Effect of methapyrilene hydrochloride on hepatic intracellular iron metabolism in vivo and in vitro

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.

Transcriptional Modulation Reveals Physiological Responses to Temperature Adaptation in Acrossocheilus fasciatus

In order to explore the molecular regulatory mechanism of temperature acclimation under long-term temperature stress in Acrossocheilus fasciatus, this study used high-throughput sequencing technology to analyze 60 days of breeding under five temperature conditions (12 °C, 16 °C, 20 °C, 24 °C, 28 °C). Compared with 20 °C, 9202, 4959 differentially expressed genes (DEGs) were discovered in low-temperature groups (12 °C, 16 °C), whereas 133 and 878 DEGs were discovered in high-temperature groups (24 °C, 28 °C), respectively. The KEGG functional enrichment analysis revealed that DEGs were primarily enriched in tight junction, PI3 K-Akt signaling pathway and protein digestion and absorption in low-temperature groups, and mainly enriched in proximal tubule bicarbonate reclamation, protein digestion and absorption, and HIF-1 signaling pathway in high-temperature groups. The viability of transcriptome sequencing-based screening of DEGs for temperature adaptation in A. fasciatus was shown by the selection of eight DEGs for further validation by quantitative real-time PCR (qRT-PCR), the findings of which were consistent with the RNA-seq data. According to the findings, protein digestion and absorption were primarily regulated by temperature variations, physiological stress was a significant regulator in regulation under high-temperature stress, and the immune system was a significant regulator in regulation under low-temperature stress. The transcriptional patterns of A. fasciatus under temperature stress are revealed in this study. This knowledge is crucial for understanding how A. fasciatus adapts to temperature and can help us better comprehend the environmental difficulties that A. fasciatus adaptation faces.

Predicting Carcinogenic Mechanisms of Non-Genotoxic Carcinogens via Combined Analysis of Global DNA Methylation and In Vitro Cell Transformation

An in vitro cell transformation assay (CTA) is useful for the detection of non-genotoxic carcinogens (NGTXCs); however, it does not provide information on their modes of action. In this study, to pursue a mechanism-based approach in the risk assessment of NGTXCs, we aimed to develop an integrated strategy comprising an in vitro Bhas 42 CTA and global DNA methylation analysis. For this purpose, 10 NGTXCs, which were also predicted to be negative through Derek/Sarah structure-activity relationship analysis, were first tested for transforming activity in Bhas 42 cells. Methylation profiles using reduced representation bisulfite sequencing were generated for seven NGTXCs that were positive in CTAs. In general, the differentially methylated regions (DMRs) within promoter regions showed slightly more bias toward hypermethylation than the DMRs across the whole genome. We also identified 13 genes associated with overlapping DMRs within the promoter regions in four NGTXCs, of which seven were hypermethylated and six were hypomethylated. Using ingenuity pathway analysis, the genes with DMRs at the CpG sites were found to be enriched in cancer-related categories, including "cell-to-cell signaling and interaction" as well as "cell death and survival". Moreover, the networks related to "cell death and survival", which were considered to be associated with carcinogenesis, were identified in six NGTXCs. These results suggest that epigenetic changes supporting cell transformation processes occur during non-genotoxic carcinogenesis. Taken together, our combined system can become an attractive component for an integrated approach for the testing and assessment of NGTXCs.

Proteomic analysis of liver proteins of mice exposed to 1,2-dichloropropane

1,2-Dichloropropane (1,2-DCP) is recognized as the causative agent for cholangiocarcinoma among offset color proof-printing workers in Japan. The aim of the present study was to characterize the molecular mechanisms of 1,2-DCP-induced hepatotoxic effects by proteomic analysis. We analyzed quantitatively the differential expression of proteins in the mouse liver and investigated the role of P450 in mediating the effects of 1,2-DCP. Male C57BL/6JJcl mice were exposed to 0, 50, 250, or 1250 ppm 1,2-DCP and treated with either 1-aminobenzotriazole (1-ABT), a nonselective P450 inhibitor, or saline, for 8 h/day for 4 weeks. Two-dimensional difference in gel electrophoresis (2D-DIGE) combined with matrix-assisted laser-desorption ionization time-of-flight mass spectrometry (MALDI-TOF/TOF/MS) was used to detect and identify proteins affected by the treatment. PANTHER overrepresentation test on the identified proteins was conducted. 2D-DIGE detected 61 spots with significantly different intensity between 0 and 250 ppm 1,2-DCP groups. Among them, 25 spots were identified by MALDI-TOF/TOF/MS. Linear regression analysis showed significant trend with 1,2-DCP level in 17 proteins in mice co-treated with 1-ABT. 1-ABT mitigated the differential expression of these proteins. The gene ontology enrichment analysis showed overrepresentation of proteins functionally related to nickel cation binding, carboxylic ester hydrolase activity, and catalytic activity. The results demonstrated that exposure to 1,2-DCP altered the expression of proteins related with catalytic and carboxylic ester hydrolase activities, and that such effect was mediated by P450 enzymatic activity.

Effect of aflatoxin B1, benzo[a]pyrene, and methapyrilene on transcriptomic and epigenetic alterations in human liver HepaRG cells

The increasing number of man-made chemicals in the environment that may pose a carcinogenic risk highlights the need for developing reliable time- and cost-effective approaches for carcinogen detection and identification. To address this issue, we investigated the utility of high-throughput microarray gene expression and next-generation genome-wide DNA methylation sequencing for the in vitro identification of genotoxic and non-genotoxic carcinogens. Terminally differentiated and metabolically competent human liver HepaRG cells were treated at minimally cytotoxic concentrations of (i) the genotoxic human liver carcinogen aflatoxin B₁ (AFB1) and its structural non-carcinogenic analog aflatoxin B₂ (AFB2); (ii) the genotoxic human lung carcinogen benzo[a]pyrene (B[a]P) and its non-carcinogenic isomer benzo[e]pyrene (B[e]P); and (iii) the non-genotoxic liver carcinogen methapyrilene for 72 h and transcriptomic and DNA methylation profiles were examined. Treatment of HepaRG cells with the liver carcinogens AFB1 and methapyrilene generated distinct gene-expression profiles, whereas B[a]P had only a slight effect on gene expression. In contrast to transcriptomic alterations, treatment of HepaRG cells with the carcinogenic and non-carcinogenic chemicals resulted in profound changes in the DNA methylation footprint; however, the correlation between gene-specific DNA methylation and gene expression changes was minimal. Among the carcinogen-altered genes, transferrin (TF) emerged as sensitive marker for an initial screening of chemicals for their potential liver carcinogenicity. Potential liver carcinogens (i.e., chemicals causing altered TF gene expression) could then be subjected to gene-expression analyses to differentiate genotoxic from non-genotoxic liver carcinogens. This approach may substantially enhance the identification and assessment of potential liver carcinogens.