Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment

Lee, Fan; Shah, Imran; Soong, Yun Ting; Xing, Jiangwa; Ng, Inn Chuan; Tasnim, Farah; Yu, Hanry

doi:10.1016/j.crtox.2021.07.003

Cited by 3 publications

(15 citation statements)

References 56 publications

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“…For example, Lee et al compared chemical-induced gene signatures (from primary rat hepatocytes using TempO-Seq data) of increasing sizes using different connectivity scoring approaches with a legacy data set (Affymetrix data from Open TG-GATES 160 ) with varying results by chemical, mechanism, and treatment concentration. 69 As expected, 8 and 200 μM of WY14643, which is a peroxisome-proliferator activated receptor alpha (PPARα) agonist, produced hits with other PPARα activators in Open TG-GATES. Similarly, valproic acid (400 and 10,000 μM) produced hits with other PPARα activators in Open TG-GATES using different vector-based approaches.…”

Section: ■ Discussionmentioning

confidence: 52%

“…First, we evaluated the reproducibility of transcriptional effects for reference chemicals in primary rat hepatocytes using TempO-Seq and Affymetrix data from OpenTG Gates. 69 We found that Jaccard and cosine similarity was more accurate than GSEA c for correctly matching extreme transcriptomic profiles of chemicals produced by TempO-Seq and Affymetrix technologies. Second, we successfully used GSEA c to calculate the concentration-dependent effects of chemicals on pathways and directional signatures 68 using an approach illustrated in Figure 4.…”

Section: Toxicologymentioning

confidence: 88%

“…The reproducibility, scalability, , and broad biological coverage of transcriptomics make it feasible to profile millions of biological samples for chemical treatments, genetic manipulations, and diseases. ,, In toxicology, connectivity mapping is being used to identify the putative targets of new chemicals, ,, to determine their impact on ecological and human health via adverse outcome pathways, to demonstrate the robustness and reproducibility of transcriptional effects across different studies and technology platforms, and to estimate biological pathway activating concentrations . This makes transcriptomic data-driven connectivity mapping a powerful tool for screening the many thousands of chemicals in commerce for putative effects and potency estimates for a broad array of cellular pathways.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, we have used multiple connectivity mapping methods presented in this review to solve three problems. First, we evaluated the reproducibility of transcriptional effects for reference chemicals in primary rat hepatocytes using TempO-Seq and Affymetrix data from OpenTG Gates . We found that Jaccard and cosine similarity was more accurate than GSEA c for correctly matching extreme transcriptomic profiles of chemicals produced by TempO-Seq and Affymetrix technologies.…”

Section: Transcriptomic Connectivity Mapping In Toxicologymentioning

confidence: 99%

“…119,120,123,126,127 Our initial findings also suggest that vector-based approaches combined with extreme transcriptomic profiles match chemicals to their known molecular targets more accurately than GSEA. 69,151 On the other hand, aggregation-based approaches could be more suitable for concentration−response modeling (see Figure 4) to estimate biological pathway activating concentrations. 68 Given the many steps involved in connectivity mapping workflows, further research on the contribution of different factors, including the choice of similarity measures, is necessary.…”

Section: Toxicologymentioning

confidence: 99%

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Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Shah

Bundy

Chambers

et al. 2022

Chem. Res. Toxicol.

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Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, patternmatching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration−response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

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Section: ■ Discussionmentioning

confidence: 52%

Section: Toxicologymentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Transcriptomic Connectivity Mapping In Toxicologymentioning

confidence: 99%

Section: Toxicologymentioning

confidence: 99%

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Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Shah

Bundy

Chambers

et al. 2022

Chem. Res. Toxicol.

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Transcriptomic-based evaluation of trichloroethylene glutathione and cysteine conjugates demonstrate phenotype-dependent stress responses in a panel of human in vitro models

Capinha

Zhang²,

Holzer³

et al. 2022

Arch Toxicol

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Environmental or occupational exposure of humans to trichloroethylene (TCE) has been associated with different extrahepatic toxic effects, including nephrotoxicity and neurotoxicity. Bioactivation of TCE via the glutathione (GSH) conjugation pathway has been proposed as underlying mechanism, although only few mechanistic studies have used cell models of human origin. In this study, six human derived cell models were evaluated as in vitro models representing potential target tissues of TCE-conjugates: RPTEC/TERT1 (kidney), HepaRG (liver), HUVEC/TERT2 (vascular endothelial), LUHMES (neuronal, dopaminergic), human induced pluripotent stem cells (hiPSC) derived peripheral neurons (UKN5) and hiPSC-derived differentiated brain cortical cultures containing all subtypes of neurons and astrocytes (BCC42). A high throughput transcriptomic screening, utilizing mRNA templated oligo-sequencing (TempO-Seq), was used to study transcriptomic effects after exposure to TCE-conjugates. Cells were exposed to a wide range of concentrations of S-(1,2-trans-dichlorovinyl)glutathione (1,2-DCVG), S-(1,2-trans-dichlorovinyl)-L-cysteine (1,2-DCVC), S-(2,2-dichlorovinyl)glutathione (2,2-DCVG), and S-(2,2-dichlorovinyl)-L-cysteine (2,2-DCVC). 1,2-DCVC caused stress responses belonging to the Nrf2 pathway and Unfolded protein response in all the tested models but to different extents. The renal model was the most sensitive model to both 1,2-DCVC and 1,2-DCVG, with an early Nrf2-response at 3 µM and hundreds of differentially expressed genes at higher concentrations. Exposure to 2,2-DCVG and 2,2-DCVC also resulted in the upregulation of Nrf2 pathway genes in RPTEC/TERT1 although at higher concentrations. Of the three neuronal models, both the LUHMES and BCC42 showed significant Nrf2-responses and at higher concentration UPR-responses, supporting recent hypotheses that 1,2-DCVC may be involved in neurotoxic effects of TCE. The cell models with the highest expression of γ-glutamyltransferase (GGT) enzymes, showed cellular responses to both 1,2-DCVG and 1,2-DCVC. Little to no effects were found in the neuronal models from 1,2-DCVG exposure due to their low GGT-expression. This study expands our knowledge on tissue specificity of TCE S-conjugates and emphasizes the value of human cell models together with transcriptomics for such mechanistic studies.

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From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

et al. 2023

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The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

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Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment

Abstract: Graphical abstract

Cited by 3 publications

References 56 publications

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Transcriptomic-based evaluation of trichloroethylene glutathione and cysteine conjugates demonstrate phenotype-dependent stress responses in a panel of human in vitro models

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

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