Interactions of Environmental Chemicals and Natural Products With ABC and SLC Transporters in the Digestive System of Aquatic Organisms

An organism’s diet is a major route of exposure to both beneficial nutrients and toxic environmental chemicals and natural products. The uptake of dietary xenobiotics in the intestine is prevented by transporters of the Solute Carrier (SLC) and ATP Binding Cassette (ABC) family. Several environmental chemicals and natural toxins have been identified to induce expression of these defense transporters in fish and aquatic invertebrates, indicating that they are substrates and can be eliminated. However, certain environmental chemicals, termed Transporter-Interfering Chemicals or TICs, have recently been shown to bind to and inhibit fish and mammalian P-glycoprotein (ABCB1), thereby sensitizing cells to toxic chemical accumulation. If and to what extent other xenobiotic defense or nutrient uptake transporters can also be inhibited by dietary TICs is still unknown. To date, most chemical-transporter interaction studies in aquatic organisms have focused on ABC-type transporters, while molecular interactions of xenobiotics with SLC-type transporters are poorly understood. In this perspective, we summarize current advances in the identification, localization, and functional analysis of protective MXR transporters and nutrient uptake systems in the digestive system of fish and aquatic invertebrates. We collate the existing literature data on chemically induced transporter gene expression and summarize the molecular interactions of xenobiotics with these transport systems. Our review emphasizes the need for standardized assays in a broader panel of commercially important fish and seafood species to better evaluate the effects of TIC and other xenobiotic interactions with physiological substrates and MXR transporters across the aquatic ecosystem and predict possible transfer to humans through consumption.

GLAM: An adaptive graph learning method for automated molecular interactions and properties predictions

Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized them, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work would take a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency.

An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents

VEGFR-2 enzyme known for physiological functioning of the cell also involves in pathological angiogenesis and tumor progression. Recently VEGFR-2 has gained the interest of researchers all around the world as a promising target for the drug design and discovery of new anticancer agents. VEGFR2 inhibitors are a major class of anticancer agents used for clinical purposes. In silico methods like virtual screening, molecular docking, molecular dynamics, pharmacophore modeling, and other computational approaches help extensively in identifying the main molecular interactions necessary for the binding of the small molecules with the respective protein target to obtain the expected pharmacological potency. In this chapter, we discussed some representative case studies of in silico techniques used to determine molecular interactions and rational drug design of VEGFR-2 inhibitors as anticancer agents.

Ecological and Molecular Interactions between Insects and Fungi

Insects and fungi represent two of the most widespread groupings of organisms in nature, occurring in every kind of ecological context and impacting agriculture and other human activities in various ways. Moreover, they can be observed to reciprocally interact, establishing a wide range of symbiotic relationships, from mutualism to antagonism. The outcome of these relationships can in turn affect the extent at which species of both organisms can exert their noxious effects, as well as the management practices which are to be adopted to counter them. In conjunction with the launch of a Special Issue of Microorganisms with the same title, this article offers a general overview of the manifold aspects related to such interactions from the perspective of implementing our capacity to regulate them in a direction more favorable for the environment, crop production and human health.