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Background Gut microbiota and microbiota-derived metabolites have been implicated in the regulation of stress-related diseases, yet their associations with chronic stress in adolescents remain unclear. Multi-omics studies on this topic in adolescents are still limited. This study aimed to characterize gut microbiota and metabolites in adolescents under chronic stress. Methods In this cross-sectional study, we assessed chronic stress in 124 adolescents aged 12–16 years using the Adolescent Life Events Scale and the Study Stress Scale. Participants were stratified by stress level into low ( n  = 42), medium ( n  = 41), and high stress ( n  = 41) groups. Fecal samples were collected from all participants for 16S rRNA gene sequencing. Subsequently, a subset of 30 adolescents with high stress and 29 low stress adolescents underwent metagenomic sequencing and untargeted metabolomics. Results Adolescents experiencing high-chronic stress showed lower alpha diversity, differential beta diversity, and a more complicated microbial network compared to those experiencing lower stress. Spearman’s rank correlation and Kruskal-Wallis test identified five genera with decreased abundances in high stress adolescents, including Faecalibacterium , Bacteroides , Akkermansia , Lachnospiraceae unclassified , and Ruminococcus ( P fdr <0.05). Additionally, 12 species showed decreased abundances and 5 increased abundances, and logistic regression analysis further revealed that the relative abundances of Bifidobacterium catenulatum , Streptococcus suis , Ruminococcus sp. CAG 108 , and Phascolarctobacterium faecium were associated with chronic stress ( P fdr <0.05), after adjusting for sex, age, fruit consumption, and body mass index. We identified 21 differential metabolites, predominantly enriched in metabolic pathways based on KEGG analysis. Moreover, 19 out of these metabolites were significantly correlated with at least one of the four species significantly associated with chronic stress. These metabolites may explain health effects of species associated with chronic stress. Conclusions Chronic stress in adolescents is associated with altered gut microbiota composition and metabolite profiles, providing insights into possible mechanisms underlying stress-related diseases and highlighting the importance of longitudinal studies to clarify temporal and causal relationships.

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.