Citation:

Chambers, B. AND I. Shah. Evaluating adaptive stress response gene signatures using transcriptomics. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 20:100179, (2021). https://doi.org/10.1016/j.comtox.2021.100179

Impact/Purpose:

Efficiently evaluating and characterizing chemicals is challenging given the multitude of indirect and non-specific routes by which chemicals induce toxicity. A key question is understanding the role that adaptive stress responses play in mitigating the effects of non-specifically acting chemicals. The stress response pathway (SRP) signatures developed in this work contribute to our understanding of the transcriptional bioactivity of adaptive stress responses. Also, they start to address the issue of crosstalk between SRPs by using a data-driven approach to resolve the contributions of effector genes to signatures. With further evaluation, we believe that these signatures could form the basis of new approach methodologies (NAMs) that can efficiently characterize non-specific bioactivity of thousands of untested chemicals. 

Description:

Stress response pathways (SRPs) mitigate the cellular effects of chemicals, but excessive perturbation can lead to adverse outcomes. Here, we investigated a computational approach to evaluate SRP activity from transcriptomic data using gene set enrichment analysis (GSEA). We extracted published gene signatures for DNA damage response (DDR), unfolded protein response (UPR), heat shock response (HSR), response to hypoxia (HPX), metal-associated response (MTL), and oxidative stress response (OSR) from the Molecular Signatures Database (MSigDB). Next, we used a gene-frequency approach to build consensus SRP signatures of varying lengths from 50 to 477 genes. We then prepared a reference dataset from perturbagens associated with SRPs from the literature with their transcriptomic profiles retrieved from public repositories. Lastly, we used receiver-operator characteristic analysis to evaluate the GSEA scores from matching transcriptomic reference profiles to SRP signatures. Our consensus signatures performed better than or as well as published signatures for 4 out of the 6 SRPs, with the best consensus signature area under the curve (% performance relative to median of published signatures) of 1.00 for DDR (109%), 0.86 for UPR (169%), 0.99 for HTS (103%), 1.00 for HPX (104%), 0.74 for MTL (150%) and 0.83 for OSR (148%). The best matches between transcriptomic profiles and SRP signatures correctly classified perturbagens in 78% and 88% of the cases by first and second rank, respectively. We believe this approach can characterize SRP activity for new chemicals using transcriptomics with further evaluation.

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