RESUMEN
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to disease predictions. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repurposing models or as a valuable resource for training such models.
RESUMEN
Tandem mass spectrometry based shotgun proteomics of distal gut microbiomes is exceedingly difficult due to the inherent complexity and taxonomic diversity of the samples. We introduce two new methodologies to improve metaproteomic studies of microbiome samples. These methods include the stable isotope labeling in mammals to permit protein quantitation across two mouse cohorts as well as the application of activity-based probes to enrich and analyze both host and microbial proteins with specific functionalities. We used these technologies to study the microbiota from the adoptive T cell transfer mouse model of inflammatory bowel disease (IBD) and compare these samples to an isogenic control, thereby limiting genetic and environmental variables that influence microbiome composition. The data generated highlight quantitative alterations in both host and microbial proteins due to intestinal inflammation and corroborates the observed phylogenetic changes in bacteria that accompany IBD in humans and mouse models. The combination of isotope labeling with shotgun proteomics resulted in the total identification of 4434 protein clusters expressed in the microbial proteomic environment, 276 of which demonstrated differential abundance between control and IBD mice. Notably, application of a novel cysteine-reactive probe uncovered several microbial proteases and hydrolases overrepresented in the IBD mice. Implementation of these methods demonstrated that substantial insights into the identity and dysregulation of host and microbial proteins altered in IBD can be accomplished and can be used in the interrogation of other microbiome-related diseases.
