Nov 19, 2024 | 2024, Journal publications, Toxicology in Vitro
Abstract
The EU-ToxRisk project (2016–2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape.
Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook» approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021–2026)).
Nov 19, 2024 | 2024, Journal publications, Redox Biology
Abstract
The activation of
ferroptosis is being pursued in cancer research as a strategy to target apoptosis-resistant cells. By contrast, in various diseases that affect the cardiovascular system, kidneys, liver, and central and peripheral nervous systems, attention is directed toward interventions that prevent ferroptotic cell death. Mechanistic insights into both research areas stem largely from studies using cellular
in vitro models. However, intervention strategies that show promise in cellular test systems often fail in
clinical trials, which raises concerns regarding the predictive validity of the utilized
in vitro models.
In this study, the human LUHMES cell line, which serves as a model for human
dopaminergic neurons, was used to characterize factors influencing the activation of
ferroptosis. Erastin and RSL-3 induced cell death that was distinct from apoptosis. Parameters such as the differentiation state of LUHMES cells, cell density, and the number and timing of medium changes were identified as determinants of sensitivity to ferroptosis activation. In differentiated LUHMES cells, interventions at mechanistically divergent sites (iron chelation, coenzyme Q
10,
peroxidase mimics, or inhibition of 12/15-lipoxygenase) provide almost complete protection from ferroptosis. LUHMES cells allowed the experimental modulation of intracellular iron concentrations and demonstrated a correlation between intracellular iron levels, the rate of
lipid peroxidation, as well as the sensitivity of the cells to ferroptotic cell death.
These findings underscore the importance of understanding the various factors that influence ferroptosis activation and highlight the need for well-characterized
in vitro models to enhance the reliability and predictive value of observations in ferroptosis research, particularly when
translating findings into
in vivo contexts.
Nov 19, 2024 | 2024, Cells, Journal publications
Abstract
Few models allow the study of neurite damage in the human central nervous system. We used here dopaminergic LUHMES neurons to establish a culture system that allows for (i) the observation of highly enriched neurites, (ii) the preparation of the neurite fraction for biochemical studies, and (iii) the measurement of neurite markers and metabolites after axotomy. LUHMES-based spheroids, plated in culture dishes, extended neurites of several thousand µm length, while all somata remained aggregated. These cultures allowed an easy microscopic observation of live or fixed neurites. Neurite-only cultures (NOC) were produced by cutting out the still-aggregated somata. The potential application of such cultures was exemplified by determinations of their protein and RNA contents. For instance, the mitochondrial TOM20 protein was highly abundant, while nuclear histone H3 was absent. Similarly, mitochondrial-encoded RNAs were found at relatively high levels, while the mRNA for a histone or the neuronal nuclear marker NeuN (RBFOX3) were relatively depleted in NOC. Another potential use of NOC is the study of neurite degeneration. For this purpose, an algorithm to quantify neurite integrity was developed. Using this tool, we found that the addition of nicotinamide drastically reduced neurite degeneration. Also, the chelation of Ca2+ in NOC delayed the degeneration, while inhibitors of calpains had no effect. Thus, NOC proved to be suitable for biochemical analysis and for studying degeneration processes after a defined cut injury.
Nov 19, 2024 | 2024, Cells, Journal publications
Abstract
The polarised expression of specific transporters in proximal tubular epithelial cells is important for the renal clearance of many endogenous and exogenous compounds. Thus, ideally, the in vitro tools utilised for predictions would have a similar expression of apical and basolateral xenobiotic transporters as in vivo. Here, we assessed the functionality of organic cation and anion transporters in proximal tubular-like cells (PTL) differentiated from human induced pluripotent stem cells (iPSC), primary human proximal tubular epithelial cells (PTEC), and telomerase-immortalised human renal proximal tubular epithelial cells (RPTEC/TERT1). Organic cation and anion transport were studied using the fluorescent substrates 4-(4-(dimethylamino)styryl)-N-methylpyridinium iodide (ASP) and 6-carboxyfluorescein (6-CF), respectively. The level and rate of intracellular ASP accumulation in PTL following basolateral application were slightly lower but within a 3-fold range compared to primary PTEC and RPTEC/TERT1 cells. The basolateral uptake of ASP and its subsequent apical efflux could be inhibited by basolateral exposure to quinidine in all models. Of the three models, only PTL showed a modest preferential basolateral-to-apical 6-CF transfer. These results show that organic cation transport could be demonstrated in all three models, but more research is needed to improve and optimise organic anion transporter expression and functionality.
Nov 19, 2024 | 2024, Altex, Journal publications
Abstract
Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the mechanisms underlying the adverse effects of xenobiotics. In the present study, two widely used human hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis, or necrosis. Benchmark concentration (BMC) response modelling was applied to transcriptomics gene co-expression networks (modules) to derive BMCs and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in the genes and modules deregulated by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics points of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and be used in a risk assessment where BMCs are paired with chemical exposure assessment.
Nov 19, 2024 | 2024, Journal publications, Molecular Informatics
Abstract
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.
