Daily Sepsis Research Analysis
Three papers stand out today: a meta-analysis of randomized trials shows anticoagulation in sepsis yields a modest survival benefit at the cost of increased bleeding; a proteomics study demonstrates first-in-disease integrated proteome, N-glycoproteome, and phosphoproteome quantification from dried blood with longitudinal sepsis sampling; and a multi-omics analysis identifies immune-metabolic heterogeneity in sepsis, highlighting GYG1 as a mechanistic driver and potential therapeutic target.
Summary
Three papers stand out today: a meta-analysis of randomized trials shows anticoagulation in sepsis yields a modest survival benefit at the cost of increased bleeding; a proteomics study demonstrates first-in-disease integrated proteome, N-glycoproteome, and phosphoproteome quantification from dried blood with longitudinal sepsis sampling; and a multi-omics analysis identifies immune-metabolic heterogeneity in sepsis, highlighting GYG1 as a mechanistic driver and potential therapeutic target.
Research Themes
- Therapeutics in sepsis: anticoagulation risk–benefit
- Field-deployable multi-omics from dried blood for sepsis phenotyping
- Immunometabolic drivers of sepsis and target discovery (GYG1)
Selected Articles
1. Mass Spectrometry-Based Quantification of Proteins and Post-Translational Modifications in Dried Blood: Longitudinal Sampling of Patients With Sepsis in Tanzania.
This proof-of-concept study establishes an integrated mass-spectrometry workflow to quantify proteome, N-glycoproteome, and phosphoproteome from dried blood in sepsis, enabling longitudinal phenotyping without plasma separation. It captures acute-phase and neutrophil-driven inflammatory signatures that partially resolve by 1 month and correlates with clinical markers; datasets are publicly available.
Impact: It introduces a scalable, field-friendly multi-omics platform for sepsis using dried blood, addressing pre-analytical variability and enabling large-scale biomarker discovery and monitoring, especially in resource-limited settings.
Clinical Implications: Dried blood microsampling coupled with MS could support decentralized sepsis diagnostics and trajectory monitoring, enabling serial sampling and transport without cold chain; targeted panels derived from these data may translate into practical assays.
Key Findings
- Integrated quantification of ~2000 proteins and ~8000 PTMs (N-glycopeptides and phosphopeptides) from dried blood was achieved across 96 samples with ~1.5 h LC-MS/MS per sample.
- Longitudinal profiles showed acute-phase response and neutrophil inflammation signatures at presentation that partially resolved by 28–42 days.
- Multiple analytes correlated with CRP, WBC counts, and Universal Vital Assessment severity scores.
- The approach avoided plasma/cell separation, reducing pre-analytical variability, and datasets were deposited (ProteomeXchange PXD060377).
Methodological Strengths
- First integrated proteome, N-glycoproteome, and phosphoproteome quantification from dried blood in a disease context with plate-based, rapid processing.
- Longitudinal sampling in a real-world, resource-limited setting with public data sharing enabling reproducibility.
Limitations
- Single-country, modest sample size (38 patients) limits generalizability and power for clinical outcome associations.
- Lack of direct head-to-head comparison with matched plasma-based assays; clinical endpoints (e.g., mortality) were not analyzed.
Future Directions: Validate candidate protein/PTM panels in larger, multi-center cohorts; benchmark against plasma/serum assays; develop targeted MS or immunoassays for clinical deployment.
The proteomic analysis of blood is routine for disease phenotyping and biomarker development. Blood is commonly separated into soluble and cellular fractions. However, this can introduce pre-analytical variability, and analysis of a single component (which is common) may ignore important pathophysiology. We have recently developed methods for the facile processing of dried blood for mass spectrometry-based quantification of the proteome, N-glycoproteome, and phosphoproteome. Here, we applied this approach to 38 patients in Tanzania who presented to the hospital with sepsis. Blood was collected on Mitra devices at presentation and 1, 3, and 28-42 days post-enrollment. Processing of 96 total samples was performed in plate-based formats and completed within 2 days. Approximately 2000 protein groups and 8000 post-translational modifications were quantified in 3 LC-MS/MS runs at ∼1.5 h per sample. Analysis of differential abundance revealed blood proteome signatures of acute phase response and neutrophilic inflammation that partially resolved at the 28-42 day timepoint. Numerous analytes correlated with clinical laboratory values for c-reactive protein and white blood cell counts, as well as the Universal Vital Assessment illness severity score. These datasets serve as proof-of-concept for large-scale MS-based (sub)phenotyping of disease using dried blood and are available via the ProteomeXchange consortium (PXD060377). SUMMARY: For the first time, we report the integrated quantitative analysis of proteins, N-glycopeptides, and phosphopeptides from dried blood specimens in a disease context. Sample collection on Mitra devices is easily incorporated into existing biobanking protocols and provides a convenient solution for sample storage and preparation for downstream mass spectrometry analysis. Signatures of sepsis are reflected in each of the analyzed proteomes and decline between presentation to the hospital and 1 month post. In addition to well-described markers, these analyses identify mediators of inflammation and innate immune signaling that would be missed in the more common analysis of cell-free plasma.
2. Efficacy and safety of anticoagulant therapy in patients with sepsis: a meta-analysis of randomized controlled trials.
Across 18 RCTs (n=8053), anticoagulation reduced 28/30-day all-cause mortality in sepsis (RR 0.92) but increased bleeding (RR 1.32). In sepsis with baseline DIC, anticoagulation improved DIC regression yet did not significantly reduce mortality.
Impact: Provides high-level evidence to inform guideline debates on anticoagulation in sepsis, quantifying a modest survival benefit alongside increased bleeding risk and clarifying effects in DIC.
Clinical Implications: Anticoagulation may be considered in selected sepsis patients with careful bleeding risk assessment and monitoring; DIC-specific strategies should prioritize regression while recognizing uncertain mortality effects.
Key Findings
- Overall 28/30-day mortality reduction with anticoagulants vs placebo/no therapy (RR 0.92, 95% CI 0.86–0.98; P=0.02).
- In baseline DIC subgroup, significant improvement in DIC regression (RR 1.62, 95% CI 1.32–2.00) but no significant mortality reduction (RR 0.87, 95% CI 0.62–1.22).
- Bleeding complications increased with anticoagulation (RR 1.32, 95% CI 1.16–1.49).
Methodological Strengths
- Inclusion limited to randomized controlled trials with predefined efficacy and safety endpoints.
- Subgroup analysis of patients with disseminated intravascular coagulation provides clinically relevant granularity.
Limitations
- Heterogeneity in anticoagulant classes, dosing, and sepsis definitions across trials; potential variability in bleeding adjudication.
- Lack of individual patient data limits precision for risk stratification and interaction analyses.
Future Directions: Conduct IPD meta-analyses and biomarker-enriched RCTs to identify patients most likely to benefit while minimizing bleeding; harmonize outcome definitions and anticoagulation protocols.
BACKGROUND: Coagulation dysfunction significantly impacts sepsis prognosis. Standardized methods for evaluating anticoagulant efficacy and safety in this population remain lacking. This study aimed to assess the efficacy and safety of anticoagulant therapy in sepsis patients. METHODS: We systematically searched PubMed, Embase, and Cochrane Library for randomized controlled trials (RCTs) comparing anticoagulants against placebo/no treatment in sepsis patients. The reduction in 28/30-day all-cause mortality or the regression of disseminated intravascular coagulation (DIC) was regarded as efficacy endpoints, while bleeding complications constituted the most prevalent adverse events. RESULTS: Eighteen RCTs involving 8053 patients were included. Anticoagulant therapy demonstrated an 8% mortality risk reduction versus placebo (relative risk [RR] 0.92, 95% CI 0.86-0.98; P = 0.02). In six studies of baseline DIC patients, anticoagulants showed non-significant mortality reduction (RR 0.87, 95% CI 0.62-1.22; P = 0.42) but significantly enhanced DIC regression (RR 1.62, 95% CI 1.32-2.00; P < 0.00001). Anticoagulants increased bleeding risk (RR 1.32, 95% CI 1.16-1.49; P < 0.0001). CONCLUSION: Anticoagulant therapy confers survival benefit in the overall sepsis population despite increased bleeding risk. While improving DIC regression in sepsis-associated DIC, mortality reduction in this subgroup lacked statistical significance. Further research should clarify anticoagulants' role in DIC-specific sepsis management.
3. Comprehensive analysis of metabolism-related genes in sepsis reveals metabolic-immune heterogeneity and highlights GYG1 as a potential therapeutic target.
An integrative analysis of bulk and single-cell transcriptomes defined an immune-metabolic risk score in sepsis and identified GYG1 as the strongest predictor enriched in myeloid cells. High-risk patients displayed neutrophil-dominant, lymphocyte-suppressed profiles; LNP-siRNA targeting of GYG1 reduced myeloid glycogen availability and inflammatory output in preclinical models.
Impact: Links metabolic programming to immune dysregulation in sepsis, delivering both a prognostic signature and a mechanistically plausible target (GYG1) with proof-of-concept silencing.
Clinical Implications: A metabolic risk score may guide prognostication and trial stratification; GYG1 inhibition represents a candidate immunometabolic therapy pending validation of efficacy and safety in humans.
Key Findings
- Two sepsis subgroups defined by metabolism-related genes showed divergent immune infiltration; high-risk was neutrophil-dominant and lymphocyte-suppressed.
- A five-gene metabolic risk score (ALPL, CYP1B1, GYG1, OLAH, VNN1) predicted outcomes and was externally validated.
- GYG1 had the strongest predictive performance and was highly expressed in monocytes, neutrophils, and proliferating myeloid cells.
- High-risk patients exhibited intensified monocyte–dendritic cell interactions and enrichment of neutrophil degranulation programs.
- LNP-siRNA targeting GYG1 reduced glycogen availability and inflammatory output in myeloid cells, improving disease outcomes in preclinical evaluation.
Methodological Strengths
- Integration of bulk transcriptomics with single-cell RNA-seq, cell–cell communication analyses, and external validation.
- Mechanistic anchoring by targeting GYG1 with LNP-siRNA to test causal relevance.
Limitations
- Abstract lacks details on experimental models, sample sizes, and specific clinical endpoints linked to the risk score.
- Translational applicability of GYG1 inhibition remains to be established in humans; potential overfitting cannot be excluded.
Future Directions: Prospective validation of the risk score across centers; mechanistic dissection of GYG1 in human myeloid cells; early-phase trials assessing safety and pharmacodynamics of GYG1-targeted approaches.
BACKGROUND: Sepsis is a life-threatening syndrome characterized by dysregulated host immune responses, yet the metabolic drivers of immune dysfunction remain poorly understood. METHODS: Here we systematically profiled metabolism-related genes (MRGs) in sepsis using bulk transcriptomic data and stratified patients into two subgroups with distinct immune infiltration profiles by MRGs, as assessed by CIBERSORT and single-cell RNA-seq integration. Machine learning identified five hub metabolic genes for constructing a metabolic risk score, whose prognostic relevance was robustly validated in an external cohort. Single cell analyses, cell-cell communication, and cell-type-specific differential expression analyses were performed to dissect the immunological context. Finally, RESULTS: Patients in the high metabolic risk group exhibited a neutrophil-dominant and lymphocyte-suppressed immune landscape, consistent across bulk and single-cell analyses. Among the five hub genes (ALPL, CYP1B1, GYG1, OLAH, VNN1), GYG1 demonstrated the strongest predictive performance and was highly expressed in monocytes, neutrophils, and proliferating myeloid cells. High-risk patients displayed intensified monocyte-dendritic cell interactions and transcriptional programs enriched in neutrophil degranulation pathways. CONCLUSIONS: This integrative multi-omics study established a robust immune-metabolic risk score system to predict sepsis patient outcomes and identified GYG1 as a metabolic driver of innate immune hyperactivation. Targeting GYG1 via LNP-siRNA delivery reduces glycogen availability and inflammatory output in myeloid cells, mitigating immune overactivation and improving disease outcomes