Amadis: A Comprehensive Database for Association Between Microbiota and Disease

The human gastrointestinal tract represents a symbiotic bioreactor that can mediate the interaction of the human host. The deployment and integration of multi-omics technologies have depicted a more complete image of the functions performed by microbial organisms. In addition, a large amount of data has been generated in a short time. However, researchers struggling to keep track of these mountains of information need a way to conveniently gain a comprehensive understanding of the relationship between microbiota and human diseases. To tackle this issue, we developed Amadis (http://gift2disease.net/GIFTED), a manually curated database that provides experimentally supported microbiota-disease associations and a dynamic network construction method. The current version of the Amadis database documents 20167 associations between 221 human diseases and 774 gut microbes across 17 species, curated from more than 1000 articles. By using the curated data, users can freely select and combine modules to obtain a specific microbe-based human disease network. Additionally, Amadis provides a user-friendly interface for browsing, searching and downloading. We hope it can serve as a useful and valuable resource for researchers exploring the associations between gastrointestinal microbiota and human diseases.

The translational network for metabolic disease – from protein interaction to disease co-occurrence

Background The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing ‘n-of-1 utility’ (n potential diseases of one patient) to human disease network—the translational disease network. Results We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. Conclusions The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

Measuring the Importance of Vertices in the Weighted Human Disease Network

Many human genetic disorders and diseases are known to be related to each other through frequently observed co-occurrences. Studying the correlations among multiple diseases provides an important avenue to better understand the common genetic background of diseases and to help develop new drugs that can treat multiple diseases. Meanwhile, network science has seen increasing applications on modeling complex biological systems, and can be a powerful tool to elucidate the correlations of multiple human diseases. In this article, known disease-gene associations were represented using a weighted bipartite network. We extracted a weighted human diseases network from such a bipartite network to show the correlations of diseases. Subsequently, we proposed a new centrality measurement for the weighted human disease network in order to quantify the importance of diseases. Using our centrality measurement to quantify the importance of vertices in the weighted human disease network, we were able to find a set of most central diseases. By investigating the 30 top diseases and their most correlated neighbors in the network, we identified disease linkages including known disease pairs and novel findings. Our research helps better understand the common genetic origin of human diseases and suggests top diseases that likely induce other related diseases.