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Related Experiment Video

Updated: Sep 11, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

Host-Microbe Multiomic Profiling Predicts Mortality in Sepsis.

Natasha Spottiswoode¹, Lucile P Neyton², Eran Mick^1,3

¹Department of Medicine, Division of Infectious Diseases.

American Journal of Respiratory and Critical Care Medicine

|August 11, 2025

Summary

This summary is machine-generated.

Understanding sepsis mortality requires considering both host and microbial factors. Integrated host-microbe models show promise for predicting sepsis outcomes in critically ill patients.

Keywords:

host response metagenomics mortality sepsis transcriptomics

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Last Updated: Sep 11, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

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Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Murine Oropharyngeal Aspiration Model of Ventilator-associated and Hospital-acquired Bacterial Pneumonia

Published on: June 28, 2018

Area of Science:

Critical care medicine
Infectious diseases
Genomics and transcriptomics

Background:

Sepsis is a major cause of death, characterized by a dysregulated host response to infection.
Previous sepsis research has largely overlooked the interplay between host and microbial factors, hindering a complete understanding of mortality drivers.

Purpose of the Study:

To identify host and microbial factors linked to sepsis mortality.
To develop prognostic classifiers for sepsis mortality prediction.

Main Methods:

Analysis of transcriptional profiling, proteomics, and metagenomics from whole blood and plasma of 321 critically ill adults.
Evaluation of associations between in-hospital mortality and host (gene expression, protein levels) and microbial (metagenomics) data.
Development of support vector machine-based prognostic classifiers.

Main Results:

Sepsis mortality correlated with increased expression of neutrophil degranulation genes, decreased T cell signaling genes, and elevated interleukin-8.
Higher microbial load and bacterial dominance were also associated with mortality.
An integrated host-microbe classifier achieved an AUC of 0.79, outperforming the APACHE-III score (AUC 0.69).

Conclusions:

Host and microbial factors are crucial in sepsis mortality.
An integrated host-microbe approach offers a novel and effective strategy for predicting sepsis mortality in critical illness.