On the usefulness of animals as a model system (part I): Overview of criteria and focus on robustness


Banning or reduction of the use of animals for laboratory experiments is a frequently-discussed societal and scientific issue. Moreover, the usefulness of animals needs to be considered in any decision process on the permission of specific animal studies. This complex issue is often simplified and generalized in the media around the question, “Are animals useful as a model?” To render an often emotional discussion about animal experimentation more rational, it is important to define “usefulness” in a structured and transparent way. To achieve such a goal, many sub-questions need to be asked, and the following aspects require clarification: (i) consistency of animal-derived data (robustness of the model system); (ii) scientific domain investigated (e.g., toxicology vs disease modelling vs therapy); (iii) measurement unit for “benefit” (inte-grating positive and negative aspects); (iv) benchmarking to alternatives; (v) definition of success criteria (how good is good enough); (vi) the procedure to assess benefit and necessity. This series of articles discusses the overall benchmarking process by specifying the six issues. The goal is to provide guidance on what needs to be clarified in scientific and political discussions. This framework should help in the future to structure available information, to identify and fill information gaps, and to arrive at rational decisions in various sub-fields of animal use. In part I of the series, we focus on the robustness of animal models. This describes the capacity of models to produce the same output/response when faced with the “same” input. Follow-up articles will cover the remaining usefulness aspects.

Studying Metabolism by NMR-Based Metabolomics


During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Specific Attenuation of Purinergic Signaling during Bortezomib-Induced Peripheral Neuropathy In Vitro

Human peripheral neuropathies are poorly understood, and the availability of experimental models limits further research. The PeriTox test uses immature dorsal root ganglia (DRG)-like neurons, derived from induced pluripotent stem cells (iPSC), to assess cell death and neurite damage. Here, we explored the suitability of matured peripheral neuron cultures for the detection of sub-cytotoxic endpoints, such as altered responses of pain-related P2X receptors. A two-step differentiation protocol, involving the transient expression of ectopic neurogenin-1 (NGN1) allowed for the generation of homogeneous cultures of sensory neurons. After >38 days of differentiation, they showed a robust response (Ca2+-signaling) to the P2X3 ligand α,β-methylene ATP. The clinical proteasome inhibitor bortezomib abolished the P2X3 signal at ≥5 nM, while 50−200 nM was required in the PeriTox test to identify neurite damage and cell death. A 24 h treatment with low nM concentrations of bortezomib led to moderate increases in resting cell intracellular Ca2+ concentration but signaling through transient receptor potential V1 (TRPV1) receptors or depolarization-triggered Ca2+ influx remained unaffected. We interpreted the specific attenuation of purinergic signaling as a functional cell stress response. A reorganization of tubulin to form dense structures around the cell somata confirmed a mild, non-cytotoxic stress triggered by low concentrations of bortezomib. The proteasome inhibitors carfilzomib, delanzomib, epoxomicin, and MG-132 showed similar stress responses. Thus, the model presented here may be used for the profiling of new proteasome inhibitors in regard to their side effect (neuropathy) potential, or for pharmacological studies on the attenuation of their neurotoxicity. P2X3 signaling proved useful as endpoint to assess potential neurotoxicants in peripheral neurons.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator


SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara’s Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

Metabolic regulation by p53 prevents R-loop-associated genomic instability


Gene-environment interactions can perturb the epigenome, triggering network alterations that participate in cancer pathogenesis. Integrating epigenomics, transcriptomics, and metabolic analyses with functional perturbation, we show that the tumor suppressor p53 preserves genomic integrity by empowering adequate levels of the universal methyl donor S-adenosylmethionine (SAM). In p53-deficient cells, perturbation of DNA methylation promotes derepression of heterochromatin, massive loss of histone H3-lysine 9 methylation, and consequent upregulation of satellite RNAs that triggers R-loop-associated replication stress and chromosomal aberrations. In p53-deficient cells, the inadequate SAM level underlies the inability to respond to perturbation because exogenous reintroduction of SAM represses satellite elements and restores the ability to cope with stress. Mechanistically, p53 transcriptionally controls genes involved in one-carbon metabolism, including Slc43a2, the methionine uptake transporter that is critical for SAM synthesis. Supported by clinical data, our findings shed light on the role of p53-mediated metabolism in preventing unscheduled R-loop-associated genomic instability.

Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation


Mitochondrial perturbation is a key event in chemical-induced organ toxicities that is incompletely understood. Here, we studied how electron transport chain (ETC) complex I, II, or III (CI, CII and CIII) inhibitors affect mitochondrial functionality, stress response activation, and cell viability using a combination of high-content imaging and TempO-Seq in HepG2 hepatocyte cells. CI and CIII inhibitors perturbed mitochondrial membrane potential (MMP) and mitochondrial and cellular ATP levels in a concentration- and time-dependent fashion and, under conditions preventing a switch to glycolysis attenuated cell viability, whereas CII inhibitors had no effect. TempO-Seq analysis of changes in mRNA expression pointed to a shared cellular response to CI and CIII inhibition. First, to define specific ETC inhibition responses, a gene set responsive toward ETC inhibition (and not to genotoxic, oxidative, or endoplasmic reticulum stress) was identified using targeted TempO-Seq in HepG2. Silencing of one of these genes, NOS3, exacerbated the impact of CI and CIII inhibitors on cell viability, indicating its functional implication in cellular responses to mitochondrial stress. Then by monitoring dynamic responses to ETC inhibition using a HepG2 GFP reporter panel for different classes of stress response pathways and applying pathway and gene network analysis to TempO-Seq data, we looked for downstream cellular events of ETC inhibition and identified the amino acid response (AAR) as being triggered in HepG2 by ETC inhibition. Through in silico approaches we provide evidence indicating that a similar AAR is associated with exposure to mitochondrial toxicants in primary human hepatocytes. Altogether, we (i) unravel quantitative, time- and concentration-resolved cellular responses to mitochondrial perturbation, (ii) identify a gene set associated with adaptation to exposure to active ETC inhibitors, and (iii) show that ER stress and an AAR accompany ETC inhibition in HepG2 and primary hepatocytes.