Comprehensive analysis of metabolism-related genes in sepsis reveals metabolic– immune heterogeneity and highlights GYG1 as a potential therapeutic target

Jie Zheng¹Kangjie Qin¹Xiaoqin Wang²Banghai Feng³Yuting Zhang¹Yiyu Wang¹Qin Han⁴Qiuyu Dai¹Xinxin Liu¹Kun Yu^1*Song Qin^1,5*

• ¹Affiliated Hospital of Zunyi Medical University, Zunyi, China
• ²The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
• ³Zunyi Hospital of Traditional Chinese Medicine, Zunyi, China
• ⁴Kweichow Moutai Hospital, renhuai, China
• ⁵Department of Rehabilitation Medicine, Affiliated Hospital of Zunyi Medical College, Zunyi, China

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.