Diagnosis and Prognosis of Sepsis Based on Use of Cytokines, Chemokines, and Growth Factors

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RATIONALE

Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

OBJECTIVES

We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

METHODS

Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

MEASUREMENTS AND MAIN RESULTS

We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

CONCLUSIONS

These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

TRIAL REGISTRATION NUMBER

ISRCTN20769191, ISRCTN12776039.

The causal effects of circulating cytokines on sepsis: a Mendelian randomization study

Background

In observational studies, sepsis and circulating levels of cytokines have been associated with unclear causality. This study used Mendelian randomization (MR) to identify the causal direction between circulating cytokines and sepsis in a two-sample study.

Methods

An MR analysis was performed to estimate the causal effect of 41 cytokines on sepsis risk. The inverse-variance weighted random-effects method, the weighted median-based method, and MR-Egger were used to analyze the data. Heterogeneity and pleiotropy were assessed using MR-Egger regression and Cochran's Q statistic.

Results

Genetically predicted beta-nerve growth factor (OR = 1.12, 95% CI [1.037-1.211], P = 0.004) increased the risk of sepsis, while RANTES (OR = 0.92, 95% CI [0.849-0.997], P = 0.041) and fibroblast growth factor (OR = 0.869, 95% CI [0.766-0.986], P = 0.029) reduced the risk of sepsis. These findings were robust in extensive sensitivity analyses. There was no clear association between the other cytokines and sepsis risk.

Conclusion

The findings of this study demonstrate that beta-nerve growth factor, RANTES, and fibroblast growth factor contribute to sepsis risk. Investigations into potential mechanisms are warranted.

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

OBJECTIVES

To identify cytokine signature clusters in patients with septic shock.

DESIGN

Prospective observational cohort study.

SETTING

Single academic center in the United States.

PATIENTS

Adult (≥ 18 yr old) patients admitted to the medical ICU with septic shock requiring vasoactive medication support.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred fourteen patients with septic shock completed cytokine measurement at time of enrollment (t 1 ) and 24 hours later (t 2 ). Unsupervised random forest analysis of the change in cytokines over time, defined as delta (t 2 -t 1 ), identified three clusters with distinct cytokine profiles. Patients in cluster 1 had the lowest initial levels of circulating cytokines that decreased over time. Patients in cluster 2 and cluster 3 had higher initial levels that decreased over time in cluster 2 and increased in cluster 3. Patients in clusters 2 and 3 had higher mortality compared with cluster 1 (clusters 1-3: 11% vs 31%; odds ratio [OR], 3.56 [1.10-14.23] vs 54% OR, 9.23 [2.89-37.22]). Cluster 3 was independently associated with in-hospital mortality (hazard ratio, 5.24; p = 0.005) in multivariable analysis. There were no significant differences in initial clinical severity scoring or steroid use between the clusters. Analysis of either t 1 or t 2 cytokine measurements alone or in combination did not reveal clusters with clear clinical significance.

CONCLUSIONS

Longitudinal measurement of cytokine profiles at initiation of vasoactive medications and 24 hours later revealed three distinct cytokine signature clusters that correlated with clinical outcomes.

Monocyte chemoattractant protein-1 as a potential marker for patients with sepsis: a systematic review and meta-analysis

Objective

To investigate the diagnostic value of monocyte chemoattractant protein-1 (MCP-1) as a biomarker for adult patients with sepsis.

Methods

Related studies on the diagnostic value of MCP-1 in adult patients with sepsis were searched in PubMed, Cochrane Library, Embase, CNKI, CBM, Web of Science, Scopus, and Wanfang Data databases (published to February 20, 2023) was performed if studies assessed the diagnostic accuracy of MCP-1 in adult patients with sepsis and provided appropriate information sufficient to construct a 2 × 2 linked table, studies were included.

Results

Data from 8 studies with a total of 805 patients were included. The combined sensitivity was 0.84 (95% CI 0.70-0.92), the specificity was 0.82 (95% CI 0.67-0.91), the combined positive likelihood ratio was 3.711 (2.119-6.500), the negative likelihood ratio was 0.287 (0.198-0.415), and the area under the working characteristic curve for combined subjects was 0.88. The diagnostic odds ratio (DOR) was 16.508 (7.632-35.706). Meta-regression analysis showed that the results were not significant. Deeks' funnel plot showed that there was no publication bias.

Conclusion

According to our meta-analysis, MCP-1 is a valuable biomarker and may provide evidence for the diagnosis of sepsis in adults.

Identification of Potential Prognosticators for Sepsis through Expression Analysis of Transcriptomic Data from Sepsis Survivors and Nonsurvivors

Background

Infection can be severely complicated by a dysregulated, whole-body inflammatory response known as sepsis. While previous research showed that genetic predisposition is linked to outcome differences, current patient characterization fails to determine which septic patients have greater tendencies to develop into severe sepsis or go into septic shock. As such, the identification of prognostic biomarkers may assist in identifying these high-risk patients and help improve the clinical management of the disease.

Objective

In this study, we aimed to identify molecular patterns involved in sepsis. We also aimed to identify essential genes associated with the disease's survival which could serve as potential prognosticators for the disease.

Methods

We used weighted gene co-expression analysis (WGCNA) to analyze GSE63042, an RNA expression dataset from 129 patients with systemic inflammatory response syndrome or sepsis, including 78 sepsis survivors and 28 sepsis nonsurvivors. This analysis included identifying gene modules that differentiate sepsis survivors from nonsurvivors and qualitatively assessing differentially expressed genes. We then used STRING's protein-protein interaction and gene ontology analysis to determine the functional and pathway relationships of the genes in the top modules. Lastly, we assessed the prognosticator abilities of the hub genes using ROC analysis.

Results

We found four diverse co-expression gene modules significantly associated with sepsis survival. Our differential gene expression analysis, combined with protein-protein interaction and gene ontology analysis, revealed that the hub genes of these modules - TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD - may serve as candidate markers for sepsis prognosis. These markers were significantly downregulated in sepsis nonsurvivors compared with sepsis survivors.

Conclusion

Weighted gene co-expression analysis, gene ontology enrichment analysis, and proteinprotein network interaction analysis of transcriptomic data from sepsis survivors and nonsurvivors revealed TAF10, SNAPIN, PSME2, PSMB9, JUNB, and CEBPD as potential biomarkers for sepsis prognosis. These genes are associated with functions related to proper immune response, and their downregulation in sepsis nonsurvivors suggests eventual immune exhaustion in late sepsis. Further analyses, however, are necessary to validate their roles in sepsis progression and patient survival.

Indirect Method for Estimation of Reference Intervals of Inflammatory Markers

Background

The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII).

Methods

C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve.

Results

The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively.

Conclusions

The proposed RII method could provide additional information for RI verification or estimation with some limitations.

Evaluation of Biomarkers from Peritoneal Fluid as Predictors of Severity for Abdominal Sepsis Patients Following Emergency Laparotomy

Purpose

Intra-abdominal infection is considered the second most common cause of sepsis and results in localized or diffused inflammation of the peritoneum. The main treatment for abdominal sepsis is an emergency laparotomy for source control. However, surgical trauma also causes inflammation, and patients become susceptible to postoperative complications. Therefore, it is necessary to identify biomarkers that can be used to distinguish sepsis from abdominal infection. This prospective study investigated whether cytokine levels in the peritoneum could predict complications and indicate severity of sepsis following emergency laparotomy.

Methods

We prospectively observed 97 patients with abdominal infection admitted to the Intensive Care Unit (ICU). After emergency laparotomy,SEPSIS-3 criteria were used for the diagnosis of sepsis or septic shock. Blood and peritoneal fluid samples were drawn at postoperative admission to the ICU and cytokine concentrations were measured by flow cytometry.

Results

Fifty-eight postoperative patients were enrolled. We found significant elevations in the peritoneal concentrations of IL-1β, IL-6, TNF-α, IL-17, and IL-2 in patients with sepsis or septic shock compared to the patients without sepsis after surgery. Positive correlations between levels of these peritoneal cytokines with APACHE II scores were found: IL-6, in particular, had the highest correlation coefficient of 0.833. Meanwhile, IL-10 in blood, MCP-1 and IL-8 in both blood and peritoneum were simultaneously increased in patients with sepsis and septic shock, and also positively correlated with disease severity.

Conclusion

The cytokine storm that occurs in the abdominal cavity after emergency laparotomy may be the main mechanism leading to sepsis. It may be valuable to measure IL-1β, IL-6, TNF-α,IL-17, IL-2, MCP-1, and IL-8 in the peritoneal fluid, combined with serum IL-10, MCP-1 and IL-8, in a panel of cytokines, to assess the severity of sepsis and predict mortality from abdominal infection after emergency laparotomy.

Effect of prebiotics, probiotics, and synbiotics on gastrointestinal outcomes in healthy adults and active adults at rest and in response to exercise-A systematic literature review

Introduction

A systematic literature search was undertaken to assess the impact of pre-, pro-, and syn-biotic supplementation on measures of gastrointestinal status at rest and in response to acute exercise.

Methods

Six databases (Ovid MEDLINE, EMBASE, Cinahl, SportsDISCUS, Web of Science, and Scopus) were used. Included were human research studies in healthy sedentary adults, and healthy active adults, involving supplementation and control or placebo groups. Sedentary individuals with non-communicable disease risk or established gastrointestinal inflammatory or functional diseases/disorders were excluded.

Results

A total of n = 1,204 participants were included from n = 37 papers reported resting outcomes, and n = 13 reported exercise-induced gastrointestinal syndrome (EIGS) outcomes. No supplement improved gastrointestinal permeability or gastrointestinal symptoms (GIS), and systemic endotoxemia at rest. Only modest positive changes in inflammatory cytokine profiles were observed in n = 3/15 studies at rest. Prebiotic studies (n = 4/5) reported significantly increased resting fecal Bifidobacteria, but no consistent differences in other microbes. Probiotic studies (n = 4/9) increased the supplemented bacterial species-strain. Only arabinoxylan oligosaccharide supplementation increased total fecal short chain fatty acid (SCFA) and butyrate concentrations. In response to exercise, probiotics did not substantially influence epithelial injury and permeability, systemic endotoxin profile, or GIS. Two studies reported reduced systemic inflammatory cytokine responses to exercise. Probiotic supplementation did not substantially influence GIS during exercise.

Discussion

Synbiotic outcomes resembled probiotics, likely due to the minimal dose of prebiotic included. Methodological issues and high risk of bias were identified in several studies, using the Cochrane Risk of Bias Assessment Tool. A major limitation in the majority of included studies was the lack of a comprehensive approach of well-validated biomarkers specific to gastrointestinal outcomes and many included studies featured small sample sizes. Prebiotic supplementation can influence gut microbial composition and SCFA concentration; whereas probiotics increase the supplemented species-strain, with minimal effect on SCFA, and no effect on any other gastrointestinal status marker at rest. Probiotic and synbiotic supplementation does not substantially reduce epithelial injury and permeability, systemic endotoxin and inflammatory cytokine profiles, or GIS in response to acute exercise.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

The effects of ageing, BMI and physical activity on blood IL-15 levels: A systematic review and meta-analyses

AIM

The purpose of the study was to test the effect of ageing, BMI, physical activity and chronic exercise on IL-15 blood concentration by meta-analyses of the literature.

METHODS

The search was performed on PubMed/MEDLINE, Web of Science, ProQuest, Embase and Cochrane databases. First meta-analysis compared blood IL-15 of healthy adults across three age groups (<35 years, 35-65 years, and >65 years), considering BMI as confounding factor; the second compared IL-15 levels between physically active and non-physically active individuals (cross-sectional studies); and the third tested the effect of chronic exercise interventions on blood IL-15 levels on participants of any age, sex, and health condition.

RESULTS

From 2582 studies retrieved, 67 were selected for the three meta-analyses (age effect: 59; physical activity cross-sectional effect: 5; chronic exercise effect: 14). Older adults had lower blood IL-15 than young and middle-aged adults (5.30 pg/ml [4.76; 5.83]; 7.11 pg/ml [6.33; 7.88]; 7.10 pg/ml [5.55; 8.65], respectively). However, the subgroup of overweight older adults had higher IL-15 than young and middle aged overweight adults; Habitual physical activity did not affect blood IL-15 (standardized mean difference [SMD] 0.61 [-0.65; 1.88], p = 0.34); Chronic exercise reduced blood IL-15 in short-term interventions (<16 weeks) (SMD -0.14 [-0.27; -0.01], p = 0.04), but not studies of >16 weeks of intervention (SMD 0.44 [-0.26; 1.15], p = 0.22).

CONCLUSION

The present meta-analyses highlight the complex interaction of age, BMI and physical activity on blood IL-15 and emphasize the need to take these factors into account when considering the role of this myokine in health throughout life.

Machine learning and murine models explain failures of clinical sepsis trials

BACKGROUND

Multiple clinical trials failed to demonstrate the efficacy of hydrocortisone, ascorbic acid, and thiamine (HAT) in sepsis. These trials were dominated by patients with pulmonary sepsis and have not accounted for differences in the inflammatory responses across varying etiologies of injury/illness. Hydrocortisone, ascorbic acid, and thiamine have previously revealed tremendous benefits in animal peritonitis sepsis models (cecal ligation and puncture [CLP]) in contradiction to the various clinical trials. The impact of HAT remains unclear in pulmonary sepsis. Our objective was to investigate the impact of HAT in pneumonia, consistent with the predominate etiology in the discordant clinical trials. We hypothesized that, in a pulmonary sepsis model, HAT would act synergistically to reduce end-organ dysfunction by the altering the inflammatory response, in a unique manner compared with CLP.

METHODS

Using Pseudomonas aeruginosa pneumonia, a pulmonary sepsis model (pneumonia [PNA]) was compared directly to previously investigated intra-abdominal sepsis models. Machine learning applied to early vital signs stratified animals into those predicted to die (pDie) versus predicted to live (pLive). Animals were then randomized to receive antibiotics and fluids (vehicle [VEH]) vs. HAT). Vitals, cytokines, vitamin C, and markers of liver and kidney function were assessed in the blood, bronchoalveolar lavage, and organ homogenates.

RESULTS

PNA was induced in 119 outbred wild-type Institute of Cancer Research mice (predicted mortality approximately 50%) similar to CLP. In PNA, interleukin 1 receptor antagonist in 72-hour bronchoalveolar lavage was lower with HAT (2.36 ng/mL) compared with VEH (4.88 ng/mL; p = 0.04). The remaining inflammatory cytokines and markers of liver/renal function showed no significant difference with HAT in PNA. PNA vitamin C levels were 0.62 mg/dL (pDie HAT), lower than vitamin C levels after CLP (1.195 mg/dL). Unlike CLP, PNA mice did not develop acute kidney injury (blood urea nitrogen: pDie, 33.5 mg/dL vs. pLive, 27.6 mg/dL; p = 0.17). Furthermore, following PNA, HAT did not significantly reduce microscopic renal oxidative stress (mean gray area: pDie, 16.64 vs. pLive, 6.88; p = 0.93). Unlike CLP where HAT demonstrated a survival benefit, HAT had no impact on survival in PNA.

CONCLUSION

Hydrocortisone, ascorbic acid, and thiamine therapy has minimal benefits in pneumonia. The inflammatory response induced by pulmonary sepsis is unique compared with the response during intra-abdominal sepsis. Consequently, different etiologies of sepsis respond differently to HAT therapy.

Prediction of acute kidney injury, sepsis and mortality in children with urinary CXCL10

BACKGROUND

To determine the associations of urinary CXC motif chemokine 10 (uCXCL10) with AKI, sepsis and pediatric intensive care unit (PICU) mortality in critically ill children, as well as its predictive value for the aforementioned issues.

METHODS

Urinary CXCL10 levels were serially measured in 342 critically ill children during the first week after PICU admission. AKI diagnosis was based on the criteria of KDIGO. Sepsis was diagnosed according to the surviving sepsis campaign's international guidelines for children.

RESULTS

Fifty-two (15.2%) children developed AKI, 132 (38.6%) were diagnosed with sepsis, and 30 (12.3%) died during the PICU stay. Both the initial and peak values of uCXCL10 remained independently associated with AKI, sepsis, septic AKI and PICU mortality. The AUCs of the initial uCXCL10 for predicting AKI, sepsis, septic AKI and PICU mortality were 0.63 (0.53-0.72), 0.62 (0.56-0.68), 0.75 (0.64-0.87) and 0.77 (0.68-0.86), respectively. The AUCs for prediction by using peak uCXCL10 were as follows: AKI 0.65 (0.56-0.75), sepsis 0.63 (0.57-0.69), septic AKI 0.76 (0.65-0.87) and PICU mortality 0.84 (0.76-0.91).

CONCLUSIONS

Urinary CXCL10 is independently associated with AKI and sepsis and may be a potential indicator of septic AKI and PICU mortality in critically ill children.

IMPACT

Urinary CXC motif chemokine 10 (uCXCL10), as an inflammatory mediator, has been proposed to be a biomarker for AKI in a specific setting. AKI biomarkers are often susceptible to confounding factors, limiting their utility as a specific biomarker, especially in heterogeneous population. This study revealed that uCXCL10 levels are independently associated with increased risk for AKI, sepsis, septic AKI and PICU mortality. A higher uCXCL10 may be predictive of septic AKI and PICU mortality in critically ill children.

Biodetection Techniques for Quantification of Chemokines

Chemokines are a class of cytokine whose special properties, together with their involvement and relevant role in various diseases, make them a restricted group of biomarkers suitable for diagnosis and monitoring. Despite their importance, biodetection techniques dedicated to the selective determination of one or more chemokines are very scarce. For some years now, the critical diagnosis of inflammatory diseases by detecting both cytokine and chemokine biomarkers, has had a strong impact on the development of multiple detection platforms. However, it would be desirable to implement methodologies with a higher degree of selectivity for chemokines, in order to provide more precise information. In addition, better development of biosensor technology applied to this specific field would make it possible to address the main challenges of detection methods for several diseases with a high incidence in the population, avoiding high costs and low sensitivity. Taking this into account, this review aims to present the state of the art of chemokine biodetection techniques and emphasize the role of these systems in the prevention, monitoring and treatment of various diseases associated with chemokines as a starting point for future developments that are also analyzed throughout the article.

Performance evaluation of presepsin using a Sysmex HISCL ‐5000 analyzer and determination of reference interval

Background

Analytical evaluation of newly developed presepsin by a Sysmex HISCL‐5000 (Sysmex, Japan) automated immune analyzer was performed.

Methods

For evaluation, sepsis patient samples were collected before treatment in an emergency department. Precision, linearity, limit of blank/limit of detection, method comparisons, and reference intervals were evaluated. Method comparisons were performed using a PATHFAST immune analyzer (LSI Medience Corporation, Japan).

Results

Precision using a 20x2x2 protocol for low (306 pg/mL) and high (1031 pg/mL) levels resulted in within‐laboratory standard deviation (95% confidence interval [CI]) and coefficient of variation (CV) %, which were as follows: 15.3 (13.1–18.7), 5.5% and 47.7, (40.5–58.1), 6.4%, respectively. Linearity using patient samples and calibrators were measured from 201 to 16,177 and 188 to 30,000 pg/mL, respectively. The regression equation was y = −23.2 + 1.008x (SE = 162.4) for low levels and y = 779.9 + 1.006x (SE = 668) for high levels. Method comparison by Passing–Bablock analysis was as follows: y = −209.77 + 1.047x (S_yx = 335.3). The correlation coefficient (95% CI) was 0.869 (0.772–0.927) with statistical significance (p < 0.001). Reference intervals from 120 normal healthy subjects showed that 300 pg/mL was the cut off. Presepsin tended to show a higher value at higher ages and in males. Presepsin showed correlation with some parameters, and the correlation coefficient (p value) were as follows: hematocrit, 0.198 (0.03); eGFR (CKD‐EPI), −0.240 (0.0129); MDRD‐eGFR, −0.194 (0.048), respectively.

Conclusion

Presepsin measurement by HISCL‐5000 showed reliable performance. Further clinical studies are required for the diagnosis and prognosis of sepsis.

Inflammatory mediators of cytokines and chemokines in sepsis: From bench to bedside

Background: Sepsis is a serious clinical condition characterized by damage to the immune system as a result of an uncontrolled response to infection. Septic patients show complications such as fever, cardiovascular shock, and/or systemic organ failure. Acute organ failure formed in sepsis mostly affects the respiratory and cardiovascular systems. In sepsis, responses including pro-inflammatory and anti-inflammatory processes in addition to the Toll-Like Receptor 4 (TLR4) signals leading to the release of inflammatory mediators have been suggested to be fundamental pathways in the pathophysiology of sepsis. Purpose: In this context, unregulated levels of sepsis-associated inflammatory mediators may increase the risk of mortality. In sepsis, infection-induced pathogens lead to a systemic inflammatory response. These systemic responses may contribute to septic shock and organ dysfunction. In the unfavorable clinical course of sepsis, an uncontrolled inflammatory response is observed. Accordingly, the mechanism of inflammatory mediators such as cytokines and chemokines in sepsis might increase. Neurotransmitters and gene regulators affect inflammatory mediators and control the inflammatory response. In this review, we aimed to show the new therapeutic targets in sepsis treatment with current studies. New clinical implications targeting inflammatory mediators in high mortality affected by the uncontrolled inflammatory response in sepsis can contribute to the understanding of the symptoms.

Magnolol protects against acute gastrointestinal injury in sepsis by down-regulating regulated on activation, normal T-cell expressed and secreted

BACKGROUND

Sepsis is a major medical challenge. Magnolol is an active constituent of Houpu that improves tissue function and exerts strong anti-endotoxin and anti-inflammatory effects, but the mechanism by which it reduces intestinal inflammation in sepsis is yet unclear.

AIM

To assess the protective effect of magnolol on intestinal mucosal epithelial cells in sepsis and elucidate the underlying mechanisms.

METHODS

Enzyme-linked immunosorbent assay was used to measure tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), IL-6, and regulated on activation, normal T-cell expressed and secreted (RANTES) levels in serum and ileal tissue in animal studies. The histopathological changes of the ileal mucosa in different groups were observed under a microscope. Cell Counting Kit-8 and cell permeability assays were used to determine the concentration of drug-containing serum that did not affect the activity of Caco2 cells but inhibited lipopolysaccharide (LPS)-induced decrease in permeability. Immunofluorescence and Western blot assays were used to detect the levels of RANTES, inhibitor of nuclear factor kappa-B kinase β (IKKβ), phosphorylated IKKβ (p-IKKβ), inhibitor of nuclear factor kappa-B kinase α (IκBα), p65, and p-p65 proteins in different groups in vitro.

RESULTS

In rats treated with LPS by intravenous tail injection in the presence or absence of magnolol, magnolol inhibited the expression of proinflammatory cytokines, IL-1β, IL-6, and TNF-α in a dose-dependent manner. In addition, magnolol suppressed the production of RANTES in LPS-stimulated sepsis rats. Moreover, in vitro studies suggested that magnolol inhibited the increase of p65 nucleation, thereby markedly downregulating the production of the phosphorylated form of IKKβ in LPS-treated Caco2 cells. Specifically, magnolol inhibited the translocation of the transcription factor nuclear factor-kappa B (NF-κB) from the cytosol into the nucleus and down-regulated the expression level of the chemokine RANTES in LPS-stimulated Caco2 cells.

CONCLUSION

Magnolol down-regulates RANTES levels by inhibiting the LPS/NF-κB signaling pathways, thereby suppressing IL-1β, IL-6, and TNF-α expression to alleviate the mucosal barrier dysfunction in sepsis.

Association between cytokine levels, sepsis severity and clinical outcomes in sepsis: a quantitative systematic review protocol

INTRODUCTION

It is widely assumed that sepsis is a life-threatening systemic inflammation caused by a dysregulated host response to infection mediated by an increase in multiple proinflammatory cytokines. The levels of key proinflammatory cytokines tumour necrosis factor, interleukin-1β and interferon γ are poorly characterised during sepsis. We believe this project will produce a 'gold-standard' document to which other reports on cytokine levels will be compared. The objective of this systematic review will be to identify key cytokine circulating levels in patients with sepsis and assess the association between these levels and morbidity and mortality outcomes related to sepsis.

METHODS AND ANALYSIS

We would include reports of any design except for case reports. Sepsis patients will comprise those with a diagnosis of sepsis, severe sepsis or septic shock. The primary exposure is levels of three proinflammatory cytokines. The primary outcome is mortality at 28 or 30 days. Study subjects can be of any age, sex or ethnicity. Studies will be restricted to the English language. Medline, Embase, Cochrane Library and Web of Science Core Collection will be searched for eligible studies. A database search will include studies from 1985 to May 2020. Two reviewers will independently screen and select studies, assess methodological quality and extract data. A meta-analysis will be performed, if possible, and the Grading of Recommendations Assessment Development and Evaluation Summary of Findings presented.

ETHICS AND DISSEMINATION

Formal ethical approval is not required as data will be extracted from existing literature. This systematic review will be disseminated through a peer-reviewed publication and at conference meetings.

PROSPERO REGISTRATION NUMBER

CRD42020179800.

Immune gene expression networks in sepsis: A network biology approach

To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.

The clinical outcomes and biomarker features of severe sepsis/septic shock with severe neutropenia: a retrospective cohort study

BACKGROUND

Severe sepsis/septic shock with severe neutropenia often leads to poor prognosis. However, it is unknown if severe neutropenia is associated with different clinical outcomes and biomarker features in severe sepsis/septic patients.

METHODS

This retrospective cohort study enrolled 141 severe sepsis/septic shock patients admitted to intensive care unit of Shanghai Children's Medical Center between January 2015 and November 2019. Patients were followed up for the development of ventilation support, the use of vasoactive drugs, continuous renal replacement therapy (CRRT) procedure, and mortality. Biomarkers that reflect the level of inflammation in severe sepsis/septic shock patients with neutropenia were compared to that in patients without neutropenia.

RESULTS

Of 141 patients enrolled, 54 patients suffered from severe sepsis/septic shock with severe neutropenia. In patients with severe sepsis/septic shock, severe neutropenia as a complication was an independent risk factor for the use of vasoactive drugs (RR 9.796; 95% CI: 3.774, 25.429; P<0.001), but not for ventilation support (RR 0.157; 95% CI: 0.06, 0.414; P<0.001), CRRT procedure (RR 1.032; 95% CI: 0.359, 2.969; P=0.953) or 28-day mortality (RR 1.405; 95% CI: 0.533, 3.708; P=0.492). Severe sepsis/septic patients with severe neutropenia had a higher plasma level of the following biomarkers: c-reaction protein (CRP) (180.5 vs. 121 mg/mL, P<0.001), procalcitonin (PCT) (12.15 vs. 2.7 ng/mL; P=0.005), interleukin (IL)-6 (316.83 vs. 55.77 pg/mL, P<0.001), IL-10 (39.165 vs. 10.09 pg/mL, P<0.001), interferon (IFN)-γ (6.155 vs. 3.71 pg/mL, P=0.016), and the percentage of regulatory T cells (Tregs) (2.7% vs. 2.09%, P=0.003). Based on the receiver operating characteristic curves, IL-10 exhibited high specificity (79.4%) in evaluating the prognosis of septic patients with neutropenia.

CONCLUSIONS

In patients with severe sepsis/septic shock, being complicated with severe neutropenia is associated with higher proportion of using vasoactive drugs, and those patients tend to have higher plasma levels of IL-6, IL-10, IFN-γ and percentage of Treg.

Clearance of inflammatory cytokines in patients with septic acute kidney injury during renal replacement therapy using the EMiC2 filter (Clic-AKI study)

BACKGROUND

The EMiC2 membrane is a medium cut-off haemofilter (45 kiloDalton). Little is known regarding its efficacy in eliminating medium-sized cytokines in sepsis. This study aimed to explore the effects of continuous veno-venous haemodialysis (CVVHD) using the EMiC2 filter on cytokine clearance.

METHODS

This was a prospective observational study conducted in critically ill patients with sepsis and acute kidney injury requiring kidney replacement therapy. We measured concentrations of 12 cytokines [Interleukin (IL) IL-1β, IL-1α, IL-2, IL-4, IL-6, IL-8, IL-10, interferon (IFN)-γ, tumour necrosis factor (TNF)-α, vascular endothelial growth factor, monocyte chemoattractant protein (MCP)-1, epidermal growth factor (EGF)] in plasma at baseline (T0) and pre- and post-dialyzer at 1, 6, 24, and 48 h after CVVHD initiation and in the effluent fluid at corresponding time points. Outcomes were the effluent and adsorptive clearance rates, mass balances, and changes in serial serum concentrations.

RESULTS

Twelve patients were included in the final analysis. All cytokines except EGF concentrations declined over 48 h (p < 0.001). The effluent clearance rates were variable and ranged from negligible values for IL-2, IFN-γ, IL-1α, IL-1β, and EGF, to 19.0 ml/min for TNF-α. Negative or minimal adsorption was observed. The effluent and adsorptive clearance rates remained steady over time. The percentage of cytokine removal was low for most cytokines throughout the 48-h period.

CONCLUSION

EMiC2-CVVHD achieved modest removal of most cytokines and demonstrated small to no adsorptive capacity despite a decline in plasma cytokine concentrations. This suggests that changes in plasma cytokine concentrations may not be solely influenced by extracorporeal removal.

TRIAL REGISTRATION

NCT03231748, registered on 27th July 2017.

Identification of the Diagnostic Signature of Sepsis Based on Bioinformatic Analysis of Gene Expression and Machine Learning

BACKGROUND

Sepsis is a life-threatening disease caused by the dysregulated host response to the infection and the major cause of death of patients in the intensive care unit (ICU).

OBJECTIVE

Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other factors. Hence, the analysis of mass data by bioinformatics tools and machine learning is a promising method for exploring early diagnosis.

METHODS

We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO) and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment analysis based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI) network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type.

RESULTS

Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub genes was used to assess GSE95233 and GSE95233 datasets, and the area under the curve (AUC) of ROC was 0.900 and 0.7988, respectively, which confirmed the efficacy of this model.

CONCLUSION

The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study may provide promising diagnostic methods for sepsis.

Plasma cytokine profiles in very preterm infants with late-onset sepsis

INTRODUCTION

Deficiencies in innate immune responses may contribute to the increased susceptibility to infection in preterm infants. In vivo cytokine profiles in response to sepsis in very preterm infants are not fully understood.

AIMS

To characterise plasma pro- and anti-inflammatory cytokine concentrations and pre-defined ratios in very preterm infants with late-onset sepsis (LOS).

METHODS

In this observational study, peripheral blood samples were collected at the time of evaluation for suspected LOS from 31 preterm infants (<30 weeks gestational age). Plasma cytokine concentrations were determined by 12-plex immunoassay.

RESULTS

IL-10, IFN-γ, IL-12p70, IP-10, IL-6 and CCL2 were elevated in the majority infants with LOS (n = 12) compared to those without LOS (n = 19). There was no difference in TNF-α, IL-1β, IL-17AF, IL-8 and IL-15 concentrations between groups. IL-10/TNF-α ratios were increased, while CCL2/IL-10 and IL-12p70/IL-10 ratios were decreased in infants with LOS compared to those without.

CONCLUSION

Very preterm infants have a marked innate inflammatory response at the time of LOS. The increase in IL-10/TNF-α ratio may indicate early immune hypo-responsiveness. Longitudinal studies with a larger number of participants are required to understand immune responses and clinical outcomes following LOS in preterm infants.

Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis

Background

This work aimed to screen key biomarkers related to sepsis progression by bioinformatics analyses.

Material/Methods

The microarray datasets of blood and neutrophils from patients with sepsis or septic shock were downloaded from Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) from 4 groups (sepsis versus normal blood samples; septic shock versus normal blood samples; sepsis neutrophils versus normal controls and septic shock neutrophils versus controls) were respectively identified followed by functional analyses. Subsequently, protein–protein network was constructed, and key functional sub-modules were extracted. Finally, receiver operating characteristic analysis was conducted to evaluate diagnostic values of key genes.

Results

There were 2082 DEGs between blood samples of sepsis patients and controls, 2079 DEGs between blood samples of septic shock patients and healthy individuals, 6590 DEGs between neutrophils from sepsis and controls, and 1056 DEGs between neutrophils from septic shock patients and normal controls. Functional analysis showed that numerous DEGs were significantly enriched in ribosome-related pathway, cell cycle, and neutrophil activation involved in immune response. In addition, TRIM25 and MYC acted as hub genes in protein–protein interaction (PPI) analyses of DEGs from microarray datasets of blood samples. Moreover, MYC (AUC=0.912) and TRIM25 (AUC=0.843) had great diagnostic values for discriminating septic shock blood samples and normal controls. RNF4 was a hub gene from PPI analyses based on datasets from neutrophils and RNF4 (AUC=0.909) was capable of distinguishing neutrophil samples from septic shock samples and controls.

Conclusions

Our findings identified several key genes and pathways related to sepsis development.