Gliotoxin, a natural product with ferroptosis inducing properties

Ferroptosis in Cancer Therapy: Mechanisms, Small Molecule Inducers, and Novel Approaches

Ferroptosis, a unique form of programmed cell death, is initiated by an excess of iron accumulation and lipid peroxidation-induced damage. There is a growing body of evidence indicating that ferroptosis plays a critical role in the advancement of tumors. The increased metabolic activity and higher iron levels in tumor cells make them particularly vulnerable to ferroptosis. As a result, the targeted induction of ferroptosis is becoming an increasingly promising approach for cancer treatment. This review offers an overview of the regulatory mechanisms of ferroptosis, delves into the mechanism of action of traditional small molecule ferroptosis inducers and their effects on various tumors. In addition, the latest progress in inducing ferroptosis using new means such as proteolysis-targeting chimeras (PROTACs), photodynamic therapy (PDT), sonodynamic therapy (SDT) and nanomaterials is summarized. Finally, this review discusses the challenges and opportunities in the development of ferroptosis-inducing agents, focusing on discovering new targets, improving selectivity, and reducing toxic and side effects.

Strain heterogeneity in a non-pathogenic fungus highlights factors contributing to virulence

Fungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri, a non-pathogenic close relative of the major pathogen Aspergillus fumigatus. In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolomic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.

Natural product-derived ferroptosis mediators

It has been 11 years since ferroptosis, a new mode of programmed cell death, was first proposed. Natural products are an important source of drug discovery. In the past five years, natural product-derived ferroptosis regulators have been discovered in an endless stream. Herein, 178 natural products discovered so far to trigger or resist ferroptosis are classified into 6 structural classes based on skeleton type, and the mechanisms of action that have been reported are elaborated upon. If pharmacodynamic data are sufficient, the structure and bioactivity relationship is also presented. This review will provide medicinal chemists with some effective ferroptosis regulators, which will promote the research of natural product-based treatment of ferroptosis-related diseases in the future.

Elucidating the mechanism of action of mycotoxins through machine learning-driven QSAR models: Focus on lipid peroxidation

Understanding the mechanisms of mycotoxin toxicity is crucial for establishing effective guidelines and preventive strategies. In this study, machine learning models based on quantitative structure-activity relationship (QSAR) were employed to predict the lipid peroxidation activity of mycotoxins. Two different algorithms using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs) have been trained using a dataset of 70 mycotoxins. The LDA model had an average correct classification rate of 91%, while the ANN model achieved a perfect 100% classification rate. Following an internal validation process, the models were utilized to predict mycotoxins with known lipid peroxidation activity. The machine learning models achieved an 88% correct classification rate for these mycotoxins. Finally, by utilizing classified algorithms, the study aimed to infer the mechanism of action related to lipid peroxidation for 91 unstudied mycotoxins. These models provide a fast, accurate, and cost-effective means to assess the potential toxicity and mechanism of action of mycotoxins. The findings of this study contribute to a comprehensive understanding of mycotoxin toxicology and assist researchers and toxicologists in evaluating health risks associated with mycotoxin exposure and developing appropriate preventive strategies and potential therapeutic interventions to mitigate the effects of mycotoxins.