Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied “dark” members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing

Transcriptomic evidence of immune activation in macroscopically normal-appearing and scarred lung tissues in idiopathic pulmonary fibrosis

•Macroscopically normal tissue in IPF patients is profoundly involved in the disease.•Immune activation is overt in normal-appearing and scarred tissue in IPF lungs.•Differences between normal-appearing and scarred tissue involve mostly epithelium. Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease manifested by overtly scarred peripheral and basilar regions and more normal-appearing central lung areas. Lung tissues from macroscopically normal-appearing (IPFn) and scarred (IPFs) areas of explanted IPF lungs were analyzed by RNASeq and compared with healthy control (HC) lung tissues. There were profound transcriptomic changes in IPFn compared with HC tissues, which included elevated expression of numerous immune-, inflammation-, and extracellular matrix-related mRNAs, and these changes were similar to those observed with IPFs compared to HC. Comparing IPFn directly to IPFs, elevated expression of epithelial mucociliary mRNAs was observed in the IPFs tissues. Thus, despite the known geographic tissue heterogeneity in IPF, the entire lung is actively involved in the disease process, and demonstrates pronounced elevated expression of numerous immune-related genes. Differences between normal-appearing and scarred tissues may thus be driven by deranged epithelial homeostasis or possibly non-transcriptomic factors