Systems Approach to Assign Expression Based Signatures to Adrenergic Drugs

Adrenergic receptors and associated drugs have been among the most thoroughly studied G‐protein coupled receptors involved in a wide variety of physiological processes. There are three main groups of adrenergic receptors (alpha1, alpha2, and beta) with each group having multiple receptor subtypes which involve different targets modulating various functions. Due to the highly conserved structures of these receptors, identifying specific and non‐specific binding partners as well as activity of the receptors have been a challenge to assign within this family. In this study, we used a systems approach, involving the use of high‐throughput drug‐perturbation profiling data from the Library of Integrated Network‐Based Cellular Signatures (LINCS) database, to assign drug receptor preference as well as downstream modulation of cellular functions. LINCS, aiming to create a network‐based understanding of gene expression when cells are exposed to different perturbing agents, have extensively profiled the transcriptomic perturbations of over 20,000 small‐molecules in various cell lines and primary cells. Existing information about receptor specificity of drugs from DrugBank for 113 adrenergic drugs was used to assign binding partners to these drugs based on the LINCS drug perturbation data. Perturbed gene expression signatures for specific receptors were then examined for enriched biological functions as well as gene expression similarities. The analysis of drugs specific for activating these receptors provides an expression‐based signature demonstrating each receptors influence on cellular functions. Defining the role different receptor subtypes have on cellular function would provide an alternative way to determine receptor preference of the more promiscuous drugs based on the similarity of their functional involvement. We used this method to identify separate classes of drugs with similar effects on our model system. As an example, we had previously been using Isoprenaline as a beta‐adrenergic agonist to modulate cytokine response in SIMA9 cells, a murine microglia cell line, and needed to identify an alternative non‐catechol drug that would also influence toll like receptor signaling pathways. Our method identified a quinolone, Procaterol, as an alternative drug, which also influences toll like receptor signaling pathways and yields a similar effect in the SIMA9 cell line. Using this systems pharmacology approach may provide a way to further elucidate receptor preference of certain drugs as well as identifying drugs with similar functional influences despite other characteristics. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .