptomics analysis of synovial fluid from the SA joints of five horses with active infection and subsequent post-treatment eradicated infection in the same joints and five horses with non-septic synovitis was performed. Eight novel mRNA transcripts were identified that were significantly upregulated (>3-fold) in horses with active SA compared to horses post-eradication of infection after treatment and horses with non-septic synovitis. Two proteins in our proteomics data corresponded to these mRNA transcripts, but were not statistically different. The transcripts used in the SeptiCyte test were not differentially expressed in our study. Our results suggest that mRNA may be a useful source of biomarkers for the eradication of joint infection in horses and warrants further investigation.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

Prognostic differences in sepsis caused by gram-negative bacteria and gram-positive bacteria: a systematic review and meta-analysis

BACKGROUND

Bacteria are the main pathogens that cause sepsis. The pathogenic mechanisms of sepsis caused by gram-negative and gram-positive bacteria are completely different, and their prognostic differences in sepsis remain unclear.

METHODS

The PubMed, Web of Science, Cochrane Library, and Embase databases were searched for Chinese and English studies (January 2003 to September 2023). Observational studies involving gram-negative (G (-))/gram-positive (G (+)) bacterial infection and the prognosis of sepsis were included. The stability of the results was evaluated by sensitivity analysis. Funnel plots and Egger tests were used to check whether there was publication bias. A meta-regression analysis was conducted on the results with high heterogeneity to identify the source of heterogeneity. A total of 6949 articles were retrieved from the database, and 45 studies involving 5586 subjects were included after screening according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Twenty-seven high-quality studies and 18 moderate-quality studies were identified according to the Newcastle‒Ottawa Scale score. There was no significant difference in the survival rate of sepsis caused by G (-) bacteria and G (+) bacteria (OR 0.95, 95% CI 0.70-1.28). Subgroup analysis according to survival follow-up time showed no significant difference. The serum concentrations of C-reactive protein (CRP) (SMD = 0.39, 95% CI 0.02-0.76), procalcitonin (SMD = 1.95, 95% CI 1.32-2.59) and tumor necrosis factor-alpha (TNF-α) (MD = 0.31, 95% CI 0.25-0.38) in the G (-) bacterial infection group were significantly higher than those in the G (+) bacterial infection group, but there was no significant difference in IL-6 (SMD = 1.33, 95% CI - 0.18-2.84) and WBC count (MD = - 0.15, 95% CI - 0.96-00.66). There were no significant differences between G (-) and G (+) bacteria in D dimer level, activated partial thromboplastin time, thrombin time, international normalized ratio, platelet count, length of stay or length of ICU stay. Sensitivity analysis of the above results indicated that the results were stable.

CONCLUSION

The incidence of severe sepsis and the concentrations of inflammatory factors (CRP, PCT, TNF-α) in sepsis caused by G (-) bacteria were higher than those caused by G (+) bacteria. The two groups had no significant difference in survival rate, coagulation function, or hospital stay. The study was registered with PROSPERO (registration number: CRD42023465051).

Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning

Background

To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis.

Methods

The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting.

Results

Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all P < 0.05). Moreover, YOD1, GADD45A and IL1R2 were upregulated, and BCL11B was downregulated in the heart, liver, lungs, and kidneys of the septic mice model.

Conclusions

We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms.

T cell activation profiles can distinguish gram negative/positive bacterial sepsis and are associated with ICU discharge

Introduction

Sepsis is a life-threatening complication resulting from a dysregulated host response to a serious infection, of which bacteria are the most common cause. A rapid differentiation of the gram negative (G^-)/gram positive (G⁺) pathogens facilitates antibiotic treatment, which in turn improves patients' survival.

Methods

We performed a prospective, observational study of adult patients in intensive care unit (ICU) unit and underwent the analysis of peripheral blood lymphocyte subsets, cytokines and other clinical indexes. The enrolled 94 patients were divided into no infection group (n=28) and bacterial sepsis group (n=66), and the latter group was subdivided into G^- (n=46) and G⁺ (n=20) sepsis subgroups.

Results

The best immune biomarker which differentiated the diagnosis of G^- sepsis from G⁺ sepsis, included activation markers of CD69, human leukocyte antigen DR (HLA-DR) on CD3⁺CD8⁺T subset. The ratio of CD3⁺CD4⁺CD69⁺T/CD3⁺CD8⁺CD69⁺T (odds ratio (OR): 0.078(0.012,0.506), P = 0.008), PCT>0.53 ng/ml (OR: 9.31(1.36,63.58), P = 0.023), and CO₂CP<26.5 mmol/l (OR: 10.99(1.29, 93.36), P = 0.028) were predictive of G^- sepsis (versus G⁺ sepsis), and the area under the curve (AUC) was 0.947. Additionally, the ratio of CD3⁺CD4⁺CD69⁺T/CD3⁺CD8⁺CD69⁺T ≤ 0.2697 was an independent risk factor for poor ICU discharge in G^- sepsis patients (HR: 0.34 (0.13, 0.88), P=0.026).

Conclusion

We conclude that enhanced activation of T cells may regulate the excessive inflammatory response of G^- bacterial sepsis, and that T cell activation profiles can rapidly distinguish G^- sepsis from G⁺ sepsis and are associated with ICU discharge.

Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies.

Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR.

Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model.

Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.

Construction and validation of a robust prognostic model based on immune features in sepsis

Purpose

Sepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.

Materials and methods

The differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.

Results

A total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value <0.01, 69 prognostic DEIRGs were obtained and the potential molecular mechanisms of DEIRGs were also clarified. According to multivariate Cox regression analysis, 22 DEIRGs were further identified to construct the prognostic model and identify patients with high risk. The Kaplan-Meier survival analysis showed that high-risk groups have higher 28-day mortality than low-risk groups (P=1.105e-13). The AUC value was 0.879 which symbolized that the prognostic model had a better accuracy to predict the 28-day mortality. The external datasets also prove that the prognostic model had an excellent prediction value. Furthermore, the results of correlation analysis showed that patients with Mars1 might have higher risk scores than Mars2-4 (P=0.002). According to the previous study, Mars1 endotype was characterized by immunoparalysis. Thus, the sepsis patients in high-risk groups might exist the immunosuppression. Between the high-risk and low-risk groups, circulating immune cells types were significantly different, and risk score was significantly negatively correlated with naive CD4+ T cells (P=0.019), activated NK cells (P=0.0045), monocytes (P=0.0134), and M1 macrophages (P=0.0002).

Conclusions

Our study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.

G-CSF as a potential early biomarker for diagnosis of bloodstream infection

Background

Cytokines play an important role in bacterial infection, and thus, we aim to find out cytokines that may be diagnostically significant in early stage of bacterial bloodstream infection.

Methods

Mice models infected with Staphylococcus aureus and Klebsiella pneumoniae were established. Then dynamic changes of nine serum cytokines were monitored within 48 hours after the infection. Cytokines with significant differences between the infected groups and control group were further analyzed. Clinical samples of patients who were suspected of bloodstream infection were collected. Then the diagnostic efficiency of screened cytokines was determined with receiver operating characteristic curve analysis.

Results

As for mice models infected by Staphylococcus aureus and Klebsiella pneumoniae, six cytokines including IL‐1β, IL‐6, IL‐12p70, G‐CSF, IFN‐γ, and TNF‐α were significantly different (P < .05) between two bacterial infected groups. As for clinical samples, three cytokines including IL‐6, IL‐12p70, and G‐CSF showed significant differences between infection group (Staphylococcus aureus and Klebsiella pneumonia group) and negative control group. With the area under curve of 0.7350 and 0.6431 for G‐CSF and IL‐6, respectively, these two cytokines were significantly different between Staphylococcus aureus and Klebsiella pneumoniae infection groups. Combination of G‐CSF and IL‐6 could improve the AUC to 0.8136.

Conclusions

G‐CSF cannot only identify bacterial bloodstream infection, but can also distinguish the infection of Staphylococcus aureus from Klebsiella pneumoniae. Further investigation should be performed concerning the diagnostic efficiency of G‐CSF in diagnosing different types of bacterial bloodstream infection.

Development of Exhausted Memory Monocytes and Underlying Mechanisms

Pathogenic inflammation and immuno-suppression are cardinal features of exhausted monocytes increasingly recognized in septic patients and murine models of sepsis. However, underlying mechanisms responsible for the generation of exhausted monocytes have not been addressed. In this report, we examined the generation of exhausted primary murine monocytes through prolonged and repetitive challenges with high dose bacterial endotoxin lipopolysaccharide (LPS). We demonstrated that repetitive LPS challenges skew monocytes into the classically exhausted Ly6Chi population, and deplete the homeostatic non-classical Ly6Clo population, reminiscent of monocyte exhaustion in septic patients. scRNAseq analyses confirmed the expansion of Ly6Chi monocyte cluster, with elevation of pathogenic inflammatory genes previously observed in human septic patients. Furthermore, we identified CD38 as an inflammatory mediator of exhausted monocytes, associated with a drastic depletion of cellular NAD+; elevation of ROS; and compromise of mitochondria respiration, representative of septic monocytes. Mechanistically, we revealed that STAT1 is robustly elevated and sustained in LPS-exhausted monocytes, dependent upon the TRAM adaptor of the TLR4 pathway. TRAM deficient monocytes are largely resistant to LPS-mediated exhaustion, and retain the non-classical homeostatic features. Together, our current study addresses an important yet less-examined area of monocyte exhaustion, by providing phenotypic and mechanistic insights regarding the generation of exhausted monocytes.

Blood transcriptomics to characterize key biological pathways and identify biomarkers for predicting mortality in melioidosis

Melioidosis is an often lethal tropical disease caused by the Gram-negative bacillus, Burkholderia pseudomallei. The study objective was to characterize transcriptomes in melioidosis patients and identify genes associated with outcome. Whole blood RNA-seq was performed in a discovery set of 29 melioidosis patients and 3 healthy controls. Transcriptomic profiles of patients who did not survive to 28 days were compared with patients who survived and healthy controls, showing 65 genes were significantly up-regulated and 218 were down-regulated in non-survivors compared to survivors. Up-regulated genes were involved in myeloid leukocyte activation, Toll-like receptor cascades and reactive oxygen species metabolic processes. Down-regulated genes were hematopoietic cell lineage, adaptive immune system and lymphocyte activation pathways. RT-qPCR was performed for 28 genes in a validation set of 60 melioidosis patients and 20 healthy controls, confirming differential expression. IL1R2, GAS7, S100A9, IRAK3, and NFKBIA were significantly higher in non-survivors compared with survivors (P < 0.005) and healthy controls (P < 0.0001). The AUROCC of these genes for mortality discrimination ranged from 0.80-0.88. In survivors, expression of IL1R2, S100A9 and IRAK3 genes decreased significantly over 28 days (P < 0.05). These findings augment our understanding of this severe infection, showing expression levels of specific genes are potential biomarkers to predict melioidosis outcomes.

Health monitoring in birds using bio-loggers and whole blood transcriptomics

Monitoring and early detection of emerging infectious diseases in wild animals is of crucial global importance, yet reliable ways to measure immune status and responses are lacking for animals in the wild. Here we assess the usefulness of bio-loggers for detecting disease outbreaks in free-living birds and confirm detailed responses using leukocyte composition and large-scale transcriptomics. We simulated natural infections by viral and bacterial pathogens in captive mallards (Anas platyrhynchos), an important natural vector for avian influenza virus. We show that body temperature, heart rate and leukocyte composition change reliably during an acute phase immune response. Using genome-wide gene expression profiling of whole blood across time points we confirm that immunostimulants activate pathogen-specific gene regulatory networks. By reporting immune response related changes in physiological and behavioural traits that can be studied in free-ranging populations, we provide baseline information with importance to the global monitoring of zoonotic diseases.

Sepsis-Pathophysiology and Therapeutic Concepts

Sepsis is a life-threatening condition and a global disease burden. Today, the heterogeneous syndrome is defined as severe organ dysfunction caused by a dysregulated host response to infection, with renewed emphasis on immune pathophysiology. Despite all efforts of experimental and clinical research during the last three decades, the ability to positively influence course and outcome of the syndrome remains limited. Evidence-based therapy still consists of basic causal and supportive measures, while adjuvant interventions such as blood purification or targeted immunotherapy largely remain without proof of effectiveness so far. With this review, we aim to provide an overview of sepsis immune pathophysiology, to update the choice of therapeutic approaches targeting different immunological mechanisms in the course of sepsis and septic shock, and to call for a paradigm shift from the pathogen to the host response as a potentially more promising angle.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

CRP/albumin ratio: A promising marker of gram-negative bacteremia in late-onset neonatal sepsis

Objective

Neonatal sepsis is a clinical condition that results in serious morbidity and mortality unless urgently diagnosed and treated. Obtaining the results of blood cultures to determine the causative agent in sepsis is a time-consuming process. The CRP/albumin ratio is an inflammatory marker that has started to be used in recent years. The aim of our study was to investigate the relationship between CRP/albumin and Gram-negative bacterial sepsis in neonates.

Material and Methods

This study was conducted on 112 premature neonates with sepsis. The patients were divided into two groups according to culture results as Gram-negative and Gram-positive bacterial sepsis. The laboratory and demographic features of the patients were obtained from the hospital records. A receiver operating characteristic curve was plotted to evaluate the predictive value of the CRP/albumin ratio for Gram-negative sepsis.

Results

CRP/albumin was significantly higher in the Gram-negative group (p<0.001). According to the receiver operating characteristic curve, the optimal cut-off value of CRP/albumin for the prediction of Gram-negative sepsis was >35.17, which had a specificity of 97% and sensitivity of 56% (AUC=0.839; 95% CI: 0.743-0.944; p<0.001). A multivariate logistic regression analysis revealed that CRP/albumin (OR=1.082, 95% CI: 1.033-1.134, p=0.001) and absolute neutrophil count (OR=1.145, 95% CI: 1.000-1.312, p=0.049) were still associated with Gram-negative sepsis after adjustment for variables found to be statistically significant in univariate analysis and correlated with Gram-negative sepsis.

Conclusion

The CRP/albumin ratio is independently related to Gram-negative sepsis in neonatal sepsis and may be useful in predicting Gram-negative bacteremia.

Quantitative and Qualitative Assessments of Cholesterol Association With Bacterial Infection Type in Sepsis and Septic Shock

BACKGROUND

Reduced cholesterol levels are associated with increased organ failure and mortality in sepsis. Cholesterol levels may vary by infection type (gram negative vs positive), possibly reflecting differences in cholesterol-mediated bacterial clearance.

METHODS

This was a secondary analysis of a combined data set of 2 prospective cohort studies of adult patients meeting Sepsis-3 criteria. Infection types were classified as gram negative, gram positive, or culture negative. We investigated quantitative (levels) and qualitative (dysfunctional high-density lipoprotein [HDL]) cholesterol differences. We used multivariable logistic regression to control for disease severity.

RESULTS

Among 171 patients with sepsis, infections were gram negative in 67, gram positive in 46, and culture negative in 47. Both gram-negative and gram-positive infections occurred in 11 patients. Total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and HDL cholesterol (HDL-C) levels were lower for culture-positive sepsis at enrollment (TC, P < .001; LDL-C, P < .001; HDL-C, P = .011) and persisted after controlling for disease severity. Similarly, cholesterol levels were lower among culture-positive patients at 48 hours (TC, P = .012; LDL-C, P = .029; HDL-C, P = .002). Triglyceride (TG) levels were lower at enrollment (P =.033) but not at 48 hours (P = .212). There were no differences in dysfunctional HDL. Among bacteremic patients, cholesterol levels were lower at enrollment (TC, P = .010; LDL-C, P = .010; HDL-C, P ≤ .001; TG, P = .005) and at 48 hours (LDL-C, P = .027; HDL-C, P < .001; TG, P = .020), except for 48 hour TC (P = .051). In the bacteremia subgroup, enrollment TC and LDL-C were lower for gram-negative versus gram-positive infections (TC, P = .039; LDL-C, P = .023).

CONCLUSION

Cholesterol levels are significantly lower among patients with culture-positive sepsis and bacteremia.

An immune-cell signature of bacterial sepsis

Dysregulation of the immune response to bacterial infection can lead to sepsis, a condition with high mortality. Multiple whole-blood gene-expression studies have defined sepsis-associated molecular signatures, but have not resolved changes in transcriptional states of specific cell types. Here, we used single-cell RNA-sequencing to profile the blood of people with sepsis (n = 29) across three clinical cohorts with corresponding controls (n = 36). We profiled total peripheral blood mononuclear cells (PBMCs, 106,545 cells) and dendritic cells (19,806 cells) across all subjects and, on the basis of clustering of their gene-expression profiles, defined 16 immune-cell states. We identified a unique CD14⁺ monocyte state that is expanded in people with sepsis and validated its power in distinguishing these individuals from controls using public transcriptomic data from subjects with different disease etiologies and from multiple geographic locations (18 cohorts, n = 1,467 subjects). We identified a panel of surface markers for isolation and quantification of the monocyte state and characterized its epigenomic and functional phenotypes, and propose a model for its induction from human bone marrow. This study demonstrates the utility of single-cell genomics in discovering disease-associated cytologic signatures and provides insight into the cellular basis of immune dysregulation in bacterial sepsis.

Diagnostic Accuracy of Procalcitonin Compared to C-Reactive Protein and Interleukin 6 in Recognizing Gram-Negative Bloodstream Infection: A Meta-Analytic Study

Objective

Gram-negative bloodstream infections (GNBSIs), especially those caused by antibiotic-resistant species, have become a public health challenge. Procalcitonin (PCT) showed promising potential in early diagnosis of GNBSI; however, little was known about its performance under different clinical settings. We here systematically assessed the diagnostic accuracy of PCT in recognizing GNBSI and made direct comparisons with C-reactive protein (CRP) and interleukin 6 (IL-6).

Methods

PubMed, Embase, ISI Web of Knowledge, and Scopus were searched from inception to March 15th, 2019. Area under the summary receiver operating characteristic curve (AUC), pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated. Hierarchical summary receiver operating characteristic (HSROC) model was used for the investigation of heterogeneity and for comparisons between markers.

Results

25 studies incorporating 50933 suspected BSI episodes were included. Pooled sensitivity and specificity for PCT were 0.71 and 0.76, respectively. The overall AUC was 0.80. The lowest AUCs were found in patients with febrile neutropenia (0.69) and hematological malignancy (0.69). The highest AUC was found in groups using electrochemiluminescence immunoassay (0.87). In direct comparisons, PCT showed better overall performance than CRP with the AUC being 0.85 (95% CI 0.81–0.87) for PCT and 0.78 (95% CI 0.74–0.81) for CRP, but the relative DORs varied with thresholds between PCT and CRP (p < 0.001). No significant difference was found either in threshold (p < 0.001). No significant difference was found either in threshold (p < 0.001). No significant difference was found either in threshold (

Conclusions

PCT was helpful in recognizing GNBSI, but the test results should be interpreted carefully with knowledge of patients' medical condition and should not serve as the only criterion for GNBSI. Further prospective studies are warranted for comparisons between different clinical settings.

Transcriptome Meta-Analysis Deciphers a Dysregulation in Immune Response-Associated Gene Signatures during Sepsis

Sepsis is a life-threatening disease induced by a systemic inflammatory response, which leads to organ dysfunction and mortality. In sepsis, the host immune response is depressed and unable to cope with infection; no drug is currently available to treat this. The lungs are frequently the starting point for sepsis. This study aimed to identify potential genes for diagnostics and therapeutic purposes in sepsis by a comprehensive bioinformatics analysis. Our criteria are to unravel sepsis-associated signature genes from gene expression datasets. Differentially expressed genes (DEGs) were identified from samples of sepsis patients using a meta-analysis and then further subjected to functional enrichment and protein‒protein interaction (PPI) network analysis for examining their potential functions. Finally, the expression of the topmost upregulated genes (ARG1, IL1R2, ELANE, MMP9) was quantified by reverse transcriptase-PCR (RT-PCR), and myeloperoxidase (MPO) expression was confirmed by immunohistochemistry (IHC) staining in the lungs of a well-established sepsis mouse model. We found that all the four genes were upregulated in semiquantitative RT-PCR studies; however, MMP9 showed a nonsignificant increase in expression. MPO staining showed strong immunoreactivity in sepsis as compared to the control. This study demonstrates the role of significant and widespread immune activation (IL1R2, MMP9), along with oxidative stress (ARG1) and the recruitment of neutrophils, in sepsis (ELANE, MPO).

Risk factors and outcomes of urosepsis in patients with calculous pyonephrosis receiving surgical intervention: a single-center retrospective study

Background

Urosepsis is a catastrophic complication, which can easily develop into septic shock and lead to death if not diagnosed early and effectively treated in time. However, there is a lack of evidence on the risk factors and outcomes in calculous pyonephrosis patients. Therefore, this study was conducted to identify risk factors and outcomes of intra- and postoperative urosepsis in this particular population.

Methods

Clinical data of 287 patients with calculous pyonephrosis were collected. In the univariate and multivariate analysis, all patients were divided into urosepsis group and non-urosepsis group. The diagnosis of urosepsis was mainly on the basis of the criteria of American College of Chest Physicians (ACCP)/Society of Critical Care Medicine (SCCM). Patient characteristics and outcomes data were analyzed, and risk factors were assessed by binary logistic regression analysis.

Results

Of 287 patients, 41 (14.3%) acquired urosepsis. Univariate analysis showed that white blood cell (WBC > 10*10^9/L) before surgery (P = 0.027), surgery types (P = 0.009), hypotension during surgery (P < 0.001) and urgent surgery (P < 0.001) were associated with intra- and postoperative urosepsis for calculous pyonephrosis patients. In multivariate analysis, hypotension during surgery and urgent surgery were closely related to intra- and postoperative urosepsis. Outcome analysis suggested that patients developing urosepsis had a longer intensive care unit (ICU) stay and postoperative hospital stay and higher mortality.

Conclusions

Hypotension during surgery and urgent surgery were risk factors of intra- and postoperative urosepsis for calculous pyonephrosis patients, which may lead to a prolonged ICU stay, postoperative hospital stay and higher mortality.

Identification of a transcriptome profile associated with improvement of organ function in septic shock patients after early supportive therapy

Background

Septic shock is the most severe complication of sepsis and this syndrome is associated with high mortality. Treatment of septic shock remains largely supportive of hemodynamics and tissue perfusion. Early changes in organ function assessed by the Sequential Organ Function Assessment (SOFA) score are highly predictive of the outcome. However, the individual patient’s response to supportive therapy is very heterogeneous, and the mechanisms underlying this variable response remain elusive. The aim of the study was to investigate the transcriptome of whole blood in septic shock patients with different responses to early supportive hemodynamic therapy assessed by changes in SOFA scores within the first 48 h from intensive care unit (ICU) admission.

Methods

We performed whole blood RNA sequencing in 31 patients: 17 classified as responders (R) and 14 as non-responders (NR). Gene expression was investigated at ICU admission (time point 1, or T1), comparing R with NR [padj < 0.01; Benjamini–Hochberg (BH)] and over time from T1 to T2 (48 h later) in R and NR independently (paired analysis, padj < 0.01; BH). Then the differences in gene expression trends over time were evaluated (Mann–Whitney, P <0.01). To identify enriched biological processes, we performed an over-representation analysis based on a right-sided hypergeometric test with Bonferroni step-down as multiple testing correction (padj < 0.05).

Results

At ICU admission, we did not identify differentially expressed genes (DEGs) between the two groups. In the transition from T1 to T2, the activation of genes involved in T cell–mediated immunity, granulocyte and natural killer (NK) cell functions, and pathogen lipid clearance was noted in the R group. Genes involved in acute inflammation were downregulated in both groups.

Conclusions

Within the limits of a small sample size, our results could suggest that early activation of genes of the adaptive immune response is associated with an improvement in organ function.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2242-3) contains supplementary material, which is available to authorized users.

The immunosuppressive face of sepsis early on intensive care unit-A large-scale microarray meta-analysis

Background

Sepsis is defined as a life-threatening condition, resulting from a dysregulated and harmful response of the hosts’ immune system to infection. Apart from this, the (over-)compensating mechanisms counterbalancing the inflammatory response have been proven to render the host susceptible to further infections and increase delayed mortality. Our study aimed to unravel the heterogeneity of immune response in early sepsis and to explain the biology behind it.

Methods

A systematic search of public repositories yielded 949 microarray samples from patients with sepsis of different infectious origin and early after clinical manifestation. These were merged into a meta-expression set, and after applying sequential conservative bioinformatics filtering, an in-deep analysis of transcriptional heterogeneity, as well as a comparison to samples of healthy controls was performed.

Results

We can identify two distinct clusters of patients (cluster 1: 655 subjects, cluster 2: 294 subjects) according to their global blood transcriptome. While both clusters exhibit only moderate differences in direct comparison, a comparison of both clusters individually to healthy controls yielded strong expression changes of genes involved in immune responses. Both comparisons found similar regulated genes, with a stronger dysregulation occurring in the larger patient cluster and implicating a loss of monocyte and T cell function, co-occurring with an activation of neutrophil granulocytes.

Conclusion

We propose a consistent—but in its extent varying—presence of immunosuppression, occurring as early in sepsis as its clinical manifestation and irrespective of the infectious origin. While certain cell types possess contradictory activation states, our finding underlines the urgent need for an early host-directed therapy of sepsis side-by-side with antibiotics.

Peripheral blood RNA gene expression in children with pneumococcal meningitis: a prospective case-control study

INTRODUCTION

Invasive pneumococcal disease (IPD), caused by Streptococcus pneumoniae, is a leading cause of pneumonia, meningitis and septicaemia worldwide, with increased morbidity and mortality in HIV-infected children.

OBJECTIVES

We aimed to compare peripheral blood expression profiles between HIV-infected and uninfected children with pneumococcal meningitis and controls, and between survivors and non-survivors, in order to provide insight into the host inflammatory response leading to poorer outcomes.

DESIGN AND SETTING

Prospective case-control observational study in a tertiary hospital in Malawi.

PARTICIPANTS

Children aged 2 months to 16 years with pneumococcal meningitis or pneumonia.

METHODS

We used the human genome HGU133A Affymetrix array to explore differences in gene expression between cases with pneumococcal meningitis (n=12) and controls, and between HIV-infected and uninfected cases, and validated gene expression profiles for 34 genes using real-time quantitative PCR (RT-qPCR) in an independent set of cases with IPD (n=229) and controls (n=13). Pathway analysis was used to explore genes differentially expressed.

RESULTS

Irrespective of underlying HIV infection, cases showed significant upregulation compared with controls of the following: S100 calcium-binding protein A12 (S100A12); vanin-1 (VNN1); arginase, liver (ARG1); matrix metallopeptidase 9 (MMP9); annexin A3 (ANXA3); interleukin 1 receptor, type II (IL1R2); CD177 molecule (CD177); endocytic adaptor protein (NUMB) and S100 calcium-binding protein A9 (S100A9), cytoskeleton-associated protein 4 (CKAP4); and glycogenin 1 (GYG1). RT-qPCR confirmed differential expression in keeping with microarray results. There was no differential gene expression in HIV-infected compared with HIV-uninfected cases, but there was significant upregulation of folate receptor 3 (FOLR3), S100A12 in survivors compared with non-survivors.

CONCLUSION

Children with IPD demonstrated increased expression in genes regulating immune activation, oxidative stress, leucocyte adhesion and migration, arginine metabolism, and glucocorticoid receptor signalling.