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Updated: Oct 2, 2025

Murine Fecal Isolation and Microbiota Transplantation
Published on: May 26, 2023
Murine fecal microbiota transfer models selectively colonize human microbes and reveal transcriptional programs
Fyza Y Shaikh1,2, Joell J Gills1,2, Fuad Mohammad1,2
1The Bloomberg-Kimmel Institute of Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Abstract:
Human gut microbial species found to associate with clinical responses to immune checkpoint inhibitors (ICIs) are often tested in mice using fecal microbiota transfer (FMT), wherein tumor responses in recipient mice may recapitulate human responses to ICI treatment. However, many FMT studies have reported only limited methodological description, details of murine cohorts, and statistical methods. To investigate the reproducibility and robustness of gut microbial species that impact ICI responses, we performed human to germ-free mouse FMT using fecal samples from patients with non-small cell lung cancer who had a pathological response or nonresponse after neoadjuvant ICI treatment. R-FMT mice yielded greater anti-tumor responses in combination with anti-PD-L1 treatment compared to NR-FMT, although the magnitude varied depending on mouse cell line, sex, and individual experiment. Detailed investigation of post-FMT mouse microbiota using 16S rRNA amplicon sequencing, with models to classify and correct for biological variables, revealed a shared presence of the most highly abundant taxa between the human inocula and mice, though low abundance human taxa colonized mice more variably after FMT. Multiple Clostridium species also correlated with tumor outcome in individual anti-PD-L1-treated R-FMT mice. RNAseq analysis revealed differential expression of T and NK cell-related pathways in responding tumors, irrespective of FMT source, with enrichment of these cell types confirmed by immunohistochemistry. This study identifies several human gut microbial species that may play a role in clinical responses to ICIs and suggests attention to biological variables is needed to improve reproducibility and limit variability across experimental murine cohorts.
Insights
Human gut microbes influence responses to immune checkpoint inhibitors (ICIs). Fecal microbiota transfer (FMT) in mice showed that specific microbes impact anti-tumor immunity, but experimental variables affect reproducibility.
Area of Science:
- Microbiome research
- Immunotherapy
- Oncology
Background:
- Gut microbial species associate with clinical responses to immune checkpoint inhibitors (ICIs).
- Fecal microbiota transfer (FMT) in mice is a common model to study these associations.
- Previous FMT studies often lack detailed methodology and statistical rigor.
Purpose of the Study:
- To investigate the reproducibility and robustness of gut microbial species impacting ICI responses.
- To analyze the impact of human gut microbiota on anti-tumor immunity in mice treated with anti-PD-L1.
- To identify specific microbial taxa and host factors influencing treatment outcomes.
Main Methods:
- Human to germ-free mouse FMT using samples from non-small cell lung cancer patients with pathological response or nonresponse to neoadjuvant ICI treatment.
- 16S rRNA amplicon sequencing to analyze mouse microbiota composition post-FMT.
- RNAseq and immunohistochemistry to assess anti-tumor immune responses in mice.
Main Results:
- FMT from responding patients (R-FMT) enhanced anti-tumor responses to anti-PD-L1 compared to non-responding (NR-FMT) mice, with variability based on mouse factors.
- High-abundance human taxa colonized mice consistently, while low-abundance taxa showed variable colonization.
- Specific Clostridium species correlated with tumor outcomes in R-FMT mice.
- Differential expression of T and NK cell pathways observed in responding tumors, irrespective of FMT source.
Conclusions:
- Human gut microbial species can influence clinical responses to ICIs.
- Experimental variables like mouse cell line, sex, and individual experiments significantly impact reproducibility in FMT studies.
- Further research focusing on biological variables is crucial for improving the reliability of murine models for ICI response prediction.
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