{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["John Hellgren"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15587"],"description":["Primary human hepatocytes from 3 different donors as 3D spheroids treated with 8 compounds (3 concentrations, 0.1xCmax, Cmax and 10xCmax) and vehicle for 7 h. Compounds have different degree of drug-induced liver injury concern.   Data for Figure 1 in connected publication. Experiment 2 of 2."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Nucleic Acid Extraction - RNA was then isolated from cell lysate following the manufacturer’s instructions using the Biomek i7 Hybrid robotic workstation (Beckman Coulter).","Sample Collection - Cells were processed by aspirating the cell culture medium and adding the lysis buffer mixture, containing Proteinase K, from the RNAdvance Cell kit (Beckman Coulter) directly to the wells of a 96-well plate while maintained on ice. The resulting cell lysates were mixed thoroughly by pipetting and incubated at room temperature for 30 minutes.","Library Construction - mRNA was selectively enriched and sequencing libraries were constructed using the KAPA mRNA HyperPrep Kit (Roche) according to the manufacturer’s recommended procedure, utilizing a Tecan Fluent® liquid handler for all automation steps. The integrity and size distribution of the resulting libraries were assessed on a Fragment Analyzer (Agilent) using the SS NGS fragment kit (1–6000 bp).","Sequencing - Sequencing was carried out on the Illumina NovaSeq 6000 platform (Illumina) with libraries loaded at a concentration of 1.75 nM according to the manufacturer's guidelines."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - RNA-seq data were processed and subjected to quality control using an internal Nextflow pipeline (v23.10). Reference genomes utilized included the human genome (GRCh38, Gencode v43). Sequencing reads were trimmed with FastP (v0.23.4) and transcript-level quantification was performed using Salmon (v1.10.1) following mapping to a transcriptome index comprising both cDNA and ncRNA entries. Quality control included STAR alignment (v2.7.11a) for mapping assessment and additional QC metrics were generated using FastQC (v0.12.1), RNAseQC (v2.3.5), and Samtools (v1.18), with results summarized in MultiQC (v1.17)."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["Illumina NovaSeq 6000"],"pubmed_abstract":["<h4>Background</h4>Early detection of drug-induced liver injury (DILI) during drug development is crucial for reducing drug attrition and ensuring the safety of patients. A versatile, biologically interpretable, and dose-dependent screening approach is therefore needed to inform early stop/go decisions and therapeutic margins.<h4>Methods</h4>We have developed AEGIS (Apoptotic Effector Genes In Safety), a preclinical DILI risk screening and prioritization tool that quantifies dose dependent perturbation of apoptosis-regulating transcription factors from transcriptomics data. We profiled transcriptomic responses after short exposures across primary human hepatocytes (PHH), HepG2/C3A cells, RAW 264.7 cells, and an acute Balb/c mouse study. From these profiles, AEGIS provides quantitative risk scores to rank and prioritize compounds and exposures.<h4>Results</h4>Here we show that AEGIS distinguishes compounds with different degree of DILI concern, achieving 86% specificity, 75% sensitivity and 90% precision in PHHs. We demonstrate versatility in data type usage and clinical translation of AEGIS with accurate predictions across species, in vitro and in vivo models, and therapeutic modalities. In addition, we apply AEGIS in a precision medicine context during drug-development within the pharmaceutical industry and investigate the contribution of underlying liver disease on DILI severity. Our findings indicate that cells from patients with metabolic dysfunction-associated steatotic liver disease (MASLD) develop more severe DILI from treatment with troglitazone, aligning with preclinical observations.<h4>Conclusions</h4>Using AEGIS early in drug discovery exemplifies a more efficient approach to identify and mitigate potential safety concerns. This can reduce the need for animal testing, and accelerates drug discovery, ultimately providing the right medicines to patients more quickly."],"study_type":["RNA-seq of coding RNA"],"species":["Homo sapiens"],"pubmed_title":["Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery"],"pubmed_authors":["John Hellgren","Bhavik Chouhan","Hellgren, J., Chouhan, B., Uatay, A., Elgendy, R., Lindgren, J., Toki, N., Bonetti, A., Chaudhari, A., Pryde, K., Andersson, P., Kalm, M., Karlsson, F., Sagemark, J., Williams, D., Tan, J., John, B., Gallon, J."],"additional_accession":[]},"is_claimable":false,"name":"RNA-seq data of primary human hepatocytes spheroids treated with compounds (experiment 2)","description":"Primary human hepatocytes from 3 different donors as 3D spheroids treated with 8 compounds (3 concentrations, 0.1xCmax, Cmax and 10xCmax) and vehicle for 7 h. Compounds have different degree of drug-induced liver injury concern.   Data for Figure 1 in connected publication. Experiment 2 of 2.","dates":{"release":"2026-01-16T00:00:00Z","modification":"2026-05-27T17:54:19.497Z","creation":"2025-09-12T12:20:06.039Z"},"accession":"E-MTAB-15587","cross_references":{"Biostudies":["E-MTAB-15586"],"EFO":["EFO_0002944","EFO_0004170","EFO_0005518","EFO_0003816","EFO_0003738","EFO_0004184"],"doi":["10.1038/s43856-025-01306-7"]}}