Understanding Heterogeneity in Biologic Phenotypes of Acute Respiratory Distress Syndrome by Leukocyte Expression Profiles

Advances in acute respiratory distress syndrome: focusing on heterogeneity, pathophysiology, and therapeutic strategies

In recent years, the incidence of acute respiratory distress syndrome (ARDS) has been gradually increasing. Despite advances in supportive care, ARDS remains a significant cause of morbidity and mortality in critically ill patients. ARDS is characterized by acute hypoxaemic respiratory failure with diffuse pulmonary inflammation and bilateral edema due to excessive alveolocapillary permeability in patients with non-cardiogenic pulmonary diseases. Over the past seven decades, our understanding of the pathology and clinical characteristics of ARDS has evolved significantly, yet it remains an area of active research and discovery. ARDS is highly heterogeneous, including diverse pathological causes, clinical presentations, and treatment responses, presenting a significant challenge for clinicians and researchers. In this review, we comprehensively discuss the latest advancements in ARDS research, focusing on its heterogeneity, pathophysiological mechanisms, and emerging therapeutic approaches, such as cellular therapy, immunotherapy, and targeted therapy. Moreover, we also examine the pathological characteristics of COVID-19-related ARDS and discuss the corresponding therapeutic approaches. In the face of challenges posed by ARDS heterogeneity, recent advancements offer hope for improved patient outcomes. Further research is essential to translate these findings into effective clinical interventions and personalized treatment approaches for ARDS, ultimately leading to better outcomes for patients suffering from ARDS.

The novel role of LCK and other PcDEGs in the diagnosis and prognosis of sepsis: Insights from bioinformatic identification and experimental validation

BACKGROUND

Programmed cell death (PCD) has emerged as a pivotal progress in pathogenesis of sepsis, but its role in identification of sepsis has not been fully understood.

METHODS

Differentially expressed genes (DEGs) were identified from the GEO database. PCD-related genes were intersected with DEGs, and key PcDEGs were identified through the protein-protein interaction (PPI) network. To pinpoint hub PcDEGs in sepsis, we applied Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Generalized Linear Model (GLM) algorithms. Additionally, the expression levels of five hub PcDEGs were validated in single cell RNA sequencing of sepsis patients and peripheral blood mononuclear cells (PBMCs) from a clinical cohort by quantitative real-time PCR (qRT-PCR). LCK expression was further determined by ELISA, and its diagnostic and prognostic value was evaluated using ROC analysis. LCK levels in the cecal ligation and puncture (CLP)-induced sepsis mouse model were assessed by Western blot and Immunofluorescence (IF). Finally, we assessed the regulatory role of LCK in cell apoptosis using flow cytometry and Western blot analysis.

RESULTS

70 PcDEGs were identified by intersecting 690 DEGs and 1254 PCD-related genes. PPI analysis identified top 15 genes based on Degree algorithm. We then identified five hub PcDEGs (LCK, IL10RA, CD3E, CD5 and ITGAM) that could serve as biomarkers through machine learning. As the expressions of LCK, IL10RA, CD3E and CD5 decreased and ITGAM expression was upregulated in septic patients. Consistently, Serum LCK concentration was reduced in septic patients, and the area under the ROC curve (AUC) of LCK was 0.753. Importantly, LCK displayed more pronounced reduction in non-survivors and those with septic shock than survivors and non-shock patients. The AUC for LCK was 0.726 in predicting mortality of septic patients. Moreover, we observed a decrease expression of LCK in the vital organs (liver, lung, spleen, thymus and PBMC) of septic mice model which mirrored observations in septic patients. Finally, we found that inhibiting LCK promoted apoptosis in Jurkat cells.

CONCLUSIONS

Our study reveals that PcDEGs are dysregulated in sepsis, and closely related to disease pathology. Our finding provides new insights into clinical identification and outcome prediction of sepsis. Of note, LCK is a new biomarker for diagnosis and prognosis, which might be a potential therapeutic target for the treatment of sepsis.

[Clinical sub-phenotypes of acute kidney injury in children and their association with prognosis]

OBJECTIVES

To investigate the clinical sub-phenotype (SP) of pediatric acute kidney injury (AKI) and their association with clinical outcomes.

METHODS

General status and initial values of laboratory markers within 24 hours after admission to the pediatric intensive care unit (PICU) were recorded for children with AKI in the derivation cohort (n=650) and the validation cohort (n=177). In the derivation cohort, a least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify death-related indicators, and a two-step cluster analysis was employed to obtain the clinical SP of AKI. A logistic regression analysis was used to develop a parsimonious classifier model with simplified metrics, and the area under the curve (AUC) was used to assess the value of this model. This model was then applied to the validation cohort and the combined derivation and validation cohort. The association between SPs and clinical outcomes was analyzed with all children with AKI as subjects.

RESULTS

In the derivation cohort, two clinical SPs of AKI (SP1 and SP2) were identified by the two-step cluster analysis using the 20 variables screened by LASSO regression, namely SPd1 group (n=536) and SPd2 group (n=114). The simplified classifier model containing eight variables (P<0.05) had an AUC of 0.965 in identifying the two clinical SPs of AKI (P<0.001). The validation cohort was clustered into SPv1 group (n=156) and SPv2 group (n=21), and the combined derivation and validation cohort was clustered into SP1 group (n=694) and SP2 group (n=133). After adjustment for confounding factors, compared with the SP1 group, the SP2 group had significantly higher incidence rates of multiple organ dysfunction syndrome and death during the PICU stay (P<0.001), and SP2 was significantly associated with the risk of death within 28 days after admission to the PICU (P<0.001).

CONCLUSIONS

This study establishes a parsimonious classifier model and identifies two clinical SPs of AKI with different clinical features and outcomes.The SP2 group has more severe disease and worse clinical prognosis.

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.

Precision Medicine in Acute Respiratory Distress Syndrome: Progress, Challenges, and the Road ahead

Several novel high-dimensional biologic measurements are increasingly being applied to biomedical sciences. Acute respiratory distress syndrome (ARDS) is a theoretically fertile ground for such approaches. Not only are these biologic and analytic tools available to better understand ARDS but also arguably, simpler approaches such as respiratory physiology has been vastly underutilized as a means of delivering precision-based care in the field. Here we review the progress made in ARDS toward discovering biologically homogeneous phenotypes, treatment responsive subgroups, the challenges to implement these discoveries at the bedside, and the road ahead that will enable precision medicine in ARDS.

Sirt3-Mediated Opa1 Deacetylation Protects Against Sepsis-Induced Acute Lung Injury by Inhibiting Alveolar Macrophage Pro-Inflammatory Polarization

Aims: Mitochondrial dynamics in alveolar macrophages (AMs) are associated with sepsis-induced acute lung injury (ALI). In this study, we aimed to investigate whether changes in mitochondrial dynamics could alter the polarization of AMs in sepsis-induced ALI and to explore the regulatory mechanism of mitochondrial dynamics by focusing on sirtuin (SIRT)3-induced optic atrophy protein 1 (OPA1) deacetylation. Results: The AMs of sepsis-induced ALI showed imbalanced mitochondrial dynamics and polarization to the M1 macrophage phenotype. In sepsis, SIRT3 overexpression promotes mitochondrial dynamic equilibrium in AMs. However, 3-(1H-1, 2, 3-triazol-4-yl) pyridine (3TYP)-specific inhibition of SIRT3 increased the mitochondrial dynamic imbalance and pro-inflammatory polarization of AMs and further aggravated sepsis-induced ALI. OPA1 is directly bound to and deacetylated by SIRT3 in AMs. In AMs of sepsis-induced ALI, SIRT3 protein expression was decreased and OPA1 acetylation was increased. OPA1 acetylation at the lysine 792 amino acid residue (OPA1-K792) promotes self-cleavage and is associated with an imbalance in mitochondrial dynamics. However, decreased acetylation of OPA1-K792 reversed the pro-inflammatory polarization of AMs and protected the barrier function of alveolar epithelial cells in sepsis-induced ALI. Innovation: Our study revealed, for the first time, the regulation of mitochondrial dynamics and AM polarization by SIRT3-mediated deacetylation of OPA1 in sepsis-induced ALI, which may serve as an intervention target for precision therapy of the disease. Conclusions: Our data suggest that imbalanced mitochondrial dynamics promote pro-inflammatory polarization of AMs in sepsis-induced ALI and that deacetylation of OPA1 mediated by SIRT3 improves mitochondrial dynamic equilibrium, thereby ameliorating lung injury. Antioxid. Redox Signal. 41, 1014-1030.

Clinical phenotype of ARDS based on K-means cluster analysis: A study from the eICU database

Purpose

To explore the characteristics of the clinical phenotype of ARDS based on Machine Learning.

Methods

This is a study on Machine Learning. Screened cases of acute respiratory distress syndrome (ARDS) in the eICU database collected basic information in the cases and clinical data on the Day 1, Day 3, and Day 7 after the diagnosis of ARDS, respectively. Using the Calinski-Harabasz criterion, Gap Statistic, and Silhouette Coefficient, we determine the optimal clustering number k value. By the K-means cluster analysis to derive clinical phenotype, we analyzed the data collected within the first 24 h. We compared it with the survival of cases under the Berlin standard classification, and also examined the phenotypic conversion within the first 24 h, on day 3, and on day 7 after the diagnosis of ARDS.

Results

We collected 5054 cases and derived three clinical phenotypes using K-means cluster analysis. Phenotype-I is characterized by fewer abnormal laboratory indicators, higher oxygen partial pressure, oxygenation index, APACHE IV score, systolic and diastolic blood pressure, and lower respiratory rate and heart rate. Phenotype-II is characterized by elevated white blood cell count, blood glucose, creatinine, temperature, heart rate, and respiratory rate. Phenotype-III is characterized by elevated age, partial pressure of carbon dioxide, bicarbonate, GCS score, albumin. The differences in ICU length of stay and in-hospital mortality were significantly different between the three phenotypes (P < 0.05), with phenotype I having the lowest in-hospital mortality (10 %) and phenotype II having the highest (31.8 %). To compare the survival analysis of ARDS patients classified by phenotype and those classified according to Berlin criteria. The results showed that the differences in survival between phenotypes were statistically significant (P < 0.05) under phenotypic classification.

Conclusions

The clinical classification of ARDS based on K-means clustering analysis is beneficial for further identifying ARDS patients with different characteristics. Compared to the Berlin standard, the new clinical classification of ARDS provides a clearer display of the survival status of different types of patients, which helps to predict patient prognosis.

Transcriptional pathways of terminal differentiation in high- and low-density blood granulocytes in sepsis

BACKGROUND

Trauma and infection induce emergency granulopoiesis. Counts of immature granulocytes and transcriptional pathways of terminal granulocytic differentiation in blood are elevated in sepsis but correlate with disease severity. This limits their performance as sepsis biomarkers in critically ill patients. We hypothesized that activation of these pathways in sepsis is attributable to immature low-density (LD) rather than mature high-density (HD) granulocytes.

METHODS

We included patients with sepsis and systemic inflammatory response syndrome (SIRS) of comparable disease severity, and additionally septic shock, on intensive or intermediate care unit admission. Blood granulocyte isolation by CD15 MicroBeads was followed by density-gradient centrifugation. Flow cytometry was used to determine counts of developmental stages (precursors) and their relative abundancies in total, HD, and LD granulocytes. Five degranulation markers were quantified in plasma by multiplex immunoassays. A set of 135 genes mapping granulocyte differentiation was assayed by QuantiGene™ Plex. CEACAM4, PLAC8, and CD63 were analyzed by qRT-PCR. Nonparametric statistical tests were applied.

RESULTS

Precursor counts appeared higher in sepsis than SIRS but did not correlate with disease severity for early immature and mature granulocytes. Precursor subpopulations were enriched at least ten-fold in LD over HD granulocytes without sepsis-SIRS differences. Degranulation markers in blood were comparable in sepsis and SIRS. Higher expression of early developmental genes in sepsis than SIRS was more pronounced in LD and less in HD than total granulocytes. Only the cell membrane protein encoding genes CXCR2 and CEACAM4 were more highly expressed in SIRS than sepsis. By qRT-PCR, the azurophilic granule genes CD63 and PLAC8 showed higher sepsis than SIRS levels in LD granulocytes and PLAC8 also in total granulocytes where its discriminatory performance resembled C-reactive protein (CRP).

CONCLUSIONS

Transcriptional programs of early terminal granulocytic differentiation distinguish sepsis from SIRS due to both higher counts of immature granulocytes and elevated expression of early developmental genes in sepsis. The sustained expression of PLAC8 in mature granulocytes likely accounts for its selection in the whole blood SeptiCyte™ LAB test. Total granulocyte PLAC8 rivals CRP as sepsis biomarker. However, infection-specific transcriptional pathways, that differentiate sepsis from sterile stress-induced granulocytosis more reliably than CRP, remain to be identified.

Glucocorticoid therapy for acute respiratory distress syndrome: Current concepts

Acute respiratory distress syndrome (ARDS), a fatal critical disease, is induced by various insults. ARDS represents a major global public health burden, and the management of ARDS continues to challenge healthcare systems globally, especially during the pandemic of the coronavirus disease 2019 (COVID-19). There remains no confirmed specific pharmacotherapy for ARDS, despite advances in understanding its pathophysiology. Debate continues about the potential role of glucocorticoids (GCs) as a promising ARDS clinical therapy. Questions regarding GC agent, dose, and duration in patients with ARDS need to be answered, because of substantial variations in GC administration regimens across studies. ARDS heterogeneity likely affects the therapeutic actions of exogenous GCs. This review includes progress in determining the GC mechanisms of action and clinical applications in ARDS, especially during the COVID-19 pandemic.

The Role of Inflammation in the Pathogenesis of Cardiogenic Shock Secondary to Acute Myocardial Infarction: A Narrative Review

Cardiogenic shock (CS) is one of the most serious complications of myocardial infarction (MI) with a high mortality rate. The timely and effective prevention and early suppression of this adverse event may influence the prognosis and outcome in patients with MI complicated by CS (MI CS). Despite the use of existing pharmaco-invasive options for maintaining an optimal pumping function of the heart in patients with MI CS, its mortality remains high, prompting the search for new approaches to pathogenetic therapy. This review considers the role of the systemic inflammatory response in the pathogenesis of MI CS. The primary processes involved in its initiation are described, including the progression from the onset of MI to the generalization of the inflammatory response and the development of multiple organ dysfunction. The approaches to anti-inflammatory therapy in patients with CS are discussed, and further promising research directions are outlined. In this review, we updated and summarized information on the inflammatory component of MI CS pathogenesis with a particular focus on its foundational aspects. This will facilitate the identification of specific inflammatory phenotypes and endotypes in MI CS and the development of targeted therapeutic strategies for this MI complication.

The human retroviral-like aspartic protease 1 (ASPRV1): From in vitro studies to clinical correlations

The human retroviral-like aspartic protease 1 (ASPRV1) is a retroviral-like protein that was first identified in the skin due to its expression in the stratum granulosum layer of the epidermis. Accordingly, it is also referred to as skin-specific aspartic protease. Similar to the retroviral polyproteins, the full-length ASPRV1 also undergoes self-proteolysis, the processing of the precursor is necessary for the autoactivation of the protease domain. ASPRV1's functions are well-established at the level of the skin: it is part of the epidermal proteolytic network and has a significant contribution to skin moisturization via the limited proteolysis of filaggrin; it is only natural protein substrate identified so far. Filaggrin and ASPRV1 are also specific for mammalians, these proteins provide unique features for the skins of these species, and the importance of filaggrin processing in hydration is proved by the fact that some ASPRV1 mutations are associated with skin diseases such as ichthyosis. ASPRV1 was also found to be expressed in macrophage-like neutrophil cells, indicating that its functions are not limited to the skin. In addition, differential expression of ASPRV1 was detected in many diseases, with yet unknown significance. The currently known enzymatic characteristics-that had been revealed mainly by in vitro studies-and correlations with pathogenic phenotypes imply potentially important functions in multiple cell types, which makes the protein a promising target of functional studies. In this review we describe the currently available knowledge and future perspective in regard to ASPRV1.

Subphenotypes in Acute Respiratory Distress Syndrome: Universal Steps Toward Treatable Traits

Patients with acute respiratory distress syndrome (ARDS) have severe respiratory impairment requiring mechanical ventilation resulting in high mortality. Despite extensive research, no effective pharmacological interventions have been identified in unselected ARDS, which has been attributed to the considerable heterogeneity. The identification of more homogeneous subgroups through phenotyping has provided a novel method to improve our pathophysiological understanding, trial design, and, most importantly, patient care through targeted interventions. The objective of this article is to outline a structured, stepwise approach toward identifying and classifying heterogeneity within ARDS and subsequently derive, validate, and integrate targeted treatment options. We present a 6-step roadmap toward the identification of effective phenotype-targeted treatments: development of distinct and reproducible subphenotypes, derivation of a possible parsimonious bedside classification method, identification of possible interventions, prospective validation of subphenotype classification, testing of subphenotype-targeted intervention prospectively in randomized clinical trial (RCT), and finally implementation of subphenotype classification and intervention in guidelines and clinical practice. Based on this framework, the current literature was reviewed. Respiratory physiology, lung morphology, and systemic inflammatory biology subphenotypes were identified. Currently, lung morphology and systemic inflammatory biology subphenotypes are being tested prospectively in RCTs.

Blood DNA methylation in post-acute sequelae of COVID-19 (PASC): a prospective cohort study

BACKGROUND

DNA methylation integrates environmental signals with transcriptional programs. COVID-19 infection induces changes in the host methylome. While post-acute sequelae of COVID-19 (PASC) is a long-term complication of acute illness, its association with DNA methylation is unknown. No universal blood marker of PASC, superseding single organ dysfunctions, has yet been identified.

METHODS

In this single centre prospective cohort study, PASC, post-COVID without PASC, and healthy participants were enrolled to investigate their symptoms association with peripheral blood DNA methylation data generated with state-of-the-art whole genome sequencing. PASC-induced quality-of-life deterioration was scored with a validated instrument, SF-36. Analyses were conducted to identify potential functional roles of differentially methylated loci, and machine learning algorithms were used to resolve PASC severity.

FINDINGS

103 patients with PASC (22.3% male, 77.7% female), 15 patients with previous COVID-19 infection but no PASC (40.0% male, 60.0% female), and 27 healthy volunteers (48.1% male, 51.9% female) were enrolled. Whole genome methylation sequencing revealed 39 differentially methylated regions (DMRs) specific to PASC, each harbouring an average of 15 consecutive positions, that differentiate patients with PASC from the two control groups. Motif analyses of PASC-regulated DMRs identify binding domains for transcription factors regulating circadian rhythm and others. Some DMRs annotated to protein coding genes were associated with changes of RNA expression. Machine learning support vector algorithm and random forest hierarchical clustering reveal 28 unique differentially methylated positions (DMPs) in the genome discriminating patients with better and worse quality of life.

INTERPRETATION

Blood DNA methylation levels identify PASC, stratify PASC severity, and suggest that DNA motifs are targeted by circadian rhythm-regulating pathways in PASC.

FUNDING

This project has been funded by the following agencies: NIH-AI173035 (A. Jaitovich and R. Alisch); and NIH-AG066179 (R. Alisch).

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BACKGROUND

Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.

METHODS

We performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.

RESULTS

Among 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.

CONCLUSIONS

Our research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

Immune cell landscapes are associated with high-grade serous ovarian cancer survival

High-grade serous ovarian cancer (HGSOC) is an aggressive disease known to develop resistance to chemotherapy. We investigated the prognostic significance of tumor cell states and potential mechanisms underlying chemotherapy resistance in HGSOC. Transcriptome deconvolution was performed to address cellular heterogeneity. Kaplan-Meier survival curves were plotted to illustrate the outcomes of patients with varying cellular abundances. The association between gene expression and chemotherapy response was tested. After adjusting for surgery status and grading, several cell states exhibited a significant correlation with patient survival. Cell states can organize into carcinoma ecotypes (CE). CE9 and CE10 were proinflammatory, characterized by higher immunoreactivity, and were associated with favorable survival outcomes. Ratios of cell states and ecotypes had better prognostic abilities than a single cell state or ecotype. A total of 1265 differentially expressed genes were identified between samples with high and low levels of C9 or CE10. These genes were partitioned into three co-expressed modules, which were associated with tumor cells and immune cells. Pogz was identified to be linked with immune cell genes and the chemotherapy response of paclitaxel. Collectively, the survival of HGSOC patients is correlated with specific cell states and ecotypes.

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.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.

Molecular Phenotypes of Acute Respiratory Distress Syndrome in the ROSE Trial Have Differential Outcomes and Gene Expression Patterns That Differ at Baseline and Longitudinally over Time

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

TRENDS IN CHOLESTEROL AND LIPOPROTEINS ARE ASSOCIATED WITH ACUTE RESPIRATORY DISTRESS SYNDROME INCIDENCE AND DEATH AMONG SEPSIS PATIENTS

ABSTRACT

Objective: Compare changes in cholesterol and lipoprotein levels occurring in septic patients with and without acute respiratory distress syndrome (ARDS) and by survivorship. Methods: We reanalyzed data from prospective sepsis studies. Cholesterol and lipoprotein levels were analyzed using univariate testing to detect changes between septic patients with or without ARDS, and among ARDS survivors compared with nonsurvivors at enrollment (first 24 h of sepsis) and 48 to 72 h later. Results: 214 patients with sepsis were included of whom 48 had ARDS and 166 did not have ARDS. Cholesterol and lipoproteins among septic ARDS versus non-ARDS showed similar enrollment levels. However, 48 to 72 h after enrollment, change in median total cholesterol (48/72 h - enrollment) was significantly different between septic ARDS (-4, interquartile range [IQR] -23.5, 6.5, n = 35) and non-ARDS (0, -10.0, 17.5, P = 0.04; n = 106). When compared by ARDS survivorship, ARDS nonsurvivors (n = 14) had lower median total cholesterol levels (75.5, IQR 68.4, 93.5) compared with ARDS survivors (113.0, IQR 84.0, 126.8, P = 0.022), and lower median enrollment low-density lipoprotein cholesterol (LDL-C) levels (27, IQR 19.5-34.5) compared with ARDS survivors (43, IQR 27-67, P = 0.013; n = 33). Apolipoprotein A-I levels were also significantly lower in ARDS nonsurvivors (n = 14) (87.6, IQR 76.45-103.64) compared with ARDS survivors (130.0, IQR 73.25-165.47, P = 0.047; n = 33). At 48 to 72 h, for ARDS nonsurvivors, median levels of low-density lipoprotein cholesterol (9.0, IQR 4.3, 18.0; n = 10), LDL-C (17.0, IQR 5.0, 29.0; n = 9), and total cholesterol (59.0, 45.3, 81.5; n = 10) were significantly lower compared with ARDS survivors' (n = 25) levels of low-density lipoprotein cholesterol (20.0, IQR 12.0-39.0, P = 0.014), LDL-C (42.0, IQR 27.0-58.0, P = 0.019), and total cholesterol (105.0, IQR 91.0, 115.0, P = 0.003). Conclusions: Change in total cholesterol was different in septic ARDS versus non-ARDS. Total cholesterol, LDL-C, and apolipoprotein A-I levels were lower in ARDS nonsurvivors compared with survivors. Future studies of dysregulated cholesterol metabolism in septic ARDS patients are needed to understand biology and links to potential therapies.

In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma

Background

Lung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.

Methods

We used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.

Results

We found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.

Conclusion

We provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

Signaling pathways and potential therapeutic targets in acute respiratory distress syndrome (ARDS)

Acute respiratory distress syndrome (ARDS) is a common condition associated with critically ill patients, characterized by bilateral chest radiographical opacities with refractory hypoxemia due to noncardiogenic pulmonary edema. Despite significant advances, the mortality of ARDS remains unacceptably high, and there are still no effective targeted pharmacotherapeutic agents. With the outbreak of coronavirus disease 19 worldwide, the mortality of ARDS has increased correspondingly. Comprehending the pathophysiology and the underlying molecular mechanisms of ARDS may thus be essential to developing effective therapeutic strategies and reducing mortality. To facilitate further understanding of its pathogenesis and exploring novel therapeutics, this review provides comprehensive information of ARDS from pathophysiology to molecular mechanisms and presents targeted therapeutics. We first describe the pathogenesis and pathophysiology of ARDS that involve dysregulated inflammation, alveolar-capillary barrier dysfunction, impaired alveolar fluid clearance and oxidative stress. Next, we summarize the molecular mechanisms and signaling pathways related to the above four aspects of ARDS pathophysiology, along with the latest research progress. Finally, we discuss the emerging therapeutic strategies that show exciting promise in ARDS, including several pharmacologic therapies, microRNA-based therapies and mesenchymal stromal cell therapies, highlighting the pathophysiological basis and the influences on signal transduction pathways for their use.

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.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort

Background

Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions.

Methods

We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme.

Findings

Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes.

Interpretation

Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

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.

ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies

The aim of these guidelines is to update the 2017 clinical practice guideline (CPG) of the European Society of Intensive Care Medicine (ESICM). The scope of this CPG is limited to adult patients and to non-pharmacological respiratory support strategies across different aspects of acute respiratory distress syndrome (ARDS), including ARDS due to coronavirus disease 2019 (COVID-19). These guidelines were formulated by an international panel of clinical experts, one methodologist and patients' representatives on behalf of the ESICM. The review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations and the quality of reporting of each study based on the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network guidelines. The CPG addressed 21 questions and formulates 21 recommendations on the following domains: (1) definition; (2) phenotyping, and respiratory support strategies including (3) high-flow nasal cannula oxygen (HFNO); (4) non-invasive ventilation (NIV); (5) tidal volume setting; (6) positive end-expiratory pressure (PEEP) and recruitment maneuvers (RM); (7) prone positioning; (8) neuromuscular blockade, and (9) extracorporeal life support (ECLS). In addition, the CPG includes expert opinion on clinical practice and identifies the areas of future research.

Cardiovascular Subphenotypes in ARDS: Diagnostic and Therapeutic Implications and Overlap with Other ARDS Subphenotypes

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical condition. Shock is a poor prognostic sign in ARDS, and heterogeneity in its pathophysiology may be a barrier to its effective treatment. Although right ventricular dysfunction is commonly implicated, there is no consensus definition for its diagnosis, and left ventricular function is neglected. There is a need to identify the homogenous subgroups within ARDS, that have a similar pathobiology, which can then be treated with targeted therapies. Haemodynamic clustering analyses in patients with ARDS have identified two subphenotypes of increasingly severe right ventricular injury, and a further subphenotype of hyperdynamic left ventricular function. In this review, we discuss how phenotyping the cardiovascular system in ARDS may align with haemodynamic pathophysiology, can aid in optimally defining right ventricular dysfunction and can identify tailored therapeutic targets for shock in ARDS. Additionally, clustering analyses of inflammatory, clinical and radiographic data describe other subphenotypes in ARDS. We detail the potential overlap between these and the cardiovascular phenotypes.

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.

Are circulating endothelial cells the next target for transcriptome-level pathway analysis in ARDS?

Acute respiratory distress syndrome (ARDS) has had no mortality-improving pharmacological intervention despite 50 years of high-caliber research due to its heterogeneity (Huppert LA, Matthay MA, Ware LB. Semin Respir Crit Care Med 40: 31-39, 2019). For the field to advance, better definitions for ARDS subgroups that more uniformly respond to therapies are needed (Bos LDJ, Scicluna BP, Ong DSY, Cremer O, van der Poll T, Schultz MJ. Am J Respir Crit Care Med 200: 42-50, 2019; Dickson RP, Schultz MJ, T van der P, Schouten LR, Falkowski NR, Luth JE, Sjoding MW, Brown CA, Chanderraj R, Huffnagle GB, Bos LDJ, Biomarker Analysis in Septic ICU Patients (BASIC) Consortium. Am J Respir Crit Care Med 201: 555-563, 2020; Sinha P, Calfee CS. Am J Respir Crit Care Med 200: 4-6, 2019; Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, NHLBI ARDS Network. Lancet Respir Med 2: 611-620, 2014; Hendrickson CM, Matthay MA. Pulm Circ 8: 1-12, 2018). A plethora of high-quality clinical research has uncovered the next generation of soluble biomarkers that provide the predictive enrichment necessary for trial recruitment; however, plasma-soluble markers do not specify the damaged organ of origin nor do they provide insight into disease mechanisms. In this perspective, we make the case for querying the transcriptome of circulating endothelial cells (CECs), which when shed from vessels after inflammatory insult, become heralds of site-specific inflammatory damage. We review the application of CEC quantification to multiple disease phenotypes (including myocardial infarction, vasculitides, cancer, and ARDS), in each case supporting the association of CEC number with disease severity. We also argue for the utility of single-cell RNA transcriptomics to the understanding of cell-specific contributions to disease pathophysiology and its potential to uncover novel insight on signals contributing to CEC shedding in ARDS.

Interrogating the sepsis host immune response using cytomics

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

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

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a PaO2/FiO2 < 200 to those with a PaO2/FiO2 ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a PaO2/FiO2 < 200 vs. PaO2/FiO2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

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

Plasma metabolic profiling implicates dysregulated lipid metabolism and glycolytic shift in hyperinflammatory ARDS

Using latent class analysis (LCA) of clinical and protein biomarkers, researchers have identified two phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and treatment responses. We investigated whether plasma metabolites differed among patients with LCA-derived hyperinflammatory and hypoinflammatory ARDS, and we tested the prognostic utility of adding metabolic clusters to LCA phenotypes. We analyzed data from 93 patients with ARDS and sepsis enrolled in a multicenter prospective cohort of critically ill patients, comparing 970 metabolites between the two LCA-derived phenotypes. In all, 188 metabolites were differentially abundant between the two LCA-derived phenotypes. After adjusting for age, sex, confounding medications, and comorbid liver and kidney disease, 82 metabolites remained significantly different. Patients with hyperinflammatory ARDS had reduced circulating lipids but high levels of pyruvate, lactate, and malate. Metabolic cluster and LCA-derived phenotypes were each significantly and independently associated with survival. Patients with hyperinflammatory ARDS may be experiencing a glycolytic shift leading to dysregulated lipid metabolism. Metabolic profiling offers prognostic information beyond what is captured by LCA phenotypes alone. Deeper biological profiling may identify key differences in pathogenesis among patients with ARDS and may lead to novel targeted therapies.

Integrating biology into clinical trial design

PURPOSE OF REVIEW

Critical care medicine revolves around syndromes, such as acute respiratory distress syndrome (ARDS), sepsis and acute kidney injury. Few interventions have shown to be effective in large clinical trials, likely because of between-patient heterogeneity. Translational evidence suggests that more homogeneous biological subgroups can be identified and that differential treatment effects exist. Integrating biological considerations into clinical trial design is therefore an important frontier of critical care research.

RECENT FINDINGS

The pathophysiology of critical care syndromes involves a multiplicity of processes, which emphasizes the difficulty of integrating biology into clinical trial design. Biological assessment can be integrated into clinical trials using predictive enrichment at trial inclusion, time-dependent variation to better understand treatment effects and biological markers as surrogate outcomes.

SUMMARY

Integrating our knowledge on biological heterogeneity into clinical trial design, which has revolutionized other medical fields, could serve as a solution to implement personalized treatment in critical care syndromes. Changing the trial design by using predictive enrichment, incorporation of the evaluation of time-dependent changes and biological markers as surrogate outcomes may improve the likelihood of detecting a beneficial effect from targeted therapeutic interventions and the opportunity to test multiple lines of treatment per patient.

Transcriptomic clustering of critically ill COVID-19 patients

BACKGROUND

Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit.

METHODS

In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings.

RESULTS

We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results.

CONCLUSIONS

These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.

Research Progress on Phenotypic Classification of Acute Respiratory Distress Syndrome: A Narrative Review

Acute respiratory distress syndrome (ARDS) is a clinical syndrome that is characterized by an acute onset and refractory hypoxemia. It remains an important contributor to high mortality in critically ill patients, and the majority of clinical randomized controlled trials on ARDS provide underwhelming findings, which is attributed in large part to its pathophysiological and clinical heterogeneity, among other aspects. It is now widely accepted that ARDS is highly heterogeneous, growing evidences support this. ARDS phenotypic and subphenotypic studies aim to further differentiate and identify ARDS heterogeneity in the hope that clinicians can benefit from it, then can diagnose ARDS faster and more accurately and provide targeted treatments. This review collates and evaluates the major phenotype-related research advances of recent years, with a specific focus on ARDS biomarkers and clinical factors.

Biomarkers of mitochondrial dysfunction in acute respiratory distress syndrome: A systematic review and meta-analysis

Introduction

Acute respiratory distress syndrome (ARDS) is one of the main causes of Intensive Care Unit morbidity and mortality. Metabolic biomarkers of mitochondrial dysfunction are correlated with disease development and high mortality in many respiratory conditions, however it is not known if they can be used to assess risk of mortality in patients with ARDS.

Objectives

The aim of this systematic review was to examine the link between recorded biomarkers of mitochondrial dysfunction in ARDS and mortality.

Methods

A systematic review of CINAHL, EMBASE, MEDLINE, and Cochrane databases was performed. Studies had to include critically ill ARDS patients with reported biomarkers of mitochondrial dysfunction and mortality. Information on the levels of biomarkers reflective of energy metabolism and mitochondrial respiratory function, mitochondrial metabolites, coenzymes, and mitochondrial deoxyribonucleic acid (mtDNA) copy number was recorded. RevMan5.4 was used for meta-analysis. Biomarkers measured in the samples representative of systemic circulation were analyzed separately from the biomarkers measured in the samples representative of lung compartment. Cochrane risk of bias tool and Newcastle-Ottawa scale were used to evaluate publication bias (Prospero protocol: CRD42022288262).

Results

Twenty-five studies were included in the systematic review and nine had raw data available for follow up meta-analysis. Biomarkers of mitochondrial dysfunction included mtDNA, glutathione coupled mediators, lactate, malondialdehyde, mitochondrial genetic defects, oxidative stress associated markers. Biomarkers that were eligible for meta-analysis inclusion were: xanthine, hypoxanthine, acetone, N-pentane, isoprene and mtDNA. Levels of mitochondrial biomarkers were significantly higher in ARDS than in non-ARDS controls (P = 0.0008) in the blood-based samples, whereas in the BAL the difference did not reach statistical significance (P = 0.14). mtDNA was the most frequently measured biomarker, its levels in the blood-based samples were significantly higher in ARDS compared to non-ARDS controls (P = 0.04). Difference between mtDNA levels in ARDS non-survivors compared to ARDS survivors did not reach statistical significance (P = 0.05).

Conclusion

Increased levels of biomarkers of mitochondrial dysfunction in the blood-based samples are positively associated with ARDS. Circulating mtDNA is the most frequently measured biomarker of mitochondrial dysfunction, with significantly elevated levels in ARDS patients compared to non-ARDS controls. Its potential to predict risk of ARDS mortality requires further investigation.

Systematic review registration

[https://www.crd.york.ac.uk/prospero], identifier [CRD42022288262].

Different treatment response to systemic corticosteroids according to white blood cell counts in severe COVID-19 patients

BACKGROUND

Limited data are available in COVID-19 patients on the prediction of treatment response to systemic corticosteroid therapy based on systemic inflammatory markers. There is a concern whether the response to systemic corticosteroid is different according to white blood cell (WBC) counts in COVID-19 patients. We aimed to assess whether WBC count is related with the clinical outcomes after treatment with systemic corticosteroids in severe COVID-19.

METHODS

We conducted a retrospective cohort study and analysed the patients hospitalised for severe COVID-19 and received systemic corticosteroids between July 2020 and June 2021. The primary endpoint was to compare the composite poor outcome of mechanical ventilation, extracorporeal membrane oxygenation, and mortality among the patients with different WBC counts.

RESULTS

Of the 585 COVID-19 patients who required oxygen supplementation and systemic corticosteroids, 145 (24.8%) belonged to the leukopoenia group, 375 (64.1%) belonged to the normal WBC group, and 65 (11.1%) belonged to the leukocytosis group. In Kaplan-Meier curve, the composite poor outcome was significantly reduced in leukopoenia group compared to leukocytosis group (log-rank p-value < 0.001). In the multivariable Cox regression analysis, leukopoenia group was significantly associated with a lower risk of the composite poor outcome compared to normal WBC group (adjusted hazard ratio [aHR] = 0.32, 95% CI 0.14-0.76, p-value = 0.009) and leukocytosis group (aHR = 0.30, 95% CI = 0.12-0.78, p-value = 0.013). There was no significant difference in aHR for composite poor outcome between leukocytosis and normal WBC group.

CONCLUSION

Leukopoenia may be related with a better response to systemic corticosteroid therapy in COVID-19 patients requiring oxygen supplementation.KEY MESSAGESIn severe COVID-19 treated with systemic corticosteroids, patients with leukopoenia showed a lower hazard for composite poor outcome compared to patients with normal white blood cell counts or leukocytosis.Leukopoenia may be a potential biomarker for better response to systemic corticosteroid therapy in COVID-19 pneumonia.

Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes

In the last decade, research on acute respiratory distress syndrome (ARDS) has made considerable progress. However, ARDS remains a leading cause of mortality in the intensive care unit. ARDS presents distinct subphenotypes with different clinical and biological features. The pathophysiologic mechanisms of ARDS may contribute to the biological variability and partially explain why some pharmacologic therapies for ARDS have failed to improve patient outcomes. Therefore, identifying ARDS variability and heterogeneity might be a key strategy for finding effective treatments. Research involving studies on biomarkers and genomic, metabolomic, and proteomic technologies is increasing. These new approaches, which are dedicated to the identification and quantitative analysis of components from biological matrixes, may help differentiate between different types of damage and predict clinical outcome and risk. Omics technologies offer a new opportunity for the development of diagnostic tools and personalized therapy in ARDS. This narrative review assesses recent evidence regarding genomics, proteomics, and metabolomics in ARDS research.

Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study

Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.

Tumor Progression Locus 2 Protects against Acute Respiratory Distress Syndrome in Influenza A Virus-Infected Mice

Excessive inflammation in patients with severe influenza disease may lead to acute lung injury that results in acute respiratory distress syndrome (ARDS). ARDS is associated with alveolar damage and pulmonary edema that severely impair gas exchange, leading to hypoxia. With no existing FDA-approved treatment for ARDS, it is important to understand the factors that lead to virus-induced ARDS development to improve prevention, diagnosis, and treatment. We have previously shown that mice deficient in the serine-threonine mitogen-activated protein kinase, Tpl2 (MAP3K8 or COT), succumb to infection with a typically low-pathogenicity strain of influenza A virus (IAV; HKX31, H3N2 [x31]). The goal of the current study was to evaluate influenza A virus-infected Tpl2-/- mice clinically and histopathologically to gain insight into the disease mechanism. We hypothesized that Tpl2-/- mice succumb to IAV infection due to development of ARDS-like disease and pulmonary dysfunction. We observed prominent signs of alveolar septal necrosis, hyaline membranes, pleuritis, edema, and higher lactate dehydrogenase (LDH) levels in the lungs of IAV-infected Tpl2-/- mice compared to wild-type (WT) mice from 7 to 9 days postinfection (dpi). Notably, WT mice showed signs of regenerating epithelium, indicative of repair and recovery, that were reduced in Tpl2-/- mice. Furthermore, biomarkers associated with human ARDS cases were upregulated in Tpl2-/- mice at 7 dpi, demonstrating an ARDS-like phenotype in Tpl2-/- mice in response to IAV infection. IMPORTANCE This study demonstrates the protective role of the serine-threonine mitogen-activated protein kinase, Tpl2, in influenza virus pathogenesis and reveals that host Tpl2 deficiency is sufficient to convert a low-pathogenicity influenza A virus infection into severe influenza disease that resembles ARDS, both histopathologically and transcriptionally. The IAV-infected Tpl2-/- mouse thereby represents a novel murine model for studying ARDS-like disease that could improve our understanding of this aggressive disease and assist in the design of better diagnostics and treatments.

Acute respiratory distress syndrome: causes, pathophysiology, and phenotypes

Acute respiratory distress syndrome (ARDS) is a common clinical syndrome of acute respiratory failure as a result of diffuse lung inflammation and oedema. ARDS can be precipitated by a variety of causes. The pathophysiology of ARDS is complex and involves the activation and dysregulation of multiple overlapping and interacting pathways of injury, inflammation, and coagulation, both in the lung and systemically. Mechanical ventilation can contribute to a cycle of lung injury and inflammation. Resolution of inflammation is a coordinated process that requires downregulation of proinflammatory pathways and upregulation of anti-inflammatory pathways. The heterogeneity of the clinical syndrome, along with its biology, physiology, and radiology, has increasingly been recognised and incorporated into identification of phenotypes. A precision-medicine approach that improves the identification of more homogeneous ARDS phenotypes should lead to an improved understanding of its pathophysiological mechanisms and how they differ from patient to patient.

The prognostic value of early measures of the ventilatory ratio in the ARDS ROSE trial

Background

The ventilatory ratio (VR, [minute ventilation × PaCO₂]/[predicted body weight × 100 × 37.5]) is associated with mortality in ARDS. The aims of this study were to test whether baseline disease severity or neuromuscular blockade (NMB) modified the relationship between VR and mortality.

Methods

This was a post hoc analysis of the PETAL-ROSE trial, which randomized moderate-to-severe ARDS patients to NMB or control. Survival among patients with different VR trajectories or VR cutoff above and below the median was assessed by Kaplan–Meier analysis. The relationships between single-day or 48-h VR trajectories with 28- or 90-day mortality were tested by logistic regression. Randomization allocation to NMB and markers of disease severity were tested as confounders by multivariable regression and interaction term analyses.

Results

Patients with worsening VR trajectories had significantly lower survival compared to those with improving VR (n = 602, p < 0.05). Patients with VR > 2 (median) at day 1 had a significantly lower 90-day survival compared to patients with VR ≤ 2 (HR 1.36, 95% CI 1.10–1.69). VR at day 1 was significantly associated with 28-day mortality (OR = 1.40, 95% CI 1.15–1.72). There was no interaction between NMB and VR for 28-day mortality. APACHE-III had a significant interaction with VR at baseline for the outcome of 28-day mortality, such that the relationship between VR and mortality was stronger among patients with lower APACHE-III. There was a significant association between rising VR trajectory and mortality that was independent of NMB, baseline PaO₂/FiO₂ ratio and generalized markers of disease severity (Adjusted OR 1.81, 95% CI 1.28–2.84 for 28-day and OR 2.07 95% CI 1.41–3.10 for 90-day mortality). APACHE-III and NMB were not effect modifiers in the relationship between VR trajectory and mortality.

Conclusions

Elevated baseline and day 1 VR were associated with higher 28-day mortality. The relationship between baseline VR and mortality was stronger among patients with lower APACHE-III. APACHE-III was not an effect modifier for the relationship between VR trajectory and mortality, so that the VR trajectory may be optimally suited for prognostication and predictive enrichment. VR was not different between patients randomized to NMB or control, indicating that VR can be utilized without correcting for NMB.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04179-7.

Identification of early biomarkers of transcriptomics in alveolar macrophage for the prognosis of intubated ARDS patients

Background

Currently, the rate of morbidity and mortality in acute respiratory distress syndrome (ARDS) remains high. One of the potential reasons for the poor and ineffective therapies is the lack of early and credible indicator of risk prediction that would help specific treatment of severely affected ARDS patients. Nevertheless, assessment of the clinical outcomes with transcriptomics of ARDS by alveolar macrophage has not been performed.

Methods

The expression data GSE116560 was obtained from the Gene Expression Omnibus databases (GEO) in NCBI. This dataset consists of 68 BAL samples from 35 subjects that were collected within 48 h of ARDS. Differentially expressed genes (DEGs) of different outcomes were analyzed using R software. The top 10 DEGs that were up- or down-regulated were analyzed using receiver operating characteristic (ROC) analysis. Kaplan–Meier survival analysis within two categories according to cut-off and the value of prediction of the clinical outcomes via DEGs was verified. GO enrichment, KEGG pathway analysis, and protein–protein interaction were also used for functional annotation of key genes.

Results

24,526 genes were obtained, including 235 up-regulated and 292 down-regulated DEGs. The gene ADORA3 was chosen as the most obvious value to predict the outcome according to the ROC and survival analysis. For functional annotation, ADORA3 was significantly augmented in sphingolipid signaling pathway, cGMP-PKG signaling pathway, and neuroactive ligand-receptor interaction. Four genes (ADORA3, GNB1, NTS, and RHO), with 4 nodes and 6 edges, had the highest score in these clusters in the protein–protein interaction network.

Conclusions

Our results show that the prognostic prediction of early biomarkers of transcriptomics as identified in alveolar macrophage in ARDS can be extended for mechanically ventilated critically ill patients. In the long term, generalizing the concept of biomarkers of transcriptomics in alveolar macrophage could add to improving precision-based strategies in the ICU patients and may also lead to identifying improved strategy for critically ill patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02130-8.

New Insights into Clinical and Mechanistic Heterogeneity of the Acute Respiratory Distress Syndrome: Summary of the Aspen Lung Conference 2021

Clinical and molecular heterogeneity are common features of human disease. Understanding the basis for heterogeneity has led to major advances in therapy for many cancers and pulmonary diseases such as cystic fibrosis and asthma. Although heterogeneity of risk factors, disease severity, and outcomes in survivors are common features of the acute respiratory distress syndrome (ARDS), many challenges exist in understanding the clinical and molecular basis for disease heterogeneity and using heterogeneity to tailor therapy for individual patients. This report summarizes the proceedings of the 2021 Aspen Lung Conference, which was organized to review key issues related to understanding clinical and molecular heterogeneity in ARDS. The goals were to review new information about ARDS phenotypes, to explore multicellular and multisystem mechanisms responsible for heterogeneity, and to review how best to account for clinical and molecular heterogeneity in clinical trial design and assessment of outcomes. The report concludes with recommendations for future research to understand the clinical and basic mechanisms underlying heterogeneity in ARDS to advance the development of new treatments for this life-threatening critical illness.

Validation of Messenger Ribonucleic Acid Markers Differentiating Among Human Acute Respiratory Distress Syndrome Subgroups in an Ovine Model of Acute Respiratory Distress Syndrome Phenotypes

Background

The discovery of biological subphenotypes in acute respiratory distress syndrome (ARDS) might offer a new approach to ARDS in general and possibly targeted treatment, but little is known about the underlying biology yet. To validate our recently described ovine ARDS phenotypes model, we compared a subset of messenger ribonucleic acid (mRNA) markers in leukocytes as reported before to display differential expression between human ARDS subphenotypes to the expression in lung tissue in our ovine ARDS phenotypes model (phenotype 1 (Ph1): hypoinflammatory; phenotype 2 (Ph2): hyperinflammatory).

Methods

We studied 23 anesthetized sheep on mechanical ventilation with observation times between 6 and 24 h. They were randomly allocated to the two phenotypes (n = 14 to Ph1 and n = 9 to Ph2). At study end, lung tissue was harvested and preserved in RNAlater. After tissue homogenization in TRIzol, total RNA was extracted and custom capture and reporter probes designed by NanoString Technologies were used to measure the expression of 14 genes of interest and the 6 housekeeping genes on a nCounter SPRINT profiler.

Results

Among the 14 mRNA markers, in all animals over all time points, 13 markers showed the same trend in ovine Ph2/Ph1 as previously reported in the MARS cohort: matrix metalloproteinase 8, olfactomedin 4, resistin, G protein-coupled receptor 84, lipocalin 2, ankyrin repeat domain 22, CD177 molecule, and transcobalamin 1 expression was higher in Ph2 and membrane metalloendopeptidase, adhesion G protein-coupled receptor E3, transforming growth factor beta induced, histidine ammonia-lyase, and sulfatase 2 expression was higher in Ph1. These expression patterns could be found when different sources of mRNA – such as blood leukocytes and lung tissue – were compared.

Conclusion

In human and ovine ARDS subgroups, similar activated pathways might be involved (e.g., oxidative phosphorylation, NF-κB pathway) that result in specific phenotypes.

COVID-19 Acute Respiratory Distress Syndrome: One Pathogen, Multiple Phenotypes

Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome arising from multiple causes with a range of clinical severity. In recent years, the potential for prognostic and predictive enrichment of clinical trials has been increased with identification of more biologically homogeneous subgroups or phenotypes within ARDS. COVID-19 ARDS also exhibits significant clinical heterogeneity despite a single causative agent. In this review the authors summarize the existing literature on COVID-19 ARDS phenotypes, including physiologic, clinical, and biological subgroups as well as the implications for improving both prognostication and precision therapy.