A novel Energy Landscape method incorporating the Latent Dirichlet Allocation topic model and the pairwise Maximum Entropy model, revealing the significant contribution of Bactericides to the development of Inflammatory Bowel Disease

1 The dysbiosis of microbiota has been reported to be associated with numerous human pathophysiological processes, including Inflammatory Bowel Disease (IBD). With advancements in highthroughput sequencing, various methods have been developed to study the alteration of microbiota in the development and progression of diseases. However, a suitable approach to assess the global stability of the microbiota in disease states through time-series microbiome data is yet to be established. In this study, we introduce a novel Energy Landscape construction method, which incorporates the Latent Dirichlet Allocation (LDA) model and the pairwise Maximum Entropy (MaxEnt) model, and demonstrate its utility by applying it to an IBD time-series dataset. Through this method, we obtained the “energy” profile for the potential patterns of microbiota to occur under disease, indicating their stability and prevalence. The results suggest the potential contribution of several microbial genera, including Bacteroides, Alistipes , and Faecalibacterium , as well as their interactions, to the development of IBD. Our proposed method provides a novel and insightful tool for understanding the alteration and stability of the microbiota under disease states and offers a more holistic view of its complex dynamics at play in microbiota-mediated disease.