AI-driven multi-omics profiling of sepsis immunity in the digestive system

Abstract

Sepsis is a life-threatening systemic inflammatory syndrome characterized by a complex immune biphasic imbalance. Monitoring of immune status has not yet been implemented in clinical practice due to lack of direct therapeutic utility. Immune dysregulation in sepsis patients is heterogeneous and dynamic. The use of artificial intelligence to drive the integration of multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics, enables biomarker monitoring and immunoassays. This review revisits gut microbes as critical illness drivers and important regulatory players in sepsis immunity. It focuses on the synthesis of clinical biomarkers of sepsis and parameters related to the gut microenvironment with the help of artificial intelligence, enabling marker identification, immunostratification and predictive modeling. This feasible clinical decision-making algorithm based on "combinatorial typing" is an important tool for realizing precision medicine for sepsis patients.

Keywords: AI; biomarkers; gut microbiota; immune response; multi-omics; sepsis.

Figure 1

AI-driven multi-omics profiling for biomarker discovery in digestive system-associated sepsis. (A) Digestive infection and onset of sepsis. (B) AI-driven multi-omics analysis workflow: Demonstrates the analytical process of multi-omics technologies: genomics (top left, DNA structure), transcriptomics (top right, single-cell RNA sequencing technology), proteomics (bottom left, protein transcription process and representative elevated cytokines in sepsis) and metabolomics (bottom right, effect of ureide metabolic pathways on T cell function). The centrally located horizontal conveyor belt symbolizes the AI-driven data analysis process, including sample collection (petri dishes, patient blood), AI computational analysis (represented by a computer) and output of the analysis results (multi-omics data with pathogenic bacteria icon representing the identified markers). (C) Biomarker discovery and clinical application.