PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a.

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A Machine Learning Framework for Predicting Drug-drug Interactions

10.21203/rs.3.rs-503867/v1 ◽

2021 ◽

Author(s):

Suyu Mei ◽

Kun Zhang

Keyword(s):

Drug Interactions ◽

Signaling Pathways ◽

Protein Interaction ◽

Drug Target ◽

Target Genes ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Model Complexity ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks

Abstract Understanding drug-drug interaction is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods commonly integrate multiple heterogeneous data sources to increase model performance but result in a high model complexity. To elucidate the molecular mechanisms behind drug-drug interactions and reserve rational biological interpretability is a major concern in computational modeling. In this study, we propose a simple representation of drug target profiles to depict drug pairs, based on which an l2-regularized logistic regression model is built to predict drug-drug interactions. In addition, we develop several statistical metrics to measure the communication intensity, interaction efficacy and action range between two drugs in the context of human protein-protein interaction networks and signaling pathways. Cross validation and independent test show that the simple feature representation via drug target profiles is effective to predict drug-drug interactions and outperforms the existing data integration methods. Statistical results show that two drugs easily interact when they target common genes, or their target genes communicate with each other via short paths in protein-protein interaction networks or through cross-talks between signaling pathways. The unravelled mechanisms provide biological insights into potential pharmacological risks of known drug-drug interactions and drug target genes.

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