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Triclosan (TCS), a commonly used antibacterial preservative, has been demonstrated to have high toxicological potential and adversely affects the water bodies. Since algae are one of the most significant primary producers on the planet, understanding the toxicological processes of TCS is critical for determining its risk in aquatic ecosystems and managing the water environment. The physiological and transcriptome changes in Euglena gracilis were studied in this study after 7 days of TCS treatment. A distinct inhibition ratio for the photosynthetic pigment content in E. gracilis was observed from 2.64% to 37.42% at 0.3–1.2 mg/L, with TCS inhibiting photosynthesis and growth of the algae by up to 38.62%. Superoxide dismutase and glutathione reductase significantly changed after exposure to TCS, compared to the control, indicating that the cellular antioxidant defense responses were induced. Based on transcriptomics, the differentially expressed genes were mainly enriched in biological processes involved in metabolism pathways and microbial metabolism in diverse environments. Integrating transcriptomics and biochemical indicators found that changed reactive oxygen species and antioxidant enzyme activities stimulating algal cell damage and the inhibition of metabolic pathways controlled by the down-regulation of differentially expressed genes were the main toxic mechanisms of TCS exposure to E. gracilis. These findings establish the groundwork for future research into the molecular toxicity to microalgae induced by aquatic pollutants, as well as provide fundamental data and recommendations for TCS ecological risk assessment.

Lithium chloride (LiCl) has been widely researched and utilized as a therapeutic option for bipolar disorder (BD). Several pathways, including cell signaling and signal transduction pathways in mammalian cells, are shown to be regulated by LiCl. LiCl can negatively control the expression and activity of PGM2, a phosphoglucomutase that influences sugar metabolism in yeast. In the presence of galactose, when yeast cells are challenged by LiCl, the phosphoglucomutase activity of PGM2p is decreased, causing an increase in the concentration of toxic galactose metabolism intermediates that result in cell sensitivity. Here, we report that the null yeast mutant strains DBP7∆ and YRF1-6∆ exhibit increased LiCl sensitivity on galactose-containing media. Additionally, we demonstrate that DBP7 and YRF1-6 modulate the translational level of PGM2 mRNA, and the observed alteration in translation seems to be associated with the 5′-untranslated region (UTR) of PGM2 mRNA. Furthermore, we observe that DBP7 and YRF1-6 influence, to varying degrees, the translation of other mRNAs that carry different 5′-UTR secondary structures.

Targeted therapy has revolutionized the treatment of poor-prognosis pediatric acute lymphoblastic leukemia (ALL) with specific genetic abnormalities. It is still being described as a new landmark therapeutic approach. The main purpose of the use of molecularly targeted drugs and immunotherapy in the treatment of ALL is to improve the treatment outcomes and reduce the doses of conventional chemotherapy, while maintaining the effectiveness of the therapy. Despite promising treatment results, there is limited clinical research on the effect of target cell therapy on the potential toxic events in children and adolescents. The recent development of highly specific molecular methods has led to an improvement in the identification of numerous unique expression profiles of acute lymphoblastic leukemia. The detection of specific genetic mutations determines patients’ risk groups, which allows for patient stratification and for an adjustment of the directed and personalized target therapies that are focused on particular molecular alteration. This review summarizes the knowledge concerning the toxicity of molecular-targeted drugs and immunotherapies applied in childhood ALL.

The concept of generating molecular structures with specific desirable characteristics underlies some of the crucial problems in drug discovery. In this paper, we present a model which constructs new molecules for specific desired properties. This model uses graph neural networks to generate molecular representations, and combining these representations with Reinforcement Learning architecture (Deep Q-Learning), builds new molecules. We used two different graph neural network architectures: Graph Convolutional Network and Graph Attention Network. We compared the molecular representations obtained from these two models with the Morgan Fingerprint representation in three separate experiments using the same Reinforcement Learning design. These experiments are single-objective optimization (drug-likeness), optimizing the penalized LogP with similarity constraint, and multi-objective optimization (drug-likeness with similarity constraint). The results show that the Reinforcement Learning models trained with molecular representations obtained from graph neural networks are more successful than the model trained with Morgan Fingerprint representation. © 2021 IEEE.