Abstract
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, in vivo validation was conducted using an LPS-induced sepsis mouse model. 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. In vivo, Gyg1 was markedly upregulated in septic mice, and LNP-mediated siRNA knockdown of Gyg1 significantly improved survival in the LPS model. Mechanistically, Gyg1 knockdown significantly reduced glycogen content in myeloid cells, attenuated IL-6 and TNF-α production, alleviated LPS-induced neutrophil, and modestly decreased CD40 expression in monocytes and dendritic cells. These results collectively suggest that Gyg1 regulates metabolic fueling of inflammatory activation and intercellular communication during sepsis. 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 in vivo, thereby highlighting its potential as a novel therapeutic target for sepsis.