Citation:

Harrill, J., L. Everett, D. Haggard, J. Bundy, C. Willis, I. Shah, K. Friedman, D. Basili, A. Middleton, AND R. Judson. Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates. TOXICOLOGY. Elsevier Science Ltd, New York, NY, 501:153694, (2024). https://doi.org/10.1016/j.tox.2023.153694

Impact/Purpose:

High-throughput transcriptomics (HTTr) profiling in human-derived cell models has been proposed as part of the first tier of a tiered new approach methods (NAMs)-based hazard evaluation strategy (Thomas et al. 2019 PMID: 30835285).  This manuscript compares a variety of different computational methods for calculating transcriptional points of departure (tPODs) from in vitro HTTr data.  The manuscript uses data for 44 chemicals tested in 8-point concentration series in MCF7 human adenocarcinoma cells cultured in two different media formulations (one containing 10% fetal bovine serum with endogenous estrogenic activity and one containing 10% charcoal-stripped fetal bovine serum with greatly reduced endogenous estrogenic activity) and three exposure durations (6, 12 and 24 hours). The computational methods are compared and discussed in terms of variability and stability across experimental conditions and accuracy using target high-throughput screening (HTS) data from 18 estrogenic receptor assays in ToxCast as a benchmark.  This information will be of use to partners and stakeholders interested in applying tPODs from in vitro transcriptomics studies in chemical risk assessment contexts.  This study was conducted with support from the Chemical Safety for Sustainability (CSS) national research program as detailed in the FY19-FY22 CSS Strategic Research Action Plan (StRAP).

Description:

This sub-product is a journal article that compares a variety of computational approaches for calculating transcriptomic points of departure (tPODs) from in vitro high-throughput transcriptomics (HTTr) screening data.  The manuscript uses a data set of 44 chemicals tested in 8-point concentration series in MCF7 cells cultured in two types of media (i.e. growth media with 10% serum containing endogenous estrogens and growth media with 10% charcoal-stripped serum where endogenous estrogens are greatly reduced) and three different exposure durations (6, 12 and 24 hours). Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

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