{"doi":"10.1101/2021.08.26.457742","title":"Transcriptomic mapping of the inter-individual variability of cellular stress response activation in primary human hepatocytes","abstract":"Abstract Background & Aims One of the early key events of drug-induced liver injury (DILI) is the activation of adaptive stress responses, a cellular mechanism to overcome stress. Given the diversity of DILI outcomes and lack in understanding of population variability, we mapped the inter-individual variability in stress response activation to improve DILI prediction. Approach & Results High-throughput transcriptome analysis of over 8,000 samples was performed in primary human hepatocytes of 50 individuals upon 8 to 24 h exposure to broad concentration ranges of stress inducers: tunicamycin to induce the unfolded protein response (UPR), diethyl maleate for the oxidative stress response, cisplatin for the DNA damage response and TNFα for NF-κB signalling. This allowed investigation of the inter-individual variability in concentration-dependent stress response activation, where the average of benchmark concentrations (BMCs) had a maximum difference of 864, 13, 13 and 259-fold between different hepatocytes for UPR, oxidative stress, DNA damage and NF-κB signalling-related genes, respectively. Hepatocytes from patients with liver disease resulted in less stress response activation. Using a population mixed-effect framework, the distribution of BMCs and maximum fold change were modelled, allowing simulation of smaller or larger PHH panel sizes. Small panel sizes systematically under-estimated the variance and resulted in low probabilities in estimating the correct variance for the human population. Moreover, estimated toxicodynamic variability factors were up to 2-fold higher than the standard uncertainty factor of 10 1/2 to account for population variability during risk assessment, exemplifying the need of data-driven variability factors. Conclusions Overall, by combining high-throughput transcriptome analysis and population modelling, improved understanding of variability in stress response activation across the human population could be established, thereby contributing towards improved prediction of DILI.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":217793,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9043,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":815065,"name":"Witold Więcek","orcid":"0000-0003-3016-1449","position":1,"is_corresponding":false},{"id":815683,"name":"Suzanna Huppelschoten","orcid":null,"position":2,"is_corresponding":false},{"id":815066,"name":"Peter Bouwman","orcid":"0000-0002-9252-5896","position":3,"is_corresponding":false},{"id":815067,"name":"Audrey Baze","orcid":"0000-0002-5783-0799","position":4,"is_corresponding":false},{"id":815684,"name":"C. Parmentier","orcid":null,"position":5,"is_corresponding":false},{"id":815685,"name":"Lysiane Richert","orcid":null,"position":6,"is_corresponding":false},{"id":65889,"name":"Richard S. Paules","orcid":"0000-0001-9106-7486","position":7,"is_corresponding":false},{"id":445368,"name":"Frédéric Y. Bois","orcid":"0000-0002-4154-0391","position":8,"is_corresponding":false},{"id":815068,"name":"Bob van de Water","orcid":"0000-0002-5839-2380","position":9,"is_corresponding":false},{"id":815064,"name":"Marije Niemeijer","orcid":"0000-0003-0045-2317","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-18T23:53:24.683898Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}