Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics

Abstract

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical–protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

Predicting mechanism of action of cellular perturbations with pathway activity signatures

“…In the context of toxicogenomics, use of low-cost, high-throughput transcriptomic technologies (Bush, et al, 2017;Bushel, et al, 2018;Subramanian, et al, 2017), combined with pasLINCS analysis may open alternative avenues for high throughput safety evaluation of commercial chemicals, pesticides, food additives/contaminants, and medical products (Kavlock, et al, 2009;Kleinstreuer, et al, 2014). Previous studies have established the potential of assigning MOA of a chemical perturbagen based on comparison of their transcriptional signatures to the signatures of chemicals with known MOA (Iwata, et al, 2017;Subramanian, et al, 2017;Wang, et al, 2018). For example, the preciously derived PI3K inhibitor signature constructed from TSes of known chemical inhibitors (Zhang, et al, 2017) showed similar level of association with L1000 mTOR pathway inhibitors as we observed with our PAS (Fig 2D).…”

Drug repositioning in cancer: The current situation in Japan

“…The main idea underlying several current methods, including in silico DR, is to identify genes whose expression levels are inversely correlated in the context of disease and drug treatment . These three approaches represent alternative and complementary approaches to the discovery of repositioned drugs.…”

Learning to Encode Cellular Responses to Systematic Perturbations with Deep Generative Models

“…While there are numerous studies using LINCS data to investigate the mechanism-of-action (MOA) of drugs and to promote clinical translation of MOA information (Iwata et al, 2017;Lamb et al, 2006;Pabon et al, 2018;Siavelis et al, 2015;Subramanian et al, 2017;Wang et al, 2016), few studies aim to use the data to learn to represent the cellular signaling system as an information encoder and examine how different perturbagens affect the system. It can be imagined that perturbing different signaling components at different levels of a signaling cascade would lead to compositional statistical structure in the data.…”