Mcl-1 suppresses abasic site repair following bile acid-induced hepatic cellular DNA damage

The application of natural language processing for the extraction of mechanistic information in toxicology

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).

Nicotine induces cell survival and chemoresistance by stimulating Mcl-1 phosphorylation and its interaction with Bak in lung cancer

Nicotine is a major carcinogen in cigarettes, which can enhance cell proliferation and metastasis and increase the chemoresistance of cancer cells. Our previous data found that nicotine promotes cell survival in lung cancer by affecting the expression of antiapoptotic protein Mcl-1, suggesting that the Mcl-1 may be a therapeutic target for patients with lung cancer. In this study, we found that the effects of drug resistance on nicotine-induced lung cancer cell lines were shown to influence the phosphorylation of Mcl-1. Moreover, nicotine induces Mcl-1 phosphorylation exclusively at the T163 site, which results in enhancement of the antiapoptotic activity of Mcl-1 and increased cell survival. Meanwhile, nicotine can reduce the sensitivity of H1299 cells to CDDP via enhancement of the binding of Mcl-1 to Bak, which inhibits the proapoptotic effect of Bak and ultimately leads to increased survival and drug resistance of lung cancer cells. Thus, nicotine-induced cell survival and chemoresistance may occur in a mechanism by stimulating Mcl-1 phosphorylation and its interaction with Bak, which may contribute to improving the efficacy of chemotherapy in the treatment of human lung cancer.