Gene expression profiling in sepsis: timing, tissue, and translational considerations

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

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.

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness

BACKGROUND

Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent MODS trajectory may shed light on underlying biology and allow for accurate prediction of those at-risk.

METHODS

Secondary analyses of publicly available gene-expression datasets. Supervised machine learning (ML) was used to identify a parsimonious set of genes associated with a persistent MODS trajectory in a training set of pediatric septic shock. We optimized model parameters and tested risk-prediction capabilities in independent validation and test datasets, respectively. We compared model performance relative to an established gene-set predictive of sepsis mortality.

FINDINGS

Patients with a persistent MODS trajectory had 568 differentially expressed genes and characterized by a dysregulated innate immune response. Supervised ML identified 111 genes associated with the outcome of interest on repeated cross-validation, with an AUROC of 0.87 (95% CI: 0.85-0.88) in the training set. The optimized model, limited to 20 genes, achieved AUROCs ranging from 0.74 to 0.79 in the validation and test sets to predict those with persistent MODS, regardless of host age and cause of organ dysfunction. Our classifier demonstrated reproducibility in identifying those with persistent MODS in comparison with a published gene-set predictive of sepsis mortality.

INTERPRETATION

We demonstrate the utility of supervised ML driven identification of the genes associated with persistent MODS. Pending validation in enriched cohorts with a high burden of organ dysfunction, such an approach may inform targeted delivery of interventions among at-risk patients.

FUNDING

H.R.W.'s NIHR35GM126943 award supported the work detailed in this manuscript. Upon his death, the award was transferred to M.N.A. M.R.A., N.S.P, and R.K were supported by NIHR21GM151703. R.K. was supported by R01GM139967.

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.

Emerging roles of microRNAs in septic cardiomyopathy

As one of the serious complications of sepsis, septic cardiomyopathy has gained more and more attention, because of its high morbidity and mortality. With the in-depth study of septic cardiomyopathy, several methods have been adopted clinically but have poor therapeutic effects due to failure to find precise therapeutic targets. In recent years, microRNAs have been found to be related to the pathogenesis, diagnosis, and treatment of septic cardiomyopathy via regulating immunity and programmed cell death. This paper reviews the role of microRNAs in septic cardiomyopathy, aiming to provide new targets for the diagnosis and treatment of septic cardiomyopathy.

Differentiating infection, colonisation, and sterile inflammation in critical illness: the emerging role of host-response profiling

Infection results when a pathogen produces host tissue damage and elicits an immune response. Critically ill patients experience immune activation secondary to both sterile and infectious insults, with overlapping clinical phenotypes and underlying immunological mechanisms. Patients also undergo a shift in microbiota with the emergence of pathogen-dominant microbiomes. Whilst the combination of inflammation and microbial shift has long challenged intensivists in the identification of true infection, the advent of highly sensitive molecular diagnostics has further confounded the diagnostic dilemma as the number of microbial detections increases. Given the key role of the host immune response in the development and definition of infection, profiling the host response offers the potential to help unravel the conundrum of distinguishing colonisation and sterile inflammation from true infection. This narrative review provides an overview of current approaches to distinguishing colonisation from infection using routinely available techniques and proposes matrices to support decision-making in this setting. In searching for new tools to better discriminate these states, the review turns to the understanding of the underlying pathobiology of the host response to infection. It then reviews the techniques available to assess this response in a clinically applicable context. It will cover techniques including profiling of transcriptome, protein expression, and immune functional assays, detailing the current state of knowledge in diagnostics along with the challenges and opportunities. The ultimate infection diagnostic tool will likely combine an assessment of both host immune response and sensitive pathogen detection to improve patient management and facilitate antimicrobial stewardship.

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.

Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis

Background

Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.

Methods

Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via "rms" package.

Results

Three different expressed MiRGs (DE-MiRGs) were identified as sepsis biomarkers. A significant difference in the immune microenvironment landscape was observed between healthy controls and sepsis patients. Among the DE-MiRGs, NDUFB3 was selected to be a potential therapeutic target and its significant elevated expression level was confirmed in sepsis using in vitro experiments and confocal microscopy, indicating its significant contribution to the mitochondrial quality imbalance in the LPS-simulated sepsis model.

Conclusion

By digging the role of these pivotal genes in immune cell infiltration, we gained a better understanding of the molecular immune mechanism in sepsis and identified potential intervention and treatment strategies.

The Role of Transcriptomics in Redefining Critical Illness

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at  https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from  https://link.springer.com/bookseries/8901 .

An immune-related gene signature predicts the 28-day mortality in patients with sepsis

Introduction

Sepsis is the leading cause of death in intensive care units and is characterized by multiple organ failure, including dysfunction of the immune system. In the present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role in the pathological process of sepsis, based on which a prognostic IRG signature for 28-day mortality prediction in patients with sepsis was developed and validated.

Methods

Weighted gene co-expression network analysis (WGCNA), Cox regression analysis and least absolute shrinkage and selection operator (LASSO) estimation were used to identify functional IRGs and construct a model for predicting the 28-day mortality. The prognostic value of the model was validated in internal and external sepsis datasets. The correlations of the IRG signature with immunological characteristics, including immune cell infiltration and cytokine expression, were explored. We finally validated the expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls using qPCR.

Results

We established a prognostic IRG signature comprising three gene members (LTB4R, HLA-DMB and IL4R). The IRG signature demonstrated good predictive performance for 28-day mortality on the internal and external validation datasets. The immune infiltration and cytokine analyses revealed that the IRG signature was significantly associated with multiple immune cells and cytokines. The molecular pathway analysis uncovered ontology enrichment in myeloid cell differentiation and iron ion homeostasis, providing clues regarding the underlying biological mechanisms of the IRG signature. Finally, qPCR detection verified the differential expression of the three IRG signature genes in blood samples from 12 sepsis patients and 12 healthy controls.

Discussion

This study presents an innovative IRG signature for 28-day mortality prediction in sepsis patients, which may be used to facilitate stratification of risky sepsis patients and evaluate patients' immune state.

Identification of novel potential molecular targets associated with pediatric septic shock by integrated bioinformatics analysis and validation of in vitro septic shock model

Background/Aim: Sepsis is a major cause of morbidity, mortality, and healthcare utilization among children all over the world. Sepsis, characterized as life-threatening organ failure, results from a dysregulated host response to infection. When combined with critically low blood pressure, it causes septic shock, resulting in high mortality  rates. The aim of this study was to perform a bioinformatic analysis of gene expression profiles to predict  septic shock risk. Methods: Four datasets related to pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to examine the functional enrichment of DEGs. STRING was used to create a protein–protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, human umbilical vein endothelial cells (HUVECs) were stimulated with supernatants of lipopolysaccharide (LPS)-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) and used to construct an in vitro septic shock model. Results: Seven-hundred seventy-one common differentially expressed genes in the four  groups were found. Of these, 433 genes showed increased expression, while 338 had reduced expression. In the DAVID analysis results, DEGs up-regulated according to gene ontology results were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Down-regulated DEGs were  significantly enriched in the regulation of immune response, T-cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Cystoscape Molecular Complex Detection (MCODE), nine down-regulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247 and four up-regulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with the bioinformatic results. Conclusion: Comparative bioinformatics analysis of data from four different septic shock studies was performed. As a result, molecular processes and important signal networks and 13 genes that we think will play a role in the development and risk prediction of septic shock are proposed. Methods: Four datasets related to Pediatric septic shock were retrieved from the Gene Expression Omnibus (GEO) database for a total of 240 patients and 83 controls. GEO2R tools based on R were used to find differentially expressed genes (DEGs). DAVID was used to examine the functional enrichment of DEGs. STRING was used to create a protein-protein interaction (PPI) network. After separately analyzing the four datasets, commonly affected genes were removed using the Venny program. Finally, HUVECs were stimulated with supernatants of LPS-stimulated RAW267.4 macrophage cells and expression of selected genes was confirmed by qRT-PCR, constructing an in vitro septic shock model. Results: There were 771 common differentially expressed genes in the 4 groups. Of these, 433 genes showed increased expression, while 338 had reducing expression. In the DAVID analysis results, DEGs upregulated by gene ontology were enriched in the regulation of innate and adaptive immune responses, complement receptor-mediated signaling, and cytokine secretion processes. Downregulated DEGs are significantly enriched in the regulation of immune response, T cell activation, antigen processing, and presentation and integral component of plasma membrane processes. According to STRING, cystoscape MCODE, and cytohubba analysis, 9 downregulated genes in the center of the PPI network, ZAP70, ITK, LAT, PRKCQ, LCK, IL2RB, FYN, CD8A, CD247, and 4 upregulated genes, MMP9, TIMP1, LCN2, HGF, were associated with septic shock. Expressions of FYN and MMP9 genes in the in vitro septic shock model were consistent with bioinformatic results. Conclusion: Important signaling networks and 13 genes potentially indicating molecular processes for the incidence, development, and risk prediction in septic shock were found using bioinformatic analysis of gene expression profiles.

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.

Immune Modulation in Sepsis, ARDS, and Covid-19 - The Road Traveled and the Road Ahead

Immune Modulation in Sepsis, ARDS, and Covid-19Leligdowicz et al. consider the history and future of immunomodulating therapies in sepsis and ARDS, including ARDS due to Covid-19, and remark on the larger challenge of clinical research on therapies for syndromes with profound clinical and biologic heterogeneity.

Na/K-ATPase suppresses LPS-induced pro-inflammatory signaling through Lyn

Na/K-ATPase (NKA), besides its ion transporter function, is a signal transducer by regulating Src family kinases (SFK). The signaling NKA contributes to oxidized LDL-induced macrophage foam cell formation and interacts with TLR4. However, its role in lipopolysaccharides (LPS)-induced signaling and glycolytic switch in macrophages remains unclear. Using peritoneal macrophages from NKA α1 haploinsufficient mice (NKA α1+/-), we found that NKA α1 haploinsufficiency led to enhanced LPS-stimulated NF-κB pathway, ROS signaling, and pro-inflammatory cytokines. Intraperitoneal injection of LPS resulted in more severe lung inflammation and injury with lower survival rate in NKA α1+/- mice. Additionally, LPS induced a higher extent of the metabolic switch from oxidative phosphorylation to glycolysis. Mechanistically, NKA α1 interacted with TLR4 and Lyn. The presence of NKA α1 in this complex attenuated Lyn activation by LPS, which subsequently restricted the downstream ROS and NF-κB signaling. In conclusion, we demonstrated that NKA α1 suppresses LPS-induced macrophage pro-inflammatory signaling through Lyn.

Comprehensive Analysis of Molecular Subtypes and Hub Genes of Sepsis by Gene Expression Profiles

Background: Sepsis is a systemic inflammatory response syndrome (SIRS) with heterogeneity of clinical symptoms. Studies further exploring the molecular subtypes of sepsis and elucidating its probable mechanisms are urgently needed.

Methods: Microarray datasets of peripheral blood in sepsis were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) analysis was conducted to screen key module genes. Consensus clustering analysis was carried out to identify distinct sepsis molecular subtypes. Subtype-specific pathways were explored using gene set variation analysis (GSVA). Afterward, we intersected subtype-related, dramatically expressed and module-specific genes to screen consensus DEGs (co-DEGs). Enrichment analysis was carried out to identify key pathways. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for screen potential diagnostic biomarkers.

Results: Patients with sepsis were classified into three clusters. GSVA showed these DEGs among different clusters in sepsis were assigned to metabolism, oxidative phosphorylation, autophagy regulation, and VEGF pathways, etc. In addition, we identified 40 co-DEGs and several dysregulated pathways. A diagnostic model with 25-gene signature was proven to be of high value for the diagnosis of sepsis. Genes in the diagnostic model with AUC values more than 0.95 in external datasets were screened as key genes for the diagnosis of sepsis. Finally, ANKRD22, GPR84, GYG1, BLOC1S1, CARD11, NOG, and LRG1 were recognized as critical genes associated with sepsis molecular subtypes.

Conclusion: There are remarkable differences in and enriched pathways among different molecular subgroups of sepsis, which may be the key factors leading to heterogeneity of clinical symptoms and prognosis in patients with sepsis. Our current study provides novel diagnostic and therapeutic biomarkers for sepsis molecular subtypes.

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.

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.

Redefining critical illness

Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence-supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside.

Differential Gene Sets Profiling in Gram-Negative and Gram-Positive Sepsis

Background

The host response to bacterial sepsis is reported to be nonspecific regardless of the causative pathogen. However, newer paradigms indicated that the host response of Gram-negative sepsis may be different from Gram-positive sepsis, and the difference has not been clearly clarified. The current study aimed to explore the difference by identifying the differential gene sets using the genome-wide technique.

Methods

The training dataset GSE6535 and the validation dataset GSE13015 were used for bioinformatics analysis. The distinct gene sets of sepsis with different infections were screened using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). The intersection gene sets based on the two algorithms were confirmed through Venn analysis. Finally, the common gene sets between GSE6535 and GSE13015 were determined by GSEA.

Results

Two immunological gene sets in GSE6535 were identified based on GSVA, which could be used to discriminate sepsis caused by Gram-positive, Gram-negative, or mixed infection. A total of 19 gene sets were obtained in GSE6535 through Venn analysis based on GSVA and GSEA, which revealed the heterogeneity of Gram-negative and Gram-positive sepsis at the molecular level. The result was also verified by analysis of the validation set GSE13015, and 40 common differential gene sets were identified between dataset GSE13015 and dataset GSE6535 by GSEA.

Conclusions

The identified differential gene sets indicated that host response may differ dramatically depending on the inciting organism. The findings offer new insight to investigate the pathophysiology of bacterial sepsis.

lncRNA GMDS‑AS1 upregulates IL‑6, TNF‑α and IL‑1β, and induces apoptosis in human monocytic THP‑1 cells via miR‑96‑5p/caspase 2 signaling

Long non-coding RNA (lncRNA) is considered a crucial modulator of the initiation and progression of several diseases. However, the roles of lncRNA in sepsis have yet to be fully elucidated. Thus, the aim of the present study was to investigate the effects of the lncRNA GDP-mannose 4,6-dehydratase antisense 1 (GMDS-AS1) and its target in order to understand its role in the pathogenesis of sepsis. An in vitro sepsis model was established by lipopolysaccharide (LPS) induction. Reverse transcription-quantitative PCR analysis was applied to detect the expression of inflammatory cytokines and the levels of GMDS-AS1, microRNA (miR)-96-5p and caspase-2 (CASP2). Flow cytometry was used to quantify the rate of apoptosis. In addition, the interaction between miR-96-5p and CASP2 was verified using a luciferase reporter assay. Western blot analysis was performed to assess the protein levels of CASP2 following alterations in GMDS-AS1 and miR-96-5p expression using transfection. The levels of interleukin (IL)-6, tumor necrosis factor-α and IL-1β were increased by LPS treatment in THP-1 cells, whereas miR-96-5p expression was downregulated. miR-96-5p overexpression inhibited LPS-induced inflammatory responses and apoptosis. In addition, GMDS-AS1 expression increased, and upregulation of GMDS-AS1 inhibited, the expression of miR-96-5p in the in vitro sepsis model. Moreover, CASP2 was confirmed to be a direct target of miR-96-5p. Therefore, the lncRNA GMDS-AS1 regulated inflammatory responses and apoptosis by modulating CASP2 and sponging miR-96-5p in LPS-induced THP-1 cells. In summary, the findings of the present study demonstrated that lncRNA GMDS-AS1 could promote the development of sepsis by targeting miR-96-5p/CASP2, indicating that the GMDS-AS1/miR-96-5p/CASP2 axis may be a new therapeutic target and potential research direction for sepsis therapy.

Past Experiences for Future Applications of Metabolomics in Critically Ill Patients with Sepsis and Septic Shocks

A disruption of several metabolic pathways in critically ill patients with sepsis indicates that metabolomics might be used as a more precise tool for sepsis and septic shock when compared with the conventional biomarkers. This article provides information regarding metabolomics studies in sepsis and septic shock patients. It has been shown that a variety of metabolomic pathways are altered in sepsis and septic shock, including amino acid metabolism, fatty acid oxidation, phospholipid metabolism, glycolysis, and tricarboxylic acid cycle. Based upon this comprehensive review, here, we demonstrate that metabolomics is about to change the world of sepsis biomarkers, not only for its utilization in sepsis diagnosis, but also for prognosticating and monitoring the therapeutic response. Additionally, the future direction regarding the establishment of studies integrating metabolomics with other molecular modalities and studies identifying the relationships between metabolomic profiles and clinical characteristics to address clinical application are discussed in this article. All of the information from this review indicates the important impact of metabolomics as a tool for diagnosis, monitoring therapeutic response, and prognostic assessment of sepsis and septic shock. These findings also encourage further clinical investigations to warrant its use in routine clinical settings.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

Genetics of Acute Respiratory Distress Syndrome: Pathways to Precision

Clinical risk factors alone fail to fully explain acute respiratory distress syndrome (ARDS) risk or ARDS death, suggesting that individual risk factors contribute. The goals of genomic ARDS studies include better mechanistic understanding, identifying dysregulated pathways that may be amenable to pharmacologic targeting, using genomic causal inference techniques to find measurable traits with meaning, and deconvoluting ARDS heterogeneity by proving reproducible subpopulations that may share a unique biology. This article discusses the latest advances in ARDS genomics, provides historical perspective, and highlights some of the ways that the coronavirus disease 2019 (COVID-19) pandemic is accelerating genomic ARDS research.

Multi-Organ Transcriptome Dynamics in a Mouse Model of Cecal Ligation and Puncture-Induced Polymicrobial Sepsis

PURPOSE

During sepsis, an excessive inflammatory immune reaction contributes to multi-organ dysfunction syndrome (MODS), a critical condition associated with high morbidity and mortality; however, the molecular mechanisms driving MODS remain elusive.

METHODS

We used RNA sequencing to characterize transcriptional changes in the early phase of sepsis, at 6, 12, 24 hour time points in lung, kidney, liver, and heart tissues, in a cecal ligation and puncture (CLP)-induced polymicrobial sepsis murine model.

RESULTS

The CLP surgery induced significant changes (adj. p-value<0.05) in expression of hundreds of transcripts in the four organs tested, with the highest number exceeding 2,000 differentially expressed genes (DEGs) in all organs at 12 hours post-CLP. Over-representation analysis by functional annotations of DEGs to the Reactome database revealed the immune system, hemostasis, lipid metabolism, signal transduction, and extracellular matrix remodeling biological processes as significantly altered in at least two organs, while metabolism of proteins and RNA were revelaed as being liver tissue specific in the early phase of sepsis.

CONCLUSION

RNA sequencing across organs and time-points in the CLP murine model allowed us to study the trajectories of transcriptome changes demonstrating alterations common across multiple organs as well as biological pathways altered in an organ-specific manner. These findings could pave new directions in the research of sepsis-induced MODS and indicate new sepsis treatment strategies.

RNA-Seq Transcriptome Analysis of Peripheral Blood From Cattle Infected With Mycobacterium bovis Across an Experimental Time Course

Bovine tuberculosis, caused by infection with members of the Mycobacterium tuberculosis complex, particularly Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including RNA sequencing, has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analysed the transcriptome of bovine whole peripheral blood samples collected at −1 week pre-infection and +1, +2, +6, +10, and +12 weeks post-infection time points. Differentially expressed genes were catalogued and evaluated at each post-infection time point relative to the −1 week pre-infection time point and used for the identification of putative candidate host transcriptional biomarkers for M. bovis infection. Differentially expressed gene sets were also used for examination of cellular pathways associated with the host response to M. bovis infection, construction of de novo gene interaction networks enriched for host differentially expressed genes, and time-series analyses to identify functionally important groups of genes displaying similar patterns of expression across the infection time course. A notable outcome of these analyses was identification of a 19-gene transcriptional biosignature of infection consisting of genes increased in expression across the time course from +1 week to +12 weeks post-infection.

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host-pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.

Sepsis Subclasses: A Framework for Development and Interpretation

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.

Label-Free Biophysical Markers from Whole Blood Microfluidic Immune Profiling Reveal Severe Immune Response Signatures

Disease manifestation and severity from acute infections are often due to hyper-aggressive host immune responses which change within minutes. Current methods for early diagnosis of infections focus on detecting low abundance pathogens, which are time-consuming, of low sensitivity, and do not reflect the severity of the pathophysiology appropriately. The approach here focuses on profiling the rapidly changing host inflammatory response, which in its over-exuberant state, leads to sepsis and death. A 15-min label-free immune profiling assay from 20 µL of unprocessed blood using unconventional L and Inverse-L shaped pillars of deterministic lateral displacement microfluidic technology is developed. The hydrodynamic interactions of deformable immune cells enable simultaneous sorting and immune response profiling in whole blood. Preliminary clinical study of 85 donors in emergency department with a spectrum of immune response states from healthy to severe inflammatory response shows correlation with biophysical markers of immune cell size, deformability, distribution, and cell counts. The speed of patient stratification demonstrated here has promising impact in deployable point-of-care systems for acute infections triage, risk management, and resource allocation at emergency departments, where clinical manifestation of infection severity may not be clinically evident as compared to inpatients in the wards or intensive care units.

Glucocorticoid-Induced Leucine Zipper: A Promising Marker for Monitoring and Treating Sepsis

Sepsis is a clinical syndrome that resulting from a dysregulated inflammatory response to infection that leads to organ dysfunction. The dysregulated inflammatory response transitions from a hyper-inflammatory phase to a hypo-inflammatory or immunosuppressive phase. Currently, no phase-specific molecular-based therapies are available for monitoring the complex immune response and treating sepsis due to individual variations in the timing and overlap of the dysregulated immune response in most patients. Glucocorticoid-induced leucine zipper (GILZ), is broadly present in multiple tissues and circumvent glucocorticoid resistance (GCR) or unwanted side effects. Recently, the characteristics of GILZ downregulation during acute hyperinflammation and GILZ upregulation during the immunosuppressive phase in various inflammatory diseases have been well documented, and the protective effects of GILZ have gained attention in the field of sepsis. However, whether GILZ could be a promising candidate biomarker for monitoring and treating septic patients remains unknown. Here, we discuss the effect of GILZ in sepsis and sepsis-induced immunosuppression.

The leukocyte non-coding RNA landscape in critically ill patients with sepsis

The extent of non-coding RNA alterations in patients with sepsis and their relationship to clinical characteristics, soluble mediators of the host response to infection, as well as an advocated in vivo model of acute systemic inflammation is unknown. Here we obtained whole blood from 156 patients with sepsis and 82 healthy subjects among whom eight were challenged with lipopolysaccharide in a clinically controlled setting (human endotoxemia). Via next-generation microarray analysis of leukocyte RNA we found that long non-coding RNA and, to a lesser extent, small non-coding RNA were significantly altered in sepsis relative to health. Long non-coding RNA expression, but not small non-coding RNA, was largely recapitulated in human endotoxemia. Integrating RNA profiles and plasma protein levels revealed known as well as previously unobserved pathways, including non-sensory olfactory receptor activity. We provide a benchmark dissection of the blood leukocyte ‘regulome’ that can facilitate prioritization of future functional studies.

Gene correlation network analysis to identify regulatory factors in sepsis

BACKGROUND AND OBJECTIVES

Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments.

METHODS

The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA.

RESULTS

A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018).

CONCLUSIONS

The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study

Background

Early and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.

Methods

In this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18–25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0–4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay.

Findings

Between Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92–0·96]), as well as at 1 day (0·87 [95% CI 0·84–0·90]), 2 days (0·85 [0·82–0·88]), and 3 days (0·74 [0·71–0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding.

Interpretation

Transcriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.

Funding

US Defense Advanced Research Projects Agency.

Overexpression of Gilz Protects Mice Against Lethal Septic Peritonitis

Sepsis in humans and experimental animals is characterized by an acute inflammatory response. glucocorticoids (GCs) are widely used for the treatment of many inflammatory disorders, yet their effectiveness in sepsis is debatable. One of the major anti-inflammatory proteins induced by GCs is glucocorticoid-induced leucine zipper (GILZ, coded by the TSC22D3 gene). We found that TSC22D3 mRNA expression is downregulated in white blood cells of human sepsis patients. Interestingly, transgenic GILZ-overexpressing mice (GILZ-tg) showed better survival rates in the cecal ligation and puncture (CLP) model of mouse sepsis. To our surprise, GILZ had only mild anti-inflammatory effects in this model, as the systemic proinflammatory response was not significantly reduced in GILZ-tg mice compared with control mice. During CLP, we observed reduced bacterial counts in blood of GILZ-tg mice compared with control mice. We found increased expression of Tsc22d3 mRNA specifically in peritoneal exudate cells in the CLP model, as well as increased capacity for bacterial phagocytosis of CD45 GILZ-tg cells compared with CD45 GILZ-wt cells. Hence, we believe that the protective effects of GILZ in the CLP model can be linked to a more efficient phagocytosis.

Insights Into a "Negative" ICU Trial Derived From Gene Expression Profiling

OBJECTIVES

Randomized controlled trials in the ICU often fail to show differences in endpoints between groups. We sought to explore reasons for this at a molecular level by analyzing transcriptomic data from a recent negative trial. Our objectives were to determine if randomization successfully balanced transcriptomic features between groups, to assess transcriptomic heterogeneity among the study subjects included, and to determine if the study drug had any effect at the gene expression level.

DESIGN

Bioinformatics analysis of transcriptomic and clinical data collected in the course of a randomized controlled trial.

SETTING

Tertiary academic mixed medical-surgical ICU.

PATIENTS

Adult, critically ill patients expected to require invasive mechanical ventilation more than 48 hours.

INTERVENTIONS

Lactoferrin or placebo delivered enterally and via an oral swab for up to 28 days.

MEASUREMENTS AND MAIN RESULTS

We found no major imbalances in transcriptomic features between groups. Unsupervised analysis did not reveal distinct clusters among patients at the time of enrollment. There were marked differences in gene expression between early and later time points. Patients in the lactoferrin group showed changes in the expression of genes associated with immune pathways known to be associated with lactoferrin.

CONCLUSIONS

In this clinical trial, transcriptomic data provided a useful complement to clinical data, suggesting that the reasons for the negative result were less likely related to the biological efficacy of the study drug, and may instead have been related to poor sensitivity of the clinical outcomes. In larger studies, transcriptomics may also prove useful in predicting response to treatment.

Longitudinal patterns of cytokine expression at the individual level in humans after laparoscopic sleeve gastrectomy

The study of the human response to injury has been hampered by the inherent heterogeneity in the models and methods used. By studying a standard injury longitudinally, using individual patient‐level analysis, we endeavoured to better describe its dynamics. We analysed clinical variables, clinical laboratory and plasma cytokines from 20 patients at five time points. Clustering analysis showed two prototype patterns of cytokine behaviour: a concordant type, where cytokines behave the same way for all patients (notably IL‐0 and TNFα), and a variable type, where different patterns of expression are seen for different patients (notably IL‐8, IL‐6 and IL‐1RA). Analysis of the cytokines at the individual patient‐level showed a strong four‐way correlation between IL‐1RA, GCSF, MIP‐1β and MCP‐1. As it holds for most patients and not just on average, this suggests that they form a network which may play a central role in the response to gastro‐intestinal injuries in humans. In conclusion, the longitudinal analysis of cytokines in a standard model allowed the identification of their underlying patterns of expression. We propose that the two prototype patterns shown may reflect the mechanism that separates the common and individual aspects of the injury response.

Biomarker Panels in Critical Care

Biomarker panels have the potential to advance the field of critical care medicine by stratifying patients according to prognosis and/or underlying pathophysiology. This article discusses the discovery and validation of biomarker panels, along with their translation to the clinical setting. The current literature on the use of biomarker panels in sepsis, acute respiratory distress syndrome, and acute kidney injury is reviewed.

Role of neuroendocrine modulation and biochemistry in the sepsis in Piaractus mesopotamicus

Sepsis is a systemic process with multifactorial pathophysiology that affects most animal species. It is responsible for high rates of morbidity and mortality. This work aimed to study the biochemical and neuroendocrine changes of the sepsis process in Piaractus mesopotamicus after Aeromonas hydrophila inoculation analyzing changes in blood leukocyte and differences in neuroendocrine-biochemical modulation using RNA-seq. Fish showed hypercortisolemia, inhibition of glucose absorption, followed by hypocortisolemia and then hyperglycemia. Thyroid hormones (T₃ and T₄) showed immediate decrease in serum and T₄ increased 6 h post-inoculation (HPI). Sepsis-induced hormonal alterations triggered changes in the metabolic pathways increasing protein and lipid catabolism, use of transient anaerobic glycolysis and liver injury. A reference transcriptome was constructed based on blood leukocytes from P. mesopotamicus. The assembly resulted in total 266,272 contigs with a N50 of 2786 bp. There was a reorganization of plasma membrane of leukocytes at the beginning of the septic process with increased expression of neuroendocrine receptors and with continuous flow of neurotransmitters, hormones and solutes with compensatory regulation at 6 HPI. Three and nine HPI seemed to be critical, the expression of a number of transcription factors was increased, including the modulatory DEGs related to glucocorticoid and thyroid hormones induced and suppressed (FDR < 0.05). Neuroendocrine modulation can regulate leukocytes and biochemical parameters of peripheral blood, being important sources for the study of the pathophysiology of sepsis. These finding highlights the importance of further studies focusing on biochemical-neuroendocrine changes in blood leukocytes and systemic sepsis.

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.

Synergistic effects of deuterium depleted water and Mentha longifolia L. essential oils on sepsis-induced liver injuries through regulation of cyclooxygenase-2

CONTEXT

Mentha longifolia L. (Lamiaceae), a traditional medicinal herb, has been highly valued for exhibiting antimicrobial, antioxidant and antispasmodic properties.

OBJECTIVE

For the first time, the synergetic anti-inflammatory effects of deuterium depleted water (DDW) and M. longifolia essential oils (ML) were investigated in experimental sepsis.

MATERIALS AND METHODS

Fifty Wistar rats were divided into 5 groups (n = 10): negative control (laparotomy), CLP, treatment groups including the combination of DDWs (15 and 30 ppm) and ML (100 mg/kg b.w) and indomethacin. At 24 h after CLP induction, lipid peroxidation (LP), glutathione (GSH), glutathione in S-transferases (GST), ferric reducing ability of plasma (FRAP), myeloperoxidase (MPO), prostaglandin E2 (PGE2), and COX-2 expression were determined in the plasma and liver tissues.

RESULTS

Compared with the CLP group, the administration of DDWs and ML significantly (p < 0.05) prevented the increase of LP, COX-2 and PGE2 levels and liver enzymes. Additionally, the decreased levels of FRAP and GSH induced by sepsis were remarkably (p < 0.05) risen by the administration of DDWs and ML in comparison to the CLP group. However, no significant (p > 0.05) differences were observed regarding GST, ALP and bilirubin levels. Our results also proved the synergistic anti-inflammatory activities of the DDWs and ML. The anti-inflammatory effects of the DDWs and ML were confirmed by histopathological studies.

DISCUSSION AND CONCLUSIONS

The combination of DDWs and ML exerted synergistic anti-inflammatory activity against CLP-induced sepsis possibly through modulating oxidative stress/antioxidant parameters.

Synergistic effects of deuterium depleted water and Mentha longifolia L. essential oils on sepsis-induced liver injuries through regulation of cyclooxygenase-2

CONTEXT

Mentha longifolia L. (Lamiaceae), a traditional medicinal herb, has been highly valued for exhibiting antimicrobial, antioxidant and antispasmodic properties.

OBJECTIVE

For the first time, the synergetic anti-inflammatory effects of deuterium depleted water (DDW) and M. longifolia essential oils (ML) were investigated in experimental sepsis.

MATERIALS AND METHODS

Fifty Wistar rats were divided into 5 groups (n = 10): negative control (laparotomy), CLP, treatment groups including the combination of DDWs (15 and 30 ppm) and ML (100 mg/kg b.w) and indomethacin. At 24 h after CLP induction, lipid peroxidation (LP), glutathione (GSH), glutathione in S-transferases (GST), ferric reducing ability of plasma (FRAP), myeloperoxidase (MPO), prostaglandin E2 (PGE2), and COX-2 expression were determined in the plasma and liver tissues.

RESULTS

Compared with the CLP group, the administration of DDWs and ML significantly (p < 0.05) prevented the increase of LP, COX-2 and PGE2 levels and liver enzymes. Additionally, the decreased levels of FRAP and GSH induced by sepsis were remarkably (p < 0.05) risen by the administration of DDWs and ML in comparison to the CLP group. However, no significant (p > 0.05) differences were observed regarding GST, ALP and bilirubin levels. Our results also proved the synergistic anti-inflammatory activities of the DDWs and ML. The anti-inflammatory effects of the DDWs and ML were confirmed by histopathological studies.

DISCUSSION AND CONCLUSIONS

The combination of DDWs and ML exerted synergistic anti-inflammatory activity against CLP-induced sepsis possibly through modulating oxidative stress/antioxidant parameters.

Biomarkers of Infection and Sepsis

The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.

Prognostic and predictive enrichment in sepsis

Sepsis is a heterogeneous disease state that is both common and consequential in critically ill patients. Unfortunately, the heterogeneity of sepsis at the individual patient level has hindered advances in the field beyond the current therapeutic standards, which consist of supportive care and antibiotics. This complexity has prompted attempts to develop a precision medicine approach, with research aimed towards stratifying patients into more homogeneous cohorts with shared biological features, potentially facilitating the identification of new therapies. Several investigators have successfully utilized leukocyte-derived mRNA and discovery-based approaches to subgroup patients on the basis of biological similarities defined by transcriptomic signatures. A critical next step is to develop a consensus sepsis subclassification system, which includes transcriptomic signatures as well as other biological and clinical data. This goal will require collaboration among various investigative groups, and validation in both existing data sets and prospective studies. Such studies are required to bring precision medicine to the bedside of critically ill patients with sepsis.

Validation of a Host Response Assay, SeptiCyte LAB, for Discriminating Sepsis from Systemic Inflammatory Response Syndrome in the ICU

RATIONALE

A molecular test to distinguish between sepsis and systemic inflammation of noninfectious etiology could potentially have clinical utility.

OBJECTIVES

This study evaluated the diagnostic performance of a molecular host response assay (SeptiCyte LAB) designed to distinguish between sepsis and noninfectious systemic inflammation in critically ill adults.

METHODS

The study employed a prospective, observational, noninterventional design and recruited a heterogeneous cohort of adult critical care patients from seven sites in the United States (n = 249). An additional group of 198 patients, recruited in the large MARS (Molecular Diagnosis and Risk Stratification of Sepsis) consortium trial in the Netherlands ( www.clinicaltrials.gov identifier NCT01905033), was also tested and analyzed, making a grand total of 447 patients in our study. The performance of SeptiCyte LAB was compared with retrospective physician diagnosis by a panel of three experts.

MEASUREMENTS AND MAIN RESULTS

In receiver operating characteristic curve analysis, SeptiCyte LAB had an estimated area under the curve of 0.82-0.89 for discriminating sepsis from noninfectious systemic inflammation. The relative likelihood of sepsis versus noninfectious systemic inflammation was found to increase with increasing test score (range, 0-10). In a forward logistic regression analysis, the diagnostic performance of the assay was improved only marginally when used in combination with other clinical and laboratory variables, including procalcitonin. The performance of the assay was not significantly affected by demographic variables, including age, sex, or race/ethnicity.

CONCLUSIONS

SeptiCyte LAB appears to be a promising diagnostic tool to complement physician assessment of infection likelihood in critically ill adult patients with systemic inflammation. Clinical trial registered with www.clinicaltrials.gov (NCT01905033 and NCT02127502).

Genetic variants in Forkhead box O1 associated with predisposition to sepsis in a Chinese Han population

Background

Genetic variant is one of the causes of sepsis patients’ mortality. Now, many studies have identified several SNPs related to sepsis. However, none of these studies were identified in a genome-wide way. We aimed to detect genetic polymorphisms of sepsis patients.

Methods

The blood samples of eight normal controls and ten sepsis patients were collected for whole exome sequencing. Then, Single Nucleotide Polymorphisms (SNPs) were selected according to quality score and number of sepsis patients who had this variants. Synonymous mutations were removed. Genes including these remaining variants were used for functional analyses. After analyses, the remaining SNPs and indels were validated in 149 normal controls and 156 sepsis patients. Finally, serum levels of proteins coded by genes including these SNPs were evaluated.

Results

After whole exome sequencing, 97 SNPs and one indel site were left. Then, functional screening was performed. Only seven SNPs were used for further validation. As a result, the rs2721068 in dominant model and rs17446614 in recessive model were associated with sepsis, and the ORs of these two SNPs were 3.24 (95%CI, 1.25, 8.44) and 0.47 (0.026, 0.88), respectively. These two SNPs were both located in Forkhead box O1 (FOXO1) gene. For rs2721068 (T/T, T/C-C/C) and rs17446614 (A/A-A/G, G/G), serum levels of foxo1 in sepsis patients were both significantly lower in normal controls.

Conclusions

We firstly reported that the rs2721068 and rs17446614 were correlated to genetic predisposition to sepsis.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4330-7) contains supplementary material, which is available to authorized users.

Identification of key miRNA‑mRNA pairs in septic mice by bioinformatics analysis

Sepsis is one of the most common causes of death among critically ill patients in intensive care units worldwide; however, the microRNAs (miRNAs/miRs) involved in the sepsis process (and their target genes) are largely unknown. The present study integrated miRNA and mRNA datasets to elucidate key sepsis-related miRNA-mRNA pairs. The datasets, GSE74952 and GSE55238 were downloaded from the Gene Expression Omnibus. By performing bioinformatics analysis such as GEO2R, miRNA target gene prediction, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and miRNA-mRNA network analysis, a total of four sepsis-related miRNA-mRNA pairs were successfully obtained. Mmu-miR-370-3p, cluster of differentiation (CD)8a, CD247, Zap70 and inhibitor of nuclear factor κ B kinase subunit β (Ikbkb) were identified as the components involved in these pairs, and these genes were enriched in the T-cell receptor signaling pathway. Finally, reverse transcription-quantitative PCR results validated that the expression levels of the four genes (CD8a, CD247, Zap70 and Ikbkb) in the sepsis model mice were consistent with the microarray analysis. In conclusion, the present study identified four sepsis-related miRNA-mRNA pairs using bioinformatics analysis. These results indicated that the candidate miRNA-mRNA pairs may be involved in the regulation of immunity in sepsis, which may in turn act as indicators or therapeutic targets for sepsis.

RNA sampling from tissue sections using infrared laser ablation

RNA was obtained from discrete locations of frozen rat brain tissue sections through infrared (IR) laser ablation using a 3-μm wavelength in transmission geometry. The ablated plume was captured in a microcentrifuge tube containing RNAse-free buffer and processed using a commercial RNA purification kit. RNA transfer efficiency and integrity were evaluated based on automated electrophoresis in microfluidic chips. Reproducible IR-laser ablation of intact RNA was demonstrated with purified RNA at laser fluences of 3-5 kJ/m² (72 ± 12% transfer efficiency) and with tissue sections at a laser fluence of 13 kJ/m² (79 ± 14% transfer efficiency); laser energies were attenuated ∼20% by the soda-lime glass slides used to support the samples. RNA integrity from tissue ablation was >90% of its original RIN value (∼7) and the purified RNA was sufficiently intact for conversion to cDNA and subsequent qPCR assay.

Precision medicine in pediatric sepsis

PURPOSE OF REVIEW

Pediatric sepsis is a heterogeneous state associated with significant morbidity and mortality, but treatment strategies are limited. Clinical trials of immunomodulators in sepsis have shown no benefit, despite having a strong biological rationale. There is considerable interest in application of a precision medicine approach to pediatric sepsis to identify patients who are more likely to benefit from targeted therapeutic interventions.

RECENT FINDINGS

Precision medicine requires a clear understanding of the molecular basis of disease. 'Omics data' and bioinformatics tools have enabled identification of endotypes of pediatric septic shock, with corresponding biological pathways. Further, using a multibiomarker-based approach, patients at highest risk of poor outcomes can be identified at disease onset. Enrichment strategies, both predictive and prognostic, may be used to optimize patient selection in clinical trials and identify a subpopulation in whom therapy of interest may be trialed. A bedside-to-bench-to-bedside model may offer clinicians pragmatic tools to aid in decision-making.

SUMMARY

Precision medicine approaches may be used to subclassify, risk-stratify, and select pediatric patients with sepsis who may benefit from new therapies. Application of precision medicine will require robust basic and translational research, rigorous clinical trials, and infrastructure to collect and analyze big data.

AKIRIN1: A Potential New Reference Gene in Human Natural Killer Cells and Granulocytes in Sepsis

Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.