Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study

Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites

Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and those without septic shock (n = 92) using a high-resolution mass spectrometer. By implementing a supervised machine learning algorithm, a prediction model based on a panel of metabolites achieved an accuracy of 87.8%. Notably, when employed on a low-resolution instrument, the model maintained its predictive performance with an accuracy of 84.2%. These results demonstrate the potential of metabolite-based algorithms to identify patients at high risk of septic shock. Our proposed workflow aims to optimize risk assessment and streamline clinical management processes in the ED, holding promise as an efficient routine test to promote timely intensive interventions and reduce septic shock mortality.

Human Peripheral Blood Leukocyte Transcriptome-Based Aging Clock Reveals Acceleration of Aging by Bacterial or Viral Infections

The aging of the population is a global concern. In the post-coronavirus disease 2019 (COVID-19) pandemic era, there are no effective methods to identify aging acceleration due to infection. In this study, we conducted whole-transcriptome sequencing on peripheral blood samples from 35 healthy individuals (22-88 years old). By analyzing the changes in mRNA, lncRNA, and miRNA expression, we investigated the characteristics of transcriptome alterations during the aging process. ceRNA networks were constructed, and 10 genes (CD248, PHGDH, SFXN2, MXRA8, NOG, TTC24, PHYKPL, CACHD1, BPGM, and TWF1) were identified as potential aging markers and used to construct an aging clock. Moreover, our aging clock categorized individuals into slow-, average-, and quick-aging groups, highlighting a link between accelerated aging and infection-related clinical parameters. Pseudotime analysis further revealed 2 distinct aging trajectories, corroborating the variations in the aging rate identified by the aging clock. Furthermore, we validated the results using the OEP001041 data set (277 healthy individuals aged 17-75), and data sets comprising patients with infectious diseases (n = 1 558). Our study revealed that infection accelerates aging via increased inflammation and oxidative stress in infectious disease patients. Besides, the aging clock exhibited alterations after infection, highlighting its potential for assessing the aging rate after patient recovery. In conclusion, our study introduces a novel aging clock to assess the aging rate in healthy individuals and those with infections, revealing a strong link between accelerated aging and infections through inflammation and oxidative stress. These findings offer valuable insights into aging mechanisms and potential strategies for healthy aging.

Advances in pediatric sepsis biomarkers - what have we learnt so far?

INTRODUCTION

Sepsis is a systemic immune dysregulation syndrome triggered by secondary infection in the host, with diagnosis based on the updated Phoenix criteria and characterized by multi-organ failure as its core pathological manifestation. It is a significant global health challenge due to its increasing incidence and high mortality rates. Recent advancements in biomarker research provide promising tools for improving early diagnosis and timely intervention, essential for better patient outcomes.

AREAS COVERED

This review examines the latest developments in pediatric sepsis biomarkers, categorized by inflammation, metabolism, organ damage, and non-coding RNAs (miRNAs, LncRNAs). We discuss the advancements in each category, highlighting the integration of diverse biomarkers and advanced technologies to enhance diagnostics, personalize therapy, and improve patient stratification.

EXPERT OPINION

Given the limited specificity and sensitivity of current markers like CRP and PCT, multicenter studies are crucial for validating new biomarkers and for developing comprehensive panel markers that combine multiple diagnostic indicators. It is also important to consider the variability in host responses to different pathogens when identifying biomarkers based on host-pathogen interactions. To advance personalized medicine, future research may prioritize the identification of specific diagnostic biomarkers for pediatric sepsis, tailored to different pathogens.

Adjunctive Corticosteroids for Hypotension in the Pediatric Cardiac ICU: Single-Center Retrospective Study, 2020-2021

OBJECTIVES

To characterize adjunctive corticosteroid use and associations between any exposure or cumulative dose and outcomes in pediatric cardiac surgical cases.

DESIGN

A retrospective cohort was admitted over 24 months (from January 2020 to December 2021).

SETTING

Single-center cardiac ICU (CICU) in a quaternary hospital.

PATIENTS

Descriptive analyses of all patients receiving hydrocortisone for hypotension. Further comparative analyses were restricted to postoperative infants (< 12 mo) exposed to corticosteroids vs. not, including propensity-score inverse weighted and matched analyses.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We examined associations between cumulative hydrocortisone dose and outcomes, including severe infection. Overall, 154 patients of all ages received steroids (91 post-surgical). Median (interquartile range [IQR]) cumulative hydrocortisone dose was 10.0 mg/kg (IQR, 6.0-21.2 mg/kg). Greater cumulative dose was associated with higher adjusted odds (95% CI) of severe infection (1.08 [95% CI, 1.03-1.12]). For comparative analyses, we identified 403 infants, including 68 with postoperative corticosteroid exposure. Propensity scores based on multiple factors, including peak modified Vasoactive-Inotropic Score (mVIS, excluding milrinone), compared outcomes and hemodynamic response with quantification of rate of mVIS fall from peak among 55 matched pairs. We failed to identify a difference in rate of mVIS fall between nonsteroid and steroid recipients (-0.162 [IQR, -0.228 to -0.053] vs. -0.160 [IQR, -0.300 to -0.046]; p = 0.674).

CONCLUSIONS

In our CICU cohort receiving adjunctive hydrocortisone for hypotension, we failed to identify a consistent signal regarding outcomes and rate of mVIS fall. Considering potential side effects, these data suggest prospective study is needed to better define the use of such treatment.

Exploration potential sepsis-ferroptosis mechanisms through the use of CETSA technology and network pharmacology

As an important self-protection response mechanism of the body, inflammation can not only remove the necrotic or even malignant cells in the body, but also take a series of targeted measures to eliminate the pathogen of foreign invasion and block the foreign substances that may affect the life and health of the body. Flavonoids have known anti-inflammatory, anti-oxidation, anti-cancer and other effects, including glycyrrhizin molecules is one of the representatives. Licochalcone D has known anti-inflammatory and antioxidant properties and is effective in the treatment of a variety of inflammatory diseases. However, the underlying mechanism for the treatment of sepsis remains unclear. In this study, the therapeutic potential of Licochalcone D for sepsis was studied by analyzing network pharmacology and molecular dynamics simulation methods. Sepsis-related genes were collected from the database to construct PPI network maps and drug-targeting network profiles. The potential mechanism of Licochalcone D in sepsis was predicted by gene ontology, KEGG and molecular dynamics simulation. Sixty drug-disease genes were subsequently validated. Go analysis showed that monomeric small molecule Licochalcone D could regulate the process of intracellular enzyme system. The KEGG pathway analysis showed that the signal pathway of the main effect was related to the calcium pathway. The results of intersections with iron death-related target genes showed that ALOX5, ALOX15B and other nine targets all had the effect of possibly improving sepsis, while GSE 54,514, GSE 95,233 and GSE 69,528 were used to analyze the survival rate and ROC curve. Five genes were screened, including ALOX5, ALOX15B, NFE2L2 and NR4A1, HIF1A. The results of molecular docking showed that ALOX5 and Licochalcone D had strong binding activity. Finally, the results of molecular dynamics simulation showed that there was good binding power between drug and target. In the present study, we utilized molecular dynamics simulation techniques to assess the binding affinity between the small-molecule ligand and the protein receptor. The simulation outcomes demonstrate that the binding interface between the ligand and receptor remains stable, with a calculated binding free energy (ΔG) of -32.47 kJ/mol. This signifies a high-affinity interaction between the ligand and receptor, suggesting the long-term stability of the small molecule under physiological conditions. These findings provide critical insights for drug development efforts. This study elucidates the therapeutic potential of Licochalcone D, a traditional Chinese medicine monomer, in improving sepsis through the regulation of ferroptosis, thereby providing a new direction and option for subsequent clinical drug development in the treatment of sepsis.

CXCR4+ PD-L1+ neutrophils are increased in non-survived septic mice

The dysregulated host response to infections can lead to sepsis, a complex disease characterized by a spectrum of clinical phenotypes. Using scRNA-seq, we analyzed the immune cell of survived and non-survived CLP-septic mice to gain insights into the immunological mechanisms by which neutrophils contribute to the hyperinflammatory phenotype. Our findings reveal that non-survived mice exhibit increased frequencies of immature CXCR4+ PD-L1+ neutrophils in the bloodstream, accompanied by an accumulation of trafficking-specific CXCR4+ PD-L1+ neutrophils into the lungs. The IFN-gamma and LPS promote the PD-L1 expression on neutrophils and an activation profile associated with inflammation and organ damage. Notably, abrogating the IFN-gamma reduced susceptibility to CLP-sepsis and diminished CXCR4+ PD-L1+ neutrophils frequency. This study provides insights into the immune cell activation profiles associated with the worsening of the CLP-sepsis, and the CXCR4+ PD-L1+ neutrophils population highlighted here represents a promising target for therapeutic modulation in clinical sepsis hyperinflammatory phenotype.

The blood transcriptional response in patients developing intensive care unit-acquired pneumonia

BACKGROUND

Immune response dysregulation has been implicated in the development of intensive care unit (ICU)-acquired pneumonia. We aimed to determine differences in the longitudinal blood transcriptional response between patients who develop ICU-acquired pneumonia (cases) and those who do not (controls).

METHODS

We performed a case-cohort study in mechanically ventilated trauma and surgery patients with ICU stays >2 days, enrolled in 30 hospitals across Europe. We collected blood for RNA sequencing at baseline, day 7 and (in cases) the day of pneumonia diagnosis. We performed gene set enrichment analysis and analysed longitudinal gene expression changes using linear mixed models. External validation was performed using an independent trauma cohort.

RESULTS

We enrolled 113 cases and 115 controls, with similar baseline characteristics. At baseline (median 2 days after ICU admission), cases showed upregulated gene pathways relating to innate immunity, haemostasis and metabolism, and downregulated adaptive immune pathways. These changes persisted at the day of pneumonia diagnosis (median 6 days, compared to day 7 in controls). In the longitudinal comparison, cases exhibited enhanced upregulation of innate immunity, adaptive immunity and haemostasis pathways, along with enhanced downregulation of metabolism pathways, relative to controls (all p<0.00001, except haemostasis p<0.05). These findings were largely externally validated. Cases had higher quantitative sepsis response signature scores (p<0.001), reflective of immune dysregulation.

CONCLUSION

Patients developing ICU-acquired pneumonia exhibit distinct blood transcriptional responses shortly after ICU admission and in the subsequent path to pneumonia, suggestive of broad immune dysfunction with both immunosuppressive and inflammatory features.

Sepsis subphenotypes, theragnostics and personalized sepsis care

Heterogeneity between critically ill patients with sepsis is a major barrier to the discovery of effective therapies. The use of machine learning techniques, coupled with improved understanding of sepsis biology, has led to the identification of patient subphenotypes. This exciting development may help overcome the problem of patient heterogeneity and lead to the identification of patient subgroups with treatable traits. Re-analyses of completed clinical trials have demonstrated that patients with different subphenotypes may respond differently to treatments. This suggests that future clinical trials that take a precision medicine approach will have a higher likelihood of identifying effective therapeutics for patients based on their subphenotype. In this review, we describe the emerging subphenotypes identified in the critically ill and outline the promising immune modulation therapies which could have a beneficial treatment effect within some of these subphenotypes. Furthermore, we will also highlight how bringing subphenotype identification to the bedside could enable a new generation of precision-medicine clinical trials.

INFORMING INTENSIVE CARE UNIT DIGITAL TWINS: DYNAMIC ASSESSMENT OF CARDIORESPIRATORY FAILURE TRAJECTORIES IN PATIENTS WITH SEPSIS

ABSTRACT

Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55-76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56-87), hospital length of stay (LOS) of 7 (IQR, 4-12) days, and ICU LOS of 2 (IQR, 1-4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2-15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6-13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0-1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3-8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

An international observational study validating gene-expression sepsis immune subgroups

BACKGROUND

Sepsis gene-expression sub-phenotypes with prognostic and theranostic potential have been discovered. These have been identified retrospectively and have not been translated to methods that could be deployed at the bedside. We aimed to identify subgroups of septic patients at high-risk of poor outcome, using a rapid, multiplex RNA-based test.

METHODS

Adults with sepsis, in the intensive care unit (ICU) were recruited from 17 sites in the United Kingdom, Sweden and France. Blood was collected at days 2-5 (S1), 6-8 (S2) and 13-15 (S3) after ICU admission and analyzed centrally. Patients were assigned into 'high' and 'low' risk groups using two models previously developed for the Immune-Profiling Panel prototype on the bioMérieux FilmArray® system.

RESULTS

357 patients were recruited (March 2021-November 2022). 69% were male with a median age of 67 years, APACHE II score of 21 and a 30% 90-day mortality rate. The proportions of high-risk patients decreased over the three sampling times (model 1: 53%, 40%, 15% and model 2: 81%, 74%, 37%). In model 1, 90-day mortality was higher in a high-risk group at each time (S1: 35% vs 24%, p = 0.04; S2: 43% vs 20%, p < 0.001; S3: 52% vs 24%, p = 0.007). In model 2, mortality was only significantly different at the second sampling time (S1: 30% vs 27%, p = 0.77; S2: 34% vs 14%, p = 0.002; S3: 35% vs 23%, p = 0.13).

CONCLUSIONS

Gene-expression diagnostics can identify patients with sepsis at high-risk of poor outcomes and could be used to identify patients for precision medicine trials.

REGISTRATION

ISRCTN11364482 Registered 24th September 2020.

Temporal Transitions of the Hyperinflammatory and Hypoinflammatory Phenotypes in Critical Illness

Rationale: Systemic molecular phenotypes of critical illness are prognostically informative, yet their temporal kinetics and implications of changing phenotypes remain incompletely understood. Objectives: To determine the temporal nature of the Hyperinflammatory and Hypoinflammatory phenotypes and assess the impact of transition between the phenotypes on mortality. Methods: We used data from one prospective observational cohort (MARS [Molecular Diagnosis and Risk Stratification of Sepsis]) and two randomized controlled trials in acute respiratory distress syndrome (ALVEOLI [Assessment of Low Tidal Volume and Elevated End-Expiratory Pressure to Obviate Lung Injury]) and sepsis (CLOVERS [Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis]). Critically ill patients with biomarkers available at multiple time points (Days 0-4) were included. We used a validated classifier model incorporating plasma IL-8, protein C, and serum bicarbonate to assign phenotypes on each day. We determined the association of longitudinal phenotype transition and 90-day all-cause mortality. Measurements and Main Results: Data from 2,407, 527, and 868 patients were included in MARS, ALVEOLI, and CLOVERS, respectively. In MARS, 36.0% were classified by the parsimonious model as Hyperinflammatory at Day 0, decreasing to 15.6% by Day 2 and 6.3% by Day 4. In ALVEOLI and CLOVERS, 26.4% and 24.8% of patients were Hyperinflammatory at Day 0, decreasing to 13.4% and 5.7% at Day 3, respectively. In all three cohorts, switching classification from Hyperinflammatory (Day 0) to Hypoinflammatory over time was associated with significantly improved mortality compared with persistently Hyperinflammatory patients. Mediation analysis indicated that only a minor proportion of this improvement could be attributed to ameliorating organ failure. Conclusions: The prevalence of the Hyperinflammatory phenotype, as assigned using a parsimonious biomarker classifier model, decreases over the first several days of critical illness, irrespective of acute respiratory distress syndrome diagnosis. The transition from Hyperinflammatory to Hypoinflammatory mediates a trajectory toward recovery beyond the resolution of organ failure.

Bundle compliance patterns in septic shock and their association with patient outcomes: an unsupervised cluster analysis

Adhering to bundle-based care recommendations within stringent time constraints presents a profound challenge. Elements within these bundles hold varying degrees of significance. We aimed to evaluate the Surviving Sepsis Campaign (SSC) hour-one bundle compliance patterns and their association with patient outcomes. Utilizing the Medical Information Mart for Intensive Care-IV 1.0 dataset, this retrospective cohort study evaluated patients with sepsis who developed shock and were admitted to the intensive care unit between 2008 and 2019. The execution of five hour-one bundle interventions were assessed. Patients with similar treatment profiles were categorized into clusters using unsupervised machine learning. Primary outcomes included in-hospital and 1-year mortality. Four clusters were identified: C#0 (n = 4716) had the poorest bundle compliance. C#1 (n = 1117) had perfect antibiotic adherence with modest fluid and serum lactate measurement adherence. C#2 (n = 850) exhibited full adherence to lactate measurement and low adherence to fluid administration, blood culture, and vasopressors, while C#3 (n = 381) achieved complete adherence to fluid administration and the highest adherence to vasopressor requirements in the entire cohort. Adjusting for covariates, C#1 and C#3 were associated with reduced odds of in-hospital mortality compared to C#0 (adjusted odds ratio [aOR] = 0·83; 95% confidence interval [CI] 0·7-0·97 and aOR = 0·7; 95% CI 0·53-0·91, respectively). C#1 exhibited significantly better 1-year survival (adjusted hazard ratio [aHR] = 0·9; 95%CI 0·81-0·99). We were able to identify distinct clusters of SSC hour-one bundle adherence patterns using unsupervised machine learning techniques, which were associated with patient outcomes.

Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome

PURPOSE

Machine learning has shown promise to detect useful subgroups of patients with sepsis from gene expression and protein data. This approach has rarely been deployed in metabolomic datasets. Metabolomic data are of interest as they capture effects from the genome, proteome, and environmental. We aimed to discover metabolic sub-phenotypes of septic shock, examine their temporal stability and association with clinical outcome.

METHODS

Analysis was performed in two double-blind randomized trials in septic shock (LeoPARDS (1402 samples from 470 patients) and VANISH (493 samples from 173 patients)). Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clustering in LeoPARDS and predicted in VANISH with an elastic net classifier.

RESULTS

Three sub-phenotypes were found. The main determinants of cluster membership were lipid species, especially lysophospholipids. Low lysophospholipid sub-phenotypes were associated with higher circulating cytokine levels. Persistence of low lysophospholipid sub-phenotypes was associated with higher mortality compared to the high lysophospholipid sub-phenotype (LeoPARDS: cluster 2 odds ratio 3.66 (95% CI 1.88-7.20), p = 0.0001, cluster 3 2.49 (1.29-4.81), p = 0.006; VANISH: cluster 2 4.13 (1.17-15.61), p = 0.03), cluster 3 3.22 (1.09-9.92), p = 0.04, vs cluster 1). We found no heterogeneity of treatment effect for any of the trial interventions by baseline metabolic sub-phenotype.

CONCLUSION

Three metabolic subgroups exist in septic shock which evolve over time. Persistence of low lysophospholipid sub-phenotypes is associated with mortality. Monitoring these subgroups could help identify patients at risk of poor outcome and direct novel therapies such as lysophospholipid supplementation.

REGISTRATION

Clinicaltirals.gov Identifiers, VANISH: ISRCTN 20769191, LeoPARDS: ISRCTN12776039.

Association of 91 Inflammatory Factors and 1400 Metabolites with Sepsis: A Mendelian Randomization Analysis

ObjectiveObservational studies suggest links between inflammatory factors, metabolites, and sepsis, yet their causality is uncertain. This study employs Mendelian Randomization (MR) to investigate the causality between these factors and sepsis, aiming to uncover the precise relationship and identify novel treatment approaches.MethodsWe used summary data from genome-wide association studies (GWAS) involving 91 inflammatory factors, 1400 metabolites as exposure, and STREPTO SEPSIS as outcome. Inverse variance weighting (IVW) and MR-Egger were used to evaluate the causal effect between exposure and outcome. Sensitivity analyses were performed using Cochrane's Q test, MR-Egger intercept method, MR-PRESSO method and leave-one-out method.ResultsThymic stromal lymphopoietin levels (TSLP) (OR = 1.269; 95%CI = 1.016-1.585; P = .036) and Interleukin 15 receptor subunit alpha levels (IL-15Rα) (OR = 0.894; 95%CI = 0.801-0.998; P = .046) had a significant causal relationship with sepsis. Forty-four metabolites were associated with sepsis, including Spermidine to choline ratio (OR = 1.447; 95%CI = 1.104-1.977; P = .009), 4-hydroxyhippurate levels (OR = 1.448; 95%CI = 1.117-1.877; P = .005), and Sphingomyelin (d18:1/20:1, d18:2/20:0) levels (OR = 1.371; 95%CI = 1.139-1.651; P < .001). TSLP was associated with 19 metabolites, and IL-15Rα was associated with 30 metabolites.ConclusionsThis study uncovers the causal link between sepsis and two inflammatory factors, TSLP and IL-15Rα, and suggests metabolites' potential in intervention. It also identifies 44 metabolites associated with sepsis, indicating possible biomarkers or therapeutic targets. The findings offer new perspectives on sepsis pathogenesis and could inform future treatment strategies.

Transcriptomics in Human Septic Shock: State of the Art

Background: Septic shock is a complex syndrome characterized by signs of intense systemic inflammation and a profound dysregulation of the immune response. Large-scale gene expression analysis is a valuable tool in this scenario because sepsis affects various cellular components and signaling pathways. Results: In this article, we provide an overview of the transcriptomic studies that investigated human sepsis from 2007 to 2024, highlighting their major contributions. Conclusions: The field, however, still faces substantial limitations and several challenges. To advance further, we believe that standardization of sample collection and data analysis, preservation of cell and tissue architecture, and integration with other omics techniques are crucial for a broader understanding of this lethal disease.

Understanding the molecular bridges between the drugs and immune cell

The interactions of drugs with the host's immune cells determine the drug's efficacy and adverse effects in patients. Nonsteroidal Anti-Inflammatory Drugs (NSAID), such as corticosteroids, NSAIDs, and immunosuppressants, affect the immune cells and alter the immune response. Molecularly, drugs can interact with immune cells via cell surface receptors, changing the antigen presentation by modifying the co-stimulatory molecules and interacting with the signaling pathways of T cells, B cells, Natural killer (NK) cells, mast cells, basophils, and macrophages. Immunotoxicity, resulting from drug-induced changes in redox status, generation of Reactive Oxygen Species (ROS)/Reactive Nitrogen Species (RNS), and alterations in antioxidant enzymes within immune cells, leads to immunodeficiency. This, in turn, causes allergic reactions, autoimmune diseases, and cytokine release syndrome (CRS). The treatment options should include the evaluation of immune status and utilization of the concept of pharmacogenomics to minimize the chances of immunotoxicity. Many strategies in redox, like targeting the redox pathway or using redox-active agents, are available for the modulation of the immune system and developing drugs. Case studies highlight significant drug-immune cell interactions and patient outcomes, underscoring the importance of understanding these complexities. The future direction focuses on the drugs to deliver antiviral therapy, new approaches to immunomodulation, and modern technologies for increasing antidote effects with reduced toxicity. In conclusion, in-depth knowledge of the interaction between drugs and immune cells is critical to protect the patient from the adverse effects of the drug and improve therapeutic outcomes of the treatment process. This review focuses on the multifaceted interactions of drugs and their consequences at the cellular levels of immune cells.

Pediatric Sepsis Phenotype in a Single-Center Cohort Covering 2010-2020: Evolution in Day 1-Day 3 Trajectory and Potential Prognostic Value

OBJECTIVES

To examine the utility of day 3 sepsis phenotype classifications compared with day 1 and whether these could be reliably identified using routine clinical data on day 1.

DESIGN

Retrospective cohort study of pediatric patients managed 2010-2014 and 2018-2020.

SETTING

Academic children's hospital.

PATIENTS

One thousand eight hundred twenty-eight children (1 mo to 18 yr old) admitted to the PICU with suspected infection who received a minimum of 7 days of systemic antibiotics.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Subjects showed significant evolution of phenotype from day 1 to day 3, with 31.7-60.9% remaining the same type. Outcomes were worst in those classifying as type D on day 3, with mortality as high as 16.6% in those that were classified as type D on both days 1 and 3, as well as 11.3% in those initially classified as type C (a lower mortality type) on day 1 but type D on day 3. Accurate statistical prediction of day 3 types using multinomial logistic regression and random forest and day 1 data was poor, despite attempts to improve performance.

CONCLUSIONS

In our retrospective cohort of patients with sepsis, we identified significant evolution in phenotype over the first 3 days of illness. Day 3 phenotypes may provide more accurate statistical prediction of outcomes, but identification of day 3 phenotypes using data available early in the course of illness is challenging. New methods will likely be required to improve performance in this area.

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery

BACKGROUND

Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis.

METHODS

Gene expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a diagnosis model. Then the CIBERSORT algorithm is used to analyze the relationship between the change of core gene expression and immune response in sepsis. Construct nomogram, DCA and CIC to further verify the reliability of the diagnosis model. The potential molecular compounds interacting with key genes were searched from the Traditional Chinese Medicine Active Compound Library (TCMACL).

RESULTS

We screened 405 DEGs, including 334 up-regulated and 71 down-regulated genes. The 308 potential genes were obtained by intersection of MEturquoise module genes in WGCNA analysis and DEGs for subsequent machine learning analysis. GO and KEGG enrichment analysis showed that sepsis was mainly related to immune response and bacterial infection. Then 113 combined machine learning algorithms are applied to construct a diagnosis model to screen 22 hub genes. Four four key genes (CD177, GNLY, ANKRD22, and IFIT1) are obtained through further analysis of PPI network constructed by 22 hub genes. Subsequently, the diagnostic model is proved to have good predictive value by nomogram, DCA and CIC. Finally, molecular compounds (Dieckol, Grosvenorine and Tellimagrandin II) were screened out as potential drugs.

CONCLUSION

113 combinated machine learning algorithms screened out four key genes that can distinguish sepsis patients. At the same time, potential therapeutic molecular compounds interacting with key genes genes were screened out by molecular docking.

Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies

Sepsis, a heterogeneous illness produced by a dysregulated host response to infection, remains a severe mortality risk. Recent discoveries in sepsis research have stressed phenotyping as a feasible strategy for tackling heterogeneity and enhancing therapy precision. Sepsis phenotyping has moved from traditional stratifications based on severity and prognosis to dynamic, phenotype-driven therapeutic options. This review covers recent progress in connecting sepsis subgroups to personalized treatments, with a focus on phenotype-based therapeutic predictions and decision-support systems. Despite ongoing challenges, such as standardizing phenotyping frameworks and incorporating findings into clinical practice, this topic has enormous promise. By investigating phenotypic variation in therapy responses, we hope to uncover new biomarkers and phenotype-driven therapeutic solutions, laying the groundwork for more effective therapies and, ultimately improving patient outcomes.

Septic Cardiomyopathy: Difficult Definition, Challenging Diagnosis, Unclear Treatment

Sepsis is a systemic inflammatory response syndrome of suspected or confirmed infectious origin, which frequently culminates in multiorgan failure, including cardiac involvement. Septic cardiomyopathy (SCM) remains a poorly defined clinical entity, lacking a formal or consensus definition and representing a significant knowledge gap in critical care medicine. It is an often-underdiagnosed complication of sepsis. The only widely accepted aspect of its definition is that SCM is a transient myocardial dysfunction occurring in patients with sepsis, which cannot be attributed to ischemia or pre-existing cardiac disease. The pathogenesis of SCM appears to be multifactorial, involving inflammatory cytokines, overproduction of nitric oxide, mitochondrial dysfunction, calcium homeostasis dysregulation, autonomic imbalance, and myocardial edema. Diagnosis primarily relies on echocardiography, with advanced tools such as tissue Doppler imaging (TDI) and global longitudinal strain (GLS) providing greater sensitivity for detecting subclinical dysfunction and guiding therapeutic decisions. Traditional echocardiographic findings, such as left ventricular ejection fraction measured by 2D echocardiography, often reflect systemic vasoplegia rather than intrinsic myocardial dysfunction, complicating accurate diagnosis. Right ventricular (RV) dysfunction, identified as a critical component of SCM in many studies, has multifactorial pathophysiology. Factors including septic cardiomyopathy itself, mechanical ventilation, hypoxemia, and hypercapnia-particularly in cases complicated by acute respiratory distress syndrome (ARDS)-increase RV afterload and exacerbate RV dysfunction. The prognostic value of cardiac biomarkers, such as troponins and natriuretic peptides, remains uncertain, as these markers primarily reflect illness severity rather than being specific to SCM. Treatment focuses on the early recognition of sepsis, hemodynamic optimization, and etiological interventions, as no targeted therapies currently exist. Emerging therapies, such as levosimendan and VA-ECMO, show potential in severe SCM cases, though further validation is needed. The lack of standardized diagnostic criteria, combined with the heterogeneity of sepsis presentations, poses significant challenges to the effective management of SCM. Future research should focus on developing cluster-based classification systems for septic shock patients by integrating biomarkers, echocardiographic findings, and clinical parameters. These advancements could clarify the underlying pathophysiology and enable tailored therapeutic strategies to improve outcomes for SCM patients.

Dynamic changes in peripheral blood lymphocyte trajectory predict the clinical outcomes of sepsis

Background

Sepsis induces profound derangements in the immune system, including lymphopenia, which correlates with immunosuppression and poor prognosis. However, most evaluations of immunosuppression in sepsis patients rely on static, sporadic lymphocyte counts, lacking dynamic modeling over the disease course. This study aimed to apply latent class mixed modeling on longitudinal lymphocyte counts to uncover heterogeneous trajectory phenotypes in sepsis patients and assess their predictive value for clinical outcomes.

Results

Four lymphocyte trajectory phenotypes were identified in the retrospective cohort (n=2,149) and externally validated (n=2,388): high-declining (α, 3.8%), stable-medium (β, 69.3%), high-increasing (γ, 3.2%), and stable-low (δ, 23.8%). The α phenotype exhibited the highest disease severity and mortality (25.9%) compared with other phenotypes in both cohorts. In the prospective cohort (n=1,056), all lymphocyte subset counts differed among phenotypes on admission (P <.001) and were lower in non-survivors (P<.05). Multivariable regression demonstrated that age, Acute Physiology and Chronic Health Evaluation-II score, heart rate, natural killer cell count, infection source, and lymphocyte trajectory phenotype were independent predictors of 28-day mortality. A nomogram combining these variables provided individualized risk estimations.

Conclusions

The lymphocyte trajectories delineated novel dynamic phenotypes associated with divergent sepsis outcomes. Incorporating longitudinal trajectory modeling and lymphocyte subsets may improve prognostic risk assessment and guide the selection of immunotherapies tailored to specific immune phenotypes in sepsis patients.

Clinical trial registration

https://www.chictr.org.cn/showproj.aspx?proj=18277, identifier ChiCTR-40 ROC-17010750.

Clinical subtypes in critically ill patients with sepsis: validation and parsimonious classifier model development

BACKGROUND

The application of sepsis subtypes to enhance personalized medicine in critically ill patients is hindered by the lack of validation across diverse cohorts and the absence of a simple classification model. We aimed to validate the previously identified SENECA clinical sepsis subtypes in multiple large ICU cohorts, and to develop parsimonious classifier models for δ-type adjudication in clinical practice.

METHODS

Data from four cohorts between 2008 and 2023 were used to assign α, β, γ and δ-type in patients fulfilling the Sepsis-3 criteria using clinical variables: (I) The Molecular diAgnosis and Risk stratification of Sepsis (MARS, n = 2449), (II) a contemporary continuation of the MARS study (MARS2, n = 2445) (III) the Dutch National Intensive Care Evaluation registry (NICE, n = 28,621) and (IV) the Medical Information Mart for Intensive Care including (MIMIC-IV, n = 18,661). K-means clustering using clinical variables was conducted to assess the optimal number of classes and compared to the SENECA subtypes. Parsimonious models were built in the SENECA derivation cohort to predict subtype membership using logistic regression, and validated in MARS and MIMIC-IV.

RESULTS

Among 52.226 patients with sepsis, the subtype distribution in MARS, MARS2 and NICE was 2-6% for the α-type, 1-5% for the β-type, 49-65% for the γ-type and 26-48% for the δ-type compared to 33%, 27%, 27% and 13% in the original SENECA derivation cohort, whereas subtype distribution in MIMIC-IV was more similar at 25%, 24%, 27% and 25%, respectively. In-hospital mortality rates were significantly different between the four cohorts for α, γ and δ-type (p < 0.001). Method-based validation showed moderate overlap with the original subtypes in both MARS and MIMIC-IV. A parsimonious model for all four subtypes had moderate to low accuracy (accuracy 62.2%), while a parsimonious classifier model with 3 variables (aspartate aminotransferase, serum lactate, and bicarbonate) had excellent accuracy in predicting the δ-type patients from all other types in the derivation cohort and moderate accuracy in the validation cohorts (MARS: area under the receiver operator characteristic curve (AUC) 0.93, 95% CI [0.92-0.94], accuracy 85.5% [84.0-86.8%]; MIMIC-IV: AUC 0.86 [0.85-0.87], accuracy 82.9% [82.4-83.4%]).

CONCLUSIONS

The distribution and mortality rates of clinical sepsis subtypes varied between US and European cohorts. A three-variable model could accurately identify the δ-type sepsis patients.

FUNCTIONAL IMMUNOPHENOTYPING FOR PRECISION THERAPIES IN SEPSIS

ABSTRACT

Sepsis remains a significant cause of morbidity and mortality worldwide. Although many more patients are surviving the acute event, a substantial number enters a state of persistent inflammation and immunosuppression, rendering them more vulnerable to infections. Modulating the host immune response has been a focus of sepsis research for the past 50 years, yet novel therapies have been few and far between. Although many septic patients have similar clinical phenotypes, pathways affected by the septic event differ not only between individuals but also within an individual over the course of illness. These differences ultimately impact overall immune function and response to treatment. Defining the immune state, or endotype, of an individual is critical to understanding which patients will respond to a particular therapy. In this review, we highlight current approaches to define the immune endotype and propose that these technologies may be used to "prescreen" individuals to determine which therapies are most likely to be beneficial.

Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment

BACKGROUND

Sepsis and shock are common complications of necrotising soft tissue infections (NSTI). Sepsis encompasses different endotypes that are associated with specific immune responses. Hyperbaric oxygen (HBO2) treatment activates the cells oxygen sensing mechanisms that are interlinked with inflammatory pathways. We aimed to identify gene expression patterns associated with effects of HBO2 treatment in patients with sepsis caused by NSTI, and to explore sepsis-NSTI profiles that are more receptive to HBO2 treatment.

METHODS

An observational cohort study examining 83 NSTI patients treated with HBO2 in the acute phase of NSTI, fourteen of whom had received two sessions of HBO2 (HBOx2 group), and another ten patients (non-HBO group) who had not been exposed to HBO2. Whole blood RNA sequencing and clinical data were collected at baseline and after the intervention, and at equivalent time points in the non-HBO group. Gene expression profiles were analysed using machine learning techniques to identify sepsis endotypes, treatment response endotypes and clinically relevant transcriptomic signatures of response to treatment.

RESULTS

We identified differences in gene expression profiles at follow-up between HBO2-treated patients and patients not treated with HBO2. Moreover, we identified two patient endotypes before and after treatment that represented an immuno-suppressive and an immune-adaptive endotype respectively, and we characterized the genetic profile of the patients that transition from the immuno-suppressive to the immune-adaptive endotype after treatment. We discovered one gene MTCO2P12 that distinguished individuals who altered their endotype in response to treatment from non-responders.

CONCLUSION

The global gene expression pattern in blood changed in response to HBO2 treatment in a direction associated with clinical biochemistry improvement, and the study provides potential novel biomarkers and pathways for monitoring HBO2 treatment effects and predicting an HBO2 responsive NSTI-sepsis profile.

TRIAL REGISTRATION

Biological material was collected during the INFECT study, registered at ClinicalTrials.gov (NCT01790698) 04/02/2013.

Recent advances of precision immunotherapy in sepsis

Precision immunotherapy signifies the administration of the required type of immune intervention tailored to the state of immune activation at the appropriate time window. The classification of patients into the different states of immune activation is usually done by either a protein blood biomarker or a molecular blood endotype that is diagnostic of the precise immune state. Evidence coming from trials of the last decade suggests that immune interventions should be split into strategies aiming to attenuate the exaggerated immune responses, restore sepsis-induced immunoparalysis (SII) and restore the vascular tone. Suggested strategies to attenuate the immune responses are anakinra, nangibotide and tocilizumab. Biomarkers that guide their use are ferritin, soluble triggering receptor expressed on myeloid cells-1 and C-reactive protein. Suggested strategies to restore SII are nivolumab, recombinant human interferon-gamma, CYT107, granulocyte macrophage colony stimulating factor and IgM-enriched immunoglobulin prepapations. Biomarkers that guide their use are the expression of the human leukocyte antigen DR on blood monocytes, the absolute lymphocyte count and blood levels of immunoglobulin M. One recently suggested strategy to restore vascular tone is adrecizumab, the use of which is guided by blood levels of bio-adrenomedulin. The use of these precision treatment strategies is still hampered by the need for large-scale randomized controlled trials.

Aiming for precision: personalized medicine through sepsis subtyping

According to the latest definition, sepsis is characterized by life-threatening organ dysfunction caused by a dysregulated host response to an infection. However, this definition fails to grasp the heterogeneous nature and the underlying dynamic pathophysiology of the syndrome. In response to this heterogeneity, efforts have been made to stratify sepsis patients into subtypes, either based on their clinical presentation or pathophysiological characteristics. Subtyping introduces the possibility of the implementation of personalized medicine, whereby each patient receives treatment tailored to their individual disease manifestation. This review explores the currently known subtypes, categorized by subphenotypes and endotypes, as well as the treatments that have been researched thus far in the context of sepsis subtypes and personalized medicine.

Whole blood ratio of CDK1/CX3CR1 mRNA expression combined to lactate refines the prediction of ICU mortality in septic patients in the Sepsis-3 era: a proof-of-concept study

Background

Transcriptomics biomarkers have been widely used to predict mortality in patients with sepsis. However, the association between mRNA levels and outcomes shows substantial variability over the course of sepsis, limiting their predictive performance. We aimed to: (a) identify and validate an mRNA biomarker signature whose association with all-cause intensive care unit (ICU) mortality is consistent at several timepoints; and (b) evaluate how this mRNA signature could be used in association with lactate levels for predictive and prognostic enrichment in sepsis.

Methods

We conducted a gene expression analysis study at two timepoints (day 1 and day 2-3 following ICU admission) using microarray data from adult septic patients to identify candidate biomarkers predictive of all-cause ICU mortality. We validated mRNA biomarkers using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) on an external validation cohort. The predictive performance of the mRNA biomarkers combination was assessed in association with lactate level to refine ICU mortality prediction.

Main results

Among 180 chips from 100 septic patients, we identified 39 upregulated and 2 downregulated differentially expressed genes (DEGs) in survivors vs. non-survivors, both at day 1 and days 2-3 following ICU admission. We combined CDK1, the hub gene of upregulated DEGs, and CX3CR1 and IL1b to compute expression ratios. The CDK1/CX3CR1 ratio had the best performance to predict all-cause ICU mortality, with an area under the ROC curve (AUROC) of 0.77 (95% confidence interval [CI] 0.88-0.66) at day 1 and of 0.82 (95% CI 0.91-0.72) at days 2-3 after ICU admission. This performance was better than that of each individual mRNA biomarker. In the external validation cohort, the predictive performance of the CDK1/CX3CR1 ratio, measured using RT-qPCR, was similar to that of lactate when measure at day 1, and higher when measured at days 2-3. Combining lactate levels and the CDK1/CX3CR1 ratio, we identify 3 groups of patients with an increasing risk of ICU-mortality, ranging from 9 to 60% with an intermediate-risk group mortality rate of 28%.

Conclusion

With stable predictive performance over the first 3 days following ICU admission, the CDK1/CX3CR1 ratio identifies three groups of septic patients with increasing ICU mortality risk. In combination with lactate, this novel biomarker strategy may be useful for sepsis patient stratification for personalized medicine trials and ICU management.

EARLY ANALYSIS OF ENDOTHELIAL MARKERS TO PREDICT SEPSIS IN THE EMERGENCY DEPARTMENT

ABSTRACT

Background: Acute infections and sepsis are a leading cause of death. These patients are primarily encountered at the emergency department (ED), where early assessment for sepsis is necessary to improve outcome. In sepsis, the inflammatory response causes several characteristic pathophysiological changes, including a dysregulated and generalized activation of the endothelium. This study aimed to analyze endothelial markers released to the blood as diagnostic biomarkers for acute infection and sepsis in the ED, as smaller studies have previously shown promising results in other settings. Methods : Serum samples from n = 312 adult patients with suspected acute infections at presentation to the ED were utilized. Patients' courses of disease and outcomes were assessed by clinical adjudication. E-selectin, P-selectin, ICAM-1, and VCAM-1 were measured by ELISAs. The accuracy of each marker for predicting bacterial infection, sepsis, and in-hospital mortality was evaluated. Results : For sepsis, E-selectin and ICAM-1 both showed an area under the receiver operating characteristic (AUROC) of 0.62, lower than procalcitonin with 0.77 (both P < 0.01) and lactate with 0.73 ( P = 0.030 and 0.046, respectively), but similar to CRP with 0.60 ( P = 0.758 and 0.876, respectively). For 28-day in-hospital mortality among patients with infection, ICAM-1 performed best with an AUROC of 0.75. Conclusions : Despite promising results in small studies and specific cohorts, particularly in intensive care units, this large-scale evaluation of four endothelial biomarkers highlights their limited diagnostic utility in a broader inclusion setup design at the earliest possible time point of evaluation.

Immune Subtypes in Sepsis: A Retrospective Cohort Study Utilizing Clustering Methodology

Background

Sepsis is a heterogeneous clinical syndrome. Identifying distinct clinical phenotypes may enable more targeted therapeutic interventions and improve patient care.

Objective

This study aims to use clustering analysis techniques to identify different immune subtypes in sepsis patients and explore their clinical relevance and prognosis.

Methods

The study included 236 patients from the EICU at Shanghai Tenth People's Hospital, who met the Sepsis 3.0 diagnostic criteria. Blood samples were collected to measure lymphocyte subsets and cytokine levels, along with demographic and clinical data. K-means clustering analysis was used to categorize patients into three groups based on immune and inflammatory markers.

Results

Three immune subtypes were identified: the high immune activation subtype (Cluster 1), characterized by high levels of CRP and WBC, high levels of T cells, NK cells, and B cells, and low levels of IL-6, IL-8, and IL-10; the moderate immune activation subtype (Cluster 2), characterized by moderate levels of CRP, WBC, T cells, NK cells, B cells, IL-6, IL-8, and IL-10; and the high inflammation and immune suppression subtype (Cluster 3), characterized by very high levels of IL-6, IL-8, and IL-10, low levels of T cells, NK cells, and B cells, and relatively lower CRP levels. Patients in Cluster 3 had a significantly increased 28-day mortality risk compared to those in Cluster 1 (HR = 21.65, 95% CI: 7.46-62.87, p < 0.001). Kaplan-Meier survival curves showed the lowest survival rates for Cluster 3 and the highest for Cluster 1, with the differences among the three groups being highly statistically significant (p < 0.0001).

Conclusion

This study identified three immune subtypes of sepsis that are significantly associated with clinical outcomes. These findings provide evidence for personalized treatment strategies to improve patient outcomes.

Identifying novel aging-related diagnostic and prognostic models and aging-targeted drugs for sepsis patients

Sepsis is defined as a dysfunctional, life-threatening response to infection leading to multiorgan dysfunction and failure. During the past decade, studies have highlighted the relationship between sepsis and aging. However, the role of aging-related mechanisms in the progression and prognosis of sepsis remains unclear. In the present study, we divided sepsis patients into High- and Low-aging groups based on the gene set variation analysis (GSVA) scores of GOBP-AGING gene set. Sepsis patients in the high-aging group exhibited higher levels of infiltration of innate immune cells, lower levels of infiltration of adaptive immune cells, and a worse prognosis than those in the Low-aging group. Additionally, the MPO to MME ratio (MPO/MME) appears to be an effective biomarker for predicting the prognosis of sepsis patients. Moreover, ARG1/SEC63 and ARG1/CDKN1C appear to be effective and robust biomarkers for the early diagnosis of sepsis patients. Finally, we found that thalidomide (TAL) significantly ameliorated LPS induced inflammation and organ injury and attenuated LPS induced cellular senescence in lung and kidney. Overall, this study provides new insights into the heterogeneity of sepsis, reveals the vital role of aging-related markers in the prognosis and diagnosis of sepsis and demonstrates that TAL is a novel aging-targeted drug for sepsis patients by attenuating LPS induced cellular senescence.

Multiomic molecular patterns of lipid dysregulation in a subphenotype of sepsis with higher shock incidence and mortality

BACKGROUND

Lipids play a critical role in defense against sepsis. We sought to investigate gene expression and lipidomic patterns of lipid dysregulation in sepsis.

METHODS

Data from four adult sepsis studies were analyzed and findings were investigated in two external datasets. Previously characterized lipid dysregulation subphenotypes of hypolipoprotein (HYPO; low lipoproteins, increased mortality) and normolipoprotein (NORMO; higher lipoproteins, lower mortality) were studied. Leukocytes collected within 24 h of sepsis underwent RNA sequencing (RNAseq) and shotgun plasma lipidomics was performed.

RESULTS

Of 288 included patients, 43% were HYPO and 57% were NORMO. HYPO patients exhibited higher median SOFA scores (9 vs 5, p = < 0.001), vasopressor use (67% vs 34%, p = < 0.001), and 28-day mortality (30% vs 16%, p = 0.004). Leukocyte RNAseq identified seven upregulated lipid metabolism genes in HYPO (PCSK9, DHCR7, LDLR, ALOX5, PLTP, FDFT1, and MSMO1) vs. NORMO patients. Lipidomics revealed lower cholesterol esters (CE, adjusted p = < 0.001), lysophosphatidylcholines (LPC, adjusted p = 0.001), and sphingomyelins (SM, adjusted p = < 0.001) in HYPO patients. In HYPO patients, DHCR7 expression strongly correlated with reductions in CE, LPC, and SM (p < 0.01), while PCSK9, MSMO1, DHCR7, PLTP, and LDLR upregulation were correlated with low LPC (p < 0.05). DHCR7, ALOX5, and LDLR correlated with reductions in SM (p < 0.05). Mortality and phenotype comparisons in two external datasets (N = 824 combined patients) corroborated six of the seven upregulated lipid genes (PCSK9, DHCR7, ALOX5, PLTP, LDLR, and MSMO1).

CONCLUSION

We identified a genetic lipid dysregulation signature characterized by seven lipid metabolism genes. Five genes in HYPO sepsis patients most strongly correlated with low CE, LPC, and SMs that mediate cholesterol storage and innate immunity.

A transcriptome-based risk model in sepsis enables prognostic prediction and drug repositioning

Septic management presented a tremendous challenge due to heterogeneous host responses. We aimed to develop a risk model for early septic stratification based on transcriptomic signature. Here, we combined genes OLAH, LY96, HPGD, and ABLIM1 into a prognostic risk score model, which demonstrated exceptional performance in septic diagnosis (AUC = 0.99-1.00) and prognosis (AUC = 0.61-0.70), outperforming that of Mars and SRS endotypes. Also, the model unveiled immunosuppressive status in high-risk patients and a poor response to hydrocortisone in low-risk individuals. Single-cell transcriptome analysis further elucidated expression patterns and effects of the four genes across immune cell types, illustrating integrated host responses reflected by this model. Upon distinct transcriptional profiles of risk subgroups, we identified fenretinide and meloxicam as therapeutic agents, which significantly improved survival in septic mice models. Our study introduced a risk model that optimized risk stratification and drug repurposing of sepsis, thereby offering a comprehensive management approach.

Neutrophil-based single-cell sequencing combined with transcriptome sequencing to explore a prognostic model of sepsis

Sepsis is a life-threatening condition influenced by various factors. Although gene expression profiling has offered new insights, accurately assessing patient risk and prognosis remains challenging. We utilized single-cell and gene expression data of sepsis patients from public databases. The Seurat package was applied for preprocessing and clustering single-cell data, focusing on neutrophils. Lasso regression identified key genes, and a prognostic model was built. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, and further analyses, including immune cell infiltration, Gene Set Enrichment Analysis (GSEA), and clinical correlation, were conducted. Several neutrophil subtypes were identified with distinct gene expression profiles. A prognostic model based on these profiles demonstrated strong predictive accuracy. Risk scores were significantly correlated with clinical features, immune responses, and key signalling pathways. This study provides a comprehensive analysis of sepsis at the molecular level. The prognostic model shows promise in predicting patient outcomes, offering potential new strategies for diagnosis and treatment.

Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis

Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine-the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g., trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.

Stratification of Experimental LPS-Induced Systemic Inflammatory Response by Expression Level of Hif1a and NFkb Genes

It was previously found that the severity of LPS-induced systemic inflammatory response (SIRS) in rats is determined by resistance to hypoxia and the level of Hif1a expression. Individual differences in the level of Hif1a and NFkb expression in the liver were studied in relation to the severity of inflammatory and immune reactions in LPS-induced SIRS in rats without previous placement in a ventilated decompression chamber. During the early periods after SIRS modeling, rats with high expression of the Hif1a and NFkb genes associated with increased expression of pro- and anti-inflammatory cytokines are identified. These animals have a high blood level of corticosterone, low number of neutrophils in the interalveolar septa, and a predominance of T cells over B cells in the peripheral blood. The obtained data can be used to develop new approaches to the individual prediction of the severity of sepsis and SIRS in intensive care units, which will increase the effectiveness of therapy and reduce mortality rate.

Research trends and hotspots on septic shock: a bibliometric study and visualization analysis

Background

Septic shock, the most severe stage of sepsis, causes potential circulatory failure and abnormal cell metabolism which are severe enough to affect prognosis, increase mortality, and impose significant burdens on the medical system. Despite a growing number of studies exploring the pathophysiology, epidemiology, and risk factors, research trends and hotspots in septic shock remain lacking. This study aims to create a visual knowledge map, identify research hotspots, and predict prospective trends based on bibliometric analysis.

Methods

We searched for publications related to septic shock in Web of Science Core Collection up to June 15, 2023. CiteSpace5.5 R2, VOS viewer and Pathfinder were used to evaluate the annual publications, countries, institutions, journals and keywords. We also analyzed the collaboration among countries, institutions and authors, and identified research hotspots and frontiers.

Results

A total of 4,208 English papers were included in the analysis, and the annual publication displayed a slow upward trend. In terms of publication volume, the top three countries were the United States, France, and Germany, and the University of Pittsburgh (the United States) ranked first (n = 85) among all institutions, with Jeanlouis Vincent from Erasmus Medical Center (Netherlands) as the most published author (n = 32). According to the collaborative network, the United States had the highest level of cooperation, and the University of Pittsburgh, the University of Toronto, and Columbia University were the institutions with the most foreign cooperation. Additionally, the co-author network revealed that scholars such as Jeanlouis Vincent, Rinaldo Bellomo, and Djillali Annane, had the strongest collaborations. The co-citation network showed that the top 3 most cited articles were: Singer M (2016), Rhodes A (2017), Dellinger RP (2013), and the top 3 most cited journals were Crit Care Med (3,664 times), N Engl J Med (3,207 times), Intens Care Med (3,096 times) in this field. In the keyword co-occurrence network, the most frequent keywords were "septic shock" (2531), "sepsis" (1667), and "mortality" (569), indicating the current research hotspots. Pathobiology, fluid therapy, and endotoxic septic shock were emerging trends in research.

Conclusion

By using bibliometrics, this study reviewed the studies in septic shock and revealed the hotspots and cutting-edge trends, including the pathogenesis of complications, the development of new biomarkers, the timing and methods of alternative treatments, and the rehabilitation trajectory, etc., which provided a reference for subsequent studies in septic shock.

Gene Expression Dysregulation in Whole Blood of Patients with Clostridioides difficile Infection

Clostridioides difficile (C. difficile) is a global threat and has significant implications for individuals and health care systems. Little is known about host molecular mechanisms and transcriptional changes in peripheral immune cells. This is the first gene expression study in whole blood from patients with C. difficile infection. We took blood and stool samples from patients with toxigenic C. difficile infection (CDI), non-toxigenic C. difficile infection (GDH), inflammatory bowel disease (IBD), diarrhea from other causes (DC), and healthy controls (HC). We performed transcriptome-wide RNA profiling on peripheral blood to identify diarrhea common and CDI unique gene sets. Diarrhea groups upregulated innate immune responses with neutrophils at the epicenter. The common signature associated with diarrhea was non-specific and shared by various other inflammatory conditions. CDI had a unique 45 gene set reflecting the downregulation of humoral and T cell memory functions. Dysregulation of immunometabolic genes was also abundant and linked to immune cell fate during differentiation. Whole transcriptome analysis of white cells in blood from patients with toxigenic C. difficile infection showed that there is an impairment of adaptive immunity and immunometabolism.

Sepsis pathogenesis and outcome are shaped by the balance between the transcriptional states of systemic inflammation and antimicrobial response

Patients with sepsis differ in their clinical presentations and immune dysregulation in response to infection, but the fundamental processes that determine this heterogeneity remain elusive. Here, we aim to understand which types of immune dysregulation characterize patients with sepsis. To that end, we investigate sepsis pathogenesis in the context of two transcriptional states: one represents the immune response to eliminate pathogens (resistance, R) and the other is associated with systemic inflammation (SI). We find that patients with sepsis share a molecular fingerprint of a low R-to-SI balance-i.e., a low R relative to the level of SI. Differences between patients with sepsis are explained by the wide diversity of R and SI states that fall under this fingerprint, such as patients with high SI, patients with low R, or both. We show how this R/SI framework can be used to guide patient stratification that is relevant to disease prognosis and management, outperforming existing classifications of sepsis.

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

Identification of host endotypes using peripheral blood transcriptomics in a prospective cohort of patients with endocarditis

OBJECTIVES

Host responses to infection are a major determinant of outcome. However, the existence of different response profiles in patients with endocarditis has not been addressed. Our objective was to apply transcriptomics to identify endotypes in patients with infective endocarditis.

METHODS

A total of 32 patients with infective endocarditis were studied. Clinical data and blood samples were collected at diagnosis and RNA sequenced. Gene expression was used to identify two clusters (endocarditis endotype 1 [EE1] and endocarditis endotype 2 [EE2]). RNA sequencing was repeated after surgery. Transcriptionally active cell populations were identified by deconvolution. Differences between endotypes in clinical data, survival, gene expression, and molecular pathways involved were assessed. The identified endotypes were recapitulated in a cohort of COVID-19 patients.

RESULTS

A total of 18 and 14 patients were assigned to EE1 and EE2, respectively, with no differences in clinical data. Patients assigned to EE2 showed an enrichment in genes related to T-cell maturation and a decrease in the activation of the signal transducer and activator of transcription protein family pathway, with higher counts of active T cells and lower counts of neutrophils. A total of 14 patients (nine in EE1 and five in EE2) were submitted to surgery. Surgery in EE2 patients shifted gene expression toward a EE1-like profile. In-hospital mortality was higher in EE1 (56% vs 14%, P = 0.027), with an adjusted hazard ratio of 12.987 (95% confidence interval 3.356-50). Translation of these endotypes to COVID-19 and non-COVID-19 septic patients yielded similar results in cell populations and outcome.

CONCLUSIONS

Gene expression reveals two endotypes in patients with acute endocarditis, with different underlying pathogenetic mechanisms, responses to surgery, and outcomes.

Shadows and lights in sepsis immunotherapy

INTRODUCTION

Sepsis remains a major global public health challenge. The host's response in sepsis involves both an exaggerated inflammatory reaction and immunosuppressive mechanisms. A better understanding of this response has shed light on the failure of anti-inflammatory therapies administered under the 'one size fits all' approach during the last decades.

AREAS COVERED

To date, patients' management has moved toward a comprehensive precision medicine approach that aims to personalize immunotherapy, whether anti-inflammatory or immunostimulatory. Large Prospective interventional randomized controlled trials validating this approach are about to start. A crucial prerequisite for these studies is to stratify patients based on biomarkers that will help defining the patients' immuno-inflammatory trajectory.

EXPERT OPINION

Some biomarkers are already available in routine clinical care, while improvements are anticipated through the standardized use of transcriptomics and other multi-omics technologies in this field. With these precautions in mind, it is reasonable to anticipate improvement in outcomes in sepsis.

Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64-0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752-0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

Targeting AMP-activated protein kinase in sepsis

Sepsis is a global health challenge marked by limited clinical options and high mortality rates. AMP-activated protein kinase (AMPK) is a cellular energy sensor that mediates multiple crucial metabolic pathways that may be an attractive therapeutic target in sepsis. Pre-clinical experimental studies have demonstrated that pharmacological activation of AMPK can offer multiple potential benefits during sepsis, including anti-inflammatory effects, induction of autophagy, promotion of mitochondrial biogenesis, enhanced phagocytosis, antimicrobial properties, and regulation of tight junction assembly. This review aims to discuss the existing evidence supporting the therapeutic potential of AMPK activation in sepsis management.

Surviving septic patients endotyped with a functional assay demonstrate active immune responses

Introduction

Sepsis is a complex clinical syndrome characterized by a heterogenous host immune response. Historically, static protein and transcriptomic metrics have been employed to describe the underlying biology. Here, we tested the hypothesis that ex vivo functional TNF expression as well as an immunologic endotype based on both IFNγ and TNF expression could be used to model clinical outcomes in sepsis patients.

Methods

This prospective, observational study of patient samples collected from the SPIES consortium included patients at five health systems enrolled over 17 months, with 46 healthy control patients, 68 ICU patients without sepsis, and 107 ICU patients with sepsis. Whole blood was collected on day 1, 4, and 7 of ICU admission. Outcomes included in-hospital and 180-day mortality and non-favorable discharge disposition defined by skilled nursing facility, long-term acute care facility, or hospice. Whole blood ELISpot assays were conducted to quantify TNF expression [stimulated by lipopolysaccharide (LPS)] and IFNγ expression (stimulated by anti-CD3/CD28 mAb), which were then used for assignment to one of four subgroups including an 'immunocompetent', 'immunosuppressed endotype', and two 'mixed' endotypes.

Results

Whole blood TNF spot-forming units were significantly increased in septic and CINS patients on days 4 and 7 compared to healthy subjects. In contrast, TNF expression per cell on days 1, 4, and 7 was significantly lower in both septic and critically ill non-septic (CINS) patients compared to healthy subjects. Early increases in total TNF expression were associated with favorable discharge disposition and lower in-hospital mortality. 'Immunocompetent' endotype patients on day 1 had a higher proportion of favorable to non-favorable discharges compared to the 'immunosuppressed' endotype. Similarly, 'immunocompetent' endotype patients on day 4 had a higher in-hospital survival compared to the 'immunosuppressed' endotype patients. Finally, among septic patients, decreased total TNF and IFNγ expression were associated with 180-day mortality.

Conclusions

Increased ex vivo whole blood TNF expression is associated with improved clinical outcomes. Further, the early 'immunocompetent' endotype is associated with favorable discharge and improved in-hospital and 180-day survival. The ability to functionally stratify septic patients based on blood cell function ex vivo may allow for identification of future immune modulating therapies.

Rapid and Robust Identification of Sepsis Using SeptiCyte RAPID in a Heterogeneous Patient Population

Background/Objective: SeptiCyte RAPID is a transcriptional host response assay that discriminates between sepsis and non-infectious systemic inflammation (SIRS) with a one-hour turnaround time. The overall performance of this test in a cohort of 419 patients has recently been described [Balk et al., J Clin Med 2024, 13, 1194]. In this study, we present the results from a detailed stratification analysis in which SeptiCyte RAPID performance was evaluated in the same cohort across patient groups and subgroups encompassing different demographics, comorbidities and disease, sources and types of pathogens, interventional treatments, and clinically defined phenotypes. The aims were to identify variables that might affect the ability of SeptiCyte RAPID to discriminate between sepsis and SIRS and to determine if any patient subgroups appeared to present a diagnostic challenge for the test. Methods: (1) Subgroup analysis, with subgroups defined by individual demographic or clinical variables, using conventional statistical comparison tests. (2) Principal component analysis and k-means clustering analysis to investigate phenotypic subgroups defined by unique combinations of demographic and clinical variables. Results: No significant differences in SeptiCyte RAPID performance were observed between most groups and subgroups. One notable exception involved an enhanced SeptiCyte RAPID performance for a phenotypic subgroup defined by a combination of clinical variables suggesting a septic shock response. Conclusions: We conclude that for this patient cohort, SeptiCyte RAPID performance was largely unaffected by key variables associated with heterogeneity in patients suspected of sepsis.

Development and optimization of a diluted whole blood ELISpot assay to test immune function

Sepsis remains a leading cause of death worldwide with no proven immunomodulatory therapies. Stratifying Patient Immune Endotypes in Sepsis ('SPIES') is a prospective, multicenter observational study testing the utility of ELISpot as a functional bioassay specifically measuring cytokine-producing cells after stimulation to identify the immunosuppressed endotype, predict clinical outcomes in septic patients, and test potential immune stimulants for clinical development. Most ELISpot protocols call for the isolation of PBMC prior to their inclusion in the assay. In contrast, we developed a diluted whole blood (DWB) ELISpot protocol that has been validated across multiple laboratories. Heparinized whole blood was collected from healthy donors and septic patients and tested under different stimulation conditions to evaluate the impact of blood dilution, stimulant concentration, blood storage, and length of stimulation on ex vivo IFNγ and TNFα production as measured by ELISpot. We demonstrate a dynamic range of whole blood dilutions that give a robust ex vivo cytokine response to stimuli. Additionally, a wide range of stimulant concentrations can be utilized to induce cytokine production. Further modifications demonstrate anticoagulated whole blood can be stored up to 24 h at room temperature without losing significant functionality. Finally, we show ex vivo stimulation can be as brief as 4 h allowing for a substantial decrease in processing time. The data demonstrate the feasibility of using ELISpot to measure the functional capacity of cells within DWB under a variety of stimulation conditions to inform clinicians on the extent of immune dysregulation in septic patients.

Sepsis phenotypes, subphenotypes, and endotypes: are they ready for bedside care?

PURPOSE OF REVIEW

Sepsis remains a leading global cause of morbidity and mortality, and despite decades of research, no effective therapies have emerged. The lack of progress in sepsis outcomes is related in part to the significant heterogeneity of sepsis populations. This review seeks to highlight recent literature regarding sepsis phenotypes and the potential for further research and therapeutic intervention.

RECENT FINDINGS

Numerous recent studies have elucidated various phenotypes, subphenotypes, and endotypes in sepsis. Clinical parameters including vital sign trajectories and microbial factors, biomarker investigation, and genomic, transcriptomic, proteomic, and metabolomic studies have illustrated numerous differences in sepsis populations with implications for prediction, diagnosis, treatment, and prognosis of sepsis.

SUMMARY

Sepsis therapies including care bundles, fluid resuscitation, and source control procedures may be better guided by validated phenotypes than universal application. Novel biomarkers may improve upon the sensitivity and specificity of existing markers and identify complications and sequelae of sepsis. Multiomics have demonstrated significant differences in sepsis populations, most notably expanding our understanding of immunosuppressed sepsis phenotypes. Despite progress, these findings may be limited by modest reproducibility and logistical barriers to clinical implementation. Further studies may translate recent findings into bedside care.

Cryo-PRO facilitates whole blood cryopreservation for single-cell RNA sequencing of immune cells from clinical samples

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) has enhanced our understanding of host immune mechanisms in small cohorts, particularly in diseases with complex and heterogeneous immune responses such as sepsis. However, PBMC isolation from blood requires technical expertise, training, and approximately two hours of onsite processing using Ficoll density gradient separation ('Ficoll') for scRNA-seq compatibility, precluding large-scale sample collection at most clinical sites. To minimize onsite processing, we developed Cryo-PRO (Cryopreservation with PBMC Recovery Offsite), a method of PBMC isolation from cryopreserved whole blood that allows immediate onsite sample cryopreservation and subsequent PBMC isolation in a central laboratory prior to sequencing. We compared scRNA-seq results from samples processed using Cryo-PRO versus standard onsite Ficoll separation in 23 patients with sepsis. Critical scRNA-seq outputs including cell substate fractions and marker genes were similar for each method across multiple cell types and substates, including an important monocyte substate enriched in patients with sepsis (Pearson correlation 0.78, p<0.001; 70% of top marker genes shared). Cryo-PRO reduced onsite sample processing time from >2 hours to <15 minutes and was reproducible across two enrollment sites, thus demonstrating potential for expanding scRNA-seq in multicenter studies of sepsis and other diseases.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer

PURPOSE

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.

SUMMARY

The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research.

CONCLUSION

The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.