A Pipeline for High Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE105050 | GEO | 2018/02/22
REPOSITORIES: GEO
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Project description:Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for in vitro toxicity screening. In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose-response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration-response points of departure. The methods are extensible to other forms of concentration-response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state.
| S-EPMC5672545 | biostudies-literature
Project description:A Pipeline for High Throughput Concentration Response Modeling of Gene Expression for Toxicogenomics
| PRJNA414472 | ENA
Project description:We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants.
2021-09-01 | GSE152128 | GEO
Project description:Introduction: Higher-throughput mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq® as an integrated genotoxicity testing approach. Objective: To determine if the high-throughput CometChip® assay and TGx-DDI biomarker analysis can be combined using human HepaRG™ cells to provide an efficient next-generation genotoxicity screening approach to identify DNA damage-inducing (DDI) chemicals. Methods: Metabolically competent human HepaRG™ cell cultures were exposed to increasing concentrations of 12 chemicals (9 DDI and 3 non-DDI) using a repeat exposure design (0 hr, 24 hr, 48 hr) in 96-well plates to measure and classify chemicals based on their ability to damage DNA. After a 4 hr recovery period, exposed cells were used to perform the Trevigen CometChip assay to assess DNA damage or used for TempO-Seq® S1500+ Targeted Transcriptome Sequencing for TGx-DDI classification purposes. Bioinformatics and statistical tools were used to classify chemicals as DDI or non-DDI using three separate analyses. Benchmark concentration analysis was also conducted for both assays for the purposes of potency ranking. Results: In combination, the CometChip® assay and the TGx-DDI were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI for all 12 chemicals were highly concordant (within four-fold) and resulted in identical potency rankings. Conclusions: These results suggest that these two assays can be integrated for efficient, high-throughput identification of DNA damage in HepaRG™ cells. This study provides evidence for the complementarity of the high-throughput CometChip® assay with TGx-DDI biomarker analysis. Integration of these two tests provides an effective and high-throughput approach to identify DDI agents. This work is a first step in accomplishing a more integrated genotoxicity testing strategy to derive mechanistic information to better inform human health risk assessment in a high-throughput manner.
2021-07-21 | GSE171360 | GEO
Project description:Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.
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Project description:New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical safety assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In the present study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening (HTS) assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high throughput hazard evaluation of environmental chemicals.
2021-01-25 | GSE162855 | GEO
Project description:High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1751 unique chemicals from the EPA’s ToxCast collection in MCF7 cells using eight concentrations and an exposure duration of 6 hours. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms-of-action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally-related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points-of-departure, predicting chemicals’ molecular mechanism(s)-of-action (MeOA) and grouping chemicals with similar bioactivity profiles.
2024-08-20 | GSE272548 | GEO
Project description:Structure probing coupled with high-throughput sequencing holds the potential to revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite major technological advances, intrinsic noise and high coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline which accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome-wide. We demonstrate on two yeast data sets that our method has greatly increased sensitivity, enabling the identification of modified regions on many more transcripts compared with existing pipelines. It also provides confident predictions at much lower coverage levels than previously reported. Our results show that statistical modeling greatly extends the scope and potential of transcriptome-wide structure probing experiments.
2016-11-01 | GSE78208 | GEO
Project description:A systematic procedure is introduced for modeling charge-neutral non-polar surfaces of ionic minerals containing polyatomic anions. By integrating distance- and charge-based clustering to identify chemical species within the mineral bulk, our pipeline, PolyCleaver, renders a variety of theoretically viable surface terminations. As a demonstrative example, this approach was applied to forsterite (Mg2SiO4), unveiling a rich interface landscape based on interactions with formaldehyde, a relevant multifaceted molecule, and more particularly in prebiotic chemistry. This high-throughput method, going beyond techniques traditionally applied in the modeling of minerals, offers new insights into the potential catalytic properties of diverse surfaces, enabling a broader exploration of synthetic pathways in complex mineral systems.
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