?Coral Genomics is combining whole genome sequencing and RNA profiling in cell- based functional assays to predict human variation in the metabolism and toxicity of compounds encountered in therapeutic and environmental exposures. RNA profiling before and after exposure provides a high-dimensional representation of changes in cell function, and the use of rapid, cost-effective profiling could enable toxicity testing in large, diverse panels of human cells. Thus, to address the need for toxicity testing that better reflects the genetic diversity of human populations, Coral developed innovative approaches to reduce the cost and increase the speed of high-throughput sequencing assays. In a successful Phase I SBIR (R43 HG010445), Coral established protocols for cost-effective sample multiplexing for rapid shallow sequencing of samples, developed a re-indexing workflow to pool samples into a single sequencing run, and combined streamlined sequencing solutions with novel functional assays and algorithms to improve the performance of polygenic risk scores (PRS). In a collaboration with the US Food and Drug Administration (FDA), Coral applied these techniques to predict patient-specific hepatocyte response profiles to acetaminophen (N = 200) and found significant interindividual variability in toxicity. Importantly, the findings show that a patient?s genotype predicts a significant portion (AUC = 0.85) of the interindividual variation, indicating the approach is sensitive to genetic diversity. Preliminary findings indicate Coral?s approach may be a sensitive means for identifying differences in toxic responses to compounds across diverse populations. Further development and testing across multiple compounds in a large, diverse sample has the potential to provide a scalable, high-throughput platform for effective toxicity testing that is more representative of the diversity of human responses. Coral proposes a Direct to Phase II SBIR in response to NIEHS?s RFA-ES-20-208 to evaluate these methods in three cell models and advance at least one model to full-scale testing with 250 patient samples and 100 compounds with known toxicity profiles.
Aim 1. Characterize the interindividual variability of RNA profile shifts in three human cell models (i.e., immune, hepatocyte, and embryoid body) exposed to ten compounds with known toxicity profiles. Select Milestones: 1) 4,500 response profiles; 2) Statistical significance of intraindividual variability vs. interindividual variability (p<0.001); 3) ? 50% of differentially expressed genes associated with exposure observable with < 1 million reads; 4) R2 > 0.3 for FAERS profile and AUC > 0.85 for Tox21 dataset.
Aim 2. Using the most predictive cell model, characterize interindividual variability of sublethal cytotoxic responses to 100 compounds with known toxicity profiles to develop a robust model for predicting systemic toxicity. Select Milestones: 1) 75,000 response profiles; 2) Identification of ? 5 compounds with high population level variability in toxicity (1-50th percentile variation >10); 3) R2 > 0.4 using chemical and transcriptional data to predict FAERS profile; 5) R2 at predicting toxicity profile of an individual across 100 compounds > 0.5.
Current methods for predicting the harmful effects of chemical exposures are not well suited to identifying the range of responses that appear when a large, diverse human population is exposed. As a result, well- intentioned policy decisions such as safe exposure limits and drug approvals may be based on findings that do not reflect the risks these chemicals pose for the most sensitive individuals. This project is designed to determine whether new methods to characterize the effects of chemicals on a large set of diverse human samples can improve policy decisions and protect human health by providing a better prediction of the range of human responses that can be expected when a chemical is used in real-world settings.