Genotoxicity of nitrosamines
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ABSTRACT:
PROVIDER: PRJNA434755 | ENA |
REPOSITORIES: ENA
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Project description:Given the positive results of quercetin in in vitro genotoxicity studies, it is of interest to test the in vivo genotoxicity of this important dietary flavonoid, especially considering possible high intake by widely available supplements. In the present study quercetin was tested in a transcriptomic analysis for genotoxicity in liver and small intestine of mice. Quercetin (0.33%) supplemented to a high-fat diet was administered to mice during 12 weeks and compared with high-fat diet feeding. Serum ALT and AST levels revealed no indications for hepatotoxicity. General microarray pathway analysis of liver and small intestinal tissue samples showed no regulation of genotoxicity related pathways. In addition, analysis of DNA damage pathways in these samples, also did not point at genotoxicity. Furthermore, comparison with a published classifier set of transcripts for identifying genotoxic compounds did not reveal any similarities with the regulation of these classifier set of transcripts by quercetin, except for two of the transcripts which were regulated in opposite direction. Available microarray datasets of known genotoxic compounds, 2-acetylaminofluorene (2-AAF) and aflatoxin B1 (AFB1) in mice were taken along as positive controls for comparison, and indeed showed genotoxic properties (regulation of genotoxic related genes) in the analyses. This transcriptomic study showed that supplementation with quercetin at ~350 mg/kg bw/day for 12 weeks in mice gave no indications of quercetin-induced genotoxicity in liver and small intestine.
2015-10-01 | GSE63227 | GEO
Project description:The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.
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Project description:Safety assessment in retroviral vector-mediated gene therapy remains challenging. In clinical trials for different blood and immune disorders, insertional mutagenesis led to myeloid and lymphoid leukemia. We previously developed the In Vitro Immortalization Assay (IVIM) and Surrogate Assay for Genotoxicity Assessment (SAGA) for pre-clinical genotoxicity prediction of integrating vectors. Murine hematopoietic stem and progenitor cells (mHSPC) transduced with mutagenic vectors acquire a proliferation advantage under limiting dilution (IVIM) and activate stem cell- and cancer-related transcriptional programs (SAGA). However, both assays present an intrinsic myeloid bias due to culture conditions. To detect lymphoid mutants, we differentiated mHSPC to mature T cells and analyzed their phenotype, insertion site pattern, and gene expression changes after transduction with retroviral vectors. Mutagenic vectors induced a block in differentiation at an early progenitor stage (double-negative 2) compared to fully differentiated untransduced mock cultures. Arrested samples harbored high-risk insertions close to Lmo2, frequently observed in clinical trials with severe adverse events. Lymphoid insertional mutants displayed a unique gene expression signature identified by the machine learning algorithm of SAGA. The gene expression-based highly sensitive molecular readout will broaden our understanding of vector-induced oncogenicity and help in pre-clinical prediction of retroviral genotoxicity.
2023-09-21 | GSE241155 | GEO
Project description:Safety assessment in retroviral vector-mediated gene therapy remains challenging. In clinical trials for different blood and immune disorders, insertional mutagenesis led to myeloid and lymphoid leukemia. We previously developed the In Vitro Immortalization Assay (IVIM) and Surrogate Assay for Genotoxicity Assessment (SAGA) for pre-clinical genotoxicity prediction of integrating vectors. Murine hematopoietic stem and progenitor cells (mHSPC) transduced with mutagenic vectors acquire a proliferation advantage under limiting dilution (IVIM) and activate stem cell- and cancer-related transcriptional programs (SAGA). However, both assays present an intrinsic myeloid bias due to culture conditions. To detect lymphoid mutants, we differentiated mHSPC to mature T cells and analyzed their phenotype, insertion site pattern, and gene expression changes after transduction with retroviral vectors. Mutagenic vectors induced a block in differentiation at an early progenitor stage (double-negative 2) compared to fully differentiated untransduced mock cultures. Arrested samples harbored high-risk insertions close to Lmo2, frequently observed in clinical trials with severe adverse events. Lymphoid insertional mutants displayed a unique gene expression signature identified by the machine learning algorithm of SAGA. The gene expression-based highly sensitive molecular readout will broaden our understanding of vector-induced oncogenicity and help in pre-clinical prediction of retroviral genotoxicity.
2023-09-21 | GSE241146 | GEO
Project description:There is a need to move from binary hazard assessment to more quantitative assessment of genotoxicity to better inform human health risk assessment and understand the relevance of positive in vitro genotoxicity findings. New approach methodologies (NAMs), including transcriptomic biomarkers combined with high-throughput technologies, enable the testing of a broad concentration range, which allows quantitative assessment of in vitro results. Initial work with the transcriptomic GENOMARK and TGx-DDI biomarkers demonstrate their potential use for hazard identification and chemical prioritization; however, no study has evaluated the concordance and complementarity of GENOMARK and TGx-DDI. The overall aim of this study is to examine if a combined approach of integrating both transcriptomic biomarkers for genotoxicity in human relevant HepaRGTM cells increases the certainty in hazard calls and potency rankings of chemicals. A sub-aim is to investigate whether GENOMARK is applicable to the TempO-Seq® high-throughput sequencing technology, to collect concentration-response data to rapidly perform hazard classification and potency ranking. Therefore, HepaRGTM cells were exposed to 10 chemicals (i.e. eight known in vivo genotoxicants and two in vivo non-genotoxicants) in increasing concentrations over 72h. TempO-Seq® was used to obtain concentration-response data for both biomarkers. Benchmark concentration (BMC) modelling of chemicals that were classified positive was conducted to obtain BMCs and transcriptomic points of departure (tPODs) for potency ranking. The results confirm that GENOMARK is applicable to TempO-Seq® since it achieved 100% predictive accuracy. In addition, a high concordance was observed in the hazard classifications and potency rankings between both biomarkers. Overall, our findings show that in vitro transcriptomic data can be used to rapidly and effectively identify genotoxic hazards while simultaneously providing additional insights on potency that is more informative in a modern hazard assessment paradigm. The results of this case study support the high value of integrating these NAMs in a weight of evidence evaluation of genotoxicity using the important human-liver cell line.
2024-03-27 | GSE241068 | GEO
Project description:There is a need to move from binary hazard assessment to more quantitative assessment of genotoxicity to better inform human health risk assessment and understand the relevance of positive in vitro genotoxicity findings. New approach methodologies (NAMs), including transcriptomic biomarkers combined with high-throughput technologies, enable the testing of a broad concentration range, which allows quantitative assessment of in vitro results. Initial work with the transcriptomic GENOMARK and TGx-DDI biomarkers demonstrate their potential use for hazard identification and chemical prioritization; however, no study has evaluated the concordance and complementarity of GENOMARK and TGx-DDI. The overall aim of this study is to examine if a combined approach of integrating both transcriptomic biomarkers for genotoxicity in human relevant HepaRGTM cells increases the certainty in hazard calls and potency rankings of chemicals. A sub-aim is to investigate whether GENOMARK is applicable to the TempO-Seq® high-throughput sequencing technology, to collect concentration-response data to rapidly perform hazard classification and potency ranking. Therefore, HepaRGTM cells were exposed to 10 chemicals (i.e. eight known in vivo genotoxicants and two in vivo non-genotoxicants) in increasing concentrations over 72h. TempO-Seq® was used to obtain concentration-response data for both biomarkers. Benchmark concentration (BMC) modelling of chemicals that were classified positive was conducted to obtain BMCs and transcriptomic points of departure (tPODs) for potency ranking. The results confirm that GENOMARK is applicable to TempO-Seq® since it achieved 100% predictive accuracy. In addition, a high concordance was observed in the hazard classifications and potency rankings between both biomarkers. Overall, our findings show that in vitro transcriptomic data can be used to rapidly and effectively identify genotoxic hazards while simultaneously providing additional insights on potency that is more informative in a modern hazard assessment paradigm. The results of this case study support the high value of integrating these NAMs in a weight of evidence evaluation of genotoxicity using the important human-liver cell line.
2024-03-27 | GSE241067 | GEO
Project description:The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode-of-action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow® and MultiFlow® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada’s New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-SeqTM assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow® and MultiFlow® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow®, and MultiFlow® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds.
2022-11-15 | GSE213454 | GEO
Project description:Application of the transcriptomic biomarker, TGx-DDI, in genotoxicity screening
| PRJNA419190 | ENA
Project description:The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.
2016-06-28 | GSE72081 | GEO
Project description:Genotoxicity induces epithelial hyperplasia and lineage infidelity via fibroblast IL-1β
| PRJNA578953 | ENA