Daily Sepsis Research Analysis
Multi-omic analyses advanced sepsis endotyping and mechanistic understanding: a JCI study linked inflammatory phenotypes in ARDS/sepsis to four mortality-associated molecular signatures centered on mitochondrial dysfunction, validated in an independent sepsis cohort. Complementary work identified mitophagy-related biomarkers and molecular subtypes with functional validation of NUP93, while a real-world study showed blood mNGS substantially increases pathogen detection and guides therapy in hemat
Summary
Multi-omic analyses advanced sepsis endotyping and mechanistic understanding: a JCI study linked inflammatory phenotypes in ARDS/sepsis to four mortality-associated molecular signatures centered on mitochondrial dysfunction, validated in an independent sepsis cohort. Complementary work identified mitophagy-related biomarkers and molecular subtypes with functional validation of NUP93, while a real-world study showed blood mNGS substantially increases pathogen detection and guides therapy in hematologic malignancy patients with sepsis after antibiotics.
Research Themes
- Mitochondria-centric biology and endotypes in sepsis/ARDS
- Mitophagy-related biomarkers and molecular subtyping
- Metagenomic next-generation sequencing to guide therapy in immunocompromised sepsis
Selected Articles
1. Longitudinal multi-omic signatures of ARDS and sepsis inflammatory phenotypes identify pathways associated with mortality.
Integrating plasma metabolomics and whole-blood transcriptomics from 160 ARDS patients stratified by inflammatory phenotype, the authors defined four mortality-associated molecular signatures that largely converged on mitochondrial dysfunction. These longitudinal signatures persisted to Day 2 and were validated in an independent critically ill sepsis cohort, revealing phenotype-specific and phenotype-independent pathways that may inform precision therapies.
Impact: This paradigm-advancing, validated multi-omic work links clinical inflammatory phenotypes to mechanistic, mortality-associated pathways centered on mitochondrial dysfunction, directly informing endotype-driven interventions in sepsis/ARDS.
Clinical Implications: Supports endotype-based risk stratification and prioritizes mitochondrial bioenergetics, fatty acid oxidation, interferon signaling, and redox pathways as therapeutic targets; enables longitudinal biomarker panels for early prognosis.
Key Findings
- Four mortality-associated molecular signatures were identified, including innate immune activation with glycolysis, hepatic/immune dysfunction with impaired fatty acid β-oxidation, interferon suppression with altered mitochondrial respiration, and redox/proliferation pathways.
- Signatures persisted from Day 0 to Day 2 and were validated in an independent critically ill sepsis cohort (EARLI).
- Within-phenotype analyses revealed distinct mortality-associated pathways, indicating both phenotype-specific and phenotype-independent biology centered on mitochondrial dysfunction.
Methodological Strengths
- Prospective trial-derived cohort with random selection by high phenotype probability and longitudinal sampling (Day 0, Day 2).
- Integrated untargeted metabolomics and transcriptomics with external validation (EARLI) using advanced multi-modal factor analysis (MEFISTO).
Limitations
- Secondary analysis of trial biospecimens with modest sample size (n=160) may limit generalizability.
- Causal inference is limited; therapeutic targets require interventional validation.
Future Directions: Prospective, multi-center validation of signature-based risk models and interventional trials targeting mitochondrial bioenergetics, fatty acid oxidation, and interferon/redox pathways in endotype-enriched populations.
BACKGROUND: Critically ill patients with acute respiratory distress syndrome (ARDS) and sepsis exhibit distinct inflammatory phenotypes with divergent clinical outcomes, but the underlying molecular mechanisms remain poorly understood. These phenotypes, derived from clinical data and protein biomarkers, were associated with metabolic differences in a pilot study. METHODS: We performed integrative multi-omics analysis of blood samples from 160 ARDS patients in the ROSE trial, randomly selecting 80 patients from each latent class analysis-defined inflammatory phenotype (Hyperinflammatory and Hypoinflammatory) with phenotype probability >0.9. Untargeted plasma metabolomics and whole blood transcriptomics at Day 0 and Day 2 were analyzed using multi-modal factor analysis (MEFISTO). The primary outcome was 90-day mortality, with validation in an independent critically ill sepsis cohort (EARLI). RESULTS: Multi-omics integration revealed four molecular signatures associated with mortality: (1) enhanced innate immune activation coupled with increased glycolysis (associated with Hyperinflammatory phenotype), (2) hepatic dysfunction and immune dysfunction paired with impaired fatty acid beta-oxidation (associated with Hyperinflammatory phenotype), (3) interferon program suppression coupled with altered mitochondrial respiration (associated with Hyperinflammatory phenotype), and (4) redox impairment and cell proliferation pathways (not associated with inflammatory phenotype). These signatures persisted through Day 2 of trial enrollment. Within-phenotype analysis revealed distinct mortality-associated pathways in each group. All molecular signatures were validated in the independent EARLI cohort. CONCLUSIONS: Inflammatory phenotypes of ARDS reflect distinct underlying biological processes with both phenotype-specific and phenotype-independent pathways influencing patient outcomes, all characterized by mitochondrial dysfunction. These findings suggest potential therapeutic targets for precise treatment strategies in critical illness. FUNDING: This work is the result of NIH funding.
2. Integrated multi-omics of mitophagy-related molecular subtype characterization and biomarker identification in sepsis.
Integrative analyses identified four mitophagy-associated gene biomarkers (RPL18, PRPF8, NUP93, CUL1) with excellent diagnostic performance and defined sepsis molecular subtypes with distinct immune landscapes. Functional experiments showed NUP93 overexpression rescues LPS-induced mitophagy impairment by restoring PINK1 and LC3B, linking biomarkers to mechanism.
Impact: Bridges computational endotyping with bench validation to position mitophagy as a diagnostic and therapeutic axis in sepsis, offering actionable biomarker candidates and a stratification framework.
Clinical Implications: Proposes a high-AUC diagnostic panel and mitophagy-informed subtypes that could guide precision diagnostics and selection of candidates for mitophagy-targeted therapies.
Key Findings
- Machine learning identified four MAG biomarkers (RPL18, PRPF8, NUP93, CUL1) with individual AUCs 0.957–0.975 and a nomogram AUC of 0.990.
- Consensus clustering based on MAG stratified sepsis into molecular subtypes with distinct immune landscapes and pathways.
- NUP93 overexpression restored PINK1 and LC3B levels in LPS-stimulated macrophages, rescuing mitophagy impairment in vitro.
Methodological Strengths
- Integration of bulk and single-cell transcriptomics with WGCNA, ssGSEA, ESTIMATE, and consensus clustering.
- Functional validation linking biomarker (NUP93) to mitophagy rescue in LPS-stimulated macrophages.
Limitations
- Predominantly in silico, retrospective dataset integration without prospective clinical validation.
- Functional assays limited to in vitro macrophage models; no in vivo validation or clinical assay readiness.
Future Directions: Prospective, multi-center validation of biomarker panels and subtypes, development of rapid assays, and preclinical/in vivo testing of mitophagy-targeted interventions.
As a life-threatening condition driven by dysregulated host responses to infection, sepsis suffers from high mortality and heterogeneity. Mitophagy is the selective removal of damaged mitochondria, which is implicated in mitigating sepsis-related damage. The systematic identification and validation of key mitophagy-associated genes (MAG) for sepsis diagnosis, stratification, and immune modulation are lacking. Bulk transcriptomic datasets were integrated for differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning. We analyzed single-cell RNA-seq data to map MAG expression, performed immune infiltration analyses by ESTIMATE, single-sample Gene Set Enrichment Analysis (ssGSEA) and conducted consensus clustering based on MAG for molecular subtyping. As a screened MAG, the role of NUP93 was functionally validated in mitophagy using lipopolysaccharide (LPS)-stimulated RAW264.7 cells with adenoviral overexpression. Integration of machine learning identified four MAG biomarkers (RPL18, PRPF8, NUP93, CUL1) with high diagnostic power (individual AUCs 0.957-0.975, nomogram AUC = 0.990). Consensus clustering based on these MAG stratified sepsis patients into distinct molecular subtypes with differing MAG expression, immune landscapes, and underlying immune-related pathways. NUP93 overexpression in vitro rescued LPS-induced mitophagy impairment by restoring mitochondrial PINK1 and LC3B levels. This study identifies RPL18, PRPF8, NUP93, and CUL1 as robust diagnostic MAG biomarkers for sepsis, demonstrates their utility in defining molecular subtypes with divergent immune microenvironments, and provides functional evidence that NUP93 promotes mitophagy during sepsis, offering novel tools for precision diagnosis and insights for targeted therapeutic strategies.
3. Application of metagenomic next-generation sequencing technology in hematologic malignancy patients with sepsis following antibiotic use.
In 119 hematologic malignancy patients with sepsis unresponsive to ≥3 days of antibiotics, blood mNGS detected pathogens far more frequently than culture (89.36% vs 25.53%) and prompted antimicrobial regimen changes in 39.49% of cases; antitumor therapy–related granulocytopenia was a risk factor for polymicrobial infection.
Impact: Demonstrates real-world utility of blood mNGS to guide therapy in immunocompromised sepsis where cultures underperform post-antibiotics, highlighting both clinical impact and interpretive challenges.
Clinical Implications: Supports incorporating blood mNGS to augment pathogen detection and tailor antimicrobials in complex, culture-negative or polymicrobial sepsis, with stewardship frameworks to manage specificity and contamination concerns.
Key Findings
- mNGS positivity for bacteria/fungi was 89.36% versus 25.53% for blood culture.
- Therapeutic modifications based on mNGS occurred in 39.49% of patients.
- Agreement with culture was low (kappa −0.202), with reported sensitivity 58.33% and specificity 0% relative to reference used.
- Granulocytopenia from antitumor therapy was a high-risk factor for polymicrobial infections.
Methodological Strengths
- Head-to-head comparison of simultaneous blood mNGS and culture in a high-risk, clinically relevant population.
- Actionability assessed via documented antimicrobial regimen changes.
Limitations
- Single-center design with no definitive gold standard and very low agreement with culture complicates performance interpretation.
- Potential contamination/background signals and lack of outcome-adjusted utility (e.g., mortality/LOS) limit conclusions.
Future Directions: Establish composite clinical/microbiologic adjudication standards, define quantitative thresholds for actionability, integrate host-response markers, and test mNGS-guided care pathways in randomized trials.
BACKGROUND: Metagenomic next-generation sequencing (mNGS) has been widely applied in clinical pathogen detection; however, its utility in patients with hematologic malignancies complicated by sepsis after antibiotic therapy requires further investigation. METHODS: A total of 119 patients with hematologic malignancies complicated by sepsis, who had received antibiotic treatment for ≥ 3 days without clinical improvement, were enrolled in the study. All patients underwent simultaneous blood culture and mNGS analysis. The diagnostic value of mNGS and its impact on optimizing anti-infective therapy were evaluated. RESULTS: For the detection of bacterial and fungal pathogens, mNGS demonstrated a significantly higher positive rate compared to blood culture (89.36% vs. 25.53%). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mNGS were 58.33%, 0.00%, 16.67%, and 0.00%, respectively. The overall agreement rate between the two methods was 13.21% (kappa = -0.202). Based on mNGS results, anti-infective treatment regimens were modified in 47 patients (39.49%). Granulocytopenia related to antitumor therapy was identified as a high-risk factor for polymicrobial infections ( CONCLUSIONS: Patients with hematologic malignancies and sepsis, particularly those with antitumor therapy-induced granulocytopenia, are at increased risk for polymicrobial infections. Blood mNGS offers a rapid and comprehensive approach to pathogen identification, showing significant potential for guiding anti-infective therapy in this patient population. CLINICAL TRIAL NUMBER: Not applicable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-025-12111-x.