MT-Tracker: A Phylogeny-Aware Algorithm for Quantifying Microbiome Transitions Across Scales and Habitats - Wenjie Zhu; Yangyang Sun; Weiwen Luo; Guosen Hou; Hao Gao; Xiaoquan Su (Mathematics)
The structural diversity of microbial communities plays a pivotal role in microbiological research and applications. However, the study of microbial transitions has remained challenging due to a lack of effective methods, limiting our understanding of microbial dynamics and their underlying mechanisms. To address this gap, we introduce MT-tracker (microbiome transition tracker), a novel algorithm designed to capture the transitional trajectories of microbial communities. Grounded in diversity and phylogenetic principles, MT-tracker reconstructs the virtual common ancestors of microbiomes at the community level. By calculating distances between microbiomes and their ancestors, MT-tracker deduces their transitional directions and probabilities, achieving a substantial speed advantage over conventional approaches. The accuracy and robustness of MT-tracker were first validated by a phylosymbiosis analysis using samples from 28 mammals and 24 nonmammal animals, describing the co-evolutionary pattern between hosts and their associated microbiomes. We then expanded the usage of MT-tracker to 456,702 microbiomes sampled world-wide, uncovering the global transitional directions among 21 ecosystems for the first time. This effort provides new insights into the macro-scale dynamic patterns of microbial communities. Additionally, MT-tracker revealed intricate longitudinal transition trends in human microbiomes over a sampling period exceeding 400 days, capturing temporal dynamics often overlooked by normal diversity analyses. In summary, MT-tracker offers robust support for the qualitative and quantitative analysis of microbial community diversity, offering significant potential for studying and utilizing the macrobiome variation.