Project description:The aim of this study was to perform the multiscale correlation between quantitative texture features phenotype of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Images from pre-biopsy 3T bpMRI scans in clinically localised prostate cancer (PCa) patients of various risk categories (n=15) were used to extract textural features. The genomic landscape of hypoxia-related genes expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the three important hypoxia-related genes with 28 radiomic texture features. Following this, cBioportal was accessed and a gene-oriented query was conducted to extract Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Correlation analysis using Pearson's coefficients calculated against each selected gene profile; survival analysis using Kaplan-Meier estimators were carried out. We found the quantitative bpMR imaging textural features, including histogram and grey level co-occurrence matrix (GLCM), correlated with hypoxia related genes (ANGPTL4, VEGFA, and P4HA1) seen on RNA sequencing using TempO-Seq method. Further radiogenomic analysis, including data accessed on cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcome, with a median survival of 81.11: 133.00 months in those with and without alterations of genes respectively. In summary, radiomic texture features of bpMRI in localised PCa correlate with the expression of hypoxia-related genes expression in prostate cancer. The expression data analysis showed that hypoxia-related genes are associated with poor survival.
Project description:We aimed to compare the baseline expression of liver function and metabolism-related genes across the three liver models. Liver in vitro models are commonly used to study liver biology, disease mechanisms, and drug metabolism. Understanding their baseline gene expression profiles is crucial for interpreting experimental results and selecting the most appropriate model for specific research questions. Experimental workflow Cell Culture: Culture HepG2, PHHs, and hiPSCs-derived HLCs under standard, untreated conditions. Cell lysis: Collect RNA lysates using BioSpyder 2x enhanced lysis buffer from each cell type. Sequencing: Ship lysates to BioClavis, 201 Dumbarton Rd, Glasgow G81 4XJ, United Kingdom and sequence using TempoSeq targeted sequencing technology: https://www.bioclavis.co.uk/temposeq Data Analysis: Perform quality control of the sequencing results, normalize the data and compare normalized gene expression profiles, focusing on liver function and metabolic genes across the three models.
Project description:We wanted to investigate the transcriptional effect of Nitrofurantoin, a known drug induced liver injury (DILI)-compound, on hiPSC-derived HLCs. The goal was to identify which srress response pathways and biological processes are activated or altered in HLCs following Nitrofurantoin exposure. The compound is known to cause hepatotoxicity through mechanisms of oxidative stress, mitochondrial dysfunction, but the precise cellular responses in human liver models remain not fully understood.
Project description:For the purpose of mechanism-based risk assessment, we investigated temporal concentration-dependent responses in cultures of PHH and HepaRG cells exposed to three cosmetics ingredients, 2,7-naphthalenediol (NPT), triclosan (TCS) and butylated hydroxytoluene (BHT), that are suspected to have liver injury liability. To facilitate the identification of early KEs and visualisation of their development over time, samples were collected at 4 time points, namely 8 and 24 hours (single exposure), 48 and 72 hours (daily repeated exposure), after exposure to a broad concentration range. Concentrations were selected based on the estimated Cmax of the cosmetic ingredients. The selected maximum concentration was set at approximately 100x Cmax, whilst the minimum tested concentration was approximately 0.2x Cmax. The large concentration range allowed to apply benchmark concentration (BMC) modelling on individual genes as well as gene co-expression networks to derive in vitro transcriptomics benchmark concentrations (BMCs) and assess their suitability to be used as PoD in chemical risk assessment.
Project description:For the purpose of mechanism-based risk assessment, we investigated temporal concentration-dependent responses in cultures of PHH and HepaRG cells exposed to three drugs, acetaminophen (APAP), cyclosporine A (CSA) and valproic acid (VPA), that have a high liability for drug-induced liver injury. To facilitate the identification of early KEs and visualisation of their development over time, samples were collected at 4 time points, namely 8 and 24 hours (single exposure), 48 and 72 hours (daily repeated exposure), after exposure to a broad concentration range. Concentrations were selected based on the reported total Cmax of each drug. As these are approved drugs currently on the market, they are not expected to induce overt adverse effects around Cmax. Therefore, the selected maximum concentration was set at approximately 30x Cmax, whilst the minimum tested concentration was approximately 0.1x Cmax. The large concentration range allowed to apply benchmark concentration (BMC) modelling on individual genes as well as gene co-expression networks to derive in vitro transcriptomics benchmark concentrations (BMCs) and assess their suitability to be used as PoD in chemical risk assessment.
Project description:The experiment investigates the effects of five well-known chemicals on the transcriptome of the HepaRG cell line, a metabolically competent hepatic cell line. The cells were treated individually with an increasing concentrations of aflatoxin B1, benzo[a]pyrene, cyclosporine A, rotenone or trichostatin A at five exposure time points followed by targeted RNA-seq using TempO-Seq technology (the panel of human whole transcriptome). The aim of the study was to explore how and to what extent the point-of-departure (POD) obtained from an in vitro transcriptomics study varied as a function of exposure time.
Project description:High throughput transcriptomics using the TempO-Seq (TM) platform for dose response modelling of 3 different cell lines treated with the compound Benzophenone-4 as part of case study to explore the application of Next Generation Risk Assessment approaches for a sunscreen active ingredient. The data generated here was produced as one part of a toolbox of technologies to inform on bioactivity.
Project description:High throughput transcriptomics using the TempO-Seq (TM) platform for dose response modelling of 3 different cell lines treated with multiple compounds as part of a study to generate a toolbox and workflow for non-animal safety assessments. The data generated here was produced as one part of a toolbox of technologies to inform on bioactivity.
Project description:High throughput transcriptomics using the TempO-Seq (TM) platform for dose response modelling of 3 different cell lines treated with multiple compounds as part of a study to generate a toolbox and workflow for non-animal safety assessments. The data generated here was produced as one part of a toolbox of technologies to inform on bioactivity and produced across multiple dates.
Project description:High throughput transcriptomics using the TempO-Seq (TM) platform for dose response modelling of 3 different cell lines treated with multiple compounds as part of a study to generate a toolbox and workflow for non-animal safety assessments. The data generated here was produced as one part of a toolbox of technologies to inform on bioactivity.