ArticleFrontiers in genetics2018
Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Analytical choices drive toxicogenomic potency estimates: a systematic evaluation of transcriptomic points of departure.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Mechanism-based new approach methodologies for in vitro detection of chemical-induced liver steatosis.Archives of toxicology · 2026Review
- Immune sentinel function of nasal mucosal epithelial cells in allergic rhinitis: a review on barrier damage and inflammatory amplification loops regulated by calcium signalling.Frontiers in immunology · 2026Review
- Steatotic liver disease induced by TCPOBOP-activated hepatic constitutive androstane receptor: primary and secondary gene responses with links to disease progression.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments.Toxicology in vitro : an international journal published in association with BIBRA · 2024Article
- Roles of transmembrane protein 135 in mitochondrial and peroxisomal functions - implications for age-related retinal disease.Frontiers in ophthalmology · 2024Article
- Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis.Chemical research in toxicology · 2023Article
- Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice.ACS medicinal chemistry letters · 2023Review
- Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.Nature biotechnology · 2023Article
- Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens.International journal of molecular sciences · 2022Review
- Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI).PLoS computational biology · 2022Article
- Review
- Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.Chemical research in toxicology · 2021Article
- Palmitate and Fructose Interact to Induce Human Hepatocytes to Produce Pro-Fibrotic Transcriptional Responses in Hepatic Stellate Cells Exposed to Conditioned Media.Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology · 2020Article
- Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.Frontiers in chemistry · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.