Molecular insights into PCB neurotoxicity: Comparing transcriptomic responses across dopaminergic neurons, population blood cells, and Parkinson's disease pathology.

Parkinson's disease (PD) is a complex neurodegenerative disorder influenced by genetic factors and environmental exposures. Polychlorinated biphenyls (PCBs), a group of synthetic organic compounds, have been identified as potential environmental risk factors for neurodegenerative diseases, including PD. We explored PCB-induced neurotoxicity mechanisms using iPSC-derived dopaminergic neurons and assessed their transcriptomic responses to varying PCB concentrations (0.01 µM, 0.5 µM, and 10 µM). Specifically, we focused on PCB-180, a congener known for its accumulation in human brains. The exposure durations were 24 h and 74 h, allowing us to capture both short-term and more prolonged effects on gene expression patterns. We observed that PCB exposure led to the suppression of oxidative phosphorylation, synaptic function, and neurotransmitter release, implicating these pathways in PCB-induced neurotoxicity. In our comparative analysis, we noted similarities in PCB-induced changes with other PD-related compounds like MPP+ and rotenone. Our findings also aligned with gene expression changes in human blood derived from a population exposed to PCBs, highlighting broader inflammatory responses. Additionally, molecular patterns seen in iPSC-derived neurons were confirmed in postmortem PD brain tissues, validating our in vitro results. In conclusion, our study offers novel insights into the multifaceted impacts of PCB-induced perturbations on various cellular contexts relevant to PD. The use of iPSC-derived dopaminergic neurons allowed us to decipher intricate transcriptomic alterations, bridging the gap between in vitro and in vivo findings. This work underscores the potential role of PCB exposure in neurodegenerative diseases like PD, emphasizing the need to consider both systemic and cell specific effects.

Genetic Variability Impacts Genotoxic and Transcriptome Responses in the Human Colon after the Consumption of Processed Red Meat Products and Those with Added Phytochemical Extracts.

The PHYTOME study investigated the effect of consuming processed meat products on outcomes related to colorectal cancer risk without testing the impact of genetic variability on these responses. This research aims to elucidate the genetic impact on apparent total N-nitroso compound (ATNC) excretion, colonic DNA adduct formation, ex vivo-induced DNA damage, and gene expression changes in colon biopsies of healthy participants. Through a systematic literature review, candidate polymorphisms were selected and then detected using TaqMan and PCR analysis. The effect of genotype on study outcomes was determined via a linear mixed model and analysis of variance. Machine learning was used to evaluate relative allele importance concerning genotoxic responses, which established a ranking of the most protective alleles and a combination of genotypes (gene scores). Participants were grouped by GSTM1 genotype and differentially expressed genes (DEGs), and overrepresented biological pathways were compared between groups. Stratifying participants by ten relevant genes revealed significant variations in outcome responses. After consumption of processed red meat, variations in NQO1 and COMT impacted responses in ATNC levels (µmol/L) (+9.56 for wildtype vs. heterozygous) and DNA adduct levels (pg/µg DNA) (+1.26 for variant vs. wildtype and +0.43 for variant vs. heterozygous), respectively. After phytochemicals were added to the meat, GSTM1 variation impacted changes in DNA adduct levels (-6.12 for deletion vs. wildtype). The gene scores correlated with these responses and DEGs were identified by GSTM1 genotype. The altered pathways specific to the GSTM1 wildtype group included 'metabolism', 'cell cycle', 'vitamin D receptor', and 'metabolism of water-soluble vitamins and co-factors'. Genotype impacted both the potential genotoxicity of processed red meat and the efficacy of protective phytochemical extracts.

Correction: Bischoff et al. The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer. Nanomaterials 2022, 12, 1256.

In the published publication [...].

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

Analysis of cell-specific transcriptional responses in human colon tissue using CIBERSORTx.

Diet is an important determinant of overall health, and has been linked to the risk of various cancers. To understand the mechanisms involved, transcriptomic responses from human intervention studies are very informative. However, gene expression analysis of human biopsy material only represents the average profile of a mixture of cell types that can mask more subtle, but relevant cell-specific changes. Here, we use the CIBERSORTx algorithm to generate single-cell gene expression from human multicellular colon tissue. We applied the CIBERSORTx to microarray data from the PHYTOME study, which investigated the effects of different types of meat on transcriptional and biomarker changes relevant to colorectal cancer (CRC) risk. First, we used single-cell mRNA sequencing data from healthy colon tissue to generate a novel signature matrix in CIBERSORTx, then we determined the proportions and gene expression of each separate cell type. After comparison, cell proportion analysis showed a continuous upward trend in the abundance of goblet cells and stem cells, and a continuous downward trend in transit amplifying cells after the addition of phytochemicals in red meat products. The dietary intervention influenced the expression of genes involved in the growth and division of stem cells, the metabolism and detoxification of enterocytes, the translation and glycosylation of goblet cells, and the inflammatory response of innate lymphoid cells. These results show that our approach offers novel insights into the heterogeneous gene expression responses of different cell types in colon tissue during a dietary intervention.

A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug-induced toxicity.

We have built a quantitative systems toxicology modeling framework focused on the early prediction of oncotherapeutic-induced clinical intestinal adverse effects. The model describes stem and progenitor cell dynamics in the small intestinal epithelium and integrates heterogeneous epithelial-related processes, such as transcriptional profiles, citrulline kinetics, and probability of diarrhea. We fitted a mouse-specific version of the model to quantify doxorubicin and 5-fluorouracil (5-FU)-induced toxicity, which included pharmacokinetics and 5-FU metabolism and assumed that both drugs led to cell cycle arrest and apoptosis in stem cells and proliferative progenitors. The model successfully recapitulated observations in mice regarding dose-dependent disruption of proliferation which could lead to villus shortening, decrease of circulating citrulline, increased diarrhea risk, and transcriptional induction of the p53 pathway. Using a human-specific epithelial model, we translated the cytotoxic activity of doxorubicin and 5-FU quantified in mice into human intestinal injury and predicted with accuracy clinical diarrhea incidence. However, for gefitinib, a specific-molecularly targeted therapy, the mice failed to reproduce epithelial toxicity at exposures much higher than those associated with clinical diarrhea. This indicates that, regardless of the translational modeling approach, preclinical experimental settings have to be suitable to quantify drug-induced clinical toxicity with precision at the structural scale of the model. Our work demonstrates the usefulness of translational models at early stages of the drug development pipeline to predict clinical toxicity and highlights the importance of understanding cross-settings differences in toxicity when building these approaches.

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.

Drug-induced intrahepatic cholestasis (DIC) is a main type of hepatic toxicity that is challenging to predict in early drug development stages. Preclinical animal studies often fail to detect DIC in humans. In vitro toxicogenomics assays using human liver cells have become a practical approach to predict human-relevant DIC. The present study was set up to identify transcriptomic signatures of DIC by applying machine learning algorithms to the Open TG-GATEs database. A total of nine DIC compounds and nine non-DIC compounds were selected, and supervised classification algorithms were applied to develop prediction models using differentially expressed features. Feature selection techniques identified 13 genes that achieved optimal prediction performance using logistic regression combined with a sequential backward selection method. The internal validation of the best-performing model showed accuracy of 0.958, sensitivity of 0.941, specificity of 0.978, and F1-score of 0.956. Applying the model to an external validation set resulted in an average prediction accuracy of 0.71. The identified genes were mechanistically linked to the adverse outcome pathway network of DIC, providing insights into cellular and molecular processes during response to chemical toxicity. Our findings provide valuable insights into toxicological responses and enhance the predictive accuracy of DIC prediction, thereby advancing the application of transcriptome profiling in designing new approach methodologies for hazard identification.

Impact of Processing Method and Storage Time on Phytochemical Concentrations in an Antioxidant-Rich Food Mixture.

Foods high in phytochemicals are known for their role in the prevention of chronic disease development, but after processing and storage, such food products may lose part of their functionality as these compounds are sensitive to the impact of processing temperature and the type of methods applied. Therefore, we measured the levels of vitamin C, anthocyanins, carotenoids, catechins, chlorogenic acid, and sulforaphane in a complex blend of fruits and vegetables, and when applied to a dry food product, after exposure to different processing methods. These levels were compared between pasteurized, pascalized (high-pressure processing), and untreated conditions. Furthermore, we established the effect of freezing and storage time on the stability of these compounds. The results showed that pascalization better preserved vitamin C and sulforaphane, whereas pasteurization resulted in higher concentrations of chlorogenic acid, carotenoids, and catechins. For samples which were frozen and thawed immediately after processing, pascalization was the optimal treatment for higher contents of lutein, cyanidin-3-glucoside, quercetin-3-glucoside, delphinidin-3-glucoside, peonidin-3-glucoside, and epicatechin gallate. Ultimately, the optimal processing method to preserve phytochemicals in fruit and vegetable products is as complex as the blend of compounds, and this decision-making would best be led by the prioritized nutrient aim of an antioxidant food product.

Reply to Kaminski, N.E.; Cohen, S.M. Comment on "Bischoff et al. The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer. Nanomaterials 2022, 12, 1256".

We appreciate the interest in our article describing transcriptome changes in a transgenic mouse model carrying an APC gene mutation and would like to reply to the reader [...].

Allele-specific expression analysis for complex genetic phenotypes applied to a unique dilated cardiomyopathy cohort.

Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein-protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth.

BACKGROUND: Rapid postnatal growth may result from exposure in utero or early life to adverse conditions and has been associated with diseases later in life and, in particular, with childhood obesity. DNA methylation, interfacing early-life exposures and subsequent diseases, is a possible mechanism underlying early-life programming. METHODS: Here, a meta-analysis of Illumina HumanMethylation 450K/EPIC-array associations of cord blood DNA methylation at single CpG sites and CpG genomic regions with rapid weight growth at 1 year of age (defined with reference to WHO growth charts) was conducted in six European-based child cohorts (ALSPAC, ENVIRONAGE, Generation XXI, INMA, Piccolipiù, and RHEA, N = 2003). The association of gestational age acceleration (calculated using the Bohlin epigenetic clock) with rapid weight growth was also explored via meta-analysis. Follow-up analyses of identified DNA methylation signals included prediction of rapid weight growth, mediation of the effect of conventional risk factors on rapid weight growth, integration with transcriptomics and metabolomics, association with overweight in childhood (between 4 and 8 years), and comparison with previous findings. RESULTS: Forty-seven CpGs were associated with rapid weight growth at suggestive p-value <1e-05 and, among them, three CpGs (cg14459032, cg25953130 annotated to ARID5B, and cg00049440 annotated to KLF9) passed the genome-wide significance level (p-value <1.25e-07). Sixteen differentially methylated regions (DMRs) were identified as associated with rapid weight growth at false discovery rate (FDR)-adjusted/Siddak p-values < 0.01. Gestational age acceleration was associated with decreasing risk of rapid weight growth (p-value = 9.75e-04). Identified DNA methylation signals slightly increased the prediction of rapid weight growth in addition to conventional risk factors. Among the identified signals, three CpGs partially mediated the effect of gestational age on rapid weight growth. Both CpGs (N=3) and DMRs (N=3) were associated with differential expression of transcripts (N=10 and 7, respectively), including long non-coding RNAs. An AURKC DMR was associated with childhood overweight. We observed enrichment of CpGs previously reported associated with birthweight. CONCLUSIONS: Our findings provide evidence of the association between cord blood DNA methylation and rapid weight growth and suggest links with prenatal exposures and association with childhood obesity providing opportunities for early prevention.

Gene expression responses reflecting 5-FU-induced toxicity: Comparison between patient colon tissue and 3D human colon organoids.

Capecitabine is a chemotherapeutic drug that is widely used as a monotherapy option in advanced cancer patients. After administration, it is converted into its active metabolite 5-fluorouracil (5-FU), a cytotoxic compound that may also induce adverse side effects in the gastrointestinal (GI) tract. Although these side effects can interfere with the continuation of the chemotherapy, diagnostic tools to detect early onset and prevention strategies are not available. In this explorative case study, we aim to identify differentially expressed genes (DEGs) that provide insight into the molecular mechanisms of toxicity induced by 5-FU in healthy colon tissue of breast cancer patients receiving capecitabine. Gene expression responses observed in patients were compared with those established in an in vitro model of healthy colon organoids. Colon biopsies from two patients with advanced breast cancer were collected before and after the treatment with capecitabine and used for RNA sequencing to determine transcriptomic responses. Differential expression analysis resulted in 31 affected genes, showing that the most affected pathways were transport of small molecules, cellular responses to stress, folate metabolism, NF-kB signalling pathway and immune system responses. The most biologically relevant genes were haemoglobin subunits encoding genes, involved in several processes; ATP12A, SLC26A3 and AQP8, involved in the transport of ions and water; TRIM31, a regulator of NF-kB signalling pathway; MST1P2 and MST1L, stimulators of macrophages. Comparison of human in vitro and in vivo responses showed that the gene expression of TRIM31 was similarly altered in the colon organoids exposed to 5-FU. Therefore, this gene constitutes a potential biomarker of colon toxicity that might be used in future in vitro drug safety design and screening.

Lead-exposure associated miRNAs in humans and Alzheimer's disease as potential biomarkers of the disease and disease processes.

Alzheimer's disease (AD) is a neurodegenerative disease that eventually affects memory and behavior. The identification of biomarkers based on risk factors for AD provides insight into the disease since the exact cause of AD remains unknown. Several studies have proposed microRNAs (miRNAs) in blood as potential biomarkers for AD. Exposure to heavy metals is a potential risk factor for onset and development of AD. Blood cells of subjects that are exposed to lead detected in the circulatory system, potentially reflect molecular responses to this exposure that are similar to the response of neurons. In this study we analyzed blood cell-derived miRNAs derived from a general population as proxies of potentially AD-related mechanisms triggered by lead exposure. Subsequently, we analyzed these mechanisms in the brain tissue of AD subjects and controls. A total of four miRNAs were identified as lead exposure-associated with hsa-miR-3651, hsa-miR-150-5p and hsa-miR-664b-3p being negatively and hsa-miR-627 positively associated. In human brain derived from AD and AD control subjects all four miRNAs were detected. Moreover, two miRNAs (miR-3651, miR-664b-3p) showed significant differential expression in AD brains versus controls, in accordance with the change direction of lead exposure. The miRNAs' gene targets were validated for expression in the human brain and were found enriched in AD-relevant pathways such as axon guidance. Moreover, we identified several AD relevant transcription factors such as CREB1 associated with the identified miRNAs. These findings suggest that the identified miRNAs are involved in the development of AD and might be useful in the development of new, less invasive biomarkers for monitoring of novel therapies or of processes involved in AD development.

Transcriptomics analysis of human iPSC-derived dopaminergic neurons reveals a novel model for sporadic Parkinson's disease.

Parkinson's disease (PD) is a progressive, neurodegenerative disease affecting over 1% of the population beyond 65 years of age. Although some PD cases are inheritable, the majority of PD cases occur in a sporadic manner. Risk factors comprise next to heredity, age, and gender also exposure to neurotoxins from for instance pesticides and herbicides. As PD is characterized by a loss of dopaminergic neurons in the substantia nigra, it is nearly impossible to access and extract these cells from patients for investigating disease mechanisms. The emergence of induced pluripotent stem (iPSC) technology allows differentiating and growing human dopaminergic neurons, which can be used for in vitro disease modeling. Here, we differentiated human iPSCs into dopaminergic neurons, and subsequently exposed the cells to increasing concentrations of the neurotoxin MPP+. Temporal transcriptomics analysis revealed a strong time- and dose-dependent response with genes over-represented across pathways involved in PD etiology such as "Parkinson's Disease", "Dopaminergic signaling" and "calcium signaling". Moreover, we validated this disease model by showing robust overlap with a meta-analysis of transcriptomics data from substantia nigra from post-mortem PD patients. The overlap included genes linked to e.g. mitochondrial dysfunction, neuron differentiation, apoptosis and inflammation. Our data shows, that MPP+-induced, human iPSC-derived dopaminergic neurons present molecular perturbations as observed in the etiology of PD. Therefore we propose iPSC-derived dopaminergic neurons as a foundation for a novel sporadic PD model to study the pathomolecular mechanisms of PD, but also to screen for novel anti-PD drugs and to develop and test new treatment strategies.

The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer.

Titanium dioxide (TiO2) is present in many different food products as the food additive E171, which is currently scrutinized due to its potential adverse effects, including the stimulation of tumor formation in the gastrointestinal tract. We developed a transgenic mouse model to examine the effects of E171 on colorectal cancer (CRC), using the Cre-LoxP system to create an Apc-gene-knockout model which spontaneously develops colorectal tumors. A pilot study showed that E171 exposed mice developed colorectal adenocarcinomas, which were accompanied by enhanced hyperplasia in epithelial cells, lymphatic nodules at the base of the polyps, and increased tumor size. In the main study, tumor formation was studied following the exposure to 5 mg/kgbw/day of E171 for 9 weeks (Phase I). E171 exposure showed a statistically nonsignificant increase in the number of colorectal tumors in these transgenic mice, as well as a statistically nonsignificant increase in the average number of mice with tumors. Gene expression changes in the colon were analyzed after exposure to 1, 2, and 5 mg/kgbw/day of E171 for 2, 7, 14, and 21 days (Phase II). Whole-genome mRNA analysis revealed the modulation of genes in pathways involved in the regulation of gene expression, cell cycle, post-translational modification, nuclear receptor signaling, and circadian rhythm. The processes associated with these genes might be involved in the enhanced tumor formation and suggest that E171 may contribute to tumor formation and progression by modulation of events related to inflammation, activation of immune responses, cell cycle, and cancer signaling.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids.

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids.

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids' responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

Replacement of Nitrite in Meat Products by Natural Bioactive Compounds Results in Reduced Exposure to N-Nitroso Compounds: The PHYTOME Project.

SCOPE: It has been proposed that endogenously form N-nitroso compounds (NOCs) are partly responsible for the link between red meat consumption and colorectal cancer (CRC) risk. As nitrite has been indicated as critical factor in the formation of NOCs, the impact of replacing the additive sodium nitrite (E250) by botanical extracts in the PHYTOME project is evaluated. METHOD AND RESULTS: A human dietary intervention study is conducted in which healthy subjects consume 300 g of meat for 2 weeks, in subsequent order: conventional processed red meat, white meat, and processed red meat with standard or reduced levels of nitrite and added phytochemicals. Consumption of red meat products enriched with phytochemicals leads to a significant reduction in the faecal excretion of NOCs, as compared to traditionally processed red meat products. Gene expression changes identify cell proliferation as main affects molecular mechanism. High nitrate levels in drinking water in combination with processed red meat intake further stimulates NOC formation, an effect that could be mitigated by replacement of E250 by natural plant extracts. CONCLUSION: These findings suggest that addition of natural extracts to conventionally processed red meat products may help to reduce CRC risk, which is mechanistically support by gene expression analyses.

Safer chemicals using less animals: kick-off of the European ONTOX project.

The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing high-level efforts of the European Commission to support development and implementation of 3Rs methods. In this respect, the European project called "ONTOX: ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment" was recently initiated with the goal to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will deliver a generic strategy to create new approach methodologies (NAMs) in order to predict systemic repeated dose toxicity effects that, upon combination with tailored exposure assessment, will enable human risk assessment. For proof-of-concept purposes, focus is put on NAMs addressing adversities in the liver, kidneys and developing brain induced by a variety of chemicals. The NAMs each consist of a computational system based on artificial intelligence and are fed by biological, toxicological, chemical and kinetic data. Data are consecutively integrated in physiological maps, quantitative adverse outcome pathway networks and ontology frameworks. Supported by artificial intelligence, data gaps are identified and are filled by targeted in vitro and in silico testing. ONTOX is anticipated to have a deep and long-lasting impact at many levels, in particular by consolidating Europe's world-leading position regarding the development, exploitation, regulation and application of animal-free methods for human risk assessment of chemicals.