Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn’s disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91–0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87–0.93) at a specificity of 0.76 to 0.98 (IQR 0.83–0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79–0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.
The type III secretion system (T3SS) plays an important role in the pathogenesis of Pseudomonas aeruginosa. Expression of the T3SS is controlled under a complicate regulatory network. In this study, we demonstrate that NrtR (PA4916) is involved in the T3SS expression and pathogenesis of P. aeruginosa in a mouse acute pneumonia model. Overexpression of the T3SS central activator ExsA or exogenous supplementation of cAMP restored the expression of T3SS in the ΔnrtR mutant, suggesting that NrtR might regulate T3SS through the cAMP-Vfr signaling pathway. Further experiments demonstrated that the decrease of cAMP content is not due to the expression change of adenylate cyclases or phosphodiesterase in the ΔnrtR mutant. As it has been shown that nadD2 is upregulated in the ΔnrtR mutant, we overexpressed nadD2 in wild type PAK, which reduced the intracellular cAMP level and the expression of the T3SS genes. Meanwhile, deletion of nadD2 in the ΔnrtR mutant restored the expression and secretion of the T3SS. Co-immunoprecipitation assay revealed an interaction between NadD2 and the catalytic domain of the adenylate cyclase CyaB. Further in vitro assay indicated that NadD2 repressed the enzymatic activity of CyaB. Therefore, we have identified a novel regulatory mechanism of T3SS in P. aeruginosa.
With the rapid development of knowledge bases (KBs), question answering (QA) based on KBs has become a hot research issue. In this paper, we propose two frameworks (i.e., a pipeline framework, an end-to-end framework) to focus on answering single-relation factoid questions. In both of two frameworks, we study the effect of context information on the quality of QA, such as the entity's notable type, out-degree. In the pipeline framework, it includes two cascaded steps: entity detection and relation detection. In the end-to-end framework, we combine char-level encoding and self-attention mechanisms, using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition, we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.
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