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.

Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.

There is a growing recognition that a good diet can help people maintain mental and physical health, while a bad one will cause the disorder of body function, and even lead to several diseases. A lot of attentions have been devoted to analyze every possible health-related factor in the daily diet, including food ingredients, additives, and cooking process. With the support of high-throughput sequencing technology, there is accumulating evidence gradually clarifying that most of these factors are mainly through the interactions with gut microbiome to trigger downstream effects. The gut microbiome may be able to act as a very sensitive mirror in response to human daily diet. A complex network of interactions among diet, gut microbiome, and health has been gradually depicted, but it is rarely discussed from a more comprehensive perspective. To this end, this review summarized the latest updates in diet-gut microbiome interactions, analyzed most identified factors involved in this process, showed the possibility of maintaining health or alleviating diseases by diet intervention, aiming to help people choose a suitable recipe more accurately.

• The significance of extracellular vesicles (EVs) from different sources in gut health was discussed. • EVs contribute to maintaining intestinal epithelial barrier integrity, modulating immune responses, and shaping gut microbiota. • Challenges in EV isolation and purification, size diversity, and cargo loading were highlighted. • Further exploration is needed for the targeted delivery, stability, and reproducibility of EVs.