Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or 'modules' enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man.Medicinal Chemistr

True substrates: The exceptional resolution and unexceptional preservation of deep time snapshots on bedding surfaces

Abstract: Rock outcrops of the sedimentary–stratigraphic record often reveal bedding planes that can be considered to be true substrates: preserved surfaces that demonstrably existed at the sediment–water or sediment–air interface at the time of deposition. These surfaces have high value as repositories of palaeoenvironmental information, revealing fossilized snapshots of microscale topography from deep time. Some true substrates are notable for their sedimentary, palaeontological and ichnological signatures that provide windows into key intervals of Earth history, but countless others occur routinely throughout the sedimentary–stratigraphic record. They frequently reveal patterns that are strikingly familiar from modern sedimentary environments, such as ripple marks, animal trackways, raindrop impressions or mudcracks: all phenomena that are apparently ephemeral in modern settings, and which form on recognizably human timescales. This paper sets out to explain why these short‐term, transient, small‐scale features are counter‐intuitively abundant within a 3.8 billion year‐long sedimentary–stratigraphic record that is known to be inherently time‐incomplete. True substrates are fundamentally related to a state of stasis in ancient sedimentation systems, and distinguishable from other types of bedding surfaces that formed from a dominance of states of deposition or erosion. Stasis is shown to play a key role in both their formation and preservation, rendering them faithful and valuable archives of palaeoenvironmental and temporal information. Further, the intersection between the time–length scale of their formative processes and outcrop expressions can be used to explain why they are so frequently encountered in outcrop investigations. Explaining true substrates as inevitable and unexceptional by‐products of the accrual of the sedimentary–stratigraphic record should shift perspectives on what can be understood about Earth history from field studies of the sedimentary–stratigraphic record. They should be recognized as providing high‐definition information about the mundane day to day operation of ancient environments, and critically assuage the argument that the incomplete sedimentary–stratigraphic record is unrepresentative of the geological past

Model-based identification of TNFα-induced IKKβ-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds

Drug-induced liver injury (DILI) has become a major problem for patients and for clinicians, academics and the pharmaceutical industry. To date, existing hepatotoxicity test systems are only poorly predictive and the underlying mechanisms are still unclear. One of the factors known to amplify hepatotoxicity is the tumor necrosis factor alpha (TNFα), especially due to its synergy with commonly used drugs such as diclofenac. However, the exact mechanism of how diclofenac in combination with TNFα induces liver injury remains elusive. Here, we combined time-resolved immunoblotting and live-cell imaging data of HepG2 cells and primary human hepatocytes (PHH) with dynamic pathway modeling using ordinary differential equations (ODEs) to describe the complex structure of TNFα-induced NFκB signal transduction and integrated the perturbations of the pathway caused by diclofenac. The resulting mathematical model was used to systematically identify parameters affected by diclofenac. These analyses showed that more than one regulatory module of TNFα-induced NFκB signal transduction is affected by diclofenac, suggesting that hepatotoxicity is the integrated consequence of multiple changes in hepatocytes and that multiple factors define toxicity thresholds. Applying our mathematical modeling approach to other DILI-causing compounds representing different putative DILI mechanism classes enabled us to quantify their impact on pathway activation, highlighting the potential of the dynamic pathway model as a quantitative tool for the analysis of DILI compounds.Toxicolog