{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:17:36Z","timestamp":1772173056408,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010148","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000}}],"reference-count":92,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004330","name":"GlaxoSmithKline","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004330","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012357","name":"LifeArc","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012357","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of \u201cfirst activation\u201d as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time-concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time-concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/anikaliu.shinyapps.io\/dili_cascades\" xlink:type=\"simple\">https:\/\/anikaliu.shinyapps.io\/dili_cascades<\/jats:ext-link>\n                    ), which allows users to query events of interest in more detail.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010148","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T13:44:10Z","timestamp":1654868650000},"page":"e1010148","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8561-4700","authenticated-orcid":true,"given":"Anika","family":"Liu","sequence":"first","affiliation":[]},{"given":"Namshik","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jordi","family":"Munoz-Muriedas","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"pcbi.1010148.ref001","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1001\/jamainternmed.2016.6008","article-title":"Failure of investigational drugs in late-stage clinical development and publication of trial results","volume":"176","author":"TJ Hwang","year":"2016","journal-title":"JAMA Internal Medicine"},{"key":"pcbi.1010148.ref002","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1038\/nrd.2016.184","article-title":"Phase II and phase III failures: 2013\u20132015","volume":"15","author":"RK Harrison","year":"2016","journal-title":"Nature Reviews Drug Discovery"},{"key":"pcbi.1010148.ref003","article-title":"Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization *","author":"JPF Bai","year":"2012"},{"key":"pcbi.1010148.ref004","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1007\/s00204-017-2045-3","article-title":"Adverse outcome pathways: opportunities, limitations and open questions","volume":"91","author":"M Leist","year":"2017","journal-title":"Archives of Toxicology"},{"key":"pcbi.1010148.ref005","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1002\/etc.34","article-title":"Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment","author":"GT Ankley","year":"2010","journal-title":"Environmental Toxicology and Chemistry"},{"key":"pcbi.1010148.ref006","unstructured":"OECD (Organisation for Economic Co-operation and Development). USERS\u2019 HANDBOOK SUPPLEMENT TO THE GUIDANCE DOCUMENT FOR DEVELOPING AND ASSESSING AOPs. OECD Environment, Health and Safety Publications Series on Testing and Assessment. 2018; 1\u201362."},{"key":"pcbi.1010148.ref007","first-page":"295","article-title":"The Environment and Disease: Association or Causation?","author":"AB Hill","year":"1965","journal-title":"Proc R Soc Med"},{"key":"pcbi.1010148.ref008","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1002\/jat.2984","article-title":"Mode of action human relevance (species concordance) framework: Evolution of the Bradford Hill considerations and comparative analysis of weight of evidence.","author":"ME Meek","year":"2014","journal-title":"Journal of Applied Toxicology"},{"key":"pcbi.1010148.ref009","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s40572-016-0079-y","article-title":"Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources.","volume":"3","author":"NO Oki","year":"2016","journal-title":"Current Environmental Health Reports"},{"key":"pcbi.1010148.ref010","first-page":"350","article-title":"An integrative data mining approach to identifying adverse outcome pathway signatures","author":"NO Oki","year":"2016","journal-title":"Toxicology"},{"key":"pcbi.1010148.ref011","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1093\/toxsci\/kfw017","article-title":"Integrating publicly available data to generate computationally predicted adverse outcome pathways for fatty liver","volume":"150","author":"SM Bell","year":"2016","journal-title":"Toxicological Sciences"},{"key":"pcbi.1010148.ref012","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1111\/risa.13423","article-title":"Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).","volume":"40","author":"LD Burgoon","year":"2020","journal-title":"Risk Analysis."},{"key":"pcbi.1010148.ref013","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1021\/acs.chemrestox.9b00040","article-title":"Development of Adverse Outcome Pathway for PPAR\u03b3Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach","volume":"32","author":"J Jeong","year":"2019","journal-title":"Chemical Research in Toxicology"},{"key":"pcbi.1010148.ref014","first-page":"8","article-title":"A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury","author":"P Kohonen","year":"2017","journal-title":"Nature Communications"},{"key":"pcbi.1010148.ref015","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/tpj.2017.17","article-title":"Toxicogenomic module associations with pathogenesis: A network-based approach to understanding drug toxicity","volume":"18","author":"JJ Sutherland","year":"2018","journal-title":"Pharmacogenomics Journal"},{"key":"pcbi.1010148.ref016","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3389\/fgene.2017.00142","article-title":"Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks.","volume":"8","author":"TM Souza","year":"2017","journal-title":"Frontiers in Genetics"},{"key":"pcbi.1010148.ref017","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.taap.2018.07.023","article-title":"Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays","volume":"356","author":"J Rooney","year":"2018","journal-title":"Toxicology and Applied Pharmacology"},{"key":"pcbi.1010148.ref018","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0200004","article-title":"Activation of Nrf2 in the liver is associated with stress resistance mediated by suppression of the growth hormone-regulated STAT5b transcription factor.","volume":"13","author":"J Rooney","year":"2018","journal-title":"PLoS ONE."},{"key":"pcbi.1010148.ref019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41572-019-0105-0","article-title":"Drug-induced liver injury","volume":"5","author":"RJ Andrade","year":"2019","journal-title":"Nature Reviews Disease Primers"},{"key":"pcbi.1010148.ref020","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1055\/s-0034-1375962","article-title":"Drug-induced liver injury and drug development: Industry perspective","volume":"34","author":"A. 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