Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach

Sepsis endotypes identified by host gene expression across global cohorts

BACKGROUND

Sepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups.

METHODS

Here, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494).

RESULTS

We identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis.

CONCLUSIONS

Our analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

Using Trajectories of Bedside Vital Signs to Identify COVID-19 Subphenotypes

BACKGROUND

Trajectories of bedside vital signs have been used to identify sepsis subphenotypes with distinct outcomes and treatment responses. The objective of this study was to validate the vitals trajectory model in a multicenter cohort of patients hospitalized with COVID-19 and to evaluate the clinical characteristics and outcomes of the resulting subphenotypes.

RESEARCH QUESTION

Can the trajectory of routine bedside vital signs identify COVID-19 subphenotypes with distinct clinical characteristics and outcomes?

STUDY DESIGN AND METHODS

The study included adult patients admitted with COVID-19 to four academic hospitals in the Emory Healthcare system between March 1, 2020, and May 31, 2022. Using a validated group-based trajectory model, we classified patients into previously defined vital sign trajectories using oral temperature, heart rate, respiratory rate, and systolic and diastolic BP measured in the first 8 h of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes. Heterogeneity of treatment effect to tocilizumab was evaluated.

RESULTS

The 7,065 patients with hospitalized COVID-19 were classified into four subphenotypes: group A (n = 1,429, 20%)-high temperature, heart rate, respiratory rate, and hypotensive; group B (1,454, 21%)-high temperature, heart rate, respiratory rate, and hypertensive; group C (2,996, 42%)-low temperature, heart rate, respiratory rate, and normotensive; and group D (1,186, 17%)-low temperature, heart rate, respiratory rate, and hypotensive. Groups A and D had higher ORs of mechanical ventilation, vasopressors, and 30-day inpatient mortality (P < .001). On comparing patients receiving tocilizumab (n = 55) with those who met criteria for tocilizumab but were admitted before its use (n = 461), there was significant heterogeneity of treatment effect across subphenotypes in the association of tocilizumab with 30-day mortality (P = .001).

INTERPRETATION

By using bedside vital signs available in even low-resource settings, we found novel subphenotypes associated with distinct manifestations of COVID-19, which could lead to preemptive and targeted treatments.

Identification of ZIP8-induced ferroptosis as a major type of cell death in monocytes under sepsis conditions

Sepsis is a heterogenous syndrome with concurrent hyperinflammation and immune suppression. A prominent feature of immunosuppression during sepsis is the dysfunction and loss of monocytes; however, the major type of cell death contributing to this depletion, as well as its underlying molecular mechanisms, are yet to be identified. In this study, we confirmed the monocyte loss in septic patients based on a pooled gene expression data of periphery leukocytes. Using the collected reference gene sets from databases and published studies, we identified ferroptosis with a greater capacity to distinguish between sepsis and control samples than other cell death types. Further investigation on the molecular drivers, by a genetic algorithm-based feature selection and a weighted gene co-expression network analysis, revealed that zrt-/irt-like protein 8 (ZIP8), encoded by SLC39A8, was closely associated with ferroptosis of monocytes during sepsis. We validated the increase of ZIP8 of monocytes with in vivo and in vitro experiments. The in vitro studies also showed that downregulation of ZIP8 alleviated the lipopolysaccharide-induced lipid peroxidation, as well as restoring the reduction of GPX4, FTH1 and xCT. These findings suggest that ferroptosis might be a key factor in the loss of monocytes during sepsis, and that the heightened expression of ZIP8 may facilitate this progression.

Alterations in leukocyte DNA methylome are associated to immunosuppression in severe clinical phenotypes of septic patients

Introduction

Sepsis patients experience a complex interplay of host pro- and anti-inflammatory processes which compromise the clinical outcome. Despite considering the latest clinical and scientific research, our comprehension of the immunosuppressive events in septic episodes remains incomplete. Additionally, a lack of data exists regarding the role of epigenetics in modulating immunosuppression, subsequently impacting patient survival.

Methods

To advance the current understanding of the mechanisms underlying immunosuppression, in this study we explored the dynamics of DNA methylation using the Infinium Methylation EPIC v1.0 BeadChip Kit in leukocytes from patients suffering from sepsis, septic shock, and critically ill patients as controls, within the first 24 h after admission in the Intensive Care Unit of a tertiary hospital.

Results and discussion

Employing two distinct analysis approaches (DMRcate and mCSEA) in comparing septic shock and critically ill patients, we identified 1,256 differentially methylated regions (DMRs) intricately linked to critical immune system pathways. The examination of the top 100 differentially methylated positions (DMPs) between septic shock and critically ill patients facilitated a clear demarcation among the three patient groups. Notably, the top 6,657 DMPs exhibited associations with organ dysfunction and lactate levels. Among the individual genes displaying significant differential methylation, IL10, TREM1, IL1B, and TNFAIP8 emerged with the most pronounced methylation alterations across the diverse patient groups when subjected to DNA bisulfite pyrosequencing analysis. These findings underscore the dynamic nature of DNA methylation profiles, highlighting the most pronounced alterations in patients with septic shock, and revealing their close association with the disease.

Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis

BACKGROUND

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.

A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY

ABSTRACT

Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the intensive care unit to elucidate temporally stable gene-expression markers between sepsis survivors and nonsurvivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients' transcriptome was sampled at alternate time points, with an area under the curve of 0.89 (95% CI, 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1) that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

ATF4 knockdown in macrophage impairs glycolysis and mediates immune tolerance by targeting HK2 and HIF-1α ubiquitination in sepsis

Strengthened glycolysis is crucial for the macrophage pro-inflammatory response during sepsis. Activating transcription factor 4 (ATF4) plays an important role in regulating glucose and lipid metabolic homeostasis in hepatocytes and adipocytes. However, its immunometabolic role in macrophage during sepsis remains largely unknown. In the present study, we found that the expression of ATF4 in peripheral blood mononuclear cells (PBMCs) was increased and associated with glucose metabolism in septic patients. Atf4 knockdown specifically decreased LPS-induced spleen macrophages and serum pro-inflammatory cytokines levels in mice. Moreover, Atf4 knockdown partially blocked LPS-induced pro-inflammatory cytokines, lactate accumulation and glycolytic capacity in RAW264.7. Mechanically, ATF4 binds to the promoter region of hexokinase II (HK2), and interacts with hypoxia inducible factor-1α (HIF-1α) and stabilizes HIF-1α through ubiquitination modification in response to LPS. Furthermore, ATF4-HIF-1α-HK2-glycolysis axis launches pro-inflammatory response in macrophage depending on the activation of mammalian target of rapamycin (mTOR). Importantly, Atf4 overexpression improves the decreased level of pro-inflammatory cytokines and lactate secretion and HK2 expression in LPS-induced tolerant macrophages. In conclusion, we propose a novel function of ATF4 as a crucial glycolytic activator contributing to pro-inflammatory response and improving immune tolerant in macrophage involved in sepsis. So, ATF4 could be a potential new target for immunotherapy of sepsis.

Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.

Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection

OBJECTIVE

Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes.

MATERIALS AND METHODS

Patients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in subphenotype clusters, trajectory patterns, and subphenotype associations with clinical outcomes and treatment responses.

RESULTS

There were 12 473 patients in training and 8256 patients in validation cohorts. DTW-HC, DTW-PAM, and GBTM models resulted in 4 consistent vitals trajectory patterns with significant agreement in clustering (71-80% agreement, P < .001): group A was hyperthermic, tachycardic, tachypneic, and hypotensive. Group B was hyperthermic, tachycardic, tachypneic, and hypertensive. Groups C and D had lower temperatures, heart rates, and respiratory rates, with group C normotensive and group D hypotensive. Group A had higher odds ratio of 30-day inpatient mortality (P < .01) and group D had significant mortality benefit from balanced crystalloids compared to saline (P < .01) in all 3 models.

DISCUSSION

DTW- and GBTM-based clustering algorithms applied to vital signs in infected patients identified consistent subphenotypes with distinct clinical outcomes and treatment responses.

CONCLUSION

Time series clustering with distinct computational approaches demonstrate similar performance and significant agreement in the resulting subphenotypes.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Development and validation of novel sepsis subphenotypes using trajectories of vital signs

PURPOSE

Sepsis is a heterogeneous syndrome and identification of sub-phenotypes is essential. This study used trajectories of vital signs to develop and validate sub-phenotypes and investigated the interaction of sub-phenotypes with treatment using randomized controlled trial data.

METHODS

All patients with suspected infection admitted to four academic hospitals in Emory Healthcare between 2014-2017 (training cohort) and 2018-2019 (validation cohort) were included. Group-based trajectory modeling was applied to vital signs from the first 8 h of hospitalization to develop and validate vitals trajectory sub-phenotypes. The associations between sub-phenotypes and outcomes were evaluated in patients with sepsis. The interaction between sub-phenotype and treatment with balanced crystalloids versus saline was tested in a secondary analysis of SMART (Isotonic Solutions and Major Adverse Renal Events Trial).

RESULTS

There were 12,473 patients with suspected infection in training and 8256 patients in validation cohorts, and 4 vitals trajectory sub-phenotypes were found. Group A (N = 3483, 28%) were hyperthermic, tachycardic, tachypneic, and hypotensive. Group B (N = 1578, 13%) were hyperthermic, tachycardic, tachypneic (not as pronounced as Group A) and hypertensive. Groups C (N = 4044, 32%) and D (N = 3368, 27%) had lower temperatures, heart rates, and respiratory rates, with Group C normotensive and Group D hypotensive. In the 6,919 patients with sepsis, Groups A and B were younger while Groups C and D were older. Group A had the lowest prevalence of congestive heart failure, hypertension, diabetes mellitus, and chronic kidney disease, while Group B had the highest prevalence. Groups A and D had the highest vasopressor use (p < 0.001 for all analyses above). In logistic regression, 30-day mortality was significantly higher in Groups A and D (p < 0.001 and p = 0.03, respectively). In the SMART trial, sub-phenotype significantly modified treatment effect (p = 0.03). Group D had significantly lower odds of mortality with balanced crystalloids compared to saline (odds ratio (OR) 0.39, 95% confidence interval (CI) 0.23-0.67, p < 0.001).

CONCLUSION

Sepsis sub-phenotypes based on vital sign trajectory were consistent across cohorts, had distinct outcomes, and different responses to treatment with balanced crystalloids versus saline.

Sleep in the intensive care unit

PURPOSE OF REVIEW

Sleep is particularly important for critically ill patients. Here, we review the latest evidence on how sleep and circadian disruption in the intensive care unit (ICU) affects physiology and clinical outcomes, as well as the most recent advances in sleep and circadian rhythm promoting interventions including therapeutics.

RECENT FINDINGS

On a molecular level, clock genes dysrhythmia and altered immunity are clearly linked, particularly in sepsis. Melatonin may also be associated with insulin sensitivity in ICU patients. Clinically, changes in sleep architecture are associated with delirium, and sleep-promoting interventions in the form of multifaceted care bundles may reduce its incidence. Regarding medications, one recent randomized controlled trial (RCT) on melatonin showed no difference in sleep quality or incidence of delirium.

SUMMARY

Further investigation is needed to establish the clinical relevance of sleep and circadian disruption in the ICU. For interventions, standardized protocols of sleep promotion bundles require validation by larger multicenter trials. Administratively, such protocols should be individualized to both organizational and independent patient needs. Incorporating pharmacotherapy such as melatonin and nocturnal dexmedetomidine requires further evaluation in large RCTs.

Identification of immune-related endoplasmic reticulum stress genes in sepsis using bioinformatics and machine learning

Background

Sepsis-induced apoptosis of immune cells leads to widespread depletion of key immune effector cells. Endoplasmic reticulum (ER) stress has been implicated in the apoptotic pathway, although little is known regarding its role in sepsis-related immune cell apoptosis. The aim of this study was to develop an ER stress-related prognostic and diagnostic signature for sepsis through bioinformatics and machine learning algorithms on the basis of the differentially expressed genes (DEGs) between healthy controls and sepsis patients.

Methods

The transcriptomic datasets that include gene expression profiles of sepsis patients and healthy controls were downloaded from the GEO database. The immune-related endoplasmic reticulum stress hub genes associated with sepsis patients were identified using the new comprehensive machine learning algorithm and bioinformatics analysis which includes functional enrichment analyses, consensus clustering, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. Next, the diagnostic model was established by logistic regression and the molecular subtypes of sepsis were obtained based on the significant DEGs. Finally, the potential diagnostic markers of sepsis were screened among the significant DEGs, and validated in multiple datasets.

Results

Significant differences in the type and abundance of infiltrating immune cell populations were observed between the healthy control and sepsis patients. The immune-related ER stress genes achieved strong stability and high accuracy in predicting sepsis patients. 10 genes were screened as potential diagnostic markers for sepsis among the significant DEGs, and were further validated in multiple datasets. In addition, higher expression levels of SCAMP5 mRNA and protein were observed in PBMCs isolated from sepsis patients than healthy donors (n = 5).

Conclusions

We established a stable and accurate signature to evaluate the diagnosis of sepsis based on the machine learning algorithms and bioinformatics. SCAMP5 was preliminarily identified as a diagnostic marker of sepsis that may affect its progression by regulating ER stress.

Methylomic and transcriptomic characterization of postoperative systemic inflammatory dysregulation

In this study, we define and validate a state of postoperative systemic inflammatory dysregulation (PSID) based on postoperative phenotypic extremes of plasma C-reactive protein concentration following major abdominal surgery. PSID manifested clinically with significantly higher rates of sepsis, complications, longer hospital stays and poorer short, and long-term outcomes. We hypothesized that PSID will be associated with, and potentially predicted by, altered patterns of genome-wide peripheral blood mononuclear cell differential DNA methylation and gene expression. We identified altered DNA methylation and differential gene expression in specific immune and metabolic pathways during PSID. Our findings suggest that dysregulation results in, or from, dramatic changes in differential DNA methylation and highlights potential targets for early detection and treatment. The combination of altered DNA methylation and gene expression suggests that dysregulation is mediated at multiple levels within specific gene sets and hence, nonspecific anti-inflammatory treatments such as corticosteroids alone are unlikely to represent an effective therapeutic strategy.

Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures

Purpose

Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis.

Methods

Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148).

Results

Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703).

Conclusion

Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06769-z.

Enriched environment causes epigenetic changes in hippocampus and improves long-term cognitive function in sepsis

Sepsis is defined as a life-threatening organ dysfunction caused by an inappropriate host response to infection. The presence of oxidative stress and inflammatory mediators in sepsis leads to dysregulated gene expression, leading to a hyperinflammatory response. Environmental conditions play an important role in various pathologies depending on the stimulus it presents. A standard environment condition (SE) may offer reduced sensory and cognitive stimulation, but an enriched environment improves spatial learning, prevents cognitive deficits induced by disease stress, and is an important modulator of epigenetic enzymes. The study evaluated the epigenetic alterations and the effects of the environmental enrichment (EE) protocol in the brain of animals submitted to sepsis by cecal ligation and perforation (CLP). Male Wistar rats were divided into sham and CLP at 24 h, 72 h, 10 days and 30 days after sepsis. Other male Wistar rats were distributed in a SE or in EE for forty-five days. Behavioral tests, analysis of epigenetic enzymes:histone acetylase (HAT), histone deacetylase (HDAC) and DNA methyltransferase (DNMT), biochemical and synaptic plasticity analyzes were performed. An increase in HDAC and DNMT activities was observed at 72 h, 10 days and 30 days. There was a positive correlation between epigenetic enzymes DNMT and HDAC 24 h, 10 days and 30 days. After EE, HDAC and DNMT enzyme activity decreased, cognitive impairment was reversed, IL1-β levels decreased and there was an increase in PSD-95 levels in the hippocampus. Interventions in environmental conditions can modulate the outcomes of long-term cognitive consequences associated with sepsis, supporting the idea of the potential benefits of EE.

Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis

Background

Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fully-automated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients.

Methods

Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.

Results

We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19.2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0.710 (CI 0.652-0.768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies (n = 7,122 genes), with an AUROC = 0.755 (CI 0.697-0.813, p = 0.286). In patients with gene expression data sampled at day 3 following admission or later, the IPP gene set had higher performance, with an AUROC = 0.804 (CI 0.643-0.964), while the total gene pool had an AUROC = 0.787 (CI 0.610-0.965, p = 0.811).

Conclusion

Using pooled publicly-available gene expression data from multiple cohorts, we showed that the IPP gene set, an immune-related transcriptomics signature conveys relevant information to predict 30-day mortality when sampled at day 1 following admission. Our data also suggests that higher predictive performance could be obtained when assaying gene expression at later time points during the course of sepsis. Prospective studies are needed to confirm these findings using the IPP gene set on its dedicated measurement platform.

The immunomodulatory effects of social isolation in mice are linked to temperature control

Living in isolation is considered an emerging societal problem that negatively affects the physical wellbeing of its sufferers in ways that we are just starting to appreciate. This study investigates the immunomodulatory effects of social isolation in mice, utilising a two-week program of sole cage occupancy followed by the testing of immune-inflammatory resilience to bacterial sepsis. Our results revealed that mice housed in social isolation showed an increased ability to clear bacterial infection compared to control socially housed animals. These effects were associated with specific changes in whole blood gene expression profile and an increased production of classical pro-inflammatory cytokines. Interestingly, equipping socially isolated mice with artificial nests as a substitute for their natural huddling behaviour reversed the increased resistance to bacterial sepsis. Together these results suggest that the control of body temperature through social housing and huddling behaviour are important factors in the regulation of the host immune response to infection in mice and might provide another example of the many ways by which living conditions influence immunity.

Admixture Mapping of Sepsis in European Individuals With African Ancestries

Sepsis is a severe systemic inflammatory response to infections that is accompanied by organ dysfunction. Although the ancestral genetic background is a relevant factor for sepsis susceptibility, there is a lack of studies using the genetic singularities of a recently admixed population to identify loci involved in sepsis susceptibility. Here we aimed to discover new sepsis loci by completing the first admixture mapping study of sepsis in Canary Islanders, leveraging their distinctive genetic makeup as a mixture of Europeans and African ancestries. We used a case-control approach and inferred local ancestry blocks from genome-wide data from 113,414 polymorphisms genotyped in 343 patients with sepsis and 410 unrelated controls, all ascertained for grandparental origin in the Canary Islands (Spain). Deviations in local ancestries between cases and controls were tested using logistic regressions, followed by fine-mapping analyses based on imputed genotypes, in silico functional assessments, and gene expression analysis centered on the region of interest. The admixture mapping analysis detected that local European ancestry in a locus spanning 1.2 megabases of chromosome 8p23.1 was associated with sepsis (lowest p = 1.37 × 10⁻⁴; Odds Ratio [OR] = 0.51; 95%CI = 0.40–0.66). Fine-mapping studies prioritized the variant rs13249564 within intron 1 of MFHAS1 gene associated with sepsis (p = 9.94 × 10⁻⁴; OR = 0.65; 95%CI = 0.50–0.84). Functional and gene expression analyses focused on 8p23.1 allowed us to identify alternative genes with possible biological plausibility such as defensins, which are well-known effector molecules of innate immunity. By completing the first admixture mapping study of sepsis, our results revealed a new genetic locus (8p23.1) harboring a number of genes with plausible implications in sepsis susceptibility.

Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock

PURPOSE

Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock.

MATERIALS AND METHODS

We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock.

RESULTS

We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed.

CONCLUSIONS

Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.

Circadian regulation of innate immunity in animals and humans and implications for human disease

Circadian rhythms are 24-h oscillating variations in physiology generated by the core circadian clock. There is now a wide body of evidence showing circadian regulation of the immune system. Innate immune cells contain the molecular circadian clock which drives rhythmic responses, from the magnitude of the inflammatory response to the numbers of circulating immune cells varying throughout the day. This leads to rhythmic presentation of disease clinically, for example the classic presentation of nocturnal asthma or the sudden development of pulmonary oedema from acute myocardial infarction first thing in the morning.

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy results in a non-specific diagnosis, and to date, a standard diagnostic test to detect sepsis in patients remains lacking. Therefore, it is vital to identify sepsis-related diagnostic genes. This study aimed to conduct an integrated analysis to assess the immune scores of samples from patients diagnosed with sepsis and normal samples, followed by weighted gene co-expression network analysis (WGCNA) to identify immune infiltration-related genes and potential transcriptome markers in sepsis. Furthermore, gene regulatory networks were established to screen diagnostic markers for sepsis based on the protein-protein interaction networks involving these immune infiltration-related genes. Moreover, we integrated WGCNA with the support vector machine (SVM) algorithm to build a diagnostic model for sepsis. Results showed that the immune score was significantly lower in the samples from patients with sepsis than in normal samples. A total of 328 and 333 genes were positively and negatively correlated with the immune score, respectively. Using the MCODE plugin in Cytoscape, we identified four modules, and through functional annotation, we found that these modules were related to the immune response. Gene Ontology functional enrichment analysis showed that the identified genes were associated with functions such as neutrophil degranulation, neutrophil activation in the immune response, neutrophil activation, and neutrophil-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed the enrichment of pathways such as primary immunodeficiency, Th1- and Th2-cell differentiation, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Finally, we identified a four-gene signature, containing the hub genes LCK, CCL5, ITGAM, and MMP9, and established a model that could be used to diagnose patients with sepsis.

Protective Effects of Dexmedetomidine on Sepsis-Induced Vascular Leakage by Alleviating Ferroptosis via Regulating Metabolic Reprogramming

Introduction

Vascular leakage plays a vital role in sepsis-induced multi-organ dysfunction. Currently, no specific measures are available for vascular leakage. Ferroptosis, as a recently recognized form of cell death, plays a crucial role in cell dysfunction. It is still unknown whether ferroptosis participates in the occurrence of organ dysfunction following sepsis. Our previous study showed that dexmedetomidine (Dex) could alleviate sepsis-induced organ dysfunction. However, whether the mechanism is related to ferroptosis is not clear.

Methods

The publicly available datasets of septic patients were reanalyzed, and septic models in vivo and vitro by cecal ligation and puncture and lipopolysaccharide-stimulated vascular endothelial cells (VECs) were applied. The occurrence of ferroptosis in septic patients and rats was observed, and the protective effects of Dex on ferroptosis, and related mechanisms on regulating metabolic reprogramming and mitochondrial fission were further studied.

Results

The transcriptomics data of patients from the GEO database showed that ferroptosis was closely related to sepsis. Sepsis induced significant ferroptosis in VECs by metabolomics analysis. The level of lipid peroxidation was increased in VECs, and the mitochondrial cristae was decreased after sepsis. Metabolomics analysis showed that Dex activated the pentose phosphate pathway and increased glutathione in VECs via up-regulation of G6PD expression. Dex could antagonize sepsis-induced the decrease in the level of Nrf2. The Nrf2 inhibitor reversed the protective effect of Dex on ferroptosis. Further study showed that Dex significantly alleviated sepsis-induced mitochondrial over-division, improved mitochondrial function, and decreased ROS, further inhibiting the ferroptosis of VECs. Dex alleviated the permeability of vessels by reducing ferroptosis and enhanced the intercellular junction of VECs.

Conclusion

Dex protects vascular leakage following sepsis by inhibiting ferroptosis. The mechanism is mainly related to metabolic reprogramming via Nrf2 up-regulation and inhibition of mitochondrial fission.

Androgen Receptor Pathway Activity Assay for Sepsis Diagnosis and Prediction of Favorable Prognosis

Introduction: Sepsis is a life-threatening complication of a bacterial infection. It is hard to predict which patients with a bacterial infection will develop sepsis, and accurate and timely diagnosis as well as assessment of prognosis is difficult. Aside from antibiotics-based treatment of the causative infection and supportive measures, treatment options have remained limited. Better understanding of the immuno-pathophysiology of sepsis is expected to lead to improved diagnostic and therapeutic solutions. Functional activity of the innate (inflammatory) and adaptive immune response is controlled by a dedicated set of cellular signal transduction pathways, that are active in the various immune cell types. To develop an immune response-based diagnostic assay for sepsis and provide novel therapeutic targets, signal transduction pathway activities have been analyzed in whole blood samples from patients with sepsis. Methods: A validated and previously published set of signal transduction pathway (STP) assays, enabling determination of immune cell function, was used to analyze public Affymetrix expression microarray data from clinical studies containing data from pediatric and adult patients with sepsis. STP assays enable quantitative measurement of STP activity on individual patient sample data, and were used to calculate activity of androgen receptor (AR), estrogen receptor (ER), JAK-STAT1/2, JAK-STAT3, Notch, Hedgehog, TGFβ, FOXO-PI3K, MAPK-AP1, and NFκB signal transduction pathways. Results: Activity of AR and TGFβ pathways was increased in children and adults with sepsis. Using the mean plus two standard deviations of normal pathway activity (in healthy individuals) as threshold for abnormal STP activity, diagnostic assay parameters were determined. For diagnosis of pediatric sepsis, the AR pathway assay showed high sensitivity (77%) and specificity (97%), with a positive prediction value (PPV) of 99% and negative prediction value (NPV) of 50%. For prediction of favorable prognosis (survival), PPV was 95%, NPV was 21%. The TGFβ pathway activity assay performed slightly less for diagnosing sepsis, with a sensitivity of 64% and specificity of 98% (PPV 99%, NPV 39%). Conclusion: The AR and TGFβ pathways have an immunosuppressive role, suggesting a causal relation between increased pathway activity and sepsis immunopathology. STP assays have been converted to qPCR assays for further evaluation of clinical utility for sepsis diagnosis and prediction of prognosis, as well as for prediction of risk at developing sepsis in patients with a bacterial infection. STPs may present novel therapeutic targets in sepsis.

Reverse Engineering of the Pediatric Sepsis Regulatory Network and Identification of Master Regulators

Sepsis remains a leading cause of death in ICUs all over the world, with pediatric sepsis accounting for a high percentage of mortality in pediatric ICUs. Its complexity makes it difficult to establish a consensus on genetic biomarkers and therapeutic targets. A promising strategy is to investigate the regulatory mechanisms involved in sepsis progression, but there are few studies regarding gene regulation in sepsis. This work aimed to reconstruct the sepsis regulatory network and identify transcription factors (TFs) driving transcriptional states, which we refer to here as master regulators. We used public gene expression datasets to infer the co-expression network associated with sepsis in a retrospective study. We identified a set of 15 TFs as potential master regulators of pediatric sepsis, which were divided into two main clusters. The first cluster corresponded to TFs with decreased activity in pediatric sepsis, and GATA3 and RORA, as well as other TFs previously implicated in the context of inflammatory response. The second cluster corresponded to TFs with increased activity in pediatric sepsis and was composed of TRIM25, RFX2, and MEF2A, genes not previously described as acting in a coordinated way in pediatric sepsis. Altogether, these results show how a subset of master regulators TF can drive pathological transcriptional states, with implications for sepsis biology and treatment.

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

Long non-coding RNA pairs to assist in diagnosing sepsis

Background

Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.

Results

Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA_i > lncRNA_j in sepsis patients and lncRNA_i < lncRNA_j in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.

Conclusion

Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07576-4.

Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Background

Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI).

Methods

Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI.

Results

We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future.

Conclusions

Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00176-y.

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.

DNA Methylation Analysis to Unravel Altered Genetic Pathways Underlying Early Onset and Late Onset Neonatal Sepsis. A Pilot Study

Background: Neonatal sepsis is a systemic condition widely affecting preterm infants and characterized by pro-inflammatory and anti-inflammatory responses. However, its pathophysiology is not yet fully understood. Epigenetics regulates the immune system, and its alteration leads to the impaired immune response underlying sepsis. DNA methylation may contribute to sepsis-induced immunosuppression which, if persistent, will cause long-term adverse effects in neonates.

Objective: To analyze the methylome of preterm infants in order to determine whether there are DNA methylation marks that may shed light on the pathophysiology of neonatal sepsis.

Design: Prospective observational cohort study performed in the neonatal intensive care unit (NICU) of a tertiary care center.

Patients: Eligible infants were premature ≤32 weeks admitted to the NICU with clinical suspicion of sepsis. The methylome analysis was performed in DNA from blood using Infinium Human Methylation EPIC microarrays to uncover methylation marks.

Results: Methylation differential analysis revealed an alteration of methylation levels in genomic regions involved in inflammatory pathways which participate in both the innate and the adaptive immune response. Moreover, differences between early and late onset sepsis as compared to normal controls were assessed.

Conclusions: DNA methylation marks can serve as a biomarker for neonatal sepsis and even contribute to differentiating between early and late onset sepsis.

Systemic viral spreading and defective host responses are associated with fatal Lassa fever in macaques

Lassa virus (LASV) is endemic in West Africa and induces a viral hemorrhagic fever (VHF) with up to 30% lethality among clinical cases. The mechanisms involved in control of Lassa fever or, in contrast, the ensuing catastrophic illness and death are poorly understood. We used the cynomolgus monkey model to reproduce the human disease with asymptomatic to mild or fatal disease. After initial replication at the inoculation site, LASV reached the secondary lymphoid organs. LASV did not spread further in nonfatal disease and was rapidly controlled by balanced innate and T-cell responses. Systemic viral dissemination occurred during severe disease. Massive replication, a cytokine/chemokine storm, defective T-cell responses, and multiorgan failure were observed. Clinical, biological, immunological, and transcriptomic parameters resembled those observed during septic-shock syndrome, suggesting that similar pathogenesis is induced during Lassa fever. The outcome appears to be determined early, as differentially expressed genes in PBMCs were associated with fatal and non-fatal Lassa fever outcome very early after infection. These results provide a full characterization and important insights into Lassa fever pathogenesis and could help to develop early diagnostic tools.

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.

Identification of Potential Biomarkers and Immune Features of Sepsis Using Bioinformatics Analysis

Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.

Dynamical modeling of pro- and anti-inflammatory cytokines in the early stage of septic shock

A dynamical model of the pathophysiological behaviors of IL18 and IL10 cytokines with their receptors is tested against data for the case of early sepsis. The proposed approach considers the surroundings (organs and bone marrow) and the different subsystems (cells and cyctokines). The interactions between blood cells, cytokines and the surroundings are described via mass balances. Cytokines are adsorbed onto associated receptors at the cell surface. The adsorption is described by the Langmuir model and gives rise to the production of more cytokines and associated receptors inside the cell. The quantities of pro and anti-inflammatory cytokines present in the body are combined to give global information via an inflammation level function which describes the patient’s state. Data for parameter estimation comes from the Sepsis 48 H database. Comparisons between patient data and simulations are presented and are in good agreement. For the IL18/IL10 cytokine pair, 5 key parameters have been found. They are linked to pro-inflammatory IL18 cytokine and show that the early sepsis is driven by components of inflammatory character.

Two gene set variation indexes as potential diagnostic tool for sepsis

Accurate diagnosis of sepsis remains challenging, new markers or combinations of markers are urgently needed. In the present study, we screened differentially expressed genes (DEGs) between sepsis and non-sepsis blood samples across three previously published gene expression data sets. Common upregulated and downregulated DEGs were ranked according to their average functional similarity. The ten genes (OLFM4, ORM1, CEP55, S100A12, S100P, LRG1, CEACAM8, MS4A4A, PLSCR1, and IL1R2) with the largest average functional similarity among the common upregulated genes and another ten genes (THEMIS, IL2RB, CD2, IL7R, CD3E, KLRB1, PVRIG, CCRR3, TGFBR3, and PLEKHA1) with the largest average functional similarity among the common downregulated genes were separately identified as the upregulated crucial gene set and the downregulated crucial gene set. Gene set variation analysis (GSVA) was used to obtain the GSVA index of each sample against the two crucial gene sets. Both the two crucial GSVA indexes may be robust markers for sepsis with high area under ROC curve. The diagnostic utility of the upregulated GSVA index was validated in another independent data set. Functional analyses revealed several sepsis-related pathways. In conclusion, we proposed two sepsis-related gene sets across multiple data sets and created two GSVA indexes with promising diagnostic value.

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.

Extensive Changes in Transcriptomic "Fingerprints" and Immunological Cells in the Large Organs of Patients Dying of Acute Septic Shock and Multiple Organ Failure Caused by Neisseria meningitidis

Background: Patients developing meningococcal septic shock reveal levels of Neisseria meningitidis (10⁶-10⁸/mL) and endotoxin (10¹-10³ EU/mL) in the circulation and organs, leading to acute cardiovascular, pulmonary and renal failure, coagulopathy and a high case fatality rate within 24 h. Objective: To investigate transcriptional profiles in heart, lungs, kidneys, liver, and spleen and immunostain key inflammatory cells and proteins in post mortem formalin-fixed, paraffin-embedded (FFPE) tissue samples from meningococcal septic shock patients. Patients and Methods: Total RNA was isolated from FFPE and fresh frozen (FF) tissue samples from five patients and two controls (acute non-infectious death). Differential expression of genes was detected using Affymetrix microarray analysis. Lung and heart tissue samples were immunostained for T-and B cells, macrophages, neutrophils and the inflammatory markers PAI-1 and MCP-1. Inflammatory mediators were quantified in lysates from FF tissues. Results: The transcriptional profiles showed a complex pattern of protein-coding and non-coding RNAs with significant regulation of pathways associated with organismal death, cell death and survival, leukocyte migration, cellular movement, proliferation of cells, cell-to-cell signaling, immune cell trafficking, and inflammatory responses in an organ-specific clustering manner. The canonical pathways including acute phase response-, EIF2-, TREM1-, IL-6-, HMBG1-, PPAR signaling, and LXR/RXR activation were associated with acute heart, pulmonary, and renal failure. Fewer genes were regulated in the liver and particularly in the spleen. The main upstream regulators were TNF, IL-1β, IL-6, RICTOR, miR-6739-3p, and CD3. Increased numbers of inflammatory cells (CD68+, MPO+, CD3+, and CD20+) were found in lungs and heart. PAI-1 inhibiting fibrinolysis and MCP-1 attracting leukocyte were found significantly present in the septic tissue samples compared to the controls. Conclusions: FFPE tissue samples can be suitable for gene expression studies as well as immunostaining of specific cells or molecules. The most pronounced gene expression patterns were found in the organs with highest levels of Neisseria meningitidis DNA. Thousands of protein-coding and non-coding RNA transcripts were altered in lungs, heart and kidneys. We identified specific biomarker panels both protein-coding and non-coding RNA transcripts, which differed from organ to organ. Involvement of many genes and pathways add up and the combined effect induce organ failure.

Toll-like Receptor 8 Stability Is Regulated by Ring Finger 216 in Response to Circulating MicroRNAs

TLR8 (Toll-like receptor 8) is an intracellular pattern recognition receptor that senses RNA in endosomes to initiate innate immune signaling through NF-κB, and mechanisms regulating TLR8 protein abundance are not completely understood. Protein degradation is a cellular process controlling protein concentrations, accomplished largely through ubiquitin transfer directed by E3 ligase proteins to substrates. In the present study, we show that TLR8 has a short half-life in THP-1 monocytes (∼1 h) and that TLR8 is ubiquitinated and degraded in the proteasome. Treatment with the TLR8 agonist R848 causes rapid depletion of TLR8 concentrations at early time points, an effect blocked by proteasomal inhibition. We show a novel role for RNF216 (ring finger protein 216), an E3 ligase that targets TLR8 for ubiquitination and degradation. RNF216 overexpression reduces TLR8 concentrations, whereas RNF216 knockdown stabilizes TLR8. We describe a potential role for TLR8 activation by circulating RNA ligands in humans with acute respiratory distress syndrome (ARDS): Plasma and extracted RNA fractions from subjects with ARDS activated TLR8 in vitro. MicroRNA (miRNA) expression profiling revealed several circulating miRNAs from subjects with ARDS. miRNA mimics promoted TLR8 proteasomal degradation in THP-1 cells. These data show that TLR8 proteasomal disposal through RNF216 in response to RNA ligands regulates TLR8 cellular concentrations and may have implications for innate immune signaling. In addition, TLR8 activation by circulating RNA ligands may be a previously underrecognized stimulus contributing to excessive innate immune signaling characteristic of ARDS.

A longitudinal study highlights shared aspects of the transcriptomic response to cardiogenic and septic shock

Background

Septic shock (SS) and cardiogenic shock (CS) are two types of circulatory shock with a different etiology. Several studies have described the molecular alterations in SS patients, whereas the molecular factors involved in CS have been poorly investigated. We aimed to assess in the whole blood of CS and SS patients, using septic patients without shock (SC) as controls, transcriptomic modifications that occur over 1 week after ICU admission and are common to the two types of shock.

Methods

We performed whole blood RNA sequencing in 21 SS, 11 CS, and 5 SC. In shock patients, blood samples were collected within 16 h from ICU admission (T1), 48 h after ICU admission (T2), and at day 7 or before discharge (T3). In controls, blood samples were available at T1 and T2. Gene expression changes over time have been studied in CS, SS, and SC separately with a paired analysis. Genes with p value < 0.01 (Benjamini-Hochberg multiple test correction) were defined differentially expressed (DEGs). We used gene set enrichment analysis (GSEA) to identify the biological processes and transcriptional regulators significantly enriched in both types of shock.

Results

In both CS and SS patients, GO terms of inflammatory response and pattern recognition receptors (PRRs) were downregulated following ICU admission, whereas gene sets of DNA replication were upregulated. At the gene level, we observed that alarmins, interleukin receptors, PRRs, inflammasome, and DNA replication genes significantly changed their expression in CS and SS, but not in SC. Analysis of transcription factor targets showed in both CS and SS patients, an enrichment of CCAAT-enhancer-binding protein beta (CEBPB) targets in genes downregulated over time and an enrichment of E2F targets in genes with an increasing expression trend.

Conclusions

This pilot study supports, within the limits of a small sample size, the role of alarmins, PRRs, DNA replication, and immunoglobulins in the pathophysiology of circulatory shock, either in the presence of infection or not. We hypothesize that these genes could be potential targets of therapeutic interventions in CS and SS.

Trial registration

ClinicalTrials.gov, NCT02141607. Registered 19 May 2014.

Sepsis Induces Prolonged Epigenetic Modifications in Bone Marrow and Peripheral Macrophages Impairing Inflammation and Wound Healing

OBJECTIVE

Sepsis represents an acute life-threatening disorder resulting from a dysregulated host response. For patients who survive sepsis, there remains long-term consequences, including impaired inflammation, as a result of profound immunosuppression. The mechanisms involved in this long-lasting deficient immune response are poorly defined. Approach and Results: Sepsis was induced using the murine model of cecal ligation and puncture. Following a full recovery period from sepsis physiology, mice were subjected to our wound healing model and wound macrophages (CD11b+, CD3-, CD19-, Ly6G-) were sorted. Post-sepsis mice demonstrated impaired wound healing and decreased reepithelization in comparison to controls. Further, post-sepsis bone marrow-derived macrophages and wound macrophages exhibited decreased expression of inflammatory cytokines vital for wound repair (IL [interleukin]-1β, IL-12, and IL-23). To evaluate if decreased inflammatory gene expression was secondary to epigenetic modification, we conducted chromatin immunoprecipitation on post-sepsis bone marrow-derived macrophages and wound macrophages. This demonstrated decreased expression of Mll1, an epigenetic enzyme, and impaired histone 3 lysine 4 trimethylation (activation mark) at NFκB (nuclear factor kappa-light-chain-enhancer of activated B cells)-binding sites on inflammatory gene promoters in bone marrow-derived macrophages and wound macrophages from postcecal ligation and puncture mice. Bone marrow transplantation studies demonstrated epigenetic modifications initiate in bone marrow progenitor/stem cells following sepsis resulting in lasting impairment in peripheral macrophage function. Importantly, human peripheral blood leukocytes from post-septic patients demonstrate a significant reduction in MLL1 compared with nonseptic controls.

CONCLUSIONS

These data demonstrate that severe sepsis induces stable mixed-lineage leukemia 1-mediated epigenetic modifications in the bone marrow, which are passed to peripheral macrophages resulting in impaired macrophage function and deficient wound healing persisting long after sepsis recovery.

Precision Medicine in Critical Illness: Sepsis and Acute Respiratory Distress Syndrome

Sepsis and the acute respiratory distress syndrome (ARDS) each cause substantial morbidity and mortality. In contrast to other lung diseases, the entire course of disease in these syndromes is measured in days to weeks rather than months to years, which raises unique challenges in achieving precision medicine. We review advances in sepsis and ARDS resulting from omics studies, including those involving genome-wide association, gene expression, targeted proteomics, and metabolomics approaches. We focus on promising evidence of biological subtypes in both sepsis and ARDS that consistently display high risk for death. In sepsis, a gene expression signature with dysregulated adaptive immune signaling has evidence for a differential response to systemic steroid therapy, whereas in ARDS, a hyperinflammatory pattern identified in plasma using targeted proteomics responded more favorably to randomized interventions including high positive end-expiratory pressure, volume conservative fluid therapy, and simvastatin therapy. These early examples suggest heterogeneous biology that may be challenging to detect by clinical factors alone and speak to the promise of a precision approach that targets the right treatment at the right time to the right patient.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

OBJECTIVES

To find and validate generalizable sepsis subtypes using data-driven clustering.

DESIGN

We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).

SETTING

Retrospective analysis.

SUBJECTS

Persons admitted to the hospital with bacterial sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.

CONCLUSIONS

The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

IL-17, IL-27, and IL-33: A Novel Axis Linked to Immunological Dysfunction During Sepsis

Sepsis is a major cause of morbidity and mortality worldwide despite numerous attempts to identify effective therapeutics. While some sepsis deaths are attributable to tissue damage caused by inflammation, most mortality is the result of prolonged immunosuppression. Ex vivo, immunosuppression during sepsis is evidenced by a sharp decrease in the production of pro-inflammatory cytokines by T cells and other leukocytes and increased lymphocyte apoptosis. This allows suppressive cytokines to exert a greater inhibitory effect on lymphocytes upon antigen exposure. While some pre-clinical and clinical trials have demonstrated utility in targeting cytokines that promote lymphocyte survival, this has not led to the approval of any therapies for clinical use. As cytokines with a more global impact on the immune system are also altered by sepsis, they represent novel and potentially valuable therapeutic targets. Recent evidence links interleukin (IL)-17, IL-27, and IL-33 to alterations in the immune response during sepsis using patient serum and murine models of peritonitis and pneumonia. Elevated levels of IL-17 and IL-27 are found in the serum of pediatric and adult septic patients early after sepsis onset and have been proposed as diagnostic biomarkers. In contrast, IL-33 levels increase in patient serum during the immunosuppressive stage of sepsis and remain high for more than 5 months after recovery. All three cytokines contribute to immunological dysfunction during sepsis by disrupting the balance between type 1, 2, and 17 immune responses. This review will describe how IL-17, IL-27, and IL-33 exert these effects during sepsis and their potential as therapeutic targets.

Activated peripheral blood mononuclear cell mediators trigger astrocyte reactivity

Sepsis is characterized by a severe and disseminated inflammation. In the central nervous system, sepsis promotes synaptic dysfunction and permanent cognitive impairment. Besides sepsis-induced neuronal dysfunction, glial cell response has been gaining considerable attention with microglial activation as a key player. By contrast, astrocytes' role during acute sepsis is still underexplored. Astrocytes are specialized immunocompetent cells involved in brain surveillance. In this context, the potential communication between the peripheral immune system and astrocytes during acute sepsis still remains unclear. We hypothesized that peripheral blood mononuclear cell (PBMC) mediators are able to affect the brain during an episode of acute sepsis. With this in mind, we first performed a data-driven transcriptome analysis of blood from septic patients to identify common features among independent clinical studies. Our findings evidenced pronounced impairment in energy-related signaling pathways in the blood of septic patients. Since astrocytes are key for brain energy homeostasis, we decided to investigate the communication between PBMC mediators and astrocytes in a rat model of acute sepsis, induced by cecal ligation and perforation (CLP). In the CLP animals, we identified widespread in vivo brain glucose hypometabolism. Ex vivo analyses demonstrated astrocyte reactivity along with reduced glutamate uptake capacity during sepsis. Also, by exposing cultured astrocytes to mediators released by PBMCs from CLP animals, we reproduced the energetic failure observed in vivo. Finally, by pharmacologically inhibiting phosphoinositide 3-kinase (PI3K), a central metabolic pathway downregulated in the blood of septic patients and reduced in the CLP rat brain, we mimicked the PBMC mediators effect on glutamate uptake but not on glucose metabolism. These results suggest that PBMC mediators are capable of directly mediating astrocyte reactivity and contribute to the brain energetic failure observed in acute sepsis. Moreover, the evidence of PI3K participation in this process indicates a potential target for therapeutic modulation.

Interleukin-18 serum levels in sepsis: Correlation with disease severity and inflammatory markers

PURPOSE

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection and a syndrome shaped by pathogen and host factors with characteristic that evolve over time. The study was conducted to evaluate the prognostic and discriminative value of IL-18 assessment in comparison to PCT, CRP, WBC in early stage of sepsis and septic shock.

METHODS

An observational and prospective study was conducted in the group of 40 ICU patients with diagnosis of sepsis or septic shock, serum PCT, IL-18, CRP and WBC measurements were performed on admission, and on the 2nd, 3rd and 5th therapy day. The level of IL-18 was determined with commercially available test according to manufacturer's protocol.

RESULTS

There were no statistically significant differences in IL-18 levels in survivors vs non-survivors and in sepsis vs septic shock subgroups the IL-18 levels were statistically significant in the course of the study except for the 5th day.

CONCLUSION

The PCT, CRP and WBC levels revealed no significant differences between any analyzed subgroups in all time points during study. According to our results the IL-18 is a biomarker better differentiating sepsis and septic shock status than PCT, CRP and WBC but with no prognostic impact.

Heterogeneity in sepsis: new biological evidence with clinical applications

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2019. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2019. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Histone Deacetylation Inhibitors as Therapy Concept in Sepsis

Sepsis is characterized by dysregulated gene expression, provoking a hyper-inflammatory response occurring in parallel to a hypo-inflammatory reaction. This is often associated with multi-organ failure, leading to the patient’s death. Therefore, reprogramming of these pro- and anti-inflammatory, as well as immune-response genes which are involved in acute systemic inflammation, is a therapy approach to prevent organ failure and to improve sepsis outcomes. Considering epigenetic, i.e., reversible, modifications of chromatin, not altering the DNA sequence as one tool to adapt the expression profile, inhibition of factors mediating these changes is important. Acetylation of histones by histone acetyltransferases (HATs) and initiating an open-chromatin structure leading to its active transcription is counteracted by histone deacetylases (HDACs). Histone deacetylation triggers a compact nucleosome structure preventing active transcription. Hence, inhibiting the activity of HDACs by specific inhibitors can be used to restore the expression profile of the cells. It can be assumed that HDAC inhibitors will reduce the expression of pro-, as well as anti-inflammatory mediators, which blocks sepsis progression. However, decreased cytokine expression might also be unfavorable, because it can be associated with decreased bacterial clearance.