Elucidating Compound Mechanism of Action by Network Perturbation Analysis

Abstract

Summary Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators, represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor-intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small molecule MoA and compound similarity.

A review of connectivity map and computational approaches in pharmacogenomics

“…Henceforth, we focus on the two largest general purpose drug perturbation data sets CMap and LINCS L1000. GEODB [21] This data set contains GEP of 13 different compounds, obtained from nine independent expression sets obtained from the Gene Expression Omnibus (GEO). Each expression set had at least six DMSO controls and six samples for compound treatment.…”

Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression

“…Following the Connectivity Map rationale (29), we reasoned that the differential expression signature following MR-silencing in human B cells represent an ideal multiplexed gene reporter assays to assess the activity of candidate small molecule inhibitors of the same MR. Since TF-targets are highly conserved across 18 distinct subtypes of human B cells, including FL and DLBCL (the rationale for using the B cell interactome for this analysis) (30), we proceeded to assess 92 compounds for which GEPs were available following perturbation of an ABC (OCI-LY3) and a GCG (OCI-LY7) DLBCL cell lines (31). Specifically, we assessed enrichment of compound-induced signatures in genes differentially expressed following siRNA-mediated silencing of validated FL-transformation MRs in SUDHL6 cells, using a two-tail GSEA (Supplementary Methods) to account for both over and under-expressed genes, to identify compounds that significantly recapitulate relevant MR silencing (Supplementary Table S3A).…”

Network pharmacology of JAK inhibitors

“…3). The latter finding indicated that JAKi treatments subtly rewire the regulatory connections within immunogenomic modules, and further illustrates the potential of network analyses to identify drug mechanisms (14), here flagging the NK receptor family for attention. Comfortingly, the strongest effects on the genomic network (cell growth transcripts in MF, effector functions in NK) corresponded to the most numerically perturbed immunocyte populations.…”

“…To go beyond simple differential expression analysis and to derive the wider network effects of long-term JAK inhibition, we turned to network-based approaches, which evaluate changes in the correlation structure of the transcriptome (differential coexpression; DCE). The underlying principle is that DCE between a pair of genes reflects a modulation of the regulatory relationship between them, resulting from a different regulatory configuration in related, yet distinct, cell types (11) or from disease or drug treatment (12)(13)(14).…”