On August 11, researchers from the University of California, San Francisco, published a paper in the Annals of the American Thoracic Society titled “Host-Microbe Multiomic Profiling Predicts Mortality in Sepsis“, which establishes a mortality prediction model for sepsis through the integration of host-microbe multiomics analysis, providing a new perspective for understanding the complex pathological mechanisms of sepsis.
The research team conducted a prospective analysis of 321 critically ill patients, collecting whole blood samples within 24 hours of admission for transcriptome sequencing, plasma proteomics analysis, and microbial metagenomic sequencing. The study found that deceased patients exhibited significantly higher expression of neutrophil degranulation-related genes (average upregulation of 2.3 times), lower expression of T cell signaling pathway genes (average downregulation of 1.8 times), and elevated levels of IL-8 (P<0.001). Notably, microbial analysis revealed a 43% increase in bacterial biomass in deceased patients, along with a higher relative abundance of pathogenic bacteria.
Based on these findings, the research team developed two predictive models: a metagenomic classifier integrating host-microbe features (AUC=0.79) and a classifier using only host transcriptome data (AUC=0.75), both of which significantly outperformed the clinically used APACHE-III score (AUC=0.69, P<0.05). This multi-omics approach also demonstrated predictive value in patients with culture-negative sepsis, suggesting its potential to overcome the limitations of traditional microbial testing.
This achievement represents three major breakthroughs: revealing for the first time the synergistic effects of host immune responses and microbial dynamics; establishing predictive tools that surpass clinical indicators; and providing a basis for developing targeted therapies against excessive neutrophil activation. The researchers particularly emphasized the association between T cell functional suppression and increased bacterial load in the model, which may explain the susceptibility of patients during the immunosuppressive phase.