RESUMEN
The intricate interplay between the gut microbiota and bone health has become increasingly recognized as a fundamental determinant of skeletal well-being. Microbiota-derived metabolites play a crucial role in dynamic interaction, specifically in bone homeostasis. In this sense, short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, indirectly promote bone formation by regulating insulin-like growth factor-1 (IGF-1). Trimethylamine N-oxide (TMAO) has been found to increase the expression of osteoblast genes, such as Runt-related transcription factor 2 (RUNX2) and bone morphogenetic protein-2 (BMP2), thus enhancing osteogenic differentiation and bone quality through BMP/SMADs and Wnt signaling pathways. Remarkably, in the context of bone infections, the role of microbiota metabolites in immune modulation and host defense mechanisms potentially affects susceptibility to infections such as osteomyelitis. Furthermore, ongoing research elucidates the precise mechanisms through which microbiota-derived metabolites influence bone cells, such as osteoblasts and osteoclasts. Understanding the multifaceted influence of microbiota metabolites on bone, from regulating homeostasis to modulating susceptibility to infections, has the potential to revolutionize our approach to bone health and disease management. This review offers a comprehensive exploration of this evolving field, providing a holistic perspective on the impact of microbiota metabolites on bone health and diseases.
RESUMEN
Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.