A genomic score prognostic of outcome in trauma patients

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.

Dysregulation of iron transport-related biomarkers in blood leukocytes is associated with poor prognosis of early trauma

Objective

The early targeted and effective diagnosis and treatment of severe trauma are crucial for patients' outcomes. Blood leukocytes act as significant effectors during the initial inflammation and activation of innate immunity in trauma. This study aims to identify hub genes related to patients' prognosis in blood leukocytes at the early stages of trauma.

Methods

The expression profiles of Gene Expression Omnibus (GEO) Series (GSE) 36809 and GSE11375 were downloaded from the GEO database. R software, GraphPad Prism 9.3.1 software, STRING database, and Cytoscape software were used to process the data and identify hub genes in blood leukocytes of early trauma.

Results

Gene Ontology (GO) analysis showed that the differentially expressed genes (DEGs) of blood leukocytes at the early stages of trauma (0-4 h, 4-8 h, and 8-12 h) were mainly involved in neutrophil activation and neutrophil degranulation, neutrophil activation involved in immune response, neutrophil mediated immunity, lymphocyte differentiation, and cell killing. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly involved in Osteoclast differentiation and Hematopoietic cell lineage. Sixty-six down-regulated DEGs and 148 up-regulated DEGs were identified and 37 hub genes were confirmed by Molecular Complex Detection (MCODE) of Cytoscape. Among the hub genes, Lipocalin 2 (LCN2), Lactotransferrin (LTF), Olfactomedin 4 (OLFM4), Resistin (RETN), and Transcobalamin 1 (TCN1) were related to prognosis and connected with iron transport closely. LCN2 and LTF were involved in iron transport and had a moderate predictive value for the poor prognosis of trauma patients, and the AUC of LCN2 and LTF was 0.7777 and 0.7843, respectively.

Conclusion

As iron transport-related hub genes in blood leukocytes, LCN2 and LTF can be used for prognostic prediction of early trauma.

Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees

We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.

Genetic and immune crosstalk between severe burns and blunt trauma: A study of transcriptomic data

Background: Severe burns and blunt trauma can lead to multiple organ dysfunction syndrome, the leading cause of death in intensive care units. In addition to infection, the degree of immune inflammatory response also affects prognosis. However, the characteristics and clinical relevance of the common mechanisms of these major diseases are still underexplored.

Methods: In the present study, we performed microarray data analysis to identify immune-related differentially expressed genes (DEGs) involved in both disease progression in burns and blunt trauma. Six analyses were subsequently performed, including gene enrichment analysis, protein‐protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, co-expression network analysis, and clinical correlation analysis.

Results: A total of 117 common immune-related DEGs was selected for subsequent analyses. Functional analysis emphasizes the important role of Th17 cell differentiation, Th1 and Th2 cell differentiation, Cytokine-cytokine receptor interaction and T cell receptor signaling pathway in these two diseases. Finally, eight core DEGs were identified using cytoHubba, including CD8A, IL10, CCL5, CD28, LCK, CCL4, IL2RB, and STAT1. The correlation analysis showed that the identified core DEGs were more or less significantly associated with simultaneous dysregulation of immune cells in blunt trauma and sepsis patients. Of these, the downregulation of CD8A and CD28 had a worse prognosis.

Conclusion: Our analysis lays the groundwork for future studies to elucidate molecular mechanisms shared in burns and blunt trauma. The functional roles of identified core immune-related DEGs and dysregulated immune cell subsets warrant further in-depth study.

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.

Multiplexed Human Gene Expression Analysis Reveals a Central Role of the TLR/mTOR/PPARγ and NFkB Axes in Burn and Inhalation Injury-Induced Changes in Systemic Immunometabolism and Long-Term Patient Outcomes

Burn patients are subject to significant acute immune and metabolic dysfunction. Concomitant inhalation injury increases mortality by 20%. In order to identify specific immune and metabolic signaling pathways in burn (B), inhalation (I), and combined burn-inhalation (BI) injury, unbiased nanoString multiplex technology was used to investigate gene expression within peripheral blood mononuclear cells (PBMCs) from burn patients, with and without inhalation injury. PBMCs were collected from 36 injured patients and 12 healthy, non-burned controls within 72 h of injury. mRNA was isolated and hybridized with probes for 1342 genes related to general immunology and cellular metabolism. From these specific gene patterns, specific cellular perturbations and signaling pathways were inferred using robust bioinformatic tools. In both B and BI injuries, elements of mTOR, PPARγ, TLR, and NF-kB signaling pathways were significantly altered within PBMC after injury compared to PBMC from the healthy control group. Using linear regression modeling, (1) DEPTOR, LAMTOR5, PPARγ, and RPTOR significantly correlated with patient BMI; (2) RPTOR significantly correlated with patient length of stay, and (3) MRC1 significantly correlated with the eventual risk of patient mortality. Identification of mediators of this immunometabolic response that can act as biomarkers and/or therapeutic targets could ultimately aid the management of burn patients.

Identification of Immune-Related Genes Concurrently Involved in Critical Illnesses Across Different Etiologies: A Data-Driven Analysis

Severe trauma and sepsis can lead to multiple organ dysfunction syndrome, which is a leading cause of death in intensive care units with mortality rates in excess of 50%. In addition to infection, the degree of immuno-inflammatory response also influences the outcome. The genomic changes observed after a variety of pathophysiological insults, such as trauma, sepsis, burns are similar, and consist of innate immune activation and adaptive immunity suppression. However, the characteristics of the shared mechanisms of aforementioned critical illnesses and the clinical relevance remain less explored. In the present study, we performed a data analysis to identify functional genes concurrently involved in critical illnesses across differing etiologies (trauma and sepsis derived from community-acquired pneumonia/abdominal source) and explored the shared signaling pathways these common genes involved in to gain insight into the underlying molecular mechanisms. A number of immune-related biological functions were found to be dysregulated in both trauma and sepsis in the present study, so we continued to identify immune-related common genes, profiled the immune cell proportion, and explored the relationships between them. The diagnostic and prognostic value of the immune-related common genes was also evaluated to address their potential clinical utilization as novel biomarkers. Notably, we identified a list of 14 immune-related genes concurrently dysregulated in trauma and sepsis showing favorable diagnostic value, among which S100P can predict prognosis of sepsis patients. Moreover, a spectrum of immune cell subsets including naïve B cells, CD8+ T cells, CD4+ memory resting T cells, activated NK cells, resting dendritic cells, plasma cells, Tregs, macrophages M0 and macrophages M1 was found to be concurrently dysregulated in both trauma and sepsis, and a close relation between above identified immune-related genes and immune cell subsets was observed. Our data-driven findings lay a foundation for future research to elucidate the pathophysiology regarding the aspect of inflammatory and immune response in critical illnesses, and suggest future studies focus on interpreting the function roles of the identified immune-related genes, as well as the reactive immune cell subsets.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery

OBJECTIVE

To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing.

SUMMARY BACKGROUND DATA

Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes.

METHODS

Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing fourteen surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury.

RESULTS

The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and post-operative time points and are already seeing evidence of physiologic perturbation between timepoints.

CONCLUSIONS

This repository allows for longitudinal, state-of-the-art genomic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.

Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Introduction

Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling.

Material and methods

This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4⁺, CD8⁺, CD14⁺, CD15⁺, and CD19⁺ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations.

Results

Ninety-four differentially expressed genes were found (CD8⁺ n = 58, CD14⁺ n = 20, CD4⁺ n = 14, CD19⁺ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4⁺, CD8⁺) and MMP25 (CD8⁺, CD14⁺). In CD4⁺ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4⁺ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8⁺ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8⁺ cells and CLEC10A was increased in CD14⁺ cells from smokers; moreover, CLEC5A (CD8⁺), CLEC7A (CD8⁺) and CLEC9A (CD19⁺) were related to smoking in supplementary analyses. CD14⁺ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3.

Conclusions

Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4⁺, CD8⁺ and CD14⁺ cells.

Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury

Recent advances in emergency medicine and the co-ordinated delivery of trauma care mean more critically-injured patients now reach the hospital alive and survive life-saving operations. Indeed, between 2008 and 2017, the odds of surviving a major traumatic injury in the UK increased by nineteen percent. However, the improved survival rates of severely-injured patients have placed an increased burden on the healthcare system, with major trauma a common cause of intensive care unit (ICU) admissions that last ≥10 days. Improved understanding of the factors influencing patient outcomes is now urgently needed. We investigated the serum metabolomic profile of fifty-five major trauma patients across three post-injury phases: acute (days 0–4), intermediate (days 5–14) and late (days 15–112). Using ICU length of stay (LOS) as a clinical outcome, we aimed to determine whether the serum metabolome measured at days 0–4 post-injury for patients with an extended (≥10 days) ICU LOS differed from that of patients with a short (<10 days) ICU LOS. In addition, we investigated whether combining metabolomic profiles with clinical scoring systems would generate a variable that would identify patients with an extended ICU LOS with a greater degree of accuracy than models built on either variable alone. The number of metabolites unique to and shared across each time segment varied across acute, intermediate and late segments. A one-way ANOVA revealed the most variation in metabolite levels across the different time-points was for the metabolites lactate, glucose, anserine and 3-hydroxybutyrate. A total of eleven features were selected to differentiate between <10 days ICU LOS vs. >10 days ICU LOS. New Injury Severity Score (NISS), testosterone, and the metabolites cadaverine, urea, isoleucine, acetoacetate, dimethyl sulfone, syringate, creatinine, xylitol, and acetone form the integrated biomarker set. Using metabolic enrichment analysis, we found valine, leucine and isoleucine biosynthesis, glutathione metabolism, and glycine, serine and threonine metabolism were the top three pathways differentiating ICU LOS with a p < 0.05. A combined model of NISS and testosterone and all nine selected metabolites achieved an AUROC of 0.824. Differences exist in the serum metabolome of major trauma patients who subsequently experience a short or prolonged ICU LOS in the acute post-injury setting. Combining metabolomic data with anatomical scoring systems allowed us to discriminate between these two groups with a greater degree of accuracy than that of either variable alone.

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors

Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors manifest unique blood leukocyte transcriptomes in late sepsis that differ from transcriptomes among sepsis survivors with rapid recovery. In a prospective cohort study of surgical ICU patients, genome-wide expression analysis was conducted on total leukocytes in human whole blood collected on days 1 and 14 from sepsis survivors who rapidly recovered or developed CCI, defined as ICU length of stay ≥ 14 days with persistent organ dysfunction. Both sepsis patients who developed CCI and those who rapidly recovered exhibited marked changes in genome-wide expression at day 1 which remained abnormal through day 14. Although summary changes in gene expression were similar between CCI patients and subjects who rapidly recovered, CCI patients exhibited differential expression of 185 unique genes compared with rapid recovery patients at day 14 (p < 0.001). The transcriptomic patterns in sepsis survivors reveal an ongoing immune dyscrasia at the level of the blood leukocyte transcriptome, consistent with persistent inflammation and immune suppression. Furthermore, the findings highlight important genes that could compose a prognostic transcriptomic metric or serve as therapeutic targets among sepsis patients that develop CCI.

Gene Expression-Based Diagnosis of Infections in Critically Ill Patients-Prospective Validation of the SepsisMetaScore in a Longitudinal Severe Trauma Cohort

OBJECTIVES

Early diagnosis of infections is pivotal in critically ill patients. Innovative gene expression-based approaches promise to deliver precise, fast, and clinically practicable diagnostic tools to bedside. This study aimed to validate the SepsisMetaScore, an 11-gene signature previously reported by our study group, in a representative longitudinal cohort of trauma patients.

DESIGN

Prospective observational cohort study.

SETTING

Surgical ICUs of the University Medical Center Goettingen, Germany.

PATIENTS

Critically ill patients with severe traumatic injuries.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Paired box gene (PAXgene) RNA blood tubes were drawn at predefined time points over the course of disease. The performance of the SepsisMetaScore was tested using targeted polymerase chain reaction and compared with Procalcitonin using area under the receiver operating characteristics analyses. The SepsisMetaScore showed significant differences between infected and noninfected patients (n = 52). It was able to accurately discriminate infectious from noninfectious acute inflammation with an area under the receiver operating characteristics of 0.92 (95% CI, 0.85-0.99) and significantly outperformed Procalcitonin (area under the receiver operating characteristics curve = 0.53; 95% CI, 0.42-0.64) early in the course of infection (p = 0.014).

CONCLUSIONS

We demonstrated the clinical utility for diagnosis of infections with higher accuracy using the SepsisMetaScore compared with Procalcitonin in a prospective cohort of severe trauma patients. Future studies should assess whether the SepsisMetaScore may substantially improve clinical practice by accurate differentiation of infections from sterile inflammation and identification of patients at risk for sepsis. Our results support further investigation of the SepsisMetaScore for the development of tailored precision treatment of critically ill patients.

Identification of Unique mRNA and miRNA Expression Patterns in Bone Marrow Hematopoietic Stem and Progenitor Cells After Trauma in Older Adults

Older adults have significantly worse morbidity and mortality after severe trauma than younger cohorts. The competency of the innate immune response decreases with advancing age, especially after an inflammatory insult. Subsequent poor outcomes after trauma are caused in part by dysfunctional leukocytes derived from the host's hematopoietic stem and progenitor cells (HSPCs). Our objective was to analyze the bone marrow (BM) HSPC transcriptomic [mRNA and microRNA (miR)] responses to trauma in older and younger adults. BM was collected intraoperatively <9 days after initial injury from trauma patients with non-mild injury [ISS ≥ 9] or with shock (lactate ≥ 2, base deficit ≥ 5, MAP ≤ 65) who underwent operative fixation of a pelvic or long bone fracture. Samples were also analyzed based on age (<55 years and ≥55 years), ISS score and transfusion in the first 24 h, and compared to age/sex-matched controls from non-cancer elective hip replacement or purchased healthy younger adult human BM aspirates. mRNA and miR expression patterns were calculated from lineage-negative enriched HSPCs. 924 genes were differentially expressed in older trauma subjects vs. age/sex-matched controls, while 654 genes were differentially expressed in younger subjects vs. age/sex-matched control. Only 68 transcriptomic changes were shared between the two groups. Subsequent analysis revealed upregulation of transcriptomic pathways related to quantity, function, differentiation, and proliferation of HSPCs in only the younger cohort. miR expression differences were also identified, many of which were associated with cell cycle regulation. In summary, differences in the BM HSPC mRNA and miR expression were identified between older and younger adult trauma subjects. These differences in gene and miR expression were related to pathways involved in HSPC production and differentiation. These differences could potentially explain why older adult patients have a suboptimal hematopoietic response to trauma. Although immunomodulation of HSPCs may be a necessary consideration to promote host protective immunity after host injury, the age related differences further highlight that patients may require an age-defined medical approach with interventions that are specific to their transcriptomic and biologic response. Also, targeting the older adult miRs may be possible for interventions in this patient population.

Multi-study reanalysis of 2,213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures

In 2019 it is estimated that more than 21,000 new acute myeloid leukemia (AML) patients will be diagnosed in the United States, and nearly 11,000 are expected to die from the disease. AML is primarily diagnosed among the elderly (median 68 years old at diagnosis). Prognoses have significantly improved for younger patients, but as much as 70% of patients over 60 years old will die within a year of diagnosis. In this study, we conducted a reanalysis of 2,213 acute myeloid leukemia patients compared to 548 healthy individuals, using curated publicly available microarray gene expression data. We carried out an analysis of normalized batch corrected data, using a linear model that included considerations for disease, age, sex, and tissue. We identified 974 differentially expressed probe sets and 4 significant pathways associated with AML. Additionally, we identified 375 age- and 70 sex-related probe set expression signatures relevant to AML. Finally, we trained a k nearest neighbors model to classify AML and healthy subjects with 90.9% accuracy. Our findings provide a new reanalysis of public datasets, that enabled the identification of new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.

A conceptual time window-based model for the early stratification of trauma patients

Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.

Metabolomics and Precision Medicine in Trauma: The State of the Field

Trauma is a major problem in the United States. Mortality from trauma is the number one cause of death under the age of 45 in the United States and is the third leading cause of death for all age groups. There are approximately 200,000 deaths per year due to trauma in the United States at a cost of over $671 billion in combined healthcare costs and lost productivity. Unsurprisingly, trauma accounts for approximately 30% of all life-years lost in the United States. Due to immense development of trauma systems, a large majority of trauma patients survive the injury, but then go on to die from complications arising from the injury. These complications are marked by early and significant metabolic changes accompanied by inflammatory responses that lead to progressive organ failure and, ultimately, death. Early resuscitative and surgical interventions followed by close monitoring to identify and rescue treatment failures are key to successful outcomes. Currently, the adequacy of resuscitation is measured using vital signs, noninvasive methods such as bedside echocardiography or stroke volume variation, and other laboratory endpoints of resuscitation, such as lactate and base deficit. However, these methods may be too crude to understand cellular and subcellular changes that may be occurring in trauma patients. Better diagnostic and therapeutic markers are needed to assess the adequacy of interventions and monitor responses at a cellular and subcellular level and inform clinical decision-making before complications are clinically apparent. The developing field of metabolomics holds great promise in the identification and application of biochemical markers toward the clinical decision-making process.

Multicohort Analysis of Whole-Blood Gene Expression Data Does Not Form a Robust Diagnostic for Acute Respiratory Distress Syndrome

OBJECTIVES

To identify a novel, generalizable diagnostic for acute respiratory distress syndrome using whole-blood gene expression arrays from multiple acute respiratory distress syndrome cohorts of varying etiologies.

DATA SOURCES

We performed a systematic search for human whole-blood gene expression arrays of acute respiratory distress syndrome in National Institutes of Health Gene Expression Omnibus and ArrayExpress. We also included the Glue Grant gene expression cohorts.

STUDY SELECTION

We included investigator-defined acute respiratory distress syndrome within 48 hours of diagnosis and compared these with relevant critically ill controls.

DATA EXTRACTION

We used multicohort analysis of gene expression to identify genes significantly associated with acute respiratory distress syndrome, both with and without adjustment for clinical severity score. We performed gene ontology enrichment using Database for Annotation, Visualization and Integrated Discovery and cell type enrichment tests for both immune cells and pneumocyte gene expression. Finally, we selected a gene set optimized for diagnostic power across the datasets and used leave-one-dataset-out cross validation to assess robustness of the model.

DATA SYNTHESIS

We identified datasets from three adult cohorts with sepsis, one pediatric cohort with acute respiratory failure, and two datasets of adult patients with trauma and burns, for a total of 148 acute respiratory distress syndrome cases and 268 critically ill controls. We identified 30 genes that were significantly associated with acute respiratory distress syndrome (false discovery rate < 20% and effect size >1.3), many of which had been previously associated with sepsis. When metaregression was used to adjust for clinical severity scores, none of these genes remained significant. Cell type enrichment was notable for bands and neutrophils, suggesting that the gene expression signature is one of acute inflammation rather than lung injury per se. Finally, an attempt to develop a generalizable diagnostic gene set for acute respiratory distress syndrome showed a mean area under the receiver-operating characteristic curve of only 0.63 on leave-one-dataset-out cross validation.

CONCLUSIONS

The whole-blood gene expression signature across a wide clinical spectrum of acute respiratory distress syndrome is likely confounded by systemic inflammation, limiting the utility of whole-blood gene expression studies for uncovering a generalizable diagnostic gene signature.

Global Gene Expression Change Induced by Major Thoracoabdominal Surgery

OBJECTIVE

To test the hypothesis that major thoracoabdominal surgery induces gene expression changes associated with adverse outcomes.

BACKGROUND

Widely different traumatic injuries evoke surprisingly similar gene expression profiles, but there is limited information on whether the iatrogenic injury caused by major surgery is associated with similar patterns.

METHODS

With informed consent, blood samples were obtained from 50 patients before and after open transhiatal esophagectomy or pancreaticoduodenectomy. Twelve cases with complicated recoveries (death, infection, venous thromboembolism) were matched with 12 cases with uneventful recoveries. Global gene expression was assayed using human microarray chips. A 2-fold change with a corrected P < 0.05 was considered differentially expressed.

RESULTS

In these 24 patients, 522 genes were differentially expressed after surgery; 248 (48%) were upregulated (innate immunity and inflammation) and 274 (52%) were downregulated [adaptive immunity (antigen presentation, T-cell function)]. Hierarchical clustering of the profile reliably predicted pre- and postoperative status. The within-patient change was 3.08 ± 0.91-fold. There was no measurable association with age, malignancy, procedure, surgery length, operative blood loss, or transfusion requirements, but was positively associated with postoperative infection (3.81 ± 0.97 vs 2.79 ± 0.73; P = 0.009) and hospital length of stay (r = 0.583, P = 0.003). Venous thromboembolism and mortality each occurred in one patient, thus no associations were possible.

CONCLUSIONS

Major surgery induces a quantifiable pattern of gene expression change that is associated with adverse outcome. This could reflect early impaired adaptive immunity and suggests potential therapeutic targets to improve postoperative recovery.

The frontline of immune response in peripheral blood

Peripheral blood is an attractive source for the discovery of disease biomarkers. Gene expression profiling of whole blood or its components has been widely conducted for various diseases. However, due to population heterogeneity and the dynamic nature of gene expression, certain biomarkers discovered from blood transcriptome studies could not be replicated in independent studies. In the meantime, it's also important to know whether a reliable biomarker is shared by several diseases or specific to certain health conditions. We hypothesized that common mechanism of immune response in blood may be shared by different diseases. Under this hypothesis, we surveyed publicly available transcriptome data on infectious and autoimmune diseases derived from peripheral blood. We examined to which extent common gene dys-regulation existed in different diseases. We also investigated whether the commonly dys-regulated genes could serve as reliable biomarkers. First, we found that a limited number of genes are frequently dys-regulated in infectious and autoimmune diseases, from which we selected 10 genes co-dysregulated in viral infections and another set of 10 genes co-dysregulated in bacterial infections. In addition to its ability to distinguish viral infections from bacterial infections, these 20 genes could assist in disease classification and monitoring of treatment effect for several infectious and autoimmune diseases. In some cases, a single gene is sufficient to serve this purpose. It was interesting that dys-regulation of these 20 genes were also observed in other types of diseases including cancer and stroke where certain genes could also serve as biomarkers for diagnosis or prognosis. Furthermore, we demonstrated that this set of 20 genes could also be used in continuous monitoring of personal health. The rich information from these commonly dys-regulated genes may find its wide application in clinical practice and personal healthcare. More validation studies and in-depth investigations are warranted in the future.

Benchmarking Sepsis Gene Expression Diagnostics Using Public Data

OBJECTIVE

In response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene "Sepsis MetaScore," the "FAIM3-to-PLAC8" ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included datasets. We also created a public repository of sepsis gene expression data to encourage their future reuse.

DATA SOURCES

We searched National Institutes of Health Gene Expression Omnibus and EBI ArrayExpress for human gene expression microarray datasets. We also included the Glue Grant trauma gene expression cohorts.

STUDY SELECTION

We selected clinical, time-matched, whole blood studies of sepsis and acute infections as compared to healthy and/or noninfectious inflammation patients. We identified 39 datasets composed of 3,241 samples from 2,604 patients.

DATA EXTRACTION

All data were renormalized from raw data, when available, using consistent methods.

DATA SYNTHESIS

Mean validation areas under the receiver operating characteristic curve for discriminating septic patients from patients with noninfectious inflammation for the Sepsis MetaScore, the FAIM3-to-PLAC8 ratio, and the Septicyte Lab were 0.82 (range, 0.73-0.89), 0.78 (range, 0.49-0.96), and 0.73 (range, 0.44-0.90), respectively. Paired-sample t tests of validation datasets showed no significant differences in area under the receiver operating characteristic curves. Mean validation area under the receiver operating characteristic curves for discriminating infected patients from healthy controls for the Sepsis MetaScore, FAIM3-to-PLAC8 ratio, and Septicyte Lab were 0.97 (range, 0.85-1.0), 0.94 (range, 0.65-1.0), and 0.71 (range, 0.24-1.0), respectively. There were few significant differences in any diagnostics due to pathogen type.

CONCLUSIONS

The three diagnostics do not show significant differences in overall ability to distinguish noninfectious systemic inflammatory response syndrome from sepsis, though the performance in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the FAIM3-to-PLAC8 ratio and Septicyte Lab. The Septicyte Lab also demonstrated significantly worse performance in discriminating infections as compared to healthy controls. Overall, public gene expression data are a useful tool for benchmarking gene expression diagnostics.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.

The diagnostic and prognostic value of systems biology research in major traumatic and thermal injury: a review

As secondary complications remain a significant cause of morbidity and mortality amongst hospitalised trauma patients, the need to develop novel approaches by which to identify patients at risk of adverse outcome is becoming increasingly important. Centred on the idea that patients who experience “poor” outcome post trauma elicit a response to injury that is distinct from those who experience “good” outcome, tailored therapeutics is an emerging concept aimed at improving current treatment regimens by promoting patient-specific therapies. Making use of recent advancements in the fields of genomics, proteomics and metabolomics, numerous groups have undertaken a systems-based approach to analysing the acute immune and inflammatory response to major traumatic and thermal injury in an attempt to uncover a single or combination of biomarkers that can identify patients at risk of adverse outcome. Early results are encouraging, with all three approaches capable of discriminating patients with “good” outcome from those who develop nosocomial infections, sepsis and multiple organ failure, with differences apparent in blood samples acquired as early as 2 h post injury. In particular, genomic data is proving to be highly informative, identifying patients at risk of “poor” outcome with a higher degree of sensitivity and specificity than statistical models built upon data obtained from existing anatomical and physiological scoring systems. Here, focussing predominantly upon human-based research, we provide an overview of the findings of studies that have investigated the immune and inflammatory response to major traumatic and thermal injury at the genomic, protein and metabolite level, and consider both the diagnostic and prognostic potential of these approaches.

Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients

RATIONALE

Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections.

OBJECTIVES

We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing.

METHODS

The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d.

MEASUREMENTS AND MAIN RESULTS

In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved.

CONCLUSIONS

Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

Patterns of gene expression among murine models of hemorrhagic shock/trauma and sepsis

Controversy remains whether the leukocyte genomic response to trauma or sepsis is dependent upon the initiating stimulus. Previous work illustrated poor correlations between historical models of murine trauma and sepsis (i.e., trauma-hemorrhage and lipopolysaccharide injection, respectively). The aim of this study is to examine the early genomic response in improved murine models of sepsis [cecal ligation and puncture (CLP)] and trauma [polytrauma (PT)] with and without pneumonia (PT+Pp). Groups of naïve, CLP, PT, and PT+Pp mice were killed at 2 h, 1 or 3 days. Total leukocytes were isolated for genome-wide expression analysis, and genes that were found to differ from control (false discovery rate adjusted P < 0.001) were assessed for fold-change differences. Spearman correlations were also performed. For all time points combined (CLP, PT, PT+Pp), there were 10,426 total genes that were found to significantly differ from naïve controls. At 2 h, the transcriptomic changes between CLP and PT showed a positive correlation (rs) of 0.446 (P < 0.0001) but were less positive thereafter. Correlations were significantly improved when we limited the analysis to common genes whose expression differed by a 1.5 fold-change. Both pathway and upstream analyses revealed the activation of genes known to be associated with pathogen-associated and damage-associated molecular pattern signaling, and early activation patterns of expression were very similar between polytrauma and sepsis at the earliest time points. This study demonstrates that the early leukocyte genomic response to sepsis and trauma are very similar in mice.

The application of a trauma index to assess injury severity and prognosis in hospitalized patients with acute trauma

OBJECTIVE

The aim of this study was to determine the application value of a trauma index (TI) to assess condition and likelihood of death in hospitalized patients with acute trauma (AT).

METHODS

Trauma index scores and injury severity scores (ISS) were assessed in 1,802 randomly selected cases of AT-hospitalized patients. The receiver operating characteristic (ROC) curve was used to compare the clinical values of TI and ISS values to predict outcomes in AT-hospitalized patients.

RESULTS

The area under the ROC curve for TI scores was 0.896 (95% CI [0.881, 0.909]), while for ISS, it was 0.792 (95% CI [0.773, 0.811]). This difference was not statistically significant (z = 3.236, P = 0.001). Potentially critical disease conditions in AT-hospitalized patients were best identified when TI scores were ≥ 16 points and ISS values were ≥ 22 points.

CONCLUSIONS

Trauma Index scores exhibited a higher resolution for outcome prediction in AT-hospitalized patients compared to ISS values. The implementation of this scale was simple, reliable, easy to learn, and could quickly identify disease, which is vital for early detection and treatment of critical trauma patients.

A Detailed Characterization of the Dysfunctional Immunity and Abnormal Myelopoiesis Induced by Severe Shock and Trauma in the Aged

The elderly are particularly susceptible to trauma, and their outcomes are frequently dismal. Such patients often have complicated clinical courses and ultimately die of infection and sepsis. Recent research has revealed that although elderly subjects have increased baseline inflammation as compared with their younger counterparts, the elderly do not respond to severe infection or injury with an exaggerated inflammatory response. Initial retrospective analysis of clinical data from the Glue Grant trauma database demonstrated that despite a similar frequency, elderly trauma patients have worse outcomes to pneumonia than younger subjects do. Subsequent analysis with a murine trauma model also demonstrated that elderly mice had increased mortality after posttrauma Pseudomonas pneumonia. Blood, bone marrow, and bronchoalveolar lavage sample analyses from juvenile and 20-24-mo-old mice showed that increased mortality to trauma combined with secondary infection in the aged are not due to an exaggerated inflammatory response. Rather, they are due to a failure of bone marrow progenitors, blood neutrophils, and bronchoalveolar lavage cells to initiate and complete an emergency myelopoietic response, engendering myeloid cells that fail to clear secondary infection. In addition, elderly people appeared unable to resolve their inflammatory response to severe injury effectively.

Developing a clinically feasible personalized medicine approach to pediatric septic shock

RATIONALE

Using microarray data, we previously identified gene expression-based subclasses of septic shock with important phenotypic differences. The subclass-defining genes correspond to adaptive immunity and glucocorticoid receptor signaling. Identifying the subclasses in real time has theranostic implications, given the potential for immune-enhancing therapies and controversies surrounding adjunctive corticosteroids for septic shock.

OBJECTIVES

To develop and validate a real-time subclassification method for septic shock.

METHODS

Gene expression data for the 100 subclass-defining genes were generated using a multiplex messenger RNA quantification platform (NanoString nCounter) and visualized using gene expression mosaics. Study subjects (n = 168) were allocated to the subclasses using computer-assisted image analysis and microarray-based reference mosaics. A gene expression score was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (n = 132).

MEASUREMENTS AND MAIN RESULTS

The NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The gene expression score identified this subclass with an area under the curve of 0.98 (95% confidence interval [CI95] = 0.96-0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (odds ratio = 2.7; CI95 = 1.2-6.0; P = 0.016), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (odds ratio = 4.1; CI95 = 1.4-12.0; P = 0.011).

CONCLUSIONS

We developed and tested a gene expression-based classification method for pediatric septic shock that meets the time constraints of the critical care environment, and can potentially inform therapeutic decisions.

Increased abundance of ADAM9 transcripts in the blood is associated with tissue damage

Background: Members of the ADAM (a disintegrin and metalloprotease domain) family have emerged as critical regulators of cell-cell signaling during development and homeostasis. ADAM9 is consistently overexpressed in various human cancers, and has been shown to play an important role in tumorigenesis. However, little is known about the involvement of ADAM9 during immune-mediated processes. Results: Mining of an extensive compendium of transcriptomic datasets identified important gaps in knowledge regarding the possible role of ADAM9 in immunological homeostasis and inflammation: 1) The abundance of ADAM9 transcripts in the blood was increased in patients with acute infection but, 2) changed very little after in vitro exposure to a wide range of pathogen-associated molecular patterns (PAMPs). 3) Furthermore it was found to increase significantly in subjects as a result of tissue injury or tissue remodeling, in absence of infectious processes. Conclusions: Our findings indicate that ADAM9 may constitute a valuable biomarker for the assessment of tissue damage, especially in clinical situations where other inflammatory markers are confounded by infectious processes.

Increased abundance of ADAM9 transcripts in the blood is associated with tissue damage

Background: Members of the ADAM (a disintegrin and metalloprotease domain) family have emerged as critical regulators of cell-cell signaling during development and homeostasis. ADAM9 is consistently overexpressed in various human cancers, and has been shown to play an important role in tumorigenesis. However, little is known about the involvement of ADAM9 during immune-mediated processes.

Results: Mining of an extensive compendium of transcriptomic datasets identified important gaps in knowledge regarding the possible role of ADAM9 in immunological homeostasis and inflammation: 1) The abundance of ADAM9 transcripts in the blood was increased in patients with acute infection but, 2) changed very little after in vitro exposure to a wide range of pathogen-associated molecular patterns (PAMPs). 3) Furthermore it was found to increase significantly in subjects as a result of tissue injury or tissue remodeling, in absence of infectious processes.

Conclusions: Our findings indicate that ADAM9 may constitute a valuable biomarker for the assessment of tissue damage, especially in clinical situations where other inflammatory markers are confounded by infectious processes.

Advanced age is associated with worsened outcomes and a unique genomic response in severely injured patients with hemorrhagic shock

INTRODUCTION

We wished to characterize the relationship of advanced age to clinical outcomes and to transcriptomic responses after severe blunt traumatic injury with hemorrhagic shock.

METHODS

We performed epidemiological, cytokine, and transcriptomic analyses on a prospective, multi-center cohort of 1,928 severely injured patients.

RESULTS

We found that there was no difference in injury severity between the aged (age ≥55, n = 533) and young (age <55, n = 1395) cohorts. However, aged patients had more comorbidities. Advanced age was associated with more severe organ failure, infectious complications, ventilator days, and intensive care unit length of stay, as well as, an increased likelihood of being discharged to skilled nursing or long-term care facilities. Additionally, advanced age was an independent predictor of a complicated recovery and 28-day mortality. Acutely after trauma, blood neutrophil genome-wide expression analysis revealed an attenuated transcriptomic response as compared to the young; this attenuated response was supported by the patients' plasma cytokine and chemokine concentrations. Later, these patients demonstrated gene expression changes consistent with simultaneous, persistent pro-inflammatory and immunosuppressive states.

CONCLUSIONS

We concluded that advanced age is one of the strongest non-injury related risk factors for poor outcomes after severe trauma with hemorrhagic shock and is associated with an altered and unique peripheral leukocyte genomic response. As the general population's age increases, it will be important to individualize prediction models and therapeutic targets to this high risk cohort.

Strategies to improve drug development for sepsis

Sepsis, a common and potentially fatal systemic illness, is triggered by microbial infection and often leads to impaired function of the lungs, kidneys or other vital organs. Since the early 1980s, a large number of therapeutic agents for the treatment of sepsis have been evaluated in randomized controlled clinical trials. With few exceptions, the results from these trials have been disappointing, and no specific therapeutic agent is currently approved for the treatment of sepsis. To improve upon this dismal record, investigators will need to identify more suitable therapeutic targets, improve their approaches for selecting candidate compounds for clinical development and adopt better designs for clinical trials.

Insights from computational modeling in inflammation and acute rejection in limb transplantation

Acute skin rejection in vascularized composite allotransplantation (VCA) is the major obstacle for wider adoption in clinical practice. This study utilized computational modeling to identify biomarkers for diagnosis and targets for treatment of skin rejection. Protein levels of 14 inflammatory mediators in skin and muscle biopsies from syngeneic grafts [n = 10], allogeneic transplants without immunosuppression [n = 10] and allografts treated with tacrolimus [n = 10] were assessed by multiplexed analysis technology. Hierarchical Clustering Analysis, Principal Component Analysis, Random Forest Classification and Multinomial Logistic Regression models were used to segregate experimental groups. Based on Random Forest Classification, Multinomial Logistic Regression and Hierarchical Clustering Analysis models, IL-4, TNF-α and IL-12p70 were the best predictors of skin rejection and identified rejection well in advance of histopathological alterations. TNF-α and IL-12p70 were the best predictors of muscle rejection and also preceded histopathological alterations. Principal Component Analysis identified IL-1α, IL-18, IL-1β, and IL-4 as principal drivers of transplant rejection. Thus, inflammatory patterns associated with rejection are specific for the individual tissue and may be superior for early detection and targeted treatment of rejection.

Host responses to sepsis vary in different low-lethality murine models

INTRODUCTION

Animal models for the study of sepsis are being increasingly scrutinized, despite their essential role for early translational research. In particular, recent studies have suggested that at the level of the leukocyte transcriptome, murine models of burns, trauma and endotoxemia markedly differ from their human equivalents, and are only weakly similar amongst themselves. We compared the plasma cytokine and leukocyte transcriptome responses between two different low-lethality murine models of polymicrobial intra-abdominal sepsis.

METHODS

Six to ten week male C57BL/6j mice underwent either the 'gold standard' cecal ligation and puncture (CLP) model of intra-abdominal sepsis or administration of a cecal slurry (CS), where cecal contents are injected intraperitoneally. Surviving mice were euthanized at two hours, one or three days after sepsis.

RESULTS

The murine leukocyte transcriptomic response to the CLP and CS models of sepsis was surprisingly dissimilar at two hours, one, and three days after sepsis. The Pearson correlation coefficient for the maximum change in expression for the entire leukocyte transcriptome that changed significantly over time (n = 19,071) was R = 0.54 (R2 = 0.297). The CS model resulted in greater magnitude of early inflammatory gene expression changes in response to sepsis with associated increased production of inflammatory chemokines and cytokines. Two hours after sepsis, CLP had more significant expression of genes associated with IL-10 signaling pathways, whereas CS had greater expression of genes related to CD28, apoptosis, IL-1 and T-cell receptor signaling. By three days, the changes in gene expression in both sepsis models were returning to baseline in surviving animals.

CONCLUSION

These analyses reveal that the murine blood leukocyte response to sepsis is highly dependent on which model of intra-abdominal sepsis is employed, despite their similar lethality. It may be difficult to extrapolate findings from one murine model to another, let alone to human sepsis.

Identifying key regulatory genes in the whole blood of septic patients to monitor underlying immune dysfunctions

There is currently no reliable tool available to measure immune dysfunction in septic patients in the clinical setting. This proof-of-concept study assesses the potential of gene expression profiling of whole blood as a tool to monitor immune dysfunction in critically ill septic patients. Whole-blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis. RNA isolated from whole-blood samples was assayed on Illumina HT-12 gene expression microarrays consisting of 48,804 probes. Microarray analysis identified 3,677 genes as differentially expressed across 5 days between septic patients and healthy controls. Of the 3,677 genes, biological pathway analysis identified 86 genes significantly downregulated in the sepsis patients were present in pathways relating to immune response. These 86 genes correspond to known immune pathways implicated in sepsis, including lymphocyte depletion, reduced T-lymphocyte activation, and deficient antigen presentation. Furthermore, expression levels of these genes correlated with clinical severity, with a significantly greater degree of downregulation found in nonsurvivors compared with survivors. The results show that whole-blood gene expression analysis can capture systemic immune dysfunctions in septic patients. Our study provides an experimental basis to support further study on the use of a gene expression-based assay, to assess immunosuppression, and to guide immunotherapy in future clinical trials.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Persistent inflammation, immunosuppression, and catabolism syndrome after severe blunt trauma

BACKGROUND

We recently proffered that a new syndrome persistent inflammation, immunosuppression, and catabolism syndrome (PICS) has replaced late multiple-organ failure as a predominant phenotype of chronic critical illness. Our goal was to validate this by determining whether severely injured trauma patients with complicated outcomes have evidence of PICS at the genomic level.

METHODS

We performed a secondary analysis of the Inflammation and Host Response to Injury database of adults with severe blunt trauma. Patients were classified into complicated, intermediate, and uncomplicated clinical trajectories. Existing genomic microarray data were compared between cohorts using Ingenuity Pathways Analysis. Epidemiologic data and outcomes were also analyzed between cohorts on admission, Day 7, and Day 14.

RESULTS

Complicated patients were older, were sicker, and required increased ventilator days compared with the intermediate/uncomplicated patients. They also had persistent leukocytosis as well as low lymphocyte and albumin levels compared with uncomplicated patients. Total white blood cell leukocyte analysis in complicated patients showed that overall genome-wide expression patterns and those patterns on Days 7 and 14 were more aberrant from control subjects than were patterns from uncomplicated patients. Complicated patients also had significant down-regulation of adaptive immunity and up-regulation of inflammatory genes on Days 7 and 14 (vs. magnitude in fold change compared with control and in magnitude compared with uncomplicated patients). On Day 7, complicated patients had significant changes in functional pathways involved in the suppression of myeloid cell differentiation, increased inflammation, decreased chemotaxis, and defective innate immunity compared with uncomplicated patients and controls. Subset analysis of monocyte, neutrophil, and T-cells supported these findings.

CONCLUSION

Genomic analysis of patients with complicated clinical outcomes exhibit persistent genomic expression changes consistent with defects in the adaptive immune response and increased inflammation. Clinical data showed persistent inflammation, immunosuppression, and protein depletion. Overall, the data support the hypothesis that patients with complicated clinical outcomes are exhibiting PICS.

LEVEL OF EVIDENCE

Epidemiologic study, level III.

Aged mice are unable to mount an effective myeloid response to sepsis

The elderly have increased morbidity and mortality following sepsis; however, the cause(s) remains unclear. We hypothesized that these poor outcomes are due in part to defects in innate immunity, rather than to an exaggerated early inflammatory response. Young (6-12 wk) or aged (20-24 mo) mice underwent polymicrobial sepsis, and subsequently, the aged mice had increased mortality and defective peritoneal bacterial clearance compared with young mice. No differences were found in the magnitude of the plasma cytokine responses. Although septic aged mice displayed equivalent or increased numbers of circulating, splenic, and bone marrow myeloid cells, some of these cells exhibited decreased phagocytosis, reactive oxygen species production, and chemotaxis. Blood leukocyte gene expression was less altered in aged versus young mice 1 d after sepsis. Aged mice had a relative inability to upregulate gene expression of pathways related to neutrophil-mediated protective immunity, chemokine/chemokine receptor binding, and responses to exogenous molecules. Expression of most MHC genes remained more downregulated in aged mice at day 3. Despite their increased myeloid response to sepsis, the increased susceptibility of aged mice to sepsis appears not to be due to an exaggerated inflammatory response, but rather, a failure to mount an effective innate immune response.

A Systems Engineering Perspective on Homeostasis and Disease

Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Mitochondrial transcription factor A, an endogenous danger signal, promotes TNFα release via RAGE- and TLR9-responsive plasmacytoid dendritic cells

Objective

Mitochondrial transcription factor A (TFAM) is normally bound to and remains associated with mitochondrial DNA (mtDNA) when released from damaged cells. We hypothesized that TFAM, bound to mtDNA (or equivalent CpG-enriched DNA), amplifies TNFα release from TLR9-expressing plasmacytoid dendritic cells (pDCs) by engaging RAGE.

Materials and Methods

Murine Flt3 ligand-expanded splenocytes obtained from C57BL/6 mice were treated with recombinant human TFAM, alone or in combination with CpG-enriched DNA with subsequent TNFα release measured by ELISA. The role of RAGE was determined by pre-treatment with soluble RAGE or heparin or by employing matching RAGE (-/-) splenocytes. TLR9 signaling was evaluated using a specific TLR9-blocking oligonucleotide and by inhibiting endosomal processing, PI3K and NF-κB. Additional studies examined whether heparin sulfate moieties or endothelin converting enzyme-1 (ECE-1)-dependent recycling of endosomal receptors were required for TFAM and CpG DNA recognition.

Main Results

TFAM augmented splenocyte TNFα release in response to CpGA DNA, which was strongly dependent upon pDCs and regulated by RAGE and TLR9 receptors. Putative TLR9 signaling pathways, including endosomal acidification and signaling through PI3K and NF-κB, were essential for splenocyte TNFα release in response to TFAM+CpGA DNA. Interestingly, TNFα release depended upon endothelin converting enzyme (ECE)-1, which cleaves and presumably activates TLR9 within endosomes. Recognition of the TFAM-CpGA DNA complex was dependent upon heparin sulfate moieties, and recombinant TFAM Box 1 and Box 2 proteins were equivalent in terms of augmenting TNFα release.

Conclusions

TFAM promoted TNFα release in a splenocyte culture model representing complex cell-cell interactions in vivo with pDCs playing a critical role. To our knowledge, this study is the first to incriminate ECE-1-dependent endosomal cleavage of TLR9 as a critical step in the signaling pathway leading to TNFα release. These findings, and others reported herein, significantly advance our understanding of sterile immune responses triggered by mitochondrial danger signals.

Epigenetics in critical illness: a new frontier

Epigenetics is the study of alterations in the function of genes that do not involve changes in the DNA sequence. Within the critical care literature, it is a relatively new and exciting avenue of research in describing pathology, clinical course, and developing targeted therapies to improve outcomes. In this paper, we highlight current research relative to critical care that is focused within the major epigenetic mechanisms of DNA methylation, histone modification, microRNA regulation, and composite epigenetic scoring. Within this emerging body of research it is quite clear that the novel therapies of the future will require clinicians to understand and navigate an even more complex and multivariate relationship between genetic, epigenetic, and biochemical mechanisms in conjunction with clinical presentation and course in order to significantly improve outcomes within the acute and critically ill population.

Changes in the epidemiology and prediction of multiple-organ failure after injury

BACKGROUND

The epidemiology of multiple-organ failure (MOF) after injury has been changing, questioning the validity of previously described prediction models. This study aimed to describe the current epidemiology of MOF. The secondary aim was development of a prediction model that could be used for early identification of patients at risk of MOF.

METHODS

A 60-month prospective epidemiologic study was undertaken at an Australian Level I trauma center. Data were collected on trauma patients that met inclusion criteria (intensive care unit [ICU] admission; Injury Severity Score [ISS] > 15; age > 18 years, head Abbreviated Injury Scale [AIS] score < 3; and survival for >48 hours). Demographics, injury severity (ISS), physiologic parameters, MOF status based on the Denver score, and outcome data were prospectively collected. Univariate analysis and multivariate logistic modeling were performed; p < 0.05 was considered significant. Data are presented as percentage or mean (SD).

RESULTS

A total of 140 patients met the inclusion criteria (age, 47 [21] years; ISS, 30 [11]; male, 69%), 21 patients (15%) developed MOF, and MOF associated mortality was 24% versus non-MOF mortality rate of 3%. Patients who developed MOF had longer ICU stays (19 [7] vs. 7 [5], p < 0.01) and had more ventilator days (18 [9] vs. 4 [4], p < 0.01). Prediction models were generated at two time points as follows: admission and 24 hours after injury. At admission, age (>65 years) and admission platelet count (<150 × 10(9)/L) were significant predictors of MOF; at 24 hours after injury, MOF was predicted by age more than 65 years, admission platelet count less than 150 × 10(9)/L, maximum creatinine of greater than 150 × 10(9)/L and minimum bilirubin of greater than 10 × 10(9)/L. Shock parameters and injury severity did not predict MOF.

CONCLUSION

The incidence of MOF (15%) is lower than reported 15 years ago; MOF remains a major cause of ICU resource use and late mortality after injury. The independent predictors of MOF have fundamentally changed, likely owing to improvements in resuscitation and critical care. Current predictors are universally available at admission and 24 hours.

LEVEL OF EVIDENCE

Epidemiologic/prognostic study, level III.

Development of a genomic metric that can be rapidly used to predict clinical outcome in severely injured trauma patients

OBJECTIVE

Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes.

DESIGN

Retrospective cohort study.

SETTING

Multi-institutional level 1 trauma centers.

PATIENTS

Data were collected from 167 severely traumatized (injury severity score >15) adult (18-55 yr) patients.

METHODS

Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric.

RESULTS

From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver-operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems.

CONCLUSIONS

A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.

MMpred: functional miRNA--mRNA interaction analyses by miRNA expression prediction

Background

MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes).

Results

We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anti-correlation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions.

Conclusions

The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses.