U.S. Environmental Protection Agency Office of Research and Development EPA www.epa.gov Thomas 2019 Bryant A. Chambers and Imran Shah Center for Computational Toxicology and Exposure, US EPA Constructing Unique Stress Signatures Identifying best performing stress signatures Stress signature activity within LINCS test set Conclusions and future directions Bryant A. Chambers l chambers.bryant@epa.gov l 919.541.4268 Finding a test set within the Library of Integrated Network- Based Cellular Signatures (LINCS) SR Pathway Accuracy in 1st Accuracy by 2nd Accuracy by 3rd DDR-400 75% 100% 100% UPR-050 100% 100% 100% HSR-GO 57% 100% 100% HPX-WINT 100% 100% 100% MTL – 200 33% 33% 67% OSR – 200 25% 50% 50% Mean Accuracy 72% 88% 91% Background and Hypothesis Key Conclusions • SRP can be accurately identified using gene signatures in transcriptomic data. • Distinct signals arise from well known chemical agents. • Consensus SRP signatures perform better than published signatures. • Consensus SRP signatures activity identified in LINCS data test set. Future Directions • Concentration-response analysis to estimate benchmark concentrations (BMC) • The lack of available methods to test non-specific chemical points of departure can be expanded to include a new approach method derived from general stress response transcriptomic assays. • Tissue and cell specificity • Expression of stress response systems is partially dependent on cell and tissue type; as such, a deeper understanding of tissue dependency must be achieved. SRP Abbreviation Reference Chemical DNA Damage Response DDR Benzo(a)pyrene Glycinamide Lasicoarpine Methylmethanesulfonate Unfolded Protein Response UPR Brefeldin A Thapsigargin Tunicamycin Heat Shock Response HSR Geldanamycin Heat with recovery Radicicol Response to Hypoxia HPX Oxygen VU-0418946-1 VU-0418946-2 Metal Response MTL Silver Nitrate Zinc Oxidative Stress Response OSR Hydrogen peroxide Tert-butyl hydrogen peroxide A) Activity of SRPs in training data set B) Performance of SRP signatures against randomized null C) Performance of SRP signatures against published signatures Accuracy of Signatures in training data set Highly gene-overlapped published signatures 𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨𝑨 Unique consensus gene space Consensus Signature Set Construction • Identified 48 relevant published signatures by inclusion of ~ key genes • Use “Wisdom of the Crowd” to score gene inclusion • Frequency based (f): 𝑓𝑓𝑖𝑖𝑖𝑖𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔−𝑓𝑓𝑜𝑜𝑜𝑜𝑜𝑜𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑓𝑓𝑖𝑖𝑖𝑖𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔+ 𝑓𝑓𝑜𝑜𝑜𝑜𝑜𝑜𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 Reference Chemicals • 49 reference chemicals were identified in the literature • 2000 GSEs found in the Gene Expression Omnibus matching • Only 35 GSEs proved sufficiently annotated DDR UPR HSR HXP MTL OSR Highly Probable Library of Integrated Network-Based Cellular Signatures (LINCS) • Searched all LINCS chemicals in pub med against stress systems • 7 terms (e.g., ‘DNA damage’, ‘er stress’, unfolded protein response’) • Totaled 32,679 searches • Pairwise mutual information (PMI) score calculated for each chemical • 𝑃𝑃𝑃𝑃𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐, 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠= log 𝐹𝐹(𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) 𝐹𝐹𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝐹𝐹(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) • Filtered to chemical with PMI > 1 and references >200 yielding a subset of 90 perturbagens • Pulled first 500 abstracts for each match and validated 68 high probability chemicals by review • Transcriptomic Dataset includes: • 11000 Profiles • 81 cell types Concentration response for HSR chemical geldanamycin Activity of SRP consensus signatures was evaluated in 68 “highly probable” chemicals in the LINCs database. Cell type aggregated data indicates broad applicability • 100% of chemicals showed activity in at least one cell line • 92% of HSR and 88% of UPR in cell aggregated data • 30/68 chemical have concentration response • Tissue and cell type is IMPORTANT DDR UPR HSR HPX MTL OSR References Ankley (2010) Environmental Toxicology and Chemistry, 29: 730-741 Simmons (2009)Toxicological Sciences 111(2,) 202–225 Judson (20