An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Prospective multicenter study identifying prognostic biomarkers and microbial profiles in severe CAP using BALF, blood mNGS, and PBMC transcriptomics

To identify potential biomarkers associated with the prognosis and severity of severe community-acquired pneumonia (SCAP), we conducted a multi-center prospective study from January 1, 2022, to December 31, 2023, enrolling 14 mild CAP and 75 SCAP patients in Shanghai, China. Patients underwent bronchoalveolar lavage fluid (BALF) and blood metagenomic next-generation sequencing (mNGS) as well as peripheral blood mononuclear cell (PBMC) transcriptomics. Among the 75 SCAP patients, 32 died within 30 days, with older age, a history of allergies, and comorbidities like cerebrovascular disease linked to worse outcomes. BALF mNGS showed greater microbial diversity, revealing a higher prevalence of pathogens, including Acinetobacter baumannii, Klebsiella pneumoniae, and Candida albicans, compared to mild CAP patients. RNA sequencing identified 431 differentially expressed genes in deceased SCAP patients, with significant alterations in immune pathways. Notably, microbial markers such as Pneumocystis jirovecii and viral markers like Human cytomegalovirus were associated with poor outcomes. Transcriptomic biomarkers, including otoferlin (OTOF), MS4A4A, and SIGLEC1, were identified as potential prognostic indicators for SCAP severity. GSEA and traditional GO/KEGG analyses identified key immune and metabolic pathways in SCAP (death) patients, including upregulation of complement activation, oxidative phosphorylation, nitrogen metabolism, while downregulation of adaptive immune response, hematopoietic cell lineage, and antigen processing pathways. These findings underscore the superiority of BALF mNGS over blood mNGS for pathogen detection, revealing microbial differences and transcriptomic alterations between mild and severe cases while identifying prognostic markers for SCAP treatment strategies.

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.

CD121b-positive neutrophils predict immunosuppression in septic shock

Background

Septic shock is linked with high mortality and significant long-term morbidity in survivors. However, the specific role of neutrophils in septic shock pathophysiology remains scarce in recent research.

Methods

Peripheral blood immune cells from healthy donors and patients with septic shock were analyzed using single-cell RNA sequencing and batch RNA sequencing. We measured serum CD121b in both patients and healthy donors. Peripheral immune cells were isolated and exposed to either a CD121b recombinant protein or a CD121b blocking antibody to evaluate the expression of inflammatory factors. Additionally, in a humanized mouse sepsis model, the expression of CD121b in neutrophils across different tissues was assessed following treatment with all-trans retinoic acid (ATRA).

Results

This study identified a subset of CD10-CD121b+ neutrophils in the peripheral blood of patients with septic shock. These patients exhibited elevated concentrations of soluble CD121b in serum and urine. Furthermore, outcomes revealed that the presence of CD121b+ neutrophils positively correlated with the severity of septic shock. These cells displayed immunosuppressive characteristics; after blocking CD121b, proinflammatory cytokines increased in peripheral immune cells. Additionally, we found that treatment with ATRA down-regulated the expression of CD121b.

Conclusions

CD121b is closely associated with the progression of septic shock and may serve as a potential predictor indicator of immunosuppression for the condition.

Dynamic Gene Attention Focus (DyGAF): Enhancing Biomarker Identification Through Dual-Model Attention Networks

The DyGAF model, which stands for Dynamic Gene Attention Focus, is specifically designed and tailored to address the challenges in biomarker detection, progression reporting of pathogen infection, and disease diagnostics. The DyGAF model introduced a novel dual-model attention-based mechanism within neural networks, combined with machine learning algorithms to enhance the process of biomarker identification. The model transcended traditional diagnostic approaches by meticulously analyzing gene expression data. DyGAF not only identified but also ranked genes based on their significance, revealing a comprehensive list of the top genes essential for disease detection and prognosis. In addition, KEGG pathways, Wiki Pathways, and Gene Ontology-based analyses provided a multileveled evaluation of the genes' roles. In our analyses, we tailored COVID-19 gene expression profile from nasopharyngeal swabs that offer a more nuanced view of the intricate interplay between the host and the virus. The genes ranked by the DyGAF model were compared against those selected by differential expression analysis and random forest feature selection methods for further validation of our model. DyGAF demonstrated its prowess in identifying important biomarkers that could enrich gene ontologies and pathways crucial for elucidating the pathogenesis of COVID-19. Furthermore, DyGAF was also employed for diagnosing COVID-19 patients by classifying gene-expression profiles with an accuracy of 94.23%. Benchmarking against other conventional models revealed DyGAF's superior performance, highlighting its effectiveness in identifying and categorizing COVID-19 cases. In summary, DyGAF model represents a significant advancement in genomic research, providing a more comprehensive and precise tool for identifying key genetic markers and unraveling the complex biological insights of a disease. The DyGAF model is available as a software package at the following link: https://github.com/hiddenntreasure/DyGAF.

Role of plasma and blood-cell co-metagenomic sequencing in precise diagnosis and severity evaluation of sepsis, a prospective cohort study in sepsis patients

PURPOSES

Sepsis caused great clinical burden all over the world. This study clarified the value of plasma metagenomic next-generation sequencing (p-mNGS) and blood cell mNGS (bc-mNGS) in sepsis diagnosis and evaluation.

METHODS

One hundred and fourty-seven blood samples were collected from sepsis patients who met sepsis 3.0 criteria. Blood culture (BC), qPCR, p-mNGS, bc-mNGS and necessary routine assays were conducted. Taking BC and qPCR as reference, diagnosis performance of p-mNGS and bc-mNGS was analyzed. Blood transcriptome was conducted to evaluate the immunological response of patients in groups with different p/bc-mNGS results. Impact of antibiotic use on different methods was also analyzed.

RESULTS

The p-mNGS demonstrated a sensitivity of 100% for bacteria/fungi and 97% for viruses, which was higher than bc-mNGS (88% for bacteria and fungi, 71% for viruses). However, bc-mNGS showed higher concordance with BC results, which indicated that co-mNGS (p-mNGS plus bc-mNGS) protocol increased sensitivity and was helpful to justify viable blood pathogens in sepsis patients. This study showed that p-mNGS(+) & bc-mNGS(+) samples represented more activated immunity response (low expression of interferon-induced genes and high expression of JAK-STAT pathway genes), poorer clinical laboratory indicators (higher Sequential Organ Failure Assessment, higher procalcitonin and higher C-reactive protein) and lower survival rate. This study also proved that the use of broad-spectrum antibiotics affected much less on p/bc-mNGS diagnostic ability than on BC.

CONCLUSIONS

This research highlighted the potential value of plasma and blood-cell co-metagenomic sequencing in precise diagnosis and severity evaluation of sepsis patients, which will benefit the management of sepsis patients.

Unsupervised Classification of the Host Response Identifies Dominant Pathobiological Signatures of Sepsis in Sub-Saharan Africa

Rationale: The global burden of sepsis is concentrated in sub-Saharan Africa, where inciting pathogens are diverse and HIV coinfection is a major driver of poor outcomes. Biological heterogeneity inherent to sepsis in this setting is poorly defined. Objectives: To identify dominant pathobiological signatures of sepsis in sub-Saharan Africa and their relationship to clinical phenotypes, patient outcomes, and biological classifications of sepsis identified in high-income countries (HICs). Methods: We analyzed two prospective cohorts of adults hospitalized with sepsis (severe infection with quick Sepsis-related Organ Failure Assessment score ⩾1) at disparate settings in Uganda (discovery cohort [Entebbe, urban], n = 242; validation cohort [Tororo, rural], n = 253). To identify pathobiological signatures in the discovery cohort, we applied unsupervised clustering to 173 soluble proteins reflecting key domains of the host response to severe infection. A random forest-derived classifier was used to predict signature assignment in the validation cohort. Measurements and Main Results: Two signatures (Uganda Sepsis Signature [USS]-1 and USS-2) were identified in the discovery cohort, distinguished by expression of proteins involved in myeloid cell and inflammasome activation, T-cell costimulation and exhaustion, and endothelial barrier dysfunction. A five-protein classifier (area under the receiver operating characteristic curve, 0.97) reproduced two signatures in the validation cohort with similar biological profiles. In both cohorts, USS-2 mapped to a more severe clinical phenotype associated with HIV and related immunosuppression, severe tuberculosis, and increased risk of 30-day mortality. Substantial biological overlap was observed between USS-2 and hyperinflammatory and reactive sepsis phenotypes identified in HICs. Conclusions: We identified prognostically enriched pathobiological signatures among patients with sepsis with diverse infections and high HIV prevalence in Uganda. Globally inclusive investigations are needed to define generalizable and context-specific mechanisms of sepsis pathobiology, with the goal of improving access to precision medicine treatment strategies.

Transcriptomic Biomarkers Associated With Microbiological Etiology and Disease Severity in Childhood Pneumonia

BACKGROUND

Challenges remain in discerning microbiologic etiology and disease severity in childhood pneumonia. Defining host transcriptomic profiles during illness may facilitate improved diagnostic and prognostic approaches.

METHODS

Using whole blood RNA sequencing from 222 hospitalized children with radiographic pneumonia and 45 age-matched controls, we identified differentially expressed (DE) genes that best identified children according to detected microbial pathogens (viral only vs bacterial only and typical vs atypical bacterial [with or without [±] viral co-detection]) and an ordinal measure of phenotypic severity (moderate, severe, very severe).

RESULTS

Overall, 135 (61%) children had viral-only detections, 15 (7%) had typical bacterial detections (± viral co-detections), and 26 (12%) had atypical bacterial detections (± viral co-detections). Eleven DE genes distinguished between viral-only and bacterial-only detections. Sixteen DE genes distinguished between atypical and typical bacterial detections (± viral co-detections). Nineteen DE genes distinguished between levels of pneumonia severity, including 4 genes also identified in the viral-only versus bacterial-only model (IGHGP, PI3, CD177, RAP1GAP1) and 4 genes from the typical versus atypical bacterial model (PRSS23, IFI27, OLFM4, ABO).

CONCLUSIONS

We identified transcriptomic biomarkers associated with microbial detections and phenotypic severity in children hospitalized with pneumonia. These DE genes are promising candidates for validation and translation into diagnostic and prognostic tools.

Two-transcript signature for differentiation and clinical outcomes in severe fever with thrombocytopenia syndrome (SFTS) patients: a double-blind, multicenter, validation study

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high mortality rate that is often underdiagnosed due to the limitations of current laboratory testing. Timely diagnosis and early identification of severe cases are crucial to improving patient outcomes and overall survival rates. This study aimed to evaluate the efficacy of two transcripts, IFI44L and PI3, in the early differentiation between SFTS virus (SFTSV) infection and bacterial sepsis, as well as in the prompt identification of severe cases during epidemic seasons. In a prospective study conducted between 1 May 2021 and 30 September 2022, we enrolled 225 patients who presented with acute fever and thrombocytopenia at four hospitals in Shandong Province, China. The two-transcript signature provided a clear distinction between SFTS and bacterial infection, achieving an area under the receiver operating characteristic curve of 0.961 (95% confidence interval [95% CI] 0.916-0.986), outperforming C-reactive protein (0.810 [95% CI 0.738-0.870]) and procalcitonin (0.764 [95% CI 0.687-0.830]). Importantly, the relative expression of the IFI44L gene was significantly elevated in fatal SFTS cases, with an area under the curve (AUC) of 0.820 (95% CI 0.727-0.914), indicating its potential as an early prognostic marker. Additionally, IFI44L and PI3 were identified as potential biomarkers for distinguishing SFTS patients with and without invasive pulmonary aspergillosis, with AUC values of 0.817 and 0.753, respectively. Our findings demonstrate that the two-transcript signature effectively distinguishes SFTSV infection from bacterial sepsis and helps identify high-risk individuals, guiding appropriate treatment during SFTS outbreak.

Harnessing the host response for precision infectious disease diagnosis

SUMMARYDetection of the presence of infection and its etiology must be accurate and timely to facilitate appropriate antimicrobial use. Diagnostic strategies that rely solely on pathogen detection often are insufficient due to poor test characteristics, inability to differentiate colonization from infection, or protracted delay to result. Understanding the human response across different pathogens on a clinical and molecular level can provide more accurate, timely, and useful answers, especially in critical illness and diagnostic uncertainty. Improvements in understanding the human immune response including genomics, protein analysis, gene expression, and cellular morphology have led to rapid innovation of new host response-based diagnostic tests. This review describes the limitations of pathogen-focused technology and the benefits of examining the breadth of immune response to diagnose infection. It then explores biomarkers that have been studied for this purpose and scrutinizes the performance of host-based multianalyte testing. Currently cleared diagnostics and those in late-stage development are described in depth, with a focus on the purpose of testing and its utility for clinicians. Finally, it concludes by examining opportunities for further host response-derived diagnostic innovation.

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework

Background

Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcriptomics data has recently emerged as a valuable resource for disease phenotyping and endotyping, making it a promising tool for predicting disease stages. Therefore, we aimed to establish an advanced machine learning framework to predict sepsis and septic shock using transcriptomics datasets with rapid turnaround methods.

Methods

We retrieved four NCBI GEO transcriptomics datasets previously generated from peripheral blood samples of healthy individuals and patients with sepsis and septic shock. The datasets were processed for bioinformatic analysis and supplemented with a series of bench experiments, leading to the identification of a hub gene panel relevant to sepsis and septic shock. The hub gene panel was used to establish a novel prediction model to distinguish sepsis from septic shock through a multistage machine learning pipeline, incorporating linear discriminant analysis, risk score analysis, and ensemble method combined with Least Absolute Shrinkage and Selection Operator analysis. Finally, we validated the prediction model with the hub gene dataset generated by RT-qPCR using peripheral blood samples from newly recruited patients.

Results

Our analysis led to identify six hub genes (GZMB, PRF1, KLRD1, SH2D1A, LCK, and CD247) which are related to NK cell cytotoxicity and septic shock, collectively termed 6-HubGss. Using this panel, we created SepxFindeR, a machine learning model that demonstrated high accuracy in predicting sepsis and septic shock and distinguishing septic shock from sepsis in a cross-database context. Remarkably, the SepxFindeR model proved compatible with RT-qPCR datasets based on the 6-HubGss panel, facilitating the identification of newly recruited patients with sepsis and septic shock.

Conclusions

Our bioinformatic approach led to the discovery of the 6-HubGss biomarker panel and the development of the SepxFindeR machine learning model, enabling accurate prediction of septic shock and distinction from sepsis with rapid processing capabilities.

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.

A host immune-related LncRNA and mRNA signature as a discriminant classifier for bacterial from non-bacterial sepsis in children

Background

The variations in non-coding RNA alterations and the host immune response between patients with bacterial and non-bacterial sepsis, along with their clinical characteristics, are largely unknown.

Methods

The landscape of long non-coding RNA (lncRNA) and mRNA in whole blood cells from pediatric patients with bacterial sepsis or non-bacterial sepsis were characterized using an Arraystar human LncRNA microarray. Weighted correlation network analysis (WGCNA) were conducted to identify immune-related LncRNA-mRNA signatures. Least absolute shrinkage and selection operator (Lasso) regression and Ridge regression analysis were employed to develop a specific LncRNA-mRNA signature, serving as a discriminant classifier for bacterial and non-bacterial sepsis in children.

Results

A total of 33 differentially expressed lncRNAs and 52 mRNAs were identified in pediatric patients with either bacterial sepsis or non-bacterial sepsis. Among these, 69 lncRNAs and mRNAs were pinpointed using WGCNA and found to be significantly correlated with clinical parameters. Further intersection analysis identified 12 lncRNAs and 16 mRNAs as immune-related signature for discerning bacterial infections in children with sepsis. Additionaly, the lncRNA-mRNA co-expression network highlighted the key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2) as an infection score model. Lasso regression analysis revealed that this infection score model achieved an area under the received operating curve (AUROC) of 0.96 in the training set and 0.86 in the validation set. Ultimately, the expression levels of these 4 key lncRNAs and mRNAs showed significant correlation with CRP or PCT levels.

Conclusion

The machine learning model, developed utilizing key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2), demonstrates robust discrimination and calibration capabilities for distinguishing between bacterial and non-bacterial sepsis in children.

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.

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.

Susceptibility to childhood sepsis, contemporary management, and future directions

Sepsis disproportionally affects children across all health-care settings and is one of the leading causes of morbidity and mortality in neonatal and paediatric age groups. As shown in the first paper in this Series, the age-specific incidence of sepsis is highest during the first years of life, before approaching adult incidence rates during adolescence. In the second paper in this Series, we focus on the unique susceptibility of paediatric patients to sepsis and how the underlying dysregulated host response relates to developmental aspects of children's immune system, genetic, perinatal, and environmental factors, and comorbidities and socioeconomic determinants of health, which often differ between children and adults. State-of-the-art clinical management of paediatric sepsis is organised around three treatment pillars-diagnosis, early resuscitation, and titration of advanced care-and we examine available treatment guidelines and the limitations of their supporting evidence. Serious evidence gaps remain in key areas of paediatric sepsis care, especially surrounding recognition, common interventions, and survivor support, and to this end we offer a research roadmap for the next decade that could accelerate targeted diagnostics and personalised use of immunomodulation. However, improving outcomes for children with sepsis relies fundamentally on systematic quality improvement in both recognition and treatment, which is the theme of the third paper in this Series. Digital health, as shown in the fourth and final paper of this Series, holds promising potential in breaking down the barriers that hinder progress in paediatric sepsis care and, ultimately, global child health.

HETEROGENEOUS EXPANSION OF POLYMORPHONUCLEAR MYELOID-DERIVED SUPPRESSOR CELLS DISTINGUISHES HIGH-RISK SEPSIS IMMUNOPHENOTYPES IN UGANDA

ABSTRACT

Background: Understanding of immune cell phenotypes associated with inflammatory and immunosuppressive host responses in sepsis is imprecise, particularly in low- and middle-income countries, where the global sepsis burden is concentrated. In these settings, elucidation of clinically relevant immunophenotypes is necessary to determine the relevance of emerging therapeutics and refine mechanistic investigations of sepsis immunopathology. Methods: In a prospective cohort of adults hospitalized with suspected sepsis in Uganda (N = 43; median age 46 years [IQR 36-59], 24 [55.8%] living with HIV, 16 [37.2%] deceased at 60 days), we combined high-dimensional flow cytometry with unsupervised machine learning and manual gating to define peripheral immunophenotypes associated with increased risk of 60-day mortality. Results: Patients who died showed heterogeneous expansion of polymorphonuclear myeloid-derived suppressor cells, with increased and decreased abundance of CD16 - PD-L1 dim and CD16 bright PD-L1 bright subsets, respectively, significantly associated with mortality. While differences between CD16 - PD-L1 dim cell abundance and mortality risk appeared consistent throughout the course of illness, those for the CD16 bright PD-L1 bright subset were more pronounced early after illness onset. Independent of HIV co-infection, depletion of CD4 + T cells, dendritic cells, and CD56 - CD16 bright NK cells were significantly associated with mortality risk, as was expansion of immature, CD56 + CD16 - CD11c + NK cells. Abundance of T cells expressing inhibitory checkpoint proteins (PD-1, CTLA-4, LAG-3) was similar between patients who died versus those who survived. Conclusions: This is the first study to define high-risk immunophenotypes among adults with sepsis in sub-Saharan Africa, an immunologically distinct region where biologically informed treatment strategies are needed. More broadly, our findings highlight the clinical importance and complexity of myeloid derived suppressor cell expansion during sepsis and support emerging data that suggest a host-protective role for PD-L1 myeloid checkpoints in acute critical illness.

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.

Dysregulation of Host-Pathogen Interactions in Sepsis: Host-Related Factors

Sepsis stands as a prominent contributor to sickness and death on a global scale. The most current consensus definition characterizes sepsis as a life-threatening organ dysfunction stemming from an imbalanced host response to infection. This definition does not capture the intricate array of immune processes at play in sepsis, marked by simultaneous states of heightened inflammation and immune suppression. This overview delves into the immune-related processes of sepsis, elaborating about mechanisms involved in hyperinflammation and immune suppression. Moreover, we discuss stratification of patients with sepsis based on their immune profiles and how this could impact future sepsis management.

Informative Subtyping of Patients with Sepsis

Sepsis pathobiology is complex. Heterogeneity refers to the clinical and biological variation within sepsis cohorts. Sepsis subtypes refer to subpopulations within sepsis cohorts derived based on these observable variations and latent features. The overarching goal of such endeavors is to enable precision immunomodulation. However, we are yet to identify immune endotypes of sepsis to achieve this goal. The sepsis subtyping field is just starting to take shape. The current subtypes in the literature do not have a core set of shared features between studies. Thus, in this narrative review, we reason that there is a need to a priori state the purpose of sepsis subtyping and minimum set of features that would be required to achieve the goal of precision immunomodulation for future sepsis.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Sepsis research: Heterogeneity as a foundation rather than an afterthought

Our understanding of sepsis has been hampered by the implicit assumption that sepsis is a homogeneous disease. In this issue of Cell Genomics, Burnham et al.1 have started to characterize the genetic variants and regulatory networks that underlie variations in the individual response to sepsis; this may eventually enable targeted intervention development.

Dysregulated STAT3 signaling and T cell immunometabolic dysfunction define a targetable, high mortality subphenotype of critically ill children

Sepsis is the leading cause of death of hospitalized children worldwide. Despite the established link between immune dysregulation and mortality in pediatric sepsis, it remains unclear which host immune factors contribute causally to adverse sepsis outcomes. Identifying modifiable pathobiology is an essential first step to successful translation of biologic insights into precision therapeutics. We designed a prospective, longitudinal cohort study of 88 critically ill pediatric patients with multiple organ dysfunction syndrome (MODS), including patients with and without sepsis, to define subphenotypes associated with targetable mechanisms of immune dysregulation. We first assessed plasma proteomic profiles and identified shared features of immune dysregulation in MODS patients with and without sepsis. We then employed consensus clustering to define three subphenotypes based on protein expression at disease onset and identified a strong association between subphenotype and clinical outcome. We next identified differences in immune cell frequency and activation state by MODS subphenotype and determined the association between hyperinflammatory pathway activation and cellular immunophenotype. Using single cell transcriptomics, we demonstrated STAT3 hyperactivation in lymphocytes from the sickest MODS subgroup and then identified an association between STAT3 hyperactivation and T cell immunometabolic dysregulation. Finally, we compared proteomics findings between patients with MODS and patients with inborn errors of immunity that amplify cytokine signaling pathways to further assess the impact of STAT3 hyperactivation in the most severe patients with MODS. Overall, these results identify a potentially pathologic and targetable role for STAT3 hyperactivation in a subset of pediatric patients with MODS who have high severity of illness and poor prognosis.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

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

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

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RATIONALE

Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

OBJECTIVES

We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

METHODS

Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

MEASUREMENTS AND MAIN RESULTS

We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

CONCLUSIONS

These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

TRIAL REGISTRATION NUMBER

ISRCTN20769191, ISRCTN12776039.

Fibrinaloid Microclots and Atrial Fibrillation

Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

Monocyte state 1 (MS1) cells in critically ill patients with sepsis or non-infectious conditions: association with disease course and host response

BACKGROUND

Sepsis is a life-threatening condition arising from an aberrant host response to infection. Recent single-cell RNA sequencing investigations identified an immature bone-marrow-derived CD14+ monocyte phenotype with immune suppressive properties termed "monocyte state 1" (MS1) in patients with sepsis. Our objective was to determine the association of MS1 cell profiles with disease presentation, outcomes, and host response characteristics.

METHODS

We used the transcriptome deconvolution method (CIBERSORTx) to estimate the percentage of MS1 cells from blood RNA profiles of patients with sepsis admitted to the intensive care unit (ICU). We compared these profiles to ICU patients without infection and to healthy controls. Host response dysregulation was further studied by gene co-expression network and gene set enrichment analyses of blood leukocytes, and measurement of 15 plasma biomarkers indicative of pathways implicated in sepsis pathogenesis.

RESULTS

Sepsis patients (n = 332) were divided into three equally-sized groups based on their MS1 cell levels (low, intermediate, and high). MS1 groups did not differ in demographics or comorbidities. The intermediate and high MS1 groups presented with higher disease severity and more often had shock. MS1 cell abundance did not differ between survivors and non-survivors, or between patients who did or did not acquire a secondary infection. Higher MS1 cell percentages were associated with downregulation of lymphocyte-related and interferon response genes in blood leukocytes, with concurrent upregulation of inflammatory response pathways, including tumor necrosis factor signaling via nuclear factor-κB. Previously described sepsis host response transcriptomic subtypes showed different MS1 cell abundances, and MS1 cell percentages positively correlated with the "quantitative sepsis response signature" and "molecular degree of perturbation" scores. Plasma biomarker levels, indicative of inflammation, endothelial cell activation, and coagulation activation, were largely similar between MS1 groups. In ICU patients without infection (n = 215), MS1 cell percentages and their relation with disease severity, shock, and host response dysregulation were highly similar to those in sepsis patients.

CONCLUSIONS

High MS1 cell percentages are associated with increased disease severity and shock in critically ill patients with sepsis or a non-infectious condition. High MS1 cell abundance likely indicates broad immune dysregulation, entailing not only immunosuppression but also anomalies reflecting exaggerated inflammatory responses.

A Transcriptomic Approach to Understand Patient Susceptibility to Pneumonia After Abdominal Surgery

OBJECTIVE

To describe immune pathways and gene networks altered following major abdominal surgery and to identify transcriptomic patterns associated with postoperative pneumonia.

BACKGROUND

Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major abdominal surgery, with postoperative pneumonia associated with an almost 5-fold increase in 30-day mortality.

METHODS

From a prospective consecutive cohort (n=150) undergoing major abdominal surgery, whole-blood RNA was collected preoperatively and at 3 time-points postoperatively (2-6, 24, and 48 h). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing.

RESULTS

Compared to preoperative sampling, 3639 genes were upregulated and 5043 downregulated at 2 to 6 hours. Pathway analysis demonstrated innate-immune activation with neutrophil degranulation and Toll-like-receptor signaling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil degranulation was associated with postoperative pneumonia acquisition ( P =0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil dysfunction and a more dysregulated host response, at 48 hours postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free ( P =0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection.

CONCLUSIONS

Major abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

A Transcriptomic Approach to Understand Patient Susceptibility to Pneumonia After Abdominal Surgery

OBJECTIVE

To describe immune pathways and gene networks altered following major abdominal surgery and to identify transcriptomic patterns associated with postoperative pneumonia.

BACKGROUND

Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major abdominal surgery, with postoperative pneumonia associated with an almost 5-fold increase in 30-day mortality.

METHODS

From a prospective consecutive cohort (n=150) undergoing major abdominal surgery, whole-blood RNA was collected preoperatively and at 3 time-points postoperatively (2-6, 24, and 48 h). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing.

RESULTS

Compared to preoperative sampling, 3639 genes were upregulated and 5043 downregulated at 2 to 6 hours. Pathway analysis demonstrated innate-immune activation with neutrophil degranulation and Toll-like-receptor signaling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil degranulation was associated with postoperative pneumonia acquisition ( P =0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil dysfunction and a more dysregulated host response, at 48 hours postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free ( P =0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection.

CONCLUSIONS

Major abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

A Transcriptomic Classifier Model Identifies High-Risk Endotypes in a Prospective Study of Sepsis in Uganda

OBJECTIVES

In high-income countries (HICs), sepsis endotypes defined by distinct pathobiological mechanisms, mortality risks, and responses to corticosteroid treatment have been identified using blood transcriptomics. The generalizability of these endotypes to low-income and middle-income countries (LMICs), where the global sepsis burden is concentrated, is unknown. We sought to determine the prevalence, prognostic relevance, and immunopathological features of HIC-derived transcriptomic sepsis endotypes in sub-Saharan Africa.

DESIGN

Prospective cohort study.

SETTING

Public referral hospital in Uganda.

PATIENTS

Adults ( n = 128) hospitalized with suspected sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Using whole-blood RNA sequencing data, we applied 19-gene and 7-gene classifiers derived and validated in HICs (SepstratifieR) to assign patients to one of three sepsis response signatures (SRS). The 19-gene classifier assigned 30 (23.4%), 92 (71.9%), and 6 (4.7%) patients to SRS-1, SRS-2, and SRS-3, respectively, the latter of which is designed to capture individuals transcriptionally closest to health. SRS-1 was defined biologically by proinflammatory innate immune activation and suppressed natural killer-cell, T-cell, and B-cell immunity, whereas SRS-2 was characterized by dampened innate immune activation, preserved lymphocyte immunity, and suppressed transcriptional responses to corticosteroids. Patients assigned to SRS-1 were predominantly (80.0% [24/30]) persons living with HIV with advanced immunosuppression and frequent tuberculosis. Mortality at 30-days differed significantly by endotype and was highest (48.1%) in SRS-1. Agreement between 19-gene and 7-gene SRS assignments was poor (Cohen's kappa 0.11). Patient stratification was suboptimal using the 7-gene classifier with 15.1% (8/53) of individuals assigned to SRS-3 deceased at 30-days.

CONCLUSIONS

Sepsis endotypes derived in HICs share biological and clinical features with those identified in sub-Saharan Africa, with major differences in host-pathogen profiles. Our findings highlight the importance of context-specific sepsis endotyping, the generalizability of conserved biological signatures of critical illness across disparate settings, and opportunities to develop more pathobiologically informed sepsis treatment strategies in LMICs.

High incidence of immune-mediated inflammatory diseases in sepsis survivors: A nationwide exposed-nonexposed epidemiological study

OBJECTIVE

Sepsis is characterized by an excessive release of inflammatory cytokines. Cytokine dysregulation is pivotal to the pathophysiology of immune-mediated inflammatory diseases (IMIDs). We aimed to analyze the incidence of IMIDs in patients who survived sepsis.

METHODS

We performed a matched-cohort study using the National Medico-Administrative Hospital database in order to analyze the association between sepsis and incident IMIDs in 2020 in France. Sepsis was defined by the combination of at least one infection diagnosis code and one organ failure code. Patients with a first sepsis diagnosed in 2020 were randomly matched with patients admitted during the same period for acute myocardial infarction (AMI) with an exact matching procedure using age, gender, and comorbidities as matching variables. The main outcome was an IMID diagnosis in a 9-month follow-up period starting the first day of hospitalization for sepsis or AMI.

RESULTS

In France, the incidence rate of IMIDs after a sepsis in 2020-analyzed in 62,257 patients-was of 7956 (95% confidence interval [95% CI] 7392-8520) per 100,000 patient-years. As compared to the AMI population, we observed an increased risk for IMIDs of 2.80 (hazard ratio [HR]; 95% CI [2.22-3.54]) starting from day 16 after admission in the sepsis population. The risk of IMIDs onset in sepsis survivors depended on the type of IMIDs and was higher for immune thrombocytopenia (5.51 [1.97-15.4]), autoimmune hemolytic anemia (HR 4.83 [1.45-16.1]), and antineutrophil cytoplasmic antibody-associated vasculitis (4.66 [2.05-10.6]). Association between sepsis and IMIDs onset appeared well balanced across pathogen categories.

CONCLUSION

Our study shows a high incidence of IMIDs among sepsis survivors.

Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity

The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.

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.

Extending the 'host response' paradigm from sepsis to cardiogenic shock: evidence, limitations and opportunities

Recent clinical and research efforts in cardiogenic shock (CS) have largely focussed on the restoration of the low cardiac output state that is the conditio sine qua non of the clinical syndrome. This approach has failed to translate into improved outcomes, and mortality has remained static at 30-50%. There is an unmet need to better delineate the pathobiology of CS to understand the observed heterogeneity of presentation and treatment effect and to identify novel therapeutic targets. Despite data in other critical illness syndromes, specifically sepsis, the role of dysregulated inflammation and immunity is hitherto poorly described in CS. High-dimensional molecular profiling, particularly through leukocyte transcriptomics, may afford opportunity to better characterise subgroups of patients with shared mechanisms of immune dysregulation. In this state-of-the-art review, we outline the rationale for considering molecular subtypes of CS. We describe how high-dimensional molecular technologies can be used to identify these subtypes, and whether they share biological features with sepsis and other critical illness states. Finally, we propose how the identification of molecular subtypes of patients may enrich future clinical trial design and identification of novel therapies for CS.

BurkHostGEN: a study protocol for evaluating variations in the Burkholderia pseudomallei and host genomes associated with melioidosis infection

Background

Melioidosis is a frequently fatal disease caused by an environmental bacterium Burkholderia pseudomallei. The disease is prevalent in northeast Thailand, particularly among rice field farmers who are at risk of bacterial exposure through contact with contaminated soil and water. However, not all exposure results in disease, and infection can manifest diverse outcomes. We postulate that genetic factors, whether from the bacterium, the host or the combination of both, may influence disease outcomes. To address this hypothesis, we aim to collect, sequence, and analyse genetic data from melioidosis patients and controls, along with isolates of B. pseudomallei obtained from patients. Additionally, we will study the metagenomics of the household water supply for both patients and controls, including the presence of B. pseudomallei.

Methods

BurkHostGEN is an ongoing observational study being conducted at Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand. We are obtaining consent from 600 melioidosis patients and 700 controls, spanning both sexes, to collect 1 mL of blood for host DNA analysis, 3 mL of blood for RNA analysis, as well as 5 L of household water supply for metagenomic analysis. Additionally, we are isolating B. pseudomallei from the melioidosis patients to obtain bacterial DNA. This comprehensive approach will allow us to identify B. pseudomallei and their paired host genetic factors associated with disease acquisition and severity. Ethical approvals have been obtained for BurkHostGEN. Host and bacterial genetic data will be uploaded to European Genome-Phenome Archive (EGA) and European Nucleotide Archive (ENA), respectively.

Conclusions

BurkHostGEN holds the potential to discover bacterial and host genetic factors associated with melioidosis infection and severity of illness. It can also support various study designs, including biomarker validation, disease pathogenesis, and epidemiological analysis not only for melioidosis but also for other infectious diseases.

Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes

PURPOSE

The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.

METHODS

This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.

RESULTS

All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made.

CONCLUSION

Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

Effects of Mitochondrial Transplantation on Transcriptomics in a Polymicrobial Sepsis Model

Previously, we demonstrated that mitochondrial transplantation has beneficial effects in a polymicrobial sepsis model. However, the mechanism has not been fully investigated. Mitochondria have their own genes, and genomic changes in sepsis are an important issue in terms of pathophysiology, biomarkers, and therapeutic targets. To investigate the changes in transcriptomic features after mitochondrial transplantation in a polymicrobial sepsis model, we used a rat model of fecal slurry polymicrobial sepsis. Total RNA from splenocytes of sham-operated (SHAM, n = 10), sepsis-induced (SEPSIS, n = 7), and sepsis receiving mitochondrial transplantation (SEPSIS + MT, n = 8) samples was extracted and we conducted a comparative transcriptome-wide analysis between three groups. We also confirmed these results with qPCR. In terms of percentage of mitochondrial mapped reads, the SEPSIS + MT group had a significantly higher mapping ratio than the others. RT1-M2 and Cbln2 were identified as highly expressed in SEPSIS + MT compared with SEPSIS. Using SHAM expression levels as another control variable, we further identified six genes (Fxyd4, Apex2l1, Kctd4, 7SK, SNORD94, and SNORA53) that were highly expressed after sepsis induction and observed that their expression levels were attenuated by mitochondrial transplantation. Changes in transcriptomic features were identified after mitochondrial transplantation in sepsis. This might provide a hint for exploring the mechanism of mitochondrial transplantation in sepsis.

A Systematic Review of Gene Expression Studies in Critically Ill Patients with Sepsis and Community-Acquired Pneumonia

(1) Background: Sepsis is present in nearly 90% of critically ill patients with community-acquired pneumonia (CAP). This systematic review updates the information on studies that have assessed gene expression profiles in critically ill septic patients with CAP. (2) Methods: We searched for studies that satisfied the following criteria: (a) expression profile in critically ill patients with sepsis due to CAP, (b) presence of a control group, and (c) adult patients. Over-representation analysis was performed with clusterProfiler using the Hallmark and Reactome collections. (3) Results: A total of 4312 differentially expressed genes (DEGs) and sRNAs were included in the enrichment analysis. In the Hallmark collection, genes regulated by nuclear factor kappa B in response to tumor necrosis factor, genes upregulated by signal transducer and activator of transcription 5 in response to interleukin 2 stimulation, genes upregulated in response to interferon-gamma, genes defining the inflammatory response, a subgroup of genes regulated by MYC-version 1 (v1), and genes upregulated during transplant rejection were significantly enriched in critically ill septic patients with CAP. Moreover, 88 pathways were identified in the Reactome database. (4) Conclusions: This study summarizes the reported DEGs in critically ill septic patients with CAP and investigates their functional implications. The results highlight the complexity of immune responses during CAP.

Peripheral Blood Omics and Other Multiplex-based Systems in Pulmonary and Critical Care Medicine

Over the last years, the use of peripheral blood-derived big datasets in combination with machine learning technology has accelerated the understanding, prediction, and management of pulmonary and critical care conditions. The goal of this article is to provide readers with an introduction to the methods and applications of blood omics and other multiplex-based technologies in the pulmonary and critical care medicine setting to better appreciate the current literature in the field. To accomplish that, we provide essential concepts needed to rationalize this approach and introduce readers to the types of molecules that can be obtained from the circulating blood to generate big datasets; elaborate on the differences between bulk, sorted, and single-cell approaches; and the basic analytical pipelines required for clinical interpretation. Examples of peripheral blood-derived big datasets used in recent literature are presented, and limitations of that technology are highlighted to qualify both the current and future value of these methodologies.

Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study

BACKGROUND

Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals.

METHODS

We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX^®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding.

RESULTS

Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively).

CONCLUSION

Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study

Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ^® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed that neutrophils and emergency granulopoiesis drove a maladaptive response during sepsis. We generated a whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune response that identified populations of immunosuppressive mature and immature neutrophils. In co-culture, CD66b⁺ sepsis neutrophils inhibited proliferation and activation of CD4⁺ T cells. Single-cell multiomic mapping of circulating hematopoietic stem and progenitor cells (HSPCs) (29,366 cells, n = 27) indicated altered granulopoiesis in patients with sepsis. These features were enriched in a patient subset with poor outcome and a specific sepsis response signature that displayed higher frequencies of IL1R2⁺ immature neutrophils, epigenetic and transcriptomic signatures of emergency granulopoiesis in HSPCs and STAT3-mediated gene regulation across different infectious etiologies and syndromes. Our findings offer potential therapeutic targets and opportunities for stratified medicine in severe infection.