Drug-induced liver injury (DILI) is a patient-specific, temporal, multifactorial pathophysiological process that cannot yet be recapitulated in a single in vitro model. Current preclinical testing ...regimes for the detection of human DILI thus remain inadequate. A systematic and concerted research effort is required to address the deficiencies in current models and to present a defined approach towards the development of new or adapted model systems for DILI prediction. This Perspective defines the current status of available models and the mechanistic understanding of DILI, and proposes our vision of a roadmap for the development of predictive preclinical models of human DILI.
Abstract
Cancer cells have a characteristic metabolism, mostly caused by alterations in signal transduction networks rather than mutations in metabolic enzymes. For metabolic drugs to be ...cancer-selective, signaling alterations need to be identified that confer a druggable vulnerability. Here, we demonstrate that many tumor cells with an acquired cancer drug resistance exhibit increased sensitivity to mechanistically distinct inhibitors of cancer metabolism. We demonstrate that this metabolic vulnerability is driven by mTORC1, which promotes resistance to chemotherapy and targeted cancer drugs, but simultaneously suppresses autophagy. We show that autophagy is essential for tumor cells to cope with therapeutic perturbation of metabolism and that mTORC1-mediated suppression of autophagy is required and sufficient for generating a metabolic vulnerability leading to energy crisis and apoptosis. Our study links mTOR-induced cancer drug resistance to autophagy defects as a cause of a metabolic liability and opens a therapeutic window for the treatment of otherwise therapy-refractory tumor patients.
Due to the high complexity of biological data it is difficult to disentangle cellular processes relying only on intuitive interpretation of measurements. A Systems Biology approach that combines ...quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes. Nevertheless, with growing complexity and increasing amount of quantitative experimental data, building realistic and reliable mathematical models can become a challenging task: the quality of experimental data has to be assessed objectively, unknown model parameters need to be estimated from the experimental data, and numerical calculations need to be precise and efficient. Here, we discuss, compare and characterize the performance of computational methods throughout the process of quantitative dynamic modeling using two previously established examples, for which quantitative, dose- and time-resolved experimental data are available. In particular, we present an approach that allows to determine the quality of experimental data in an efficient, objective and automated manner. Using this approach data generated by different measurement techniques and even in single replicates can be reliably used for mathematical modeling. For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically. Our results show that deterministic derivative-based optimization employing the sensitivity equations in combination with a multi-start strategy based on latin hypercube sampling outperforms the other methods by orders of magnitude in accuracy and speed. Finally, we investigated transformations that yield a more efficient parameterization of the model and therefore lead to a further enhancement in optimization performance. We provide a freely available open source software package that implements the algorithms and examples compared here.
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease characterized by patchy scarring of the distal lung with limited therapeutic options and poor prognosis. Here, ...we show that conditional deletion of the ubiquitin ligase Nedd4-2 (Nedd4l) in lung epithelial cells in adult mice produces chronic lung disease sharing key features with IPF including progressive fibrosis and bronchiolization with increased expression of Muc5b in peripheral airways, honeycombing and characteristic alterations in the lung proteome. NEDD4-2 is implicated in the regulation of the epithelial Na
channel critical for proper airway surface hydration and mucus clearance and the regulation of TGFβ signaling, which promotes fibrotic remodeling. Our data support a role of mucociliary dysfunction and aberrant epithelial pro-fibrotic response in the multifactorial disease pathogenesis. Further, treatment with the anti-fibrotic drug pirfenidone reduced pulmonary fibrosis in this model. This model may therefore aid studies of the pathogenesis and therapy of IPF.
Systemic iron homeostasis is mainly controlled by the liver through synthesis of the peptide hormone hepcidin (encoded by Hamp), the key regulator of duodenal iron absorption and macrophage iron ...release. Here we show that the liver-specific microRNA miR-122 is important for regulating Hamp mRNA expression and tissue iron levels. Efficient and specific depletion of miR-122 by injection of a locked-nucleic-acid-modified (LNA-modified) anti-miR into WT mice caused systemic iron deficiency, characterized by reduced plasma and liver iron levels, mildly impaired hematopoiesis, and increased extramedullary erythropoiesis in the spleen. Moreover, miR-122 inhibition increased the amount of mRNA transcribed by genes that control systemic iron levels, such as hemochromatosis (Hfe), hemojuvelin (Hjv), bone morphogenetic protein receptor type 1A (Bmpr1a), and Hamp. Importantly, miR-122 directly targeted the 3′ untranslated region of 2 mRNAs that encode activators of hepcidin expression, Hfe and Hjv. These data help to explain the increased Hamp mRNA levels and subsequent iron deficiency in mice with reduced miR-122 levels and establish a direct mechanistic link between miR-122 and the regulation of systemic iron metabolism.
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the ...identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.
Chemical dimerizers are powerful tools for non‐invasive manipulation of enzyme activities in intact cells. Here we introduce the first rapidly reversible small‐molecule‐based dimerization system and ...demonstrate a sufficiently fast switch‐off to determine kinetics of lipid metabolizing enzymes in living cells. We applied this new method to induce and stop phosphatidylinositol 3‐kinase (PI3K) activity, allowing us to quantitatively measure the turnover of phosphatidylinositol 3,4,5‐trisphosphate (PIP3) and its downstream effectors by confocal fluorescence microscopy as well as standard biochemical methods.
Little helper: Chemical dimerizers are powerful tools for manipulation of enzyme activities in intact cells. The first rapidly reversible chemical dimerization system is introduced, which permits to determine kinetics of lipid metabolizing enzymes in living cells. This new method was applied to induce and stop phosphatidylinositol 3‐kinase activity, allowing to quantitatively measure 3,4,5‐trisphosphate turnover.
ALK-positive NSCLC patients demonstrate initial responses to ALK tyrosine kinase inhibitor (TKI) treatments, but eventually develop resistance, causing rapid tumor relapse and poor survival rates. ...Growing evidence suggests that the combination of drug and immune therapies greatly improves patient survival; however, due to the low immunogenicity of the tumors, ALK-positive patients do not respond to currently available immunotherapies. Tumor-associated macrophages (TAMs) play a crucial role in facilitating lung cancer growth by suppressing tumoricidal immune activation and absorbing chemotherapeutics. However, they can also be programmed toward a pro-inflammatory tumor suppressive phenotype, which represents a highly active area of therapy development. Iron loading of TAMs can achieve such reprogramming correlating with an improved prognosis in lung cancer patients. We previously showed that superparamagnetic iron oxide nanoparticles containing core-cross-linked polymer micelles (SPION-CCPMs) target macrophages and stimulate pro-inflammatory activation. Here, we show that SPION-CCPMs stimulate TAMs to secrete reactive nitrogen species and cytokines that exert tumoricidal activity. We further show that SPION-CCPMs reshape the immunosuppressive Eml4-Alk lung tumor microenvironment (TME) toward a cytotoxic profile hallmarked by the recruitment of CD8+ T cells, suggesting a multifactorial benefit of SPION-CCPM application. When intratracheally instilled into lung cancer-bearing mice, SPION-CCPMs delay tumor growth and, after first line therapy with a TKI, halt the regrowth of relapsing tumors. These findings identify SPIONs-CCPMs as an adjuvant therapy, which remodels the TME, resulting in a delay in the appearance of resistant tumors.
Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle ...progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.
Synopsis
Mathematical modeling and quantitative experiments identify the abundance of components of the AKT and ERK signaling pathways as the key determinant of the cell type‐specific regulation of Epo‐induced proliferation.
Cell type‐specific dynamics of Epo‐induced signal activation are determined by the different abundance of key signaling proteins.
A dynamic pathway model adapted to experimentally measured, cell type‐specific protein abundance can faithfully predict the Epo‐induced dynamics of AKT, ERK, and S6 activation.
Based on snapshot measurements of protein abundance, the mathematical model predicts the cell type‐specific impact of AKT and MEK inhibitors on Epo‐induced proliferation in human cells.
Mathematical modeling and quantitative experiments identify the abundance of components of the AKT and ERK signaling pathways as the key determinant of the cell type‐specific regulation of Epo‐induced proliferation.
Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral ...responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model‐based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose‐dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient‐specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient‐specific pathway desensitization, while the abundance of STAT2 predicts the patient‐specific IFNα signal response.
Synopsis
Mathematical modeling based on quantitative data reveals the molecular mechanisms that cause hypersensitization of interferon alpha (IFNα) signaling by pre‐exposure with a low dose of IFNα and desensitization with a high dose of IFNα.
IFNα induces the formation of the pSTAT1:pSTAT1, pSTAT1:pSTAT2 and pSTAT1:pSTAT2:IRF9 (ISGF3) transcription factor complexes in a temporal and dose‐dependent order.
IRF9 is induced with low doses of IFNα and causes pathway hypersensitization by favoring the ISGF3 transcription factor complex.
Both USP18 and SOCS1 foster desensitization of the IFNα signal transduction pathway.
The patient‐specific abundance of USP18 defines the desensitization threshold, while the abundance of STAT2 is a predictor for the antiviral response of an individual patient.
Mathematical modeling based on quantitative data reveals the molecular mechanisms that cause hypersensitization of interferon alpha (IFNα) signaling by pre‐exposure with a low dose of IFNα and desensitization with a high dose of IFNα.
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