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

Biomarkers in sepsis

Sepsis is a life-threatening condition characterized by dysregulated host response to infection leading to organ dysfunction. Despite advances in understanding its pathology, sepsis remains a global health concern and remains a major contributor to mortality. Timely identification is crucial for improving clinical outcomes, as delayed treatment significantly impacts survival. Accordingly, biomarkers play a pivotal role in diagnosis, risk stratification, and management. This review comprehensively discusses various biomarkers in sepsis and their potential application in antimicrobial stewardship and risk assessment. Biomarkers such as white blood cell count, neutrophil to lymphocyte ratio, erythrocyte sedimentation rate, C-reactive protein, interleukin-6, presepsin, and procalcitonin have been extensively studied for their diagnostic and prognostic value as well as in guiding antimicrobial therapy. Furthermore, this review explores the role of biomarkers in risk stratification, emphasizing the importance of identifying high-risk patients who may benefit from specific therapeutic interventions. Moreover, the review discusses the emerging field of transcriptional diagnostics and metagenomic sequencing. Advances in sequencing have enabled the identification of host response signatures and microbial genomes, offering insight into disease pathology and aiding species identification. In conclusion, this review provides a comprehensive overview of the current understanding and future directions of biomarker-based approaches in sepsis diagnosis, management, and personalized therapy.

Biomarkers in pulmonary infections: a clinical approach

Severe acute respiratory infections, such as community-acquired pneumonia, hospital-acquired pneumonia, and ventilator-associated pneumonia, constitute frequent and lethal pulmonary infections in the intensive care unit (ICU). Despite optimal management with early appropriate empiric antimicrobial therapy and adequate supportive care, mortality remains high, in part attributable to the aging, growing number of comorbidities, and rising rates of multidrug resistance pathogens. Biomarkers have the potential to offer additional information that may further improve the management and outcome of pulmonary infections. Available pathogen-specific biomarkers, for example, Streptococcus pneumoniae urinary antigen test and galactomannan, can be helpful in the microbiologic diagnosis of pulmonary infection in ICU patients, improving the timing and appropriateness of empiric antimicrobial therapy since these tests have a short turnaround time in comparison to classic microbiology. On the other hand, host-response biomarkers, for example, C-reactive protein and procalcitonin, used in conjunction with the clinical data, may be useful in the diagnosis and prediction of pulmonary infections, monitoring the response to treatment, and guiding duration of antimicrobial therapy. The assessment of serial measurements overtime, kinetics of biomarkers, is more informative than a single value. The appropriate utilization of accurate pathogen-specific and host-response biomarkers may benefit clinical decision-making at the bedside and optimize antimicrobial stewardship.

Enhancing sepsis biomarker development: key considerations from public and private perspectives

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

A 29-mRNA host-response classifier identifies bacterial infections following liver transplantation - a pilot study

PURPOSE

Infections are common complications in patients following liver transplantation (LTX). The early diagnosis and prognosis of these infections is an unmet medical need even when using routine biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT). Therefore, new approaches are necessary.

METHODS

In a prospective, observational pilot study, we monitored 30 consecutive patients daily between days 0 and 13 following LTX using the 29-mRNA host classifier IMX-BVN-3b that determine the likelihood of bacterial infections and viral infections. True infection status was determined using clinical adjudication. Results were compared to the accuracy of CRP and PCT for patients with and without bacterial infection due to clinical adjudication.

RESULTS

Clinical adjudication confirmed bacterial infections in 10 and fungal infections in 2 patients. 20 patients stayed non-infected until day 13 post-LTX. IMX-BVN-3b bacterial scores were increased directly following LTX and decreased until day four in all patients. Bacterial IMX-BVN-3b scores detected bacterial infections in 9 out of 10 patients. PCT concentrations did not differ between patients with or without bacterial, whereas CRP was elevated in all patients with significantly higher levels in patients with bacterial infections.

CONCLUSION

The 29-mRNA host classifier IMX-BVN-3b identified bacterial infections in post-LTX patients and did so earlier than routine biomarkers. While our pilot study holds promise future studies will determine whether these classifiers may help to identify post-LTX infections earlier and improve patient management.

CLINICAL TRIAL NOTATION

German Clinical Trials Register: DRKS00023236, Registered 07 October 2020, https://drks.de/search/en/trial/DRKS00023236.

Integrated Analysis Identified TGFBI as a Biomarker of Disease Severity and Prognosis Correlated with Immune Infiltrates in Patients with Sepsis

Background

Sepsis is a major contributor to morbidity and mortality among hospitalized patients. This study aims to identify markers associated with the severity and prognosis of sepsis, providing new approaches for its management and treatment.

Methods

Data were mined from the Gene Expression Omnibus (GEO) databases and were analyzed by multiple statistical methods like the Spearman correlation coefficient, Kaplan-Meier analysis, Cox regression analysis, and functional enrichment analysis. Candidate indicator' associations with immune infiltration and roles in sepsis development were evaluated. Additionally, we employed techniques such as flow cytometry and neutral red staining to evaluate its impact on macrophage functions like polarization and phagocytosis.

Results

Twenty-eight genes were identified as being closely linked to the severity of sepsis, among which transforming growth factor beta induced (TGFBI) emerged as a distinct marker for predicting clinical outcomes. Notably, reductions in TGFBI expression during sepsis correlate with poor prognosis and rapid disease progression. Elevated expression of TGFBI has been observed to mitigate abnormalities in sepsis-related immune cell infiltration that are critical to the pathogenesis and prognosis of the disease, including but not limited to type 17 T helper cells and activated CD8 T cells. Moreover, the protein-protein interaction network revealed the top ten genes that interact with TGFBI, showing significant involvement in the regulation of the actin cytoskeleton, extracellular matrix-receptor interactions, and phagosomes. These are pivotal elements in the formation of phagocytic cups by macrophages, squaring the findings of the Human Protein Atlas. Additionally, we discovered that TGFBI expression was significantly higher in M2-like macrophages, and its upregulation was found to inhibit lipopolysaccharide-induced polarization and phagocytosis in M1-like macrophages, thereby playing a role in preventing the onset of inflammation.

Conclusion

TGFBI warrants additional exploration as a promising biomarker for assessing illness severity and prognosis in patients with sepsis, considering its significant association with immunological and inflammatory responses in this condition.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

Causal associations between circulating cytokines and risk of sepsis and related outcomes: a two-sample Mendelian randomization study

Introduction

Sepsis represents a critical medical condition that arises due to an imbalanced host reaction to infection. Central to its pathophysiology are cytokines. However, observational investigations that explore the interrelationships between circulating cytokines and susceptibility to sepsis frequently encounter challenges pertaining to confounding variables and reverse causality.

Methods

To elucidate the potential causal impact of cytokines on the risk of sepsis, we conducted two-sample Mendelian randomization (MR) analyses. Genetic instruments tied to circulating cytokine concentrations were sourced from genome-wide association studies encompassing 8,293 Finnish participants. We then evaluated their links with sepsis and related outcomes using summary-level data acquired from the UK Biobank, a vast multicenter cohort study involving over 500,000 European participants. Specifically, our data spanned 11,643 sepsis cases and 474,841 controls, with subsets including specific age groups, 28-day mortality, and ICU-related outcomes.

Results and Discussion

MR insights intimated that reduced genetically-predicted interleukin-10 (IL-10) levels causally correlated with a heightened sepsis risk (odds ratio [OR] 0.68, 95% confidence interval [CI] 0.52-0.90, P=0.006). An inverse relationship emerged between monocyte chemoattractant protein-1 (MCP-1) and sepsis-induced mortality. Conversely, elevated macrophage inflammatory protein 1 beta (MIP1B) concentrations were positively linked with both sepsis incidence and associated mortality. These revelations underscore the causal impact of certain circulating cytokines on sepsis susceptibility and its prognosis, hinting at the therapeutic potential of modulating these cytokine levels. Additional research is essential to corroborate these connections.

Systems immunology of transcriptional responses to viral infection identifies conserved antiviral pathways across macaques and humans

Viral pandemics and epidemics pose a significant global threat. While macaque models of viral disease are routinely used, it remains unclear how conserved antiviral responses are between macaques and humans. Therefore, we conducted a cross-species analysis of transcriptomic data from over 6,088 blood samples from macaques and humans infected with one of 31 viruses. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses that are consistent across infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Leveraging longitudinal data from experimental challenges, we identify virus-specific response kinetics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Our results underscore macaque studies as a powerful tool for understanding viral pathogenesis and immune responses that translate to humans, with implications for viral therapeutic development and pandemic preparedness.

Microbiological Diagnosis of Pulmonary Aspergillus Infections

As microbiological tests play an important role in our diagnostic algorithms and clinical approach towards patients at-risk for pulmonary aspergillosis, a good knowledge of the diagnostic possibilities and especially their limitations is extremely important. In this review, we aim to reflect critically on the available microbiological diagnostic modalities for diagnosis of pulmonary aspergillosis and formulate some future prospects. Timely start of adequate antifungal treatment leads to a better patient outcome, but overuse of antifungals should be avoided. Current diagnostic possibilities are expanding, and are mainly driven by enzyme immunoassays and lateral flow device tests for the detection of Aspergillus antigens. Most of these tests are directed towards similar antigens, but new antibodies towards different targets are under development. For chronic forms of pulmonary aspergillosis, anti-Aspergillus IgG antibodies and precipitins remain the cornerstone. More studies on the possibilities and limitations of molecular testing including targeting resistance markers are ongoing. Also, metagenomic next-generation sequencing is expanding our future possibilities. It remains important to combine different test results and interpret them in the appropriate clinical context to improve performance. Test performances may differ according to the patient population and test results may be influenced by timing, the tested matrix, and prophylactic and empiric antifungal therapy. Despite the increasing armamentarium, a simple blood or urine test for the diagnosis of aspergillosis in all patient populations at-risk is still lacking. Research on diagnostic tools is broadening from a pathogen focus on biomarkers related to the patient and its immune system.

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.

Methods

Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.

Results

Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).

Discussion

The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

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

Antibiotic failure: Beyond antimicrobial resistance

Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

BACKGROUND

Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.

METHODS

We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.

RESULTS

Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.

CONCLUSION

This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

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

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

Rapid identification of sepsis in the emergency department

Objectives

Recent research has helped define the complex pathways in sepsis, affording new opportunities for advancing diagnostics tests. Given significant advances in the field, a group of academic investigators from emergency medicine, intensive care, pathology, and pharmacology assembled to develop consensus around key gaps and potential future use for emerging rapid host response diagnostics assays in the emergency department (ED) setting.

Methods

A modified Delphi study was conducted that included 26 panelists (expert consensus panel) from multiple specialties. A smaller steering committee first defined a list of Delphi statements related to the need for and future potential use of a hypothetical sepsis diagnostic test in the ED. Likert scoring was used to assess panelists agreement or disagreement with statements. Two successive rounds of surveys were conducted and consensus for statements was operationally defined as achieving agreement or disagreement of 75% or greater.

Results

Significant gaps were identified related to current tools for assessing risk of sepsis in the ED. Strong consensus indicated the need for a test providing an indication of the severity of dysregulated host immune response, which would be helpful even if it did not identify the specific pathogen. Although there was a relatively high degree of uncertainty regarding which patients would most benefit from the test, the panel agreed that an ideal host response sepsis test should aim to be integrated into ED triage and thus should produce results in less than 30 minutes. The panel also agreed that such a test would be most valuable for improving sepsis outcomes and reducing rates of unnecessary antibiotic use.

Conclusion

The expert consensus panel expressed strong consensus regarding gaps in sepsis diagnostics in the ED and the potential for new rapid host response tests to help fill these gaps. These finding provide a baseline framework for assessing key attributes of evolving host response diagnostic tests for sepsis in the ED.

Biophysical Changes of Leukocyte Activation (and NETosis) in the Cellular Host Response to Sepsis

Sepsis, the leading cause of mortality in hospitals, currently lacks effective early diagnostics. A new cellular host response test, the IntelliSep test, may provide an indicator of the immune dysregulation characterizing sepsis. The objective of this study was to examine the correlation between the measurements performed using this test and biological markers and processes associated with sepsis. Phorbol myristate acetate (PMA), an agonist of neutrophils known to induce neutrophil extracellular trap (NET) formation, was added to whole blood of healthy volunteers at concentrations of 0, 200, and 400 nM and then evaluated using the IntelliSep test. Separately, plasma from a cohort of subjects was segregated into Control and Diseased populations and tested for levels of NET components (citrullinated histone (cit-H3) DNA and neutrophil elastase (NE) DNA) using customized ELISA assays and correlated with ISI scores from the same patient samples. Significant increases in IntelliSep Index (ISI) scores were observed with increasing concentrations of PMA in healthy blood (0 and 200: p < 10-10; 0 and 400: p < 10-10). Linear correlation was observed between the ISI and quantities of NE DNA and Cit-H3 DNA in patient samples. Together these experiments demonstrate that the IntelliSep test is associated with the biological processes of leukocyte activation and NETosis and may indicate changes consistent with sepsis.

The Role of Transcriptomics in Redefining Critical Illness

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

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza

BACKGROUND

Indiscriminate use of antimicrobials and antimicrobial resistance is a public health threat. IMX-BVN-1, a 29-host mRNA classifier, provides two separate scores that predict likelihoods of bacterial and viral infections in patients with suspected acute infections. We validated the performance of IMX-BVN-1 in adults attending acute health care settings with suspected influenza.

METHOD

We amplified 29-host response genes in RNA extracted from blood by NanoString nCounter. IMX-BVN-1 calculated two scores to predict probabilities of bacterial and viral infections. Results were compared against the infection status (no infection; highly probable/possible infection; confirmed infection) determined by clinical adjudication.

RESULTS

Amongst 602 adult patients (74.9% ED, 16.9% ICU, 8.1% outpatients), 7.6% showed in-hospital mortality and 15.5% immunosuppression. Median IMX-BVN-1 bacterial and viral scores were higher in patients with confirmed bacterial (0.27) and viral (0.62) infections than in those without bacterial (0.08) or viral (0.21) infection, respectively. The AUROC distinguishing bacterial from nonbacterial illness was 0.81 and 0.87 when distinguishing viral from nonviral illness. The bacterial top quartile's positive likelihood ratio (LR) was 4.38 with a rule-in specificity of 88%; the bacterial bottom quartile's negative LR was 0.13 with a rule-out sensitivity of 96%. Similarly, the viral top quartile showed an infinite LR with rule-in specificity of 100%; the viral bottom quartile had a LR of 0.22 and a rule-out sensitivity of 85%.

CONCLUSION

IMX-BVN-1 showed high accuracy for differentiating bacterial and viral infections from noninfectious illness in patients with suspected influenza. Clinical utility of IMX-BVN will be validated following integration into a point of care system.

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a P_aO₂/F_iO₂ < 200 to those with a P_aO₂/F_iO₂ ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a P_aO₂/F_iO₂ < 200 vs. P_aO₂/F_iO₂ ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data

Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. Pre-processing normalisation steps significantly reduce the number of variables in the data and thereby increase the power of statistical testing, but can potentially discard valuable and insightful classification features. A systematic assessment of applying transcript level filtering on the robustness and stability of ML based RNA-seq classification remains to be fully explored. In this report we examine the impact of filtering out low count transcripts and those with influential outliers read counts on downstream ML analysis for sepsis biomarker discovery using elastic net regularised logistic regression, L1-reguarlised support vector machines and random forests. We demonstrate that applying a systematic objective strategy for removal of uninformative and potentially biasing biomarkers representing up to 60% of transcripts in different sample size datasets, including two illustrative neonatal sepsis cohorts, leads to substantial improvements in classification performance, higher stability of the resulting gene signatures, and better agreement with previously reported sepsis biomarkers. We also demonstrate that the performance uplift from gene filtering depends on the ML classifier chosen, with L1-regularlised support vector machines showing the greatest performance improvements with our experimental data.

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

Introduction

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

Methods

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

Results

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

Discussion

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

Endotoxin in Sepsis: Methods for LPS Detection and the Use of Omics Techniques

Lipopolysaccharide (LPS) or endotoxin, the major cell wall component of Gram-negative bacteria, plays a pivotal role in the pathogenesis of sepsis. It is able to activate the host defense system through interaction with Toll-like receptor 4, thus triggering pro-inflammatory mechanisms. A large amount of LPS induces inappropriate activation of the immune system, triggering an exaggerated inflammatory response and consequent extensive organ injury, providing the basis of sepsis damage. In this review, we will briefly describe endotoxin's molecular structure and its main pathogenetic action during sepsis. In addition, we will summarize the main different available methods for endotoxin detection with a special focus on the wider spectrum offered by omics technologies (genomics, transcriptomics, proteomics, and metabolomics) and promising applications of these in the identification of specific biomarkers for sepsis.

Benchmarking transcriptional host response signatures for infection diagnosis

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

Immune modulation after traumatic brain injury

Traumatic brain injury (TBI) induces instant activation of innate immunity in brain tissue, followed by a systematization of the inflammatory response. The subsequent response, evolved to limit an overwhelming systemic inflammatory response and to induce healing, involves the autonomic nervous system, hormonal systems, and the regulation of immune cells. This physiological response induces an immunosuppression and tolerance state that promotes to the occurrence of secondary infections. This review describes the immunological consequences of TBI and highlights potential novel therapeutic approaches using immune modulation to restore homeostasis between the nervous system and innate immunity.

A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients

Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed that a multiplex transcriptomic panel prototype termed Immune Profiling Panel (IPP) could capture the dysregulation of immune responses of ICU COVID-19 patients at admission. Nine transcripts were associated with mortality in univariate analysis and this 9-mRNA signature remained significantly associated with mortality in a multivariate analysis that included age, SOFA and Charlson scores. Using a machine learning model with these 9 mRNA, we could predict the 28-day survival status with an Area Under the Receiver Operating Curve (AUROC) of 0.764. Interestingly, adding patients’ age to the model resulted in increased performance to predict the 28-day mortality (AUROC reaching 0.839). This prototype IPP demonstrated that such a tool, upon clinical/analytical validation and clearance by regulatory agencies could be used in clinical routine settings to quickly identify patients with higher risk of death requiring thus early aggressive intensive care.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Serial Measurements of Protein Biomarkers in Sepsis-Induced Acute Respiratory Distress Syndrome

The role of early, serial measurements of protein biomarkers in sepsis-induced acute respiratory distress syndrome (ARDS) is not clear.

OBJECTIVES

To determine the differences in soluble receptor for advanced glycation end-products (sRAGEs), angiopoietin-2, and surfactant protein-D (SP-D) levels and their changes over time between sepsis patients with and without ARDS.

DESIGN SETTING AND PARTICIPANTS

Prospective observational cohort study of adult patients admitted to the medical ICU at Grady Memorial Hospital within 72 hours of sepsis diagnosis.

MAIN OUTCOMES AND MEASURES

Plasma sRAGE, angiopoietin-2, and SP-D levels were measured for 3 consecutive days after enrollment. The primary outcome was ARDS development, and the secondary outcome of 28-day mortality. The biomarker levels and their changes over time were compared between ARDS and non-ARDS patients and between nonsurvivors and survivors.

RESULTS

We enrolled 111 patients, and 21 patients (18.9%) developed ARDS. The three biomarker levels were not significantly different between ARDS and non-ARDS patients on all 3 days of measurement. Nonsurvivors had higher levels of all three biomarkers than did survivors on multiple days. The changes of the biomarker levels over time were not different between the outcome groups. Logistic regression analyses showed association between day 1 SP-D level and mortality (odds ratio, 1.52; 95% CI, 1.03-2.24; p = 0.03), and generalized estimating equation analyses showed association between angiopoietin-2 levels and mortality (estimate 0.0002; se 0.0001; p = 0.04).

CONCLUSIONS AND RELEVANCE

Among critically ill patients with sepsis, sRAGE, angiopoietin-2, and SP-D levels were not significantly different between ARDS and non-ARDS patients but were higher in nonsurvivors compared with survivors. The trend toward higher levels of sRAGE and SP-D, but not of angiopoietin-2, in ARDS patients may indicate the importance of epithelial injury in sepsis-induced ARDS. Changes of the biomarker levels over time were not different between the outcome groups.

Biomarkers for the Prediction and Judgement of Sepsis and Sepsis Complications: A Step towards precision medicine?

Sepsis and septic shock are a major public health concern and are still associated with high rates of morbidity and mortality. Whilst there is growing understanding of different phenotypes and endotypes of sepsis, all too often treatment strategies still only employ a “one-size-fits-all” approach. Biomarkers offer a unique opportunity to close this gap to more precise treatment approaches by providing insight into clinically hidden, yet complex, pathophysiology, or by individualizing treatment pathways. Predicting and evaluating systemic inflammation, sepsis or septic shock are essential to improve outcomes for these patients. Besides opportunities to improve patient care, employing biomarkers offers a unique opportunity to improve clinical research in patients with sepsis. The high rate of negative clinical trials in this field may partly be explained by a high degree of heterogeneity in patient cohorts and a lack of understanding of specific endotypes or phenotypes. Moving forward, biomarkers can support the selection of more homogeneous cohorts, thereby potentially improving study conditions of clinical trials. This may finally pave the way to a precision medicine approach to sepsis, septic shock and complication of sepsis in the future.

A training curriculum for retrieving, structuring, and aggregating information derived from the biomedical literature and large-scale data repositories

Background: Biomedical research over the past two decades has become data and information rich. This trend has been in large part driven by the development of systems-scale molecular profiling capabilities and by the increasingly large volume of publications contributed by the biomedical research community. It has therefore become important for early career researchers to learn to leverage this wealth of information in their own research. Methods: Here we describe in detail a training curriculum focusing on the development of foundational skills necessary to retrieve, structure, and aggregate information available from vast stores of publicly available information. It is provided along with supporting material and an illustrative use case. The stepwise workflow encompasses; 1) Selecting a candidate gene; 2) Retrieving background information about the gene; 3) Profiling its literature; 4) Identifying in the literature instances where its transcript abundance changes in blood of patients; 5) Retrieving transcriptional profiling data from public blood transcriptome and reference datasets; and 6) Drafting a manuscript, submitting it for peer-review, and publication. Results: This resource may be leveraged by instructors who wish to organize hands-on workshops. It can also be used by independent trainees as a self-study toolkit. The workflow presented as proof-of-concept was designed to establish a resource for assessing a candidate gene’s potential utility as a blood transcriptional biomarker. Trainees will learn to retrieve literature and public transcriptional profiling data associated with a specific gene of interest. They will also learn to extract, structure, and aggregate this information to support downstream interpretation efforts as well as the preparation of a manuscript. Conclusions: This resource should support early career researchers in their efforts to acquire skills that will permit them to leverage the vast amounts of publicly available large-scale profiling data.

OMiCC: An expanded and enhanced platform for meta-analysis of public gene expression data

OMiCC (OMics Compendia Commons) is a biologist-friendly web platform that facilitates data reuse and integration. Users can search over 40,000 publicly available gene expression studies, annotate and curate samples, and perform meta-analysis. Since the initial publication, we have incorporated RNA-seq datasets, compendia sharing, RESTful API support, and an additional meta-analysis method based on random effects. Here, we provide a step-by-step guide for using OMiCC.

For complete details on the use and execution of this protocol, please refer to Shah et al. (2016).

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OMiCC (OMics Compendia Commons) is a free web-based tool for gene expression data reuse

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Search publicly available studies to perform sample group comparisons to explore a disease

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In meta-analysis, multiple studies are combined to identify coherent signals

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OMiCC supports crowd-sharing and users can share their own analyses with the community

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS

ABSTRACT

Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.

Modes of action and diagnostic value of miRNAs in sepsis

Sepsis is a clinical syndrome defined as a dysregulated host response to infection resulting in life-threatening organ dysfunction. Sepsis is a major public health concern associated with one in five deaths worldwide. Sepsis is characterized by unbalanced inflammation and profound and sustained immunosuppression, increasing patient susceptibility to secondary infections and mortality. microRNAs (miRNAs) play a central role in the control of many biological processes, and deregulation of their expression has been linked to the development of oncological, cardiovascular, neurodegenerative and metabolic diseases. In this review, we discuss the role of miRNAs in sepsis pathophysiology. Overall, miRNAs are seen as promising biomarkers, and it has been proposed to develop miRNA-based therapies for sepsis. Yet, the picture is not so straightforward because of the versatile and dynamic features of miRNAs. Clearly, more research is needed to clarify the expression and role of miRNAs in sepsis, and to promote the use of miRNAs for sepsis management.

Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures

Purpose

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

Methods

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

Results

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

Conclusion

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

Supplementary Information

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

Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity

Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a "leave-one-disease-out" analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = -0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = -0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10^-3) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10^-9). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity.

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

Background

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

Methods

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

Results

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

Conclusion

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

Personalized Medicine for the Critically Ill Patient: A Narrative Review

Personalized Medicine (PM) is rapidly advancing in everyday medical practice. Technological advances allow researchers to reach patients more than ever with their discoveries. The critically ill patient is probably the most complex of all, and personalized medicine must make serious efforts to fulfill the desire to “treat the individual, not the disease”. The complexity of critically ill pathologies arises from the severe state these patients and from the deranged pathways of their diseases. PM constitutes the integration of basic research into clinical practice; however, to make this possible complex and voluminous data require processing through even more complex mathematical models. The result of processing biodata is a digitized individual, from which fragments of information can be extracted for specific purposes. With this review, we aim to describe the current state of PM technologies and methods and explore its application in critically ill patients, as well as some of the challenges associated with PM in intensive care from the perspective of economic, approval, and ethical issues. This review can help in understanding the complexity of, P.M.; the complex processes needed for its application in critically ill patients, the benefits that make the effort of implementation worthwhile, and the current challenges of PM.

Host Transcriptional Signatures Predict Etiology in Community-Acquired Pneumonia: Potential Antibiotic Stewardship Tools

Background:

Current approaches for pathogen identification in community-acquired pneumonia (CAP) remain suboptimal, leaving most patients without a microbiological diagnosis. If better diagnostic tools were available for differentiating between viral and bacterial CAP, unnecessary antibacterial therapy could be avoided in viral CAP patients.

Methods:

In 156 adults hospitalized with CAP classified to have bacterial, viral, or mixed viral-bacterial infection based on microbiological testing or both microbiological testing and procalcitonin (PCT) levels, we aimed to identify discriminatory host transcriptional signatures in peripheral blood samples acquired at hospital admission, by applying Dual-color-Reverse-Transcriptase-Multiplex-Ligation-dependent-Probe-Amplification (dc-RT MLPA).

Results:

In patients classified by microbiological testing, a 9-transcript signature showed high accuracy for discriminating bacterial from viral CAP (AUC 0.91, 95% CI 0.85-0.96), while a 10-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.91, 95% CI 0.86-0.96). In patients classified by both microbiological testing and PCT levels, a 13-transcript signature showed excellent accuracy for discriminating bacterial from viral CAP (AUC 1.00, 95% CI 1.00-1.00), while a 7-transcript signature similarly discriminated mixed viral-bacterial from viral CAP (AUC 0.93, 95% CI 0.87-0.98).

Conclusion:

Our findings support host transcriptional signatures in peripheral blood samples as a potential tool for guiding clinical decision-making and antibiotic stewardship in CAP.

An 8-gene machine learning model improves clinical prediction of severe dengue progression

BACKGROUND

Each year 3-6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed.

METHODS

We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were "locked" prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD.

RESULTS

We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2-100) sensitivity and 79.7% (95% CI 75.5-83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7-25.6) and 99.0% (95% CI 97.7-100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3-94.1) sensitivity and 39.7% (95% CI 34.7-44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression.

CONCLUSIONS

The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

Digital PCR applications for the diagnosis and management of infection in critical care medicine

Infection (either community acquired or nosocomial) is a major cause of morbidity and mortality in critical care medicine. Sepsis is present in up to 30% of all ICU patients. A large fraction of sepsis cases is driven by severe community acquired pneumonia (sCAP), which incidence has dramatically increased during COVID-19 pandemics. A frequent complication of ICU patients is ventilator associated pneumonia (VAP), which affects 10–25% of all ventilated patients, and bloodstream infections (BSIs), affecting about 10% of patients. Management of these severe infections poses several challenges, including early diagnosis, severity stratification, prognosis assessment or treatment guidance. Digital PCR (dPCR) is a next-generation PCR method that offers a number of technical advantages to face these challenges: it is less affected than real time PCR by the presence of PCR inhibitors leading to higher sensitivity. In addition, dPCR offers high reproducibility, and provides absolute quantification without the need for a standard curve. In this article we reviewed the existing evidence on the applications of dPCR to the management of infection in critical care medicine. We included thirty-two articles involving critically ill patients. Twenty-three articles focused on the amplification of microbial genes: (1) four articles approached bacterial identification in blood or plasma; (2) one article used dPCR for fungal identification in blood; (3) another article focused on bacterial and fungal identification in other clinical samples; (4) three articles used dPCR for viral identification; (5) twelve articles quantified microbial burden by dPCR to assess severity, prognosis and treatment guidance; (6) two articles used dPCR to determine microbial ecology in ICU patients. The remaining nine articles used dPCR to profile host responses to infection, two of them for severity stratification in sepsis, four focused to improve diagnosis of this disease, one for detecting sCAP, one for detecting VAP, and finally one aimed to predict progression of COVID-19. This review evidences the potential of dPCR as a useful tool that could contribute to improve the detection and clinical management of infection in critical care medicine.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans

OBJECTIVE

Acute Pancreatitis (AP) is sudden onset pancreas inflammation that causes systemic injury with a wide and markedly heterogeneous range of clinical consequences. Here, we hypothesized that this observed clinical diversity corresponds to diversity in molecular subtypes that can be identified in clinical and multiomics data.

SUMMARY BACKGROUND DATA

Observational cohort study. n = 57 for the discovery cohort (clinical, transcriptomics, proteomics, and metabolomics data) and n = 312 for the validation cohort (clinical and metabolomics data).

METHODS

We integrated coincident transcriptomics, proteomics, and metabolomics data at serial time points between admission to hospital and up to 48 hours after recruitment from a cohort of patients presenting with acute pancreatitis. We systematically evaluated 4 different metrics for patient similarity using unbiased mathematical, biological, and clinical measures of internal and external validity.We next compared the AP molecular endotypes with previous descriptions of endotypes in a critically ill population with acute respiratory distress syndrome (ARDS).

RESULTS

Our results identify 4 distinct and stable AP molecular endotypes. We validated our findings in a second independent cohort of patients with AP.We observed that 2 endotypes in AP recapitulate disease endotypes previously reported in ARDS.

CONCLUSIONS

Our results show that molecular endotypes exist in AP and reflect biological patterns that are also present in ARDS, suggesting that generalizable patterns exist in diverse presentations of critical illness.

A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures

BACKGROUND

Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.

METHODS

Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.

FINDINGS

Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts.

INTERPRETATION

The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

29 m6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation - 102 Transcriptomic Dataset Analyses

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m⁶A-RNA methylation (epitranscriptomic) regulators (m⁶A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m⁶A-RMRs were upregulated; and most m⁶A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m⁶A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m⁶A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m⁶A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m⁶A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m⁶A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m⁶A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m⁶A-RMRs were more than downregulated m⁶A-RMRs in cancer types; five types of cancers upregulated ≥10 m⁶A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m⁶A-RMRs; (9) 86% of m⁶A-RMRs were differentially expressed in the six clusters of CD4⁺Foxp3⁺ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m⁶A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m⁶A-RMRs, and inhibition of CD40 upregulated m⁶A-RMRs; (11) cytokines and interferons modulated m⁶A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m⁶A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m⁶A-RMRs; (14) m⁶A writer RBM15 and one m⁶A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m⁶A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m⁶A eraser FTO and two m⁶A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m⁶A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.

Pediatric sepsis biomarkers for prognostic and predictive enrichment

Sepsis is a major public health problem in children throughout the world. Given that the treatment guidelines emphasize early recognition, there is interest in developing biomarkers of sepsis, and most attention is focused on diagnostic biomarkers. While there is a need for ongoing discovery and development of diagnostic biomarkers for sepsis, this review will focus on less well-known applications of sepsis biomarkers. Among patients with sepsis, the biomarkers can give information regarding the risk of poor outcome from sepsis, risk of sepsis-related organ dysfunction, and subgroups of patients with sepsis who share underlying biological features potentially amenable to targeted therapeutics. These types of biomarkers, beyond the traditional concept of diagnosis, address the important concepts of prognostic and predictive enrichment, which are key components of bringing the promise of precision medicine to the bedside of children with sepsis.

The Use of Multiplexing to Identify Cytokine and Chemokine Networks in the Immune-Inflammatory Response to Trauma

Significance: The immunoinflammatory responses that follow trauma contribute to clinical trajectory and patient outcomes. While remarkable advances have been made in trauma services and injury management, clarity on how the immune system in humans responds to trauma is lagging. Recent Advances: Multiplexing platforms have transformed our ability to analyze comprehensive immune mediator responses in human trauma. In parallel, with the establishment of large data sets, computational methods have been adapted to yield new insights based on mediator patterns. These efforts have added an important data layer to the emerging multiomic characterization of the human response to injury. Critical Issues: Outcome after trauma is greatly affected by the host immunoinflammatory response. Excessive or sustained responses can contribute to organ damage. Hence, understanding the pathophysiology behind traumatic injury is of vital importance. Future Directions: This review summarizes our work in the study of circulating immune mediators in trauma patients. Our foundational studies into dynamic patterns of inflammatory mediators represent an important contribution to the concepts and computational challenges that these large data sets present. We hope to see further integration and understanding of multiomics strategies in the field of trauma that can aid in patient endotyping and in potentially identifiying certain therapeutic targets in the future. Antioxid. Redox Signal. 35, 1393-1406.

Diagnostic Challenges in Sepsis

Purpose of Review

Sepsis is a leading cause of death worldwide. Groundbreaking international collaborative efforts have culminated in the widely accepted surviving sepsis guidelines, with iterative improvements in management strategies and definitions providing important advances in care for patients. Key to the diagnosis of sepsis is identification of infection, and whilst the diagnostic criteria for sepsis is now clear, the diagnosis of infection remains a challenge and there is often discordance between clinician assessments for infection.

Recent Findings

We review the utility of common biochemical, microbiological and radiological tools employed by clinicians to diagnose infection and explore the difficulty of making a diagnosis of infection in severe inflammatory states through illustrative case reports. Finally, we discuss some of the novel and emerging approaches in diagnosis of infection and sepsis.

Summary

While prompt diagnosis and treatment of sepsis is essential to improve outcomes in sepsis, there remains no single tool to reliably identify or exclude infection. This contributes to unnecessary antimicrobial use that is harmful to individuals and populations. There is therefore a pressing need for novel solutions. Machine learning approaches using multiple diagnostic and clinical inputs may offer a potential solution but as yet these approaches remain experimental.