Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia

mSystems

卷:11

Research Article

Samantha Franklin^1,2  Pranoti Sahasrabhojane^3,4  Tomo Hayase⁴  Eiko Hayase⁴  Chia-Chi Chang⁵  Jayastu Senapati⁵  Sai Prasad Desikan⁵  Tapan Kadia⁵  Philip L. Lorenzi⁶  Robert R. Jenq^4,7  Samuel A. Shelburne^3,4  Jessica Galloway-Peña^1,2,4 

[1] Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

[2] Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

[3] Department of Veterinary Pathobiology, Texas A&M University, College Station, Texas, USA

[4] Department of Infectious Disease, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

[5] Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

[6] Interdisciplinary Graduate Program in Genetics and Genomics, Texas A&M University, College Station, Texas, USA

[7] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA

关键词: microbiome;    metabolome;    neutropenic fever;    bacteremia;    machine learning;    network analysis;   

DOI  :  10.1128/msystems.01343-25

 received in 2025-09-16, accepted in 2026-03-04,  发布年份 2026

来源: American Society for Microbiology

【 摘 要 】

ABSTRACTNeutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic [AUROC] = 0.769) identified higher levels of Akkermansia, Enterobacter, Escherichia–Shigella, and Flavonifractor as predictors of infectious NF, while Collinsella, Lachnospiraceae, Coprococcus, and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella, Butyrate, Lachnospiraceae, Ruminococcaceae, and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome–metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts.IMPORTANCEOur study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.

【 授权许可】

CC BY   
Copyright © 2026 Franklin et al.

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