<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter/><organism>Rattus norvegicus</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-13996</full_dataset_link><description>Petroleum substances are complex mixtures of hydrocarbons with vast structural diversity, posing significant challenges for toxicity assessment. To evaluate their safety under the REACH legislation, in vivo toxicological tests, particularly prenatal developmental toxicity assessments, are mandated. However, these tests are resource-intensive. Given the substantial number of compositionally and biologically similar petroleum substances, a more efficient approach is essential to meet regulatory requirements while supporting the industry's competitiveness and innovation. Petroleum substance toxicity is frequently attributed to the presence of polycyclic aromatic hydrocarbons (PAHs). PAHs’ underlying mechanism of action involves the aryl hydrocarbon receptor (AhR) activation as a molecular initiating event followed by cytochrome P450 induction, which in turn catalyzes the transformation of PAH to epoxide-containing reactive metabolites. To evaluate the dependence of this adverse outcome pathway, an AhR-knockout (AhR-KO) rat model was orally exposed to Benzo[a]Pyrene (BaP), a prototypical PAH. This study offers valuable insights into the AhR-dependent and -independent maternal toxicity and fetal developmental effects of BaP exposure, employing transcriptomic assessments to enhance the comprehension of the underlying mechanisms across four distinct tissues: blood, kidney, liver, and thymus. This research contributes toward a more efficient approach to assessing the safety of diverse petroleum substances, in alignment with regulatory objectives and the interests of the industry.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Library Construction - tracted total RNA were first treated by Ribo-depletion using the NEXTFLEX Ribonaut rRNA Depletion. For this, 1ug of total were used for all solid tissue, and 143 ng for the blood (corresponding of the smallest extracted amount for animal 19). Library preparation was then made using the NEXTFLEX Rapid Directional RNA-Seq Kit 2.0, with the corresponding unique dual indexes</sample_protocol><sample_protocol>Nucleic Acid Extraction - The total RNA of all 96 samples were extracted using different methods depending on the tissue of origins. Solid tissues (Liver, Kidney and Thymus) were extracted using the miRNeasy Mini Kit (Qiagen, Cat.No. 217004). For this, small pieces of tissue were cut and dissolved in 700 µl of Qiazol using the MiniBeadBeaterPlus for two cycles of 15 seconds and 5 minutes, respectively. The blood was processed with the RNeasy Protect Animal Blood Kit, (Qiagen , Cat. No. 73224). All extracted total RNAs were then assessed for quality and dosed using a bioAnalyser</sample_protocol><sample_protocol>Sample Collection - Representative samples of blood, gross lesions, kidney, liver, thymus and uterus were collected from all animals and preserved in 10% neutral buffered formalin or snap frozen in liquid nitrogen and stored in deep freezer (&lt;-75°C). Organs were collected at scheduled necropsy.</sample_protocol><sample_protocol>Sequencing - The ribo-depleted libraries were sequenced on an Illumina NovaSeq 6000 using a S2 200 cycles v1.5 flowcell. After base calling and demultiplexing, we obtained an average of 43.9 million cluster passing quality control for all 96 samples (with a median of 43.45M, with a range from 26.1M to 55.1M).</sample_protocol><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>organisation</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Data were normalized using DESeq2 standard R package algorithm</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq 6000</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Rattus norvegicus</species><additional_accession>ERP159301</additional_accession><pubmed_authors>Florian Caiment</pubmed_authors></additional><is_claimable>false</is_claimable><name>Polyaromatic Hydrocarbon Driven Developmental Toxicity is Dependent on the Aryl Hydrocarbon Receptor: A case study with Benzo(a)Pyrene</name><description>Petroleum substances are complex mixtures of hydrocarbons with vast structural diversity, posing significant challenges for toxicity assessment. To evaluate their safety under the REACH legislation, in vivo toxicological tests, particularly prenatal developmental toxicity assessments, are mandated. However, these tests are resource-intensive. Given the substantial number of compositionally and biologically similar petroleum substances, a more efficient approach is essential to meet regulatory requirements while supporting the industry's competitiveness and innovation. Petroleum substance toxicity is frequently attributed to the presence of polycyclic aromatic hydrocarbons (PAHs). PAHs’ underlying mechanism of action involves the aryl hydrocarbon receptor (AhR) activation as a molecular initiating event followed by cytochrome P450 induction, which in turn catalyzes the transformation of PAH to epoxide-containing reactive metabolites. To evaluate the dependence of this adverse outcome pathway, an AhR-knockout (AhR-KO) rat model was orally exposed to Benzo[a]Pyrene (BaP), a prototypical PAH. This study offers valuable insights into the AhR-dependent and -independent maternal toxicity and fetal developmental effects of BaP exposure, employing transcriptomic assessments to enhance the comprehension of the underlying mechanisms across four distinct tissues: blood, kidney, liver, and thymus. This research contributes toward a more efficient approach to assessing the safety of diverse petroleum substances, in alignment with regulatory objectives and the interests of the industry.</description><dates><release>2025-06-29T00:00:00Z</release><modification>2025-01-27T15:00:07.423Z</modification><creation>2024-04-09T10:50:55.192Z</creation></dates><accession>E-MTAB-13996</accession><cross_references><ENA>ERP159301</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>