To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
Glucocorticoids (GCs), such as prednisolone (PRED), are widely prescribed anti-inflammatory drugs, but their use may induce glucose intolerance and diabetes. GC-induced beta cell dysfunction contributes to these diabetogenic effects through mechanisms that remain to be elucidated. In this study, we hypothesized that activation of the unfolded protein response (UPR) following endoplasmic reticulum (ER) stress could be one of the underlying mechanisms involved in GC-induced beta cell dysfunction. We report here that PRED did not affect basal insulin release but time-dependently inhibited glucose-stimulated insulin secretion in INS-1E cells. PRED treatment also decreased both PDX1 and insulin expression, leading to a marked reduction in cellular insulin content. These PRED-induced detrimental effects were found to be prevented by prior treatment with the glucocorticoid receptor (GR) antagonist RU486 and associated with activation of two of the three branches of the UPR. Indeed, PRED induced a GR-mediated activation of both ATF6 and IRE1/XBP1 pathways but was found to reduce the phosphorylation of PERK and its downstream substrate eIF2α. These modulations of ER stress pathways were accompanied by upregulation of calpain 10 and increased cleaved caspase 3, indicating that long term exposure to PRED ultimately promotes apoptosis. Taken together, our data suggest that the inhibition of insulin biosynthesis by PRED in the insulin-secreting INS-1E cells results, at least in part, from a GR-mediated impairment in ER homeostasis which may lead to apoptotic cell death.
