Predicting the seed microbiome using phylogeny-driven machine learning.

backgroundThe composition of the seed-associated bacterial microbiome can reflect host evolutionary relationships, a pattern consistent with phylosymbiosis. While machine learning offers new opportunities to predict microbial community composition, existing models often require prior microbial profiles or environmental variables, limiting their application to unsampled hosts. Here, we tested whether plant nuclear internal transcribed spacer (ITS) sequences, used as a marker of host relatedness, can predict species-level seed-associated bacterial communities using 16S rRNA data from 61 plant species.

resultsWe introduced customized machine learning models that use sequence-based Hamming distances to capture plant host relatedness. Among the tested models, the Hamming Distance-based k-Nearest Neighbor model (HD-KNN) achieved the highest overall predictive accuracy, yielding an average Jensen-Shannon divergence (JSD) of 0.276 between observed and predicted microbiome profiles. HD-KNN performed particularly well within densely sampled host groups, including Brassicaceae and Poaceae, where closely related reference species were available. In contrast, Hamming Distance-based Gaussian Process Regression (HD-GPR) showed slightly better performance for phylogenetically isolated species, suggesting that model performance depends on host representation within the training dataset.

conclusionsOur framework demonstrates that plant nuclear ITS-derived host relatedness carries a partial predictive signal for seed-associated bacterial microbiome composition. These results provide a foundation for low-input predictive modelling of seed-associated bacteria and may help prioritise microbiome predictions for unsampled plant species when closely related reference species are available. However, our conclusions are strictly limited to seed-associated bacterial communities and should not be directly generalized to fungal communities or other plant compartments, such as the rhizosphere or phyllosphere, which may be shaped by different environmental filtering mechanisms.