Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling

Low salinity influences the dose-dependent transcriptomic responses of oysters to cadmium

Species in estuaries tend to undergo both cadmium (Cd) and low salinity stress. However, how low salinity affects the Cd toxicity has not been fully understood. Investigating the impacts of low salinity on the dose-response relationships between Cd and biological endpoints has potential to enhance our understanding of the combined effects of low salinity and Cd. In this work, changes in the transcriptomes of Pacific oysters were analyzed following exposure to Cd (5, 20, 80 μg/L Cd2+) under normal (31.4 psu) and low (15.7 psu) salinity conditions, and then the dose-response relationship between Cd and transcriptome was characterized in a high-throughput manner. The benchmark dose (BMD) of gene expression, as a point of departure (POD), was also calculated based on the fitted dose-response model. We found that low salinity treatment significantly influenced the dose-response relationships between Cd and transcripts in oysters indicated by altered dose-response curves. In details, a total of 219 DEGs were commonly fitted to best models under both normal and low salinity conditions. Nearly three quarters of dose-response curves varied with salinity condition. Some monotonic dose-response curves in normal salinity condition even were replaced by nonmonotonic curves in low salinity condition. Low salinity treatment decreased the PODs of differentially expressed genes induced by Cd, suggesting that gene differential expression was more prone to being triggered by Cd in low salinity condition. The changed sensitivity to Cd in low salinity condition should be taken into consideration when using oyster as an indicator to assess the ecological risk of Cd pollution in estuaries.

Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos

High-throughput transcriptomics (HTTr) is increasingly applied to zebrafish embryos to survey the toxicological effects of environmental chemicals. Before the adoption of this approach in regulatory testing, it is essential to characterize background noise in order to guide experimental designs. We thus empirically quantified the HTTr false discovery rate (FDR) across different embryo pool sizes, sample sizes, and concentration groups for toxicology studies. We exposed zebrafish embryos to 0.1% dimethyl sulfoxide (DMSO) for 5 days. Pools of 1, 5, 10, and 20 embryos were created (n = 24 samples for each pool size). Samples were sequenced on the TempO-Seq platform and then randomly assigned to mock treatment groups before differentially expressed gene (DEG), pathway, and benchmark concentration (BMC) analyses. Given that all samples were treated with DMSO, any significant DEGs, pathways, or BMCs are false positives. As expected, we found decreasing FDRs for DEG and pathway analyses with increasing pool and sample sizes. Similarly, FDRs for BMC analyses decreased with increasing pool size and concentration groups, with more stringent BMC premodel filtering reducing BMC FDRs. Our study provides foundational data for determining appropriate experiment designs for regulatory toxicity testing with HTTr in zebrafish embryos.

Identify organic contaminants of high-concern based on non-targeted toxicity testing and non-targeted LC-HRMS analysis in tap water and source water along the Yangtze River

Many organic pollutants were detected in tap water (TW) and source water (SW) along the Yangtze River. However, the potential toxic effects and the high-concern organics (HCOs) which drive the effect are still unknown. Here, a non-targeted toxicity testing method based on the concentration-dependent transcriptome and non-targeted LC-HRMS analysis combining tiered filtering were used to reveal the overall biological effects and chemical information. Subsequently, we developed a qualitative pathway-structure relationship (QPSR) model to effectively match the biological and chemical information and successfully identified HCOs in TW and SW along the Yangtze River by potential substructures of HCOs. Non-targeted toxicity testing found that the biological potency of both TW and SW was stronger in the downstream of the Yangtze River, and disruption of the endocrine system and cancer were the main drivers of the effect. In addition, non-targeted LC-HRMS analysis combined with retention time prediction results identified 3220 and 631 high-confidence compound structures in positive and negative ion modes, respectively. Then, QPSR model was further implied and identified a total of 103 HCOs, containing 35 industrial chemicals, 30 PPCPs, 26 pesticides, and 12 hormones in TW and SW, respectively. Among them, the neuroactive and hormonal compounds oxoamide, 8-iso-16-cyclohexyl-tetranor prostaglandin E2, E Keppra, and Tocris-0788 showed the highest frequency of detection, which were identified in more than 1/3 of the samples. The strategy of combining non-targeted toxicity testing and non-targeted LC-HRMS analysis will support comprehensive biological effect assessment, identification of HCOs, and risk control of mixtures.

Characterizing temporal variability and repeatability of dose-dependent functional genomics approach for evaluating triclosan toxification

Dose-dependent functional genomics approach has shown great advantage in identifying the molecular initiating event (MIE) of chemical toxification and yielding point of departure (POD) at genome-wide scale. However, POD variability and repeatability derived from experimental design (settings of dose, replicate number, and exposure time) has not been fully determined. In this work, we evaluated POD profiles perturbed by triclosan (TCS) using dose-dependent functional genomics approach in Saccharomyces cerevisiae at multiple time points (9 h, 24 h and 48 h). The full dataset (total 9 concentrations with 6 replicates per treatment) at 9 h was subsampled 484 times to generate subsets of 4 dose groups (Dose A - Dose D with varied concentration range and spacing) and 5 replicate numbers (2 reps - 6 reps). Firstly, given the accuracy of POD and the experimental cost, the POD profiles from 484 subsampled datasets demonstrated that the Dose C group (space narrow at high concentrations and wide dose range) with three replicates was best choice at both gene and pathway levels. Secondly, the variability of POD was found to be relatively robustness and stability across different experimental designs, but POD was more dependent on the dose range and interval than the number of replicates. Thirdly, MIE of TCS toxification was identified to be the glycerophospholipid metabolism pathway at all-time points, supporting the ability of our approach to accurately recognize MIE of chemical toxification at both short- and long-term exposure. Finally, we identified and validated 13 key mutant strains involved in MIE of TCS toxification, which could serve as biomarkers for TCS exposure. Taken together, our work evaluated the repeatability of dose-dependent functional genomics approach and the variability of POD and MIE of TCS toxification, which will benefit the experimental design for future dose-dependent functional genomics study.

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

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.