Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis

Activated peripheral blood mononuclear cell mediators trigger astrocyte reactivity

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

When not to start antibiotics: avoiding antibiotic overuse in the intensive care unit

BACKGROUND

Most intensive care unit (ICU) patients receive broad-spectrum antibiotics. While lifesaving in some, in others these treatments may be unnecessary and place patients at risk of antibiotic-associated harms.

OBJECTIVES

To review the literature exploring how we diagnose infection in patients in the ICU and address the safety and utility of a 'watchful waiting' approach to antibiotic initiation with selected patients in the ICU.

SOURCES

A semi-structured search of PubMed and Cochrane Library databases for articles published in English during the past 15 years was conducted.

CONTENT

Distinguishing infection from non-infectious mimics in ICU patients is uniquely challenging. At present, we do not have access to a rapid point-of-care test that reliably differentiates between individuals who need antibiotics and those who do not. A small number of studies have attempted to compare early aggressive versus conservative antimicrobial strategies in the ICU. However, this body of literature is small and not robust enough to guide practice.

IMPLICATIONS

This issue will not likely be resolved until there are diagnostic tests that rapidly and reliably identify the presence or absence of infection in the ICU population. In the meantime, prospective trials that identify clinical situations wherein it is safe to delay or withhold antibiotic initiation in the ICU until the presence of an infection is proven are warranted.

Emerging strategies for the noninvasive diagnosis of nosocomial pneumonia

Introduction: Hospital-acquired pneumonia is a common and therapeutically challenging diagnosis that can lead to severe sepsis, critical illness, and respiratory failure. In this review, we focus on efforts to enhance microbiological diagnosis of hospital-acquired pneumonia, including ventilator-associated pneumonia.

Areas covered: A systematic literature review was conducted by searching Medline from inception to December 2018, including hand-searching of the reference lists for additional studies. The search strategy comprised the following common search terms: hospital pneumonia OR nosocomial pneumonia OR noninvasive OR molecular diagnostic tests (OR point-of-care systems OR VOC [i.e. volatile organic compounds]) OR rapid (or simple or quick test), including brand names for the most common commercial tests.

Expert opinion: In recent years, the microbiological diagnosis of respiratory pathogens has improved significantly by the development and implementation of molecular diagnostic tests for pneumonia. Real-time polymerase chain reaction, hybridization, and mass spectrometry-based platforms dominate the scene, with microarray-based assays, multiplex polymerase chain reaction, and MALDI-TOF mass spectrometry capable of detecting the determinants of antimicrobial resistance (mainly β-lactamase genes). Introducing these assays into routine clinical practice for rapid identification of the causative microbes and their resistance patterns could transform the care of pneumonia, improving antimicrobial selection, de-escalation, and stewardship.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.

A Comparative Review of Equine SIRS, Sepsis, and Neutrophils

The most recent definition of sepsis in human medicine can be summarized as organ dysfunction caused by a dysregulated host response to infection. In equine medicine, although no consensus definition is available, sepsis is commonly described as a dysregulated host systemic inflammatory response to infection. Defense against host infection is the primary role of innate immune cells known as neutrophils. Neutrophils also contribute to host injury during sepsis, making them important potential targets for sepsis prevention, diagnosis, and treatment. This review will present both historical and updated perspectives on the systemic inflammatory response (SIRS) and sepsis; it will also discuss the impact of sepsis on neutrophils, and the impact of neutrophils during sepsis. Future identification of clinically relevant sepsis diagnosis and therapy depends on a more thorough understanding of disease pathogenesis across species. To gain this understanding, there is a critical need for research that utilizes a clearly defined, and consistently applied, classification system for patients diagnosed with, and at risk of developing, sepsis.

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. We address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways. Semi-supervised training of a feed forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context. We find that computational generalization of signaling insights substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

Non-utility of sepsis scores for identifying infection in surgical intensive care unit patients

BACKGROUND

The Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) scores replaced the Systemic Inflammatory Response System (SIRS) criteria for defining sepsis, and are often utilized to identify infection, however remain understudied in surgical populations.

METHODS

Daily SOFA, qSOFA, and SIRS scores were prospectively collected in a surgical/trauma intensive care unit (ICU), comparing scores between patients with and without new infection. Multivariable analysis controlled for ICU type and pre-existing infection.

RESULTS

Scores were recorded for 1942 patient-days, including 1385 (71%) with no infection, 439 (23%) with existing/treated infection, and 120 (6.2%) with new infection. Scores were globally elevated, with 98% having SOFA score ≥2, 82% with qSOFA score ≥2, and 92% meeting ≥2 SIRS criteria. Neither univariate nor multivariate analysis revealed a correlation between SOFA, qSOFA, or SIRS score and infection.

CONCLUSION

No scores correlated with new infection, potentially related to increased existing inflammation in this population.

SUMMARY

The Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) have replaced the Systemic Inflammatory Response System (SIRS) criteria for sepsis, however are not well investigated in surgical populations or for identifying infections, as they are often used in practice. In this study, neither daily SOFA, qSOFA, nor SIRS criteria correlated with new infection in a population of critically ill surgical patients. Scores were globally elevated in non-infected patients, potentially related to high levels of existing inflammation in this population.

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

Background

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

Methods

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

Results

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

Conclusion

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

Altered expression of TIAM1 in endotoxin-challenged airway epithelial cells and rodent septic models

BACKGROUND

In sepsis, reorganization of the actin cytoskeleton in the epithelium during inflammation will lead to a breakdown of epithelial barrier integrity, and contribute to the pathogenesis of sepsis, but the exact changes of various components regulating the actin cytoskeleton pathway remain unclear.

METHODS

We used lipopolysaccharide (LPS) challenged primary epithelial cells cultured at the air-liquid interface (ALI) to mimic epithelial barrier dysfunction during sepsis. Then we detected differential expression of T-lymphoma invasion and metastasis 1 (TIAM1) gene in lung epithelial cells and septic models.

RESULTS

LPS induced barrier dysfunction in human tracheobronchial epithelial cells (HTBEs) as measured by statistically significant changes in ionic and macromolecular permeability. We observed differential expression of TIAM1 gene. The protein expression of TIAM1 was decreased after LPS challenge, in human bronchial epithelial cells. Furthermore, the expression levels of both TIAM1 mRNA and protein were decreased in lungs of septic rodent models.

CONCLUSIONS

Given that expression levels of TIAM1 have been associated with mortality among sepsis patients, our findings have the potential for the development of diagnostic and treatment strategies relevant for patient management.

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO)

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse.

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.

Research Advances in Biomarker for Sepsis

Sepsis is one of the most common causes of death in severely injured patients worldwide. The early detection of sepsis still has to be solved in clinical practice. The delayed diagnosis often contributes to inappropriate antimicrobial treatment and subsequent high mortality. Sepsis biomarkers are produced during the host response to infection. Traditional biomarkers are polypeptides and/or proteins derived from this response. Omics-based biomarkers are screening out from all kinds of molecules of host response while high-throughout omics technologies are emerging. This review describes traditional and potential omics-based sepsis biomarkers from currently available literatures. The combination of these biomarkers would refine the identification of sepsis for further clinical and experimental sepsis studies.

Acute Respiratory Distress Syndrome Neutrophils Have a Distinct Phenotype and Are Resistant to Phosphoinositide 3-Kinase Inhibition

RATIONALE

Acute respiratory distress syndrome is refractory to pharmacological intervention. Inappropriate activation of alveolar neutrophils is believed to underpin this disease's complex pathophysiology, yet these cells have been little studied.

OBJECTIVES

To examine the functional and transcriptional profiles of patient blood and alveolar neutrophils compared with healthy volunteer cells, and to define their sensitivity to phosphoinositide 3-kinase inhibition.

METHODS

Twenty-three ventilated patients underwent bronchoalveolar lavage. Alveolar and blood neutrophil apoptosis, phagocytosis, and adhesion molecules were quantified by flow cytometry, and oxidase responses were quantified by chemiluminescence. Cytokine and transcriptional profiling were used in multiplex and GeneChip arrays.

MEASUREMENTS AND MAIN RESULTS

Patient blood and alveolar neutrophils were distinct from healthy circulating cells, with increased CD11b and reduced CD62L expression, delayed constitutive apoptosis, and primed oxidase responses. Incubating control cells with disease bronchoalveolar lavage recapitulated the aberrant functional phenotype, and this could be reversed by phosphoinositide 3-kinase inhibitors. In contrast, the prosurvival phenotype of patient cells was resistant to phosphoinositide 3-kinase inhibition. RNA transcriptomic analysis revealed modified immune, cytoskeletal, and cell death pathways in patient cells, aligning closely to sepsis and burns datasets but not to phosphoinositide 3-kinase signatures.

CONCLUSIONS

Acute respiratory distress syndrome blood and alveolar neutrophils display a distinct primed prosurvival profile and transcriptional signature. The enhanced respiratory burst was phosphoinositide 3-kinase-dependent but delayed apoptosis and the altered transcriptional profile were not. These unexpected findings cast doubt over the utility of phosphoinositide 3-kinase inhibition in acute respiratory distress syndrome and highlight the importance of evaluating novel therapeutic strategies in patient-derived cells.

Sepsis and septic shock

For more than two decades, sepsis was defined as a microbial infection that produces fever (or hypothermia), tachycardia, tachypnoea and blood leukocyte changes. Sepsis is now increasingly being considered a dysregulated systemic inflammatory and immune response to microbial invasion that produces organ injury for which mortality rates are declining to 15-25%. Septic shock remains defined as sepsis with hyperlactataemia and concurrent hypotension requiring vasopressor therapy, with in-hospital mortality rates approaching 30-50%. With earlier recognition and more compliance to best practices, sepsis has become less of an immediate life-threatening disorder and more of a long-term chronic critical illness, often associated with prolonged inflammation, immune suppression, organ injury and lean tissue wasting. Furthermore, patients who survive sepsis have continuing risk of mortality after discharge, as well as long-term cognitive and functional deficits. Earlier recognition and improved implementation of best practices have reduced in-hospital mortality, but results from the use of immunomodulatory agents to date have been disappointing. Similarly, no biomarker can definitely diagnose sepsis or predict its clinical outcome. Because of its complexity, improvements in sepsis outcomes are likely to continue to be slow and incremental.

The Clinical Challenge of Sepsis Identification and Monitoring

Jean-Louis Vincent outlines why combinations of biomarkers will be central to the future of sepsis diagnosis.

The use and risks of antibiotics in critically ill patients

INTRODUCTION

The altered pathophysiology in critically ill patients presents a unique challenge in both the diagnosis of infection and the appropriate prescription of antibiotics. In this context, the importance of effective and timely treatment needs to be weighed against the individual and community harms associated with antibiotic collateral damage and antibiotic resistance.

AREAS COVERED

We evaluate the principles of antibiotic use in critically ill patients, including dose optimisation, use of combination antibiotic therapy, therapeutic drug monitoring, appropriate antibiotic therapy duration, de-escalation, and utilisation of sepsis biomarkers. We also describe the potential risks associated with antibiotic therapy including antibiotic resistance, delayed treatment, treatment failure, and collateral damage.

EXPERT OPINION

Prescribing teams must be aware of the impact of critical illness on their patients and tailor antibiotic therapy appropriately to prevent the significant harms associated with suboptimal antibiotic administration.

Prostaglandin E₂ constrains systemic inflammation through an innate lymphoid cell-IL-22 axis

Systemic inflammation, which results from the massive release of proinflammatory molecules into the circulatory system, is a major risk factor for severe illness, but the precise mechanisms underlying its control are not fully understood. We observed that prostaglandin E2 (PGE2), through its receptor EP4, is down-regulated in human systemic inflammatory disease. Mice with reduced PGE2 synthesis develop systemic inflammation, associated with translocation of gut bacteria, which can be prevented by treatment with EP4 agonists. Mechanistically, we demonstrate that PGE2-EP4 signaling acts directly on type 3 innate lymphoid cells (ILCs), promoting their homeostasis and driving them to produce interleukin-22 (IL-22). Disruption of the ILC-IL-22 axis impairs PGE2-mediated inhibition of systemic inflammation. Hence, the ILC-IL-22 axis is essential in protecting against gut barrier dysfunction, enabling PGE2-EP4 signaling to impede systemic inflammation.

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.

Personalized Medicine for Sepsis

Sepsis is a complex syndrome triggered by infection and characterized by systemic deregulation of immune and inflammatory pathways. It is a major cause of death worldwide and results in the widespread use of antibiotics and substantial health care costs. In a vicious circle, sepsis treatment promotes the emergence of highly virulent and resistant pathogens and devastating nosocomial infections. Sepsis is a heterogeneous disease affecting many people worldwide. Because individual patients have different inflammatory responses and unique profiles of immune activation against pathogens, the most effective way to advance the treatment of sepsis is probably through a tailored approach. The advent of high-throughput technologies and the remarkable progress in the field of bioinformatics has allowed the subclassification of many pathological conditions. This has potential to provide better understanding of life-threatening infections in people. The study of host factors, however, needs to be integrated with studies on bacterial signaling in both symbiotic and pathogenic bacteria. Sepsis is certainly the sum of multiple host-microbial interactions and the metagenome should be extensively investigated. Personalized medicine is probably the only strategy able to deconstruct and reassemble our knowledge about sepsis, and its use should allow us to understand and manipulate sepsis as a wide, interconnected phenomenon with myriad variables and peculiarities. In this study, the recent advances in this area, the major challenges that remain, and the reasons why the septic patient should be approached as a superorganism are discussed.

A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts

BACKGROUND

Systemic inflammation is a whole body reaction having an infection-positive (i.e., sepsis) or infection-negative origin. It is important to distinguish between these two etiologies early and accurately because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on peripheral blood RNAs could be discovered that would (1) determine which patients with systemic inflammation had sepsis, (2) be robust across independent patient cohorts, (3) be insensitive to disease severity, and (4) provide diagnostic utility. The goal of this study was to identify and validate such a molecular classifier.

METHODS AND FINDINGS

We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICUs). Biomarker discovery utilized an Australian cohort (n = 105) consisting of 74 cases (sepsis patients) and 31 controls (post-surgical patients with infection-negative systemic inflammation) recruited at five tertiary care settings in Brisbane, Australia, from June 3, 2008, to December 22, 2011. A four-gene classifier combining CEACAM4, LAMP1, PLA2G7, and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte Lab, was validated using reverse transcription quantitative PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Patients for validation were selected from the Molecular Diagnosis and Risk Stratification of Sepsis study (ClinicalTrials.gov, NCT01905033), which recruited ICU patients from the Academic Medical Center in Amsterdam and the University Medical Center Utrecht. Patients recruited from November 30, 2012, to August 5, 2013, were eligible for inclusion in the present study. Validation cohort 1 (n = 59) consisted entirely of unambiguous cases and controls; SeptiCyte Lab gave an area under curve (AUC) of 0.95 (95% CI 0.91-1.00) in this cohort. ROC curve analysis of an independent, more heterogeneous group of patients (validation cohorts 2-5; 249 patients after excluding 37 patients with an infection likelihood of "possible") gave an AUC of 0.89 (95% CI 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility of SeptiCyte Lab was evaluated by comparison to various clinical and laboratory parameters available to a clinician within 24 h of ICU admission. SeptiCyte Lab was significantly better at differentiating cases from controls than all tested parameters, both singly and in various logistic combinations, and more than halved the diagnostic error rate compared to procalcitonin in all tested cohorts and cohort combinations. Limitations of this study relate to (1) cohort compositions that do not perfectly reflect the composition of the intended use population, (2) potential biases that could be introduced as a result of the current lack of a gold standard for diagnosing sepsis, and (3) lack of a complete, unbiased comparison to C-reactive protein.

CONCLUSIONS

SeptiCyte Lab is a rapid molecular assay that may be clinically useful in managing ICU patients with systemic inflammation. Further study in population-based cohorts is needed to validate this assay for clinical use.

Utility of immune response-derived biomarkers in the differential diagnosis of inflammatory disorders

Differentiating between inflammatory disorders is difficult, but important for a rational use of antimicrobial agents. Biomarkers reflecting the host immune response may offer an attractive strategy to predict the etiology of an inflammatory process and can thus be of help in decision making. We performed a review of the literature to evaluate the diagnostic value of inflammatory biomarkers in adult patients admitted to the hospital with suspected systemic acute infections. Elevated procalcitonin (PCT) concentrations indicate a bacterial infection in febrile patients with an auto-immune disease, rather than a disease flare. CD64 expression on neutrophils can discriminate between non-infectious systemic inflammation and sepsis, and limited evidence suggests the same for decoy receptor 3. PCT is useful for both diagnosing bacterial infection complicating influenza and guiding antibiotic treatment in lower respiratory tract infections in general. In undifferentiated illnesses, increased CD35 expression on neutrophils distinguishes bacterial from viral infections. Compared to bacterial infections, invasive fungal infections are characterized by low concentrations of PCT. No biomarker predicting a specific infecting agent could be identified.

Comparative transcriptomic analysis between an artificially induced SIRS in healthy individuals and spontaneous sepsis

Sepsis is defined as a syndrome combining a systemic inflammatory response with a documented infection. It may progress to more serious cases such as septic shock following the failure of one or more organs and the emergence of hemodynamic defects. Assuming that the emergence of serious septic syndromes may be partially explained by the early loss of regulation of the inflammatory response, we decided to compare, in a transcriptomic perspective, the biological mechanisms expressed during an induced systemic inflammatory response with those expressed during severe septic syndromes. By using open-access transcriptomic databases, we first studied the kinetics of an induced inflammatory response. The use of functional analysis helped us identify discriminating biological mechanisms, such as the mTOR signaling pathway, between the pathological cases of sepsis and non-pathological (i.e., the artificially induced SIRS) cases.

What was old is new again: using the host response to diagnose infectious disease

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, focusing on both pathogen discovery and the host response. Here, we review how 'omics' technologies improve sepsis diagnosis, early pathogen identification and personalize therapy. Such host response diagnostics are possible due to the confluence of advanced laboratory techniques (e.g., transcriptomics, metabolomics, proteomics) along with advanced mathematical modeling such as machine learning techniques. The road ahead is promising, but obstacles remain before the impact of such advanced diagnostic modalities is felt at the bedside.

Temporal profile of inflammatory response to fracture and hemorrhagic shock: Proposal of a novel long-term survival murine multiple trauma model

Hemorrhagic shock (hS) interacts with the posttraumatic immune response and fracture healing in multiple trauma. Due to the lack of a long-term survival multiple trauma animal models, no standardized analysis of fracture healing referring the impact of multiple trauma on fracture healing was performed. We propose a new long-term survival (21 days) murine multiple trauma model combining hS (microsurgical cannulation of carotid artery, withdrawl of blood and continuously blood pressure measurement), femoral (osteotomy/external fixation) and tibial fracture (3-point bending technique/antegrade nail). The posttraumatic immune response was measured via IL-6, sIL-6R ELISA. The hS was investigated via macrohemodynamics, blood gas analysis, wet-dry lung ration and histologic analysis of the shock organs. We proposed a new murine long-term survival (21 days) multiple trauma model mimicking clinical relevant injury patterns and previously published human posttraumatic immune response. Based on blood gas analysis and histologic analysis of shock organs we characterized and standardized our murine multiple trauma model. Furthermore, we revealed hemorrhagic shock as a causative factor that triggers sIL-6R formation underscoring the fundamental pathophysiologic role of the transsignaling mechanism in multiple trauma.

Dynamic expression of leukocyte innate immune genes in whole blood from horses with lipopolysaccharide-induced acute systemic inflammation

Background

In horses, insights into the innate immune processes in acute systemic inflammation are limited even though these processes may be highly important for future diagnostic and therapeutic advances in high-mortality disease conditions as the systemic inflammatory response syndrome (SIRS) and sepsis. Therefore, the aim of this study was to investigate the expression of 31 selected blood leukocyte immune genes in an equine model of acute systemic inflammation to identify significantly regulated genes and to describe their expression dynamics during a 24-h experimental period. Systemic inflammation was induced in 6 adult horses by the intravenous injection of 1 μg lipopolysaccharide (LPS) per kg btw. Sixteen blood samples were collected for each horse at predetermined intervals and analyzed by reverse transcription quantitative real-time PCR. Post-induction expression levels for each gene were compared with baseline levels.

Results

Systemic inflammation was confirmed by the presence of clinical and hematological changes which were consistent with SIRS. The clinical response to LPS was transient and brief as all horses except one showed unaltered general demeanor after 24 h. Twenty-two leukocyte genes were significantly regulated at at least one time point during the experimental period. By close inspection of the temporal responses the dynamic changes in mRNA abundance revealed a very rapid onset of both pro- and anti-inflammatory mediators and a substantial variation in both expression magnitudes and duration of changes between genes. A majority of the 22 significantly regulated genes peaked within the first 8 h after induction, and an on-going, albeit tightly controlled, regulation was seen after 24 h despite approximate clinical recovery.

Conclusions

This first broad study of gene expressions in blood leukocytes during equine acute LPS-induced systemic inflammation thoroughly characterized a highly regulated and dynamic innate immune response. These results provide new insights into the molecular mechanisms of equine systemic inflammation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-015-0450-5) contains supplementary material, which is available to authorized users.

Solid and Suspension Microarrays for Microbial Diagnostics

Advancements in molecular technologies have provided new platforms that are being increasingly adopted for use in the clinical microbiology laboratory. Among these, microarray methods are particularly well suited for diagnostics as they allow multiplexing, or the ability to test for multiple targets simultaneously from the same specimen. Microarray technologies commonly used for the detection and identification of microbial targets include solid-state microarrays, electronic microarrays and bead suspension microarrays. Microarray methods have been applied to microbial detection, genotyping and antimicrobial resistance gene detection. Microarrays can offer a panel approach to diagnose specific patient presentations, such as respiratory or gastrointestinal infections, and can discriminate isolates by genotype for tracking epidemiology and outbreak investigations. And, as more information has become available on specific genes and pathways involved in antimicrobial resistance, we are beginning to be able to predict susceptibility patterns based on sequence detection for particular organisms. With further advances in automated microarray processing methods and genotype-phenotype prediction algorithms, these tests will become even more useful as an adjunct or replacement for conventional antimicrobial susceptibility testing, allowing for more rapid selection of targeted therapy for infectious diseases.

Lysosome and Cytoskeleton Pathways Are Robustly Enriched in the Blood of Septic Patients: A Meta-Analysis of Transcriptomic Data

Background. Sepsis is a leading cause of mortality in intensive care units worldwide. A better understanding of the blood systems response to sepsis should expedite the identification of biomarkers for early diagnosis and therapeutic interventions. Methods. We analyzed microarray studies whose data is available from the GEO repository and which were performed on the whole blood of septic patients and normal controls. Results. We identified 6 cohorts consisting of 450 individuals (sepsis = 323, control = 127) providing genome-wide messenger RNA (mRNA) expression data. Through meta-analysis we found the “Lysosome” and “Cytoskeleton” pathways were upregulated in human sepsis patients relative to controls, in addition to previously known signaling pathways (including MAPK, TLR). The key regulatory genes in the “Lysosome” pathway include lysosomal acid hydrolases (e.g., protease cathepsin A, D) as well as the major (LAMP1, 2) and minor (SORT1, LAPTM4B) membrane proteins. In contrast, pathways related to “Ribosome”, “Spliceosome” and “Cell adhesion molecules” were found to be downregulated, along with known pathways for immune dysfunction. Overall, our study revealed distinct mRNA activation profiles and protein-protein interaction networks in blood of human sepsis. Conclusions. Our findings suggest that aberrant mRNA expression in the lysosome and cytoskeleton pathways may play a pivotal role in the molecular pathobiology of human sepsis.

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.

Synthesis and biological evaluation of novel indole-2-one and 7-aza-2-oxindole derivatives as anti-inflammatory agents

Sepsis, a typically acute inflammatory disease, is the biggest cause of death in ICU (intensive care unit). Novel anti-inflammatory alternatives are still in urgent need. In this study, we designed and synthesized 30 indole-2-one and 7-aza-2-oxindole derivatives based on the skeleton of tenidap, and their anti-inflammatory activity was determined by evaluating the inhibitory potency against lipopolysaccharide (LPS)-stimulated tumor necrosis factor (TNF)-α and interleukin (IL)-6 release in RAW264.7 macrophages. Quantitative SAR (structure-activity relationship) analysis revealed that a high molecular polarizability and low lipid/water partition coefficient (ALogP) in indole-2-one are beneficial for anti-inflammatory activity. Moreover, compounds 7i and 8e inhibited the expression of TNF-α, IL-6, COX-2, PGES, and iNOS in LPS-stimulated macrophages, and 7i exhibited a significant protection from LPS-induced septic death in mouse models. These data present a series of new indole-2-one compounds with potential therapeutic effects in acute inflammatory diseases.

Respiratory infections in patients undergoing mechanical ventilation

Lower respiratory tract infections in mechanically ventilated patients are a frequent cause of antibiotic treatment in intensive-care units. These infections present as severe sepsis or septic shock with respiratory dysfunction in intubated patients. Purulent respiratory secretions are needed for diagnosis, but distinguishing between pneumonia and tracheobronchitis is not easy. Both presentations are associated with longlasting mechanical ventilation and extended intensive-care unit stay, providing a rationale for antibiotic treatment initiation. Differentiation of colonisers from true pathogens is difficult, and microbiological data show Staphylococcus aureus and Pseudomonas aeruginosa to be of great concern because of clinical outcomes and therapeutic challenges. Key management issues include identification of the pathogen, choice of initial empirical antibiotic, and decisions with regard to the resolution pattern.

Inflammation biomarkers and delirium in critically ill patients

Introduction

Delirium is a common occurrence in critically ill patients and is associated with an increase in morbidity and mortality. Septic patients with delirium may differ from a general critically ill population. The aim of this investigation was to study the relationship between systemic inflammation and the development of delirium in septic and non-septic critically ill patients.

Methods

We performed a prospective cohort study in a 20-bed mixed intensive care unit (ICU) including 78 (delirium = 31; non-delirium = 47) consecutive patients admitted for more than 24 hours. At enrollment, patients were allocated to septic or non-septic groups according to internationally agreed criteria. Delirium was diagnosed using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) during the first 72 hours of ICU admission. Blood samples were collected within 12 hours of enrollment for determination of tumor necrosis factor (TNF)-α, soluble TNF Receptor (STNFR)-1 and -2, interleukin (IL)-1β, IL-6, IL-10 and adiponectin.

Results

Out of all analyzed biomarkers, only STNFR1 (P = 0.003), STNFR2 (P = 0.005), adiponectin (P = 0.005) and IL-1β (P < 0.001) levels were higher in delirium patients. Adjusting for sepsis and sedation, these biomarkers were also independently associated with delirium occurrence. However, none of them were significant influenced by sepsis.

Conclusions

STNFR1, STNFR2, adiponectin and IL-1β were associated with delirium. Sepsis did not modify the relationship between the biomarkers and delirium occurrence.

Inhibition of sepsis-induced inflammatory response by β1-adrenergic antagonists

BACKGROUND

Although previous studies have described potential benefits of nonselective β-adrenergic antagonist therapy in sepsis, there is a paucity of data on the use of β1-selective antagonists (B1AA). The purposes of this study were to describe the effects of B1AA on survival in septic animals and to explore for molecular mechanisms of potential treatment benefit.

METHODS

C57BL/6 male mice received intraperitoneal injection of lipopolysaccharide. Continuous infusion of a B1AA (esmolol) or an equal volume of saline (control) was initiated at 4 hours after injection. Kaplan-Meier survival analysis at 120 hours was used to explore for mortality differences. A subgroup of animals was sacrificed for microarray expression analysis. Top candidate genes were validated in vitro and in silico. Expression of our candidate genes in a human microarray database (GSE28750) was explored.

RESULTS

B1AA infusion resulted in increased survival (p = 0.001) at 120 hours. Mean survival difference was 23.6 hours (p = 0.002). Hazard ratio for mortality with B1AA is 0.43 (95% confidence interval, 0.26-0.72). Immunologic disease (p = 0.0003-0.036) and cell death/survival (p = 0.0001-0.042) were significantly associated with improved survival in septic mice treated with B1AA. Further analysis of the gene structure revealed that eight genes shared common promoter activating sequence for NFKB and/or BRCA1 motifs. Analysis of a human sepsis database identified the up-regulation of CAMP (p = 0.032) and TNFSF10 (p = 0.001) genes in septic patients compared with healthy controls.

CONCLUSION

Continuous infusion of a B1AA initiated after septic insult improves survival at 5 days in a murine model. Benefits may be caused by modulation of gene expression in immunologic pathways leading to an increase in CAMP and TNFSF10 expression. This observed effect may be explained by the activation of NFKB and BRCA1 genes involved in immune response and cell repair pathways. Our findings support further investigation of the use of B1AA in the treatment of sepsis.

Fast differentiation of SIRS and sepsis from blood plasma of ICU patients using Raman spectroscopy

Currently, there is no biomarker that can reliable distinguish between infectious and non-infectious systemic inflammatory response syndrome (SIRS). However, such a biomarker would be of utmost importance for early identification and stratification of patients at risk to initiate timely and appropriate antibiotic treatment. Within this proof of principle study, the high potential of Raman spectroscopy for the fast differentiation of non-infectious SIRS and sepsis is demonstrated. Blood plasma collected from 70 patients from the intensive care unit (31 patients with sepsis and 39 patients classified with SIRS without infection) was analyzed by means of Raman spectroscopy. A PCA-LDA based classification model was trained with Raman spectra from test samples and yielded for sepsis a sensitivity of 1.0 and specificity of 0.82. These results have been confirmed with an independent dataset (prediction accuracy 80%).

The beginning of personalized medicine in sepsis: small steps to a bright future

There is a growing recognition that there is a need for a more personalized approach towards sepsis care. In most clinical trials investigating novel therapeutic interventions against sepsis, patients have been considered a rather homogeneous population. However, there is probably more individual variability between septic patients than previously considered. The pathophysiology of sepsis is a complex and dynamic process that originates from the host immune response to infection and varies according to the genetic predisposition, immune status and co-morbid conditions of the host, the type of pathogen and the site and extent of infection. Until now, efforts to stratify septic patients according to their immune profile were hampered by the lack of specific biomarkers. Recent advances in molecular medicine have made it possible to develop tools that will facilitate a faster and more precise diagnosis of infection. Individual variability between each patient's responses to infection can assist in tailoring therapeutic interventions to the individual's disease profile and monitoring treatment response. In this review, we describe those recent advances in genomics and theragnostics, which are slowly entering clinical practice and which will make possible a more personalized approach to each septic patient in the next decade.

Molecular diagnosis of sepsis: New aspects and recent developments

By shortening the time to pathogen identification and allowing for detection of organisms missed by blood culture, new molecular methods may provide clinical benefits for the management of patients with sepsis. While a number of reviews on the diagnosis of sepsis have recently been published we here present up-to-date new developments including multiplex PCR, mass spectrometry and array techniques. We focus on those techniques that are commercially available and for which clinical studies have been performed and published.

Sepsis biomarkers: an omics perspective

Sepsis is a common cause of death in hospitalized patients worldwide. The early detection of sepsis remains a great challenge for clinicians, and delayed diagnosis frequently undermines treatment efforts, thereby contributing to high mortality. Omics technologies allow high-throughput screening of sepsis biomarkers. This review describes currently available and novel sepsis biomarkers in the context of genomics, transcriptomics, proteomics, and metabolomics. The combination of these technologies can help refine the diagnosis of sepsis. This review paper serves as a reference for future studies that employ an integrated, multi-omics approach to disease identification.