Analysis of Time-Series Gene Expression Data to Explore Mechanisms of Chemical-Induced Hepatic Steatosis Toxicity

Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of disease, ranging from simple fatty liver through steatosis with inflammation and necrosis to cirrhosis. One of the most challenging problems in biomedical research and within the chemical industry is to understand the underlying mechanisms of complex disease, and complex adverse outcome pathways (AOPs). Based on a set of 28 steatotic chemicals with gene expression data measured on primary hepatocytes at three times (2, 8, and 24 h) and three doses (low, medium, and high), we identified genes and pathways, defined as molecular initiating events (MIEs) and key events (KEs) of steatosis using a combination of a time series and pathway analyses. Among the genes deregulated by these compounds, the study highlighted OSBPL9, ALDH7A1, MYADM, SLC51B, PRDX6, GPAT3, TMEM135, DLGDA5, BCO2, APO10LA, TSPAN6, NEURL1B, and DUSP1. Furthermore, pathway analysis indicated deregulation of pathways related to lipid accumulation, such as fat digestion and absorption, linoleic and linolenic acid metabolism, calcium signaling pathway, fatty acid metabolism, peroxisome, retinol metabolism, and steroid metabolic pathways in a time dependent manner. Such transcription profile analysis can help in the understanding of the steatosis evolution over time generated by chemical exposure

sAOP:linking chemical stressors to adverse outcomes pathway networks

International audienceMotivation: Adverse outcome pathway (AOP) is a toxicological concept proposed to provide a mechanistic representation of biological perturbation over different layers of biological organization. Although AOPs are by definition chemical-agnostic, many chemical stressors can putatively interfere with one or several AOPs and such information would be relevant for regulatory decision-making. Results: With the recent development of AOPs networks aiming to facilitate the identification of interactions among AOPs, we developed a stressor-AOP network (sAOP). Using the 'cytotoxitiy burst' (CTB) approach, we mapped bioactive compounds from the ToxCast data to a list of AOPs reported in AOP-Wiki database. With this analysis, a variety of relevant connections between chemicals and AOP components can be identified suggesting multiple effects not observed in the simplified 'one-biological perturbation to one-adverse outcome' model. The results may assist in the prioritization of chemicals to assess risk-based evaluations in the context of human health

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure

Cellular uptake and intracellular phosphorylation of GS-441524:Implications for its effectiveness against COVID-19

GS-441524 is an adenosine analog and the parent nucleoside of the prodrug remdesivir, which has received emergency approval for treatment of COVID-19. Recently, GS-441524 has been proposed to be effective in the treatment of COVID-19, perhaps even being superior to remdesivir for treatment of this disease. Evaluation of the clinical effectiveness of GS-441524 requires understanding of its uptake and intracellular conversion to GS-441524 triphosphate, the active antiviral substance. We here discuss the potential impact of these pharmacokinetic steps of GS-441524 on the formation of its active antiviral substance and effectiveness for treatment of COVID-19. Available protein expression data suggest that several adenosine transporters are expressed at only low levels in the epithelial cells lining the alveoli in the lungs, i.e., the alveolar cells or pneumocytes from healthy lungs. This may limit uptake of GS-441524. Importantly, cellular uptake of GS-441524 may be reduced during hypoxia and inflammation due to decreased expression of adenosine transporters. Similarly, hypoxia and inflammation may lead to reduced expression of adenosine kinase, which is believed to convert GS-441524 to GS-441524 monophosphate, the perceived rate-limiting step in the intracellular formation of GS-441524 triphosphate. Moreover, increases in extracellular and intracellular levels of adenosine, which may occur during critical illnesses, has the potential to competitively decrease cellular uptake and phosphorylation of GS-441524. Taken together, tissue hypoxia and severe inflammation in COVID-19 may lead to reduced uptake and phosphorylation of GS-441524 with lowered therapeutic effectiveness as a potential outcome. Hypoxia may be particularly critical to the ability of GS-441524 to eliminate SARS-CoV-2 from tissues with low basal expression of adenosine transporters, such as alveolar cells. This knowledge may also be relevant to treatments with other antiviral adenosine analogs and anticancer adenosine analogs as well

Evolution of substrate recognition sites (SRSs) in cytochromes P450 from Apiaceae exemplified by the CYP71AJ subfamily

Background Large proliferations of cytochrome P450 encoding genes resulting from gene duplications can be termed as ‘blooms’, providing genetic material for the genesis and evolution of biosynthetic pathways. Furanocoumarins are allelochemicals produced by many of the species in Apiaceaous plants belonging to the Apioideae subfamily of Apiaceae and have been described as being involved in the defence reaction against phytophageous insects.[br/] Results A bloom in the cytochromes P450 CYP71AJ subfamily has been identified, showing at least 2 clades and 6 subclades within the CYP71AJ subfamily. Two of the subclades were functionally assigned to the biosynthesis of furanocoumarins. Six substrate recognition sites (SRS1-6) important for the enzymatic conversion were investigated in the described cytochromes P450 and display significant variability within the CYP71AJ subfamily. Homology models underline a significant modification of the accession to the iron atom, which might explain the difference of the substrate specificity between the cytochromes P450 restricted to furanocoumarins as substrates and the orphan CYP71AJ.[br/] Conclusion Two subclades functionally assigned to the biosynthesis of furanocoumarins and four other subclades were identified and shown to be part of two distinct clades within the CYP71AJ subfamily. The subclades show significant variability within their substrate recognition sites between the clades, suggesting different biochemical functions and providing insights into the evolution of cytochrome P450 ‘blooms’ in response to environmental pressures