Prediction of sporulating Firmicutes from uncultured gut microbiota using SpoMAG, an ensemble learning tool.

Sporulation represents a key adaptive strategy among Firmicutes, facilitating bacterial persistence under environmental stress while mediating host colonization, transmission dynamics, and microbiome stability. Despite the recognized ecological and biomedical significance of spore-forming Bacilli and Clostridia, most taxa remain uncultivated, limiting phenotypic characterization of their sporulation capacity. To bridge this knowledge gap, we developed SpoMAG, an ensemble machine learning framework that predicts sporulation potential of metagenome-assembled genomes (MAGs) through supervised classification models trained on the presence/absence of 160 sporulation-associated genes. This R-based tool integrates Random Forest and support vector machine algorithms, achieving probabilistic predictions with high performance (AUC = 92.2%, F1-score = 88.2%). Application to fecal metagenomes from humans, cattle, poultry, and swine identified 63 putatively spore-forming MAGs exhibiting distinct host- and order-specific patterns. Bacilli MAGs from Bacillales and Paenibacillales orders showed high sporulation probabilities and gene richness, while Clostridia MAGs exhibited more heterogeneous profiles. Predictions included undercharacterized families in the spore-forming perspective, such as Acetivibrionaceae, Christensenellaceae, and UBA1381, expanding the known phylogenetic breadth of sporulation capacity. Nine genes were consistently present across all predicted spore-formers (namely