Daily Sepsis Research Analysis
Three papers advance sepsis science across therapeutics, methodology, and pathogenesis. A red blood cell membrane–fused, methyl-branched DNase I nanocarrier degrades NETs/cfDNA and prevents organ dysfunction in septic mice, a causal forest analysis of the VANISH RCT identifies actionable heterogeneity by serum potassium, and dual host–pathogen transcriptomics maps Cronobacter turicensis strategies during CNS invasion in vivo.
Summary
Three papers advance sepsis science across therapeutics, methodology, and pathogenesis. A red blood cell membrane–fused, methyl-branched DNase I nanocarrier degrades NETs/cfDNA and prevents organ dysfunction in septic mice, a causal forest analysis of the VANISH RCT identifies actionable heterogeneity by serum potassium, and dual host–pathogen transcriptomics maps Cronobacter turicensis strategies during CNS invasion in vivo.
Research Themes
- Targeted degradation of NETs/cfDNA as adjunctive sepsis therapy
- Causal machine learning to uncover heterogeneous treatment effects in septic shock
- In vivo host–pathogen transcriptomics in neonatal sepsis pathogens
Selected Articles
1. Archaea-inspired deoxyribonuclease I liposomes prevent multiple organ dysfunction in sepsis.
A red blood cell membrane–fused, methyl-branched liposomal DNase I formulation efficiently degraded NETs and cfDNA, prolonged circulation, reprogrammed innate immune activation, and prevented organ dysfunction in septic mice. This platform supports NETs/cfDNA clearance as a tractable therapeutic axis in sepsis.
Impact: Introduces a mechanistically targeted nanotherapy that addresses a validated sepsis driver (NETs/cfDNA) with improved pharmacokinetics. If translated, it could reshape adjunctive treatment strategies.
Clinical Implications: Supports development of DNase-based adjuncts for sepsis, with potential patient selection using cfDNA/NETs biomarkers and combination with standard source control and antibiotics.
Key Findings
- DNase I/Rm-Lipo efficiently cleared NETs and cfDNA in activated neutrophils.
- The formulation prolonged DNase I circulation time and suppressed neutrophil activation while modulating macrophage polarization.
- In septic mice, DNase I/Rm-Lipo mitigated inflammation and prevented multiple organ dysfunction.
Methodological Strengths
- Rational nanocarrier design combining methyl-branched lipids with red blood cell membrane for stability and stealth.
- Multiscale evaluation across immune cell assays and in vivo septic mouse model demonstrating mechanism and efficacy.
Limitations
- Preclinical study without human safety or efficacy data.
- Details of sepsis model standardization, dose–response, and long-term immunogenicity are not provided in the abstract.
Future Directions: Translate to large-animal models, define dosing and immunogenicity, and design early-phase trials with NETs/cfDNA biomarkers for enrichment.
Neutrophil extracellular traps (NETs) and circulating cell-free DNA (cfDNA) are pivotal in driving excessive inflammation and organ damage during sepsis, with their levels correlating positively with sepsis severity in both patients and murine models. Despite the ability of deoxyribonuclease I (DNase I) to degrade NETs and cfDNA, its short half-life and rapid degradation limit its therapeutic effectiveness. To address this challenge, we developed a methyl-branched liposome fused with a red blood cell membrane for the systemic delivery of DNase I (DNase I/Rm-Lipo). The efficacy of DNase I/Rm-Lipo was evaluated in the stimulated immune cells and septic model. The data confirmed that DNase I/Rm-Lipo efficiently removed excess NETs and cfDNA in activated neutrophils. Following injection, DNase I/Rm-Lipo exhibited an extended circulation time, effectively suppressing neutrophil activation and regulating macrophage polarization to mitigate inflammation and prevent organ dysfunction in septic mice. These findings highlight the therapeutic potential of DNase I/Rm-Lipo as a promising candidate for sepsis management by targeting the degradation of NETs and cfDNA.
2. Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.
Across classical, lasso, and causal forest approaches on VANISH septic shock RCT data, serum potassium consistently emerged as an HTE driver, with a causal forest–derived threshold of 4.68 mmol/L separating subgroups with divergent 28-day survival risk differences. Extracting root splits offers a clinically interpretable, data-driven path to define subgroups.
Impact: Demonstrates a practical causal ML workflow to turn HTE signals into actionable subgroup thresholds in septic shock, potentially informing precision vasopressor selection.
Clinical Implications: Suggests serum potassium–based stratification could guide vasopressin versus norepinephrine use, pending prospective validation of the 4.68 mmol/L threshold.
Key Findings
- All analytic frameworks identified heterogeneous treatment effects linked to serum potassium.
- Causal forest root splits most commonly occurred on serum potassium with a mean threshold of 4.68 mmol/L.
- Risk differences for 28-day survival were 0.069 (≤4.68 mmol/L) vs −0.257 (>4.68 mmol/L), indicating divergent treatment effects across strata.
Methodological Strengths
- Triangulation across classical interaction tests, hierarchical lasso, and causal forests on RCT data.
- Use of honest causal trees and explicit extraction of root splits to yield interpretable subgroup thresholds.
Limitations
- Secondary analysis without external validation; causal forest HTE signal had modest statistical support (p=0.124).
- Thresholds are data-derived and require prospective confirmation before clinical adoption.
Future Directions: Prospective, pre-specified validation of potassium-based subgroups and integration with multivariable HTE models to guide vasopressor therapy.
BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the treatment effect. However, individual significant interactions may not always yield clinically actionable subgroups, particularly for continuous covariates. Non-parametric causal machine learning approaches are flexible alternatives for estimating HTEs across many possible treatment effect modifiers in a single analysis. METHODS: We conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. We used classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. Causal forests comprise honest causal trees, which use sample splitting to determine tree splits and estimate treatment effects separately. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects. RESULTS: All three models found evidence of HTE with serum potassium levels. Univariable logistic regression OR 0.435 (95%CI [0.270, 0.683]. p = 0.0004), hierarchical lasso logistic regression standardised OR: 0.604 (95% CI 0.259, 0.701), lambda = 0.0049. Hierarchical lasso kept the interaction between the treatment and serum potassium, sodium level, minimum temperature, platelet count and presence of ischemic heart disease. The causal forest approach found some evidence of HTE (p = 0.124). When extracting root splits, the modal split was on serum potassium (mean applied threshold of 4.68 mmol/L). When dividing the patient population into subgroups based on the mean initial root threshold, risk differences in being alive at 28 days were 0.069 (95%CI [-0.032, 0.169]) and - 0.257 (95%CI [-0.368, -0.146]) with serum potassium ≤ 4.68 and > 4.68 respectively. CONCLUSIONS: The causal forest agreed with the data-adaptive and classical method of subgroup analysis in identifying HTE by serum potassium. Whilst classical and data-adaptive methods may identify sources of HTE, they do not immediately suggest subgroup splits which are clinically actionable. The extraction of root splits in causal forests is a novel approach to obtaining data-derived subgroups, to be further investigated.
3. Uncovering the pathogenic mechanisms of Cronobacter turicensis: A dual transcriptomics study using a zebrafish larvae model.
Dual RNA-seq in a zebrafish CNS invasion model mapped 1,432 bacterial and 80 host DE genes, revealing Cronobacter programs in denitrification/anaerobic respiration, chemotaxis, surface structures, and secretion systems alongside host inflammatory and NF-κB signaling.
Impact: Provides in vivo, simultaneous host–pathogen transcriptomes during CNS invasion by a neonatal sepsis pathogen, generating hypothesis-rich targets for intervention.
Clinical Implications: Although preclinical, identified bacterial pathways and host responses could inform diagnostics (e.g., biomarkers) and future anti-virulence or immunomodulatory strategies for neonatal sepsis/meningitis.
Key Findings
- Dual RNA-seq identified 1,432 differentially expressed bacterial genes and 80 host genes during CNS invasion.
- Cronobacter upregulated denitrification/anaerobic respiration, chemotaxis, surface structures, and secretion systems.
- Host zebrafish showed upregulation of inflammatory processes, cytokine-mediated signaling, and NF-κB pathways.
Methodological Strengths
- In vivo zebrafish model enabling CNS invasion and spatially targeted sampling.
- Simultaneous host–pathogen transcriptomics providing a systems view of infection.
Limitations
- Zebrafish larvae may not fully recapitulate human neonatal pathophysiology.
- Functional validation of candidate virulence factors and causality is not presented.
Future Directions: Validate key bacterial pathways and host targets functionally, and extend to mammalian neonatal models to bridge to clinical translation.
PURPOSE: Cronobacter (C.) is an emerging opportunistic pathogen representing a significant cause of mortality in neonatal patients with bacteremia and meningitis. The pathobiology of Cronobacter mediated meningitis has primarily been investigated using in vitro models. In this study, we used zebrafish to investigate in vivo the infection strategy of the sepsis/meningitis-causing strain C. turicensis z3032 (LMG 23827T) and the immune response of zebrafish larvae after central nervous system (CNS) invasion. Global gene expression profiles of both organisms were analyzed using RNA-Seq. METHODS: Injection of bacteria into the yolk sac resulted in proliferation of bacteria and translocation to different tissues, including the brain. Infected larval heads were obtained by microdissection and dual RNA-sequencing was performed on host and pathogen simultaneously. RESULTS: A total of 1432 genes in C. turicensis z3032 and 80 genes in zebrafish were found to be differentially expressed. Upregulated virulence genes in C. turicensis included those encoding for denitrification and anaerobic respiration, chemotaxis, surface structures, and secretion systems. In zebrafish, transcriptional changes included inflammatory processes, cytokine mediated signaling pathways, and NF-kB signaling as the primary GO categories for upregulated genes in response to infection. CONCLUSION: The dual transcriptomics approach provided a unique opportunity to create a comprehensive catalog of differentially expressed genes in both the pathogen and the host, offering new insights into the infection strategies of C. turicensis and zebrafish immune response.