In this study, the researchers used MetaPhlAn 4, a metagenomic profiling method, to improve the analysis of the mouse gut microbiome. By combining multiple datasets and additional samples, they were able to identify several diet-related microbial biomarkers, including previously unknown ones.
Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenom-ics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirm-ing the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehen-sive profiling.
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