# Integrated Host-Microbe Metagenomics for Sepsis Pathogen Surveillance, Subphenotyping and Outcome Prediction

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $662,381

## Abstract

SUMMARY
Sepsis is a leading cause of death in hospitalized patients and involves a dysregulated host inflammatory
response to infection. Despite decades of clinical trials, no new, effective treatments have been identified, and
mortality rates remain unacceptably high. Several factors have contributed to this impasse, starting with the
complex challenge of accurately detecting the microbial pathogens precipitating sepsis. Heterogeneity across
patient populations is a second key barrier to the development of effective treatment strategies, given that sepsis
can be caused by a broad diversity of microbes, and immune responses vary widely between patients. In
addition, while disease trajectories and interventional needs differ dramatically between individuals, no
prognostic tools exist that account for both the host response and pathogen, the two primary drivers of sepsis
pathobiology. Here we seek to address key gaps in our understanding and treatment of sepsis by leveraging
blood and plasma specimens collected from 1563 patients enrolled across 60 medical centers in the CLOVERS
trial, the largest study of septic shock to date. Aim 1 seeks to identify known and novel pathogens responsible
for septic shock using metagenomic sequencing of host and microbe, a novel culture-independent method for
studying sepsis. Aim 2 will combine host gene expression and microbial metagenomic data to discover novel
sepsis subphenotypes and evaluate their associations with clinical outcomes. Aim 3 will use machine learning
to develop a multiomic classifier that predicts sepsis mortality by incorporating host transcriptomic, microbial
metagenomic, and clinical data. Together, these aims will accelerate our understanding of sepsis pathogenesis,
and could redefine our diagnostic, therapeutic, and prognostic strategies, ultimately improving patient outcomes.

## Key facts

- **NIH application ID:** 10943602
- **Project number:** 1R01AI185511-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Charles Langelier
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $662,381
- **Award type:** 1
- **Project period:** 2024-06-21 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10943602

## Citation

> US National Institutes of Health, RePORTER application 10943602, Integrated Host-Microbe Metagenomics for Sepsis Pathogen Surveillance, Subphenotyping and Outcome Prediction (1R01AI185511-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10943602. Licensed CC0.

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