Daily Sepsis Research Analysis
Analyzed 13 papers and selected 3 impactful papers.
Summary
Three studies advance sepsis science and care: a preclinical mechanistic paper identifies PHKA2 as a direct thymol target that reprograms macrophages and limits cardiomyocyte pyroptosis in septic cardiomyopathy; an ICU cohort uses unsupervised clustering to define three candidemia phenotypes with distinct outcomes; and a multicenter prospective study shows presepsin outperforms conventional biomarkers for sepsis diagnosis and 30-day mortality prediction.
Research Themes
- Mechanistic immunometabolic targeting in septic cardiomyopathy
- Data-driven phenotyping for invasive fungal sepsis
- Improved sepsis diagnosis and risk stratification using presepsin
Selected Articles
1. Targeting PHKA2 by Thymol alleviates sepsis induced cardiomyocyte pyroptosis via FOXA1/KLF4-mediated macrophage polarization.
In CLP septic mice and LPS-stimulated macrophage–cardiomyocyte co-cultures, thymol improved cardiac function, reduced macrophage TNF-α release, and limited cardiomyocyte pyroptosis by promoting M2 polarization. Mechanistically, thymol directly bound PHKA2 at Glu546, enhanced its kinase activity to phosphorylate FOXA1, and activated Klf4 transcription; macrophage-specific Klf4 deletion abrogated benefits.
Impact: This work uncovers a previously unrecognized PHKA2–FOXA1–KLF4 axis linking macrophage polarization to cardiomyocyte pyroptosis in septic cardiomyopathy and validates PHKA2 as a direct small-molecule target of thymol.
Clinical Implications: Findings nominate the PHKA2–FOXA1–KLF4 pathway as a therapeutic target for septic cardiomyopathy and support early translational studies of thymol or PHKA2 modulators with biomarker-driven patient selection.
Key Findings
- Thymol improved cardiac function and survival indicators in CLP septic mice and reduced cardiomyocyte pyroptosis by decreasing macrophage TNF-α secretion.
- Thymol promoted macrophage polarization toward an M2 phenotype; macrophage-specific Klf4 deletion abolished cardioprotective effects.
- Target deconvolution identified PHKA2 as a direct thymol target at Glu546, enhancing kinase activity, phosphorylating FOXA1, and driving nuclear PHKA2 binding to the Klf4 promoter to activate transcription.
Methodological Strengths
- Orthogonal target validation using proteomics, docking, CETSA, DARTS, and SPR to confirm direct PHKA2 binding
- Integration of in vivo CLP model, in vitro co-culture, and macrophage-specific Klf4 knockout for causal inference
Limitations
- Preclinical animal and cell models; human validation absent
- Dosing, pharmacokinetics/pharmacodynamics, and safety not reported; experiment-level sample sizes not detailed in abstract
Future Directions: Validate the PHKA2–FOXA1–KLF4 axis in human tissues, define PK/PD and safety profiles, perform dose–response and large-animal studies, and develop biomarker-guided early-phase clinical trials.
BACKGROUND: Sepsis-induced crosstalk between macrophages and cardiomyocytes is a critical pathogenic mechanism underlying cardiac dysfunction. However, clinically effective pharmacological interventions to mitigate the associated cytokine storm remain limited. PURPOSE: In this study, we aimed to investigate the role of thymol, a natural small-molecule bioactive compound, in regulating macrophage function in septic cardiomyopathy (SCM). METHODS: The following methodologies were employed to investigate the cardioprotective effects of thymol in sepsis and its underlying mechanisms. For in vivo studies, a septic mouse model was established via cecal ligation and puncture (CLP), followed by thymol administration. Cardiac function and macrophage responses were assessed using survival analysis, echocardiography, hematoxylin and eosin (HE) staining, TUNEL assay, flow cytometry, and immunofluorescence staining. In vitro, a co-culture system of RAW264.7 macrophages and HL-1 cardiomyocytes stimulated with lipopolysaccharide (LPS) was used to examine macrophage‑cardiomyocyte interactions. Mechanistic exploration involved proteomic sequencing, western blotting, quantitative polymerase chain reaction (qPCR), and biochemical analyses. Key signaling pathways were further validated by chromatin immunoprecipitation (ChIP) and co‑immunoprecipitation (Co‑IP). Additionally, Krüppel-like factor 4 (Klf4) knockout mice, RNA sequencing (RNA-Seq), liquid chromatography‑tandem mass spectrometry (LC‑MS/MS)‑based proteomics, molecular docking, cellular thermal shift assay (CETSA), drug affinity responsive target stability (DARTS) assay, and surface plasmon resonance (SPR) were utilized to identify the direct molecular target of thymol and elucidate its regulatory role in downstream signaling pathways. RESULTS: Thymol treatment ameliorated cardiac dysfunction and promoted macrophage polarization toward the M2 phenotype. Furthermore, thymol reduced the secretion of tumor necrosis factor-α (TNF-α) by macrophages, thereby inhibiting cardiomyocyte pyroptosis. Mechanistically, RNA-Seq analysis revealed that thymol upregulated KLF4 expression, and macrophage-specific deletion of Klf4 abolished the beneficial effects of thymol on heart failure. We also identified phosphorylase kinase alpha 2 (PHKA2) as a direct binding target of thymol at GLU 546. This interaction enhanced PHKA2 kinase activity, leading to phosphorylation of the downstream transcription factor forkhead box A1 (FOXA1). Importantly, phosphorylated PHKA2 translocated into the nucleus and bound to the promoter region of Klf4, facilitating its transcriptional activation. CONCLUSIONS: Our findings demonstrate that thymol alleviates the cytokine storm in SCM, highlighting its potential as a therapeutic candidate for this condition.
2. Decoding candidemia in critically ill patients: unsupervised clustering identifies three unique phenotypes.
In 492 ICU patients with candidemia across 16 French ICUs, factor analysis and hierarchical clustering identified three phenotypes with distinct sources and severities and markedly different 90-day mortality. Age, cirrhosis, and SAPS II independently predicted death, while echinocandin use and proven catheter-related source were protective; immunodepression per se was not associated with mortality.
Impact: Provides a reproducible, data-driven framework to stratify candidemia in the ICU, revealing actionable risk factors and protective features that can inform empiric therapy and source-control strategies.
Clinical Implications: Phenotype-based risk stratification may guide early echinocandin use, prioritize aggressive source control, and tailor monitoring intensity; patients with cirrhosis and higher SAPS II warrant heightened vigilance.
Key Findings
- Three candidemia phenotypes were identified: (1) severely immunosuppressed, high SAPS II; (2) elderly, cirrhosis, early digestive source; (3) younger, lower severity, catheter-related source.
- Ninety-day mortality differed significantly across phenotypes: 72.9% (phenotype 1), 70.4% (phenotype 2), and 50.3% (phenotype 3).
- Independent predictors of mortality were age, cirrhosis, and SAPS II; echinocandin use and proven catheter-related source were protective; immunodepression was not independently associated.
Methodological Strengths
- Multicenter ICU cohort with substantial sample size across 16 centers
- Rigorous unsupervised learning (FAMD + HCPC) with survival analyses (KM, Cox) to link phenotypes to outcomes
Limitations
- Retrospective design with potential residual confounding and missing data
- Lack of external validation and possible limited generalizability beyond French ICUs
Future Directions: Externally validate phenotypes, integrate microbiology and genomic data, and test phenotype-targeted management strategies in prospective trials.
BACKGROUND: Candidemia displays significant clinical heterogeneity in critically ill patients. This study aimed to identify distinct clinical phenotypes and to assess their association with 90-day mortality. METHODS: We conducted a multicenter retrospective cohort study of 492 intensive care unit (ICU) patients with candidemia from 16 French ICUs (2015-2023). We performed a factor analysis of mixed data (FAMD) incorporating both categorical and continuous baseline variables, followed by hierarchical clustering on principal components (HCPC). Survival analysis was performed with Kaplan-Meier curves and Cox proportional hazards models. RESULTS: Overall, 90-day mortality for the 492 patients (median age: 64 years, 69.1% male) with candidemia was 62.6%. Three different phenotypes emerged from FAMD followed by HCPC: Phenotype 1 (n = 70, 14.2%) comprised patients with severe immunosuppression, mostly due to hematological malignancies (82.9%), and high severity scores (SAPS II:70); Phenotype 2 (n = 223, 45.3%) corresponded to elderly cirrhotic patients (19.3%) with early-onset digestive candidemia; Phenotype 3 (n = 199, 40.5%) comprised younger patients with lower severity scores and catheter-related candidemia. Mortality differed significantly between phenotypes: 72.9% (Phenotype 1), 70.4% (Phenotype 2), and 50.3% (Phenotype 3) (p < 0.001). Independent predictors of mortality included age (aHR: 1.01, 95% CI: 1.00-1.02; p = 0.003), cirrhosis (aHR: 1.90, 95% CI: 1.39-2.60; p < 0.001), SAPS II (aHR: 1.01, 95% CI: 1.01-1.02; p < 0.001), echinocandin use (aHR = 0.49, 95% CI: 0.39-0.63; p < 0.001) and proven catheter-related candidemia (protective; aHR: 0.48, 95% CI: 0.33-0.69; p < 0.001). Immunodepression was not associated with mortality. CONCLUSION: Unsupervised clustering identified three clinically different candidemia phenotypes with different outcomes. Cirrhosis, higher age and illness severity were associated with mortality, whereas a catheter-related source of infection was protective.
3. Presepsin outperforms conventional biomarkers for Sepsis diagnosis and 30-day mortality prediction: A multicenter prospective study in a north African ICU cohort.
Across two ICUs (n=184), presepsin showed superior diagnostic accuracy over PCT, CRP, IL-6/IL-8 to distinguish sepsis and septic shock and was the strongest 30-day mortality predictor (AUC 0.821; cut-off 2721.52 pg/mL). In multivariable analysis, presepsin was the only independent predictor of mortality.
Impact: Head-to-head, prospective comparison establishes presepsin as a superior diagnostic and prognostic biomarker and provides a validated mortality cut-off in a critically ill North African cohort.
Clinical Implications: Supports integrating presepsin into ICU sepsis diagnostic panels and risk stratification algorithms, potentially enabling earlier targeted therapy and informed resource allocation.
Key Findings
- Presepsin achieved the highest diagnostic AUCs for sepsis vs pulmonary infection (AUC 0.941) and septic shock vs pulmonary infection (AUC 0.959), outperforming PCT, CRP, IL-6/IL-8 by DeLong’s test.
- Presepsin best predicted 30-day mortality (AUC 0.821) with a validated cut-off of 2721.52 pg/mL; non-survivors had much higher levels (8315.8 vs 1453.5 pg/mL).
- In multivariable logistic regression, presepsin was the only independent predictor of 30-day mortality.
Methodological Strengths
- Prospective multicenter enrollment with standardized single timepoint sampling within 6 hours of admission
- Head-to-head biomarker comparison in the same cohort with ROC/DeLong testing and multivariable adjustment
Limitations
- Modest sample size and regional cohort may limit generalizability
- Single baseline measurement without longitudinal dynamics or external validation
Future Directions: Validate cut-offs across diverse populations, assess serial presepsin kinetics, and integrate into clinical decision pathways with impact-on-outcome trials.
BACKGROUND: Sepsis remains a leading cause of intensive care unit (ICU) mortality, necessitating the identification of accurate biomarkers for diagnosis and prognosis. Presepsin (sCD14-ST), generated through monocyte/macrophage phagocytosis of bacterial components, offers infection-specific signals. This study compared the diagnostic and prognostic performance of with of procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-8 (IL-8) within the same patient cohort. PATIENTS AND METHODS: This prospective multicenter cohort study enrolled 184 participants across two Algerian tertiary ICUs: 60 healthy controls, 30 patients with pulmonary infection, 46 with sepsis, and 48 with septic shock (Sepsis-3 criteria). All biomarkers were measured using a single blood sample collected within six hours of admission. Diagnostic accuracy was assessed using ROC curve analysis and DeLong's test, and independent predictors of 30-day mortality were identified using binary logistic regression. RESULTS: All biomarkers differed significantly between the groups (p < 0.001). Presepsin achieved the highest diagnostic accuracy for septic shock versus healthy controls (AUC = 0.880) and was the best discriminator between sepsis and pulmonary infection (AUC = 0.941) and between septic shock and pulmonary infection (AUC = 0.959), significantly outperforming all the comparators by DeLong's test. The 30-day mortality rate was 51.1%. Non-survivors had markedly higher presepsin levels than survivors (8315.8 vs. 1453.5 pg/mL; p < 0.001). Presepsin yielded the best prognostic AUC for 30-day mortality (AUC = 0.821; cut-off: 2721.52 pg/mL), whereas PCT, CRP, IL-8, and WBC showed near-chance discrimination. In multivariable regression analysis, presepsin was the only independent predictor of mortality. CONCLUSION: Presepsin outperformed all evaluated biomarkers for sepsis diagnosis and 30-day mortality prediction, emerging as the sole independent prognostic marker after adjustment for confounding factors. These findings support its integration into ICU sepsis panels and provide the first validated presepsin mortality cut-off in a critically ill North African population.