Drug prioritization using the semantic properties of a knowledge graph

Abstract Compounds that are candidates for drug repurposing can be ranked by leveraging knowledge available in the biomedical literature and databases. This knowledge, spread across a variety of sources, can be integrated within a knowledge graph, which thereby comprehensively describes known relationships between biomedical concepts, such as drugs, diseases, genes, etc. Our work uses the semantic information between drug and disease concepts as features, which are extracted from an existing knowledge graph that integrates 200 different biological knowledge sources. RepoDB, a standard drug repurposing database which describes drug-disease combinations that were approved or that failed in clinical trials, is used to train a random forest classifier. The 10-times repeated 10-fold cross-validation performance of the classifier achieves a mean area under the receiver operating characteristic curve (AUC) of 92.2%. We apply the classifier to prioritize 21 preclinical drug repurposing candidates that have been suggested for Autosomal Dominant Polycystic Kidney Disease (ADPKD). Mozavaptan, a vasopressin V2 receptor antagonist is predicted to be the drug most likely to be approved after a clinical trial, and belongs to the same drug class as tolvaptan, the only treatment for ADPKD that is currently approved. We conclude that semantic properties of concepts in a knowledge graph can be exploited to prioritize drug repurposing candidates for testing in clinical trials

Comprehensive transcriptome analysis of fluid shear stress altered gene expression in renal epithelial cells

Renal epithelial cells are exposed to mechanical forces due to flow-induced shear stress within the nephrons. Shear stress is altered in renal diseases caused by tubular dilation, obstruction, and hyperfiltration, which occur to compensate for lost nephrons. Fundamental in regulation of shear stress are primary cilia and other mechano-sensors, and defects in cilia formation and function have profound effects on development and physiology of kidneys and other organs. We applied RNA sequencing to get a comprehensive overview of fluid-shear regulated genes and pathways in renal epithelial cells. Functional enrichment-analysis revealed TGF-β, MAPK, and Wnt signaling as core signaling pathways up-regulated by shear. Inhibitors of TGF-β and MAPK/ERK signaling modulate a wide range of mechanosensitive genes, identifying these pathways as master regulators of shear-induced gene expression. However, the main down-regulated pathway, that is, JAK/STAT, is independent of TGF-β and MAPK/ERK. Other up-regulated cytokine pathways include FGF, HB-EGF, PDGF, and CXC. Cellular responses to shear are modified at several levels, indicated by altered expression of genes involved in cell-matrix, cytoskeleton, and glycocalyx remodeling, as well as glycolysis and cholesterol metabolism. Cilia ablation abolished shear induced expression of a subset of genes, but genes involved in TGF-β, MAPK, and Wnt signaling were hardly affected, suggesting that other mechano-sensors play a prominent role in the shear stress response of renal epithelial cells. Modulations in signaling due to variations in fluid shear stress are relevant for renal physiology and pathology, as suggested by elevated gene expression at pathological levels of shear stress compared to physiological shear

Comparative transcriptomics of shear stress treated Pkd1(-/-) cells and pre-cystic kidneys reveals pathways involved in early polycystic kidney disease

Molecular Technology and Informatics for Personalised Medicine and HealthFunctional Genomics of Systemic Disorder

Meta-analysis of polycystic kidney disease expression profiles defines strong involvement of injury repair processes

Functional Genomics of Systemic DisordersMolecular Technology and Informatics for Personalised Medicine and Healt

Chemical functionalization of graphene

Contains fulltext : 217398.pdf (publisher's version ) (Open Access

Genomic expression catalogue of a global collection of BCG vaccine strains show evidence for highly diverged metabolic and cell-wall adaptations.

Although Bacillus Calmette-Guérin (BCG) vaccines against tuberculosis have been available for more than 90 years, their effectiveness has been hindered by variable protective efficacy and a lack of lasting memory responses. One factor contributing to this variability may be the diversity of the BCG strains that are used around the world, in part from genomic changes accumulated during vaccine production and their resulting differences in gene expression. We have compared the genomes and transcriptomes of a global collection of fourteen of the most widely used BCG strains at single base-pair resolution. We have also used quantitative proteomics to identify key differences in expression of proteins across five representative BCG strains of the four tandem duplication (DU) groups. We provide a comprehensive map of single nucleotide polymorphisms (SNPs), copy number variation and insertions and deletions (indels) across fourteen BCG strains. Genome-wide SNP characterization allowed the construction of a new and robust phylogenic genealogy of BCG strains. Transcriptional and proteomic profiling revealed a metabolic remodeling in BCG strains that may be reflected by altered immunogenicity and possibly vaccine efficacy. Together, these integrated-omic data represent the most comprehensive catalogue of genetic variation across a global collection of BCG strains