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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
Published on: February 23, 2024
Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic
Joshua A Harrill1, Logan J Everett1, Derik E Haggard2
1Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC, USA.
Abstract:
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.
Insights
Computational methods significantly impact chemical hazard assessments using high-throughput transcriptomics (HTTr). Different methods yield vastly different transcriptional points of departure (tPODs), highlighting the need for careful selection and validation in risk assessment.
Area of Science:
- Toxicology and computational biology
- Development and application of New Approach Methodologies (NAMs) for chemical safety assessment
Background:
- High-throughput transcriptomics (HTTr) is a promising New Approach Methodology (NAM) for chemical hazard identification.
- Accurate calculation of transcriptional points of departure (tPODs) from HTTr data is crucial for risk assessment.
- Various computational methods exist for tPOD calculation, but their performance and impact on results are not fully understood.
Purpose of the Study:
- To evaluate and compare different computational methods for calculating tPODs from HTTr data.
- To assess the influence of computational methods versus experimental conditions on tPOD values.
- To compare calculated tPODs with external in vitro data for a set of estrogenic compounds.
Main Methods:
- Utilized six datasets from MCF7 cells treated with 44 chemicals across different exposure durations (6, 12, 24 hr) and media formulations.
- Compared tPOD calculation methods using individual gene and gene set signature data processed by ToxCast Pipeline 2 (tcplfit2), BMDExpress, and PLIER (Pathway Level Information ExtractoR).
- Evaluated methods by comparing calculated tPODs to in vitro points of departure (PODs) from high-throughput screening (HTS) assays for estrogenic compounds.
Main Results:
- tPODs varied by several orders of magnitude between different computational methods for the same chemical and experimental conditions.
- Computational methods had a greater impact on tPOD variability than experimental conditions.
- PLIER and BMDExpress generally yielded higher tPODs compared to an external reference set of PODs.
- tPODs from HTTr in MCF7 cells were often higher than PODs from broad in vitro ToxCast assays.
Conclusions:
- The choice of computational method significantly influences tPOD values derived from HTTr data.
- Validation against external data is essential for selecting appropriate tPOD calculation methods.
- Testing chemicals across multiple cell types and readout technologies is necessary for comprehensive in vitro hazard screening.
