Using machine learning to classify the immunosuppressive activity of per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFASs), one of the persistent organic pollutants, have immunosuppressive effects. The evaluation of this effect has been the focus of regulatory toxicology. In this investigation, 146 PFASs (immunosuppressive or nonimmunosuppressive) and corresponding concentration gradients were collected from literature, and their structures were characterized by using Dragon descriptors. Feature importance analysis and stepwise feature elimination are used for feature selection. Three machine learning (ML) methods, namely Random Forest (RF), Extreme Gradient Boosting Machine (XGB), and Categorical Boosting Machine (CB), were utilized for model development. The model interpretability was explored by feature importance analysis and correlation analysis. The findings indicated that the three models developed have exhibited excellent performance. Among them, the best-performing RF model has an average AUC score of 0.9720 for the testing set. The results of the feature importance analysis demonstrated that concentration, SpPosA_X, IVDE, R2s, and SIC2 were the crucial molecular features. Applicability domain analysis was also performed to determine reliable prediction boundaries for the model. In conclusion, this study is the first application of ML models to investigate the immunosuppressive activity of PFASs. The variables used in the models can help understand the mechanism of the immunosuppressive activity of PFASs, allow researchers to more effectively assess the immunosuppressive potential of a large number of PFASs, and thus better guide environmental and health risk assessment efforts.

HNF4A as a potential target of PFOA and PFOS leading to hepatic steatosis: Integrated molecular docking, molecular dynamic and transcriptomic analyses.

Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are indeed among the most well known and extensively studied Per- and polyfluoroalkyl substances (PFASs), and increasing evidence confirm their effects on human health, especially liver steatosis. Nonetheless, the molecular mechanisms of their initiation of hepatic steatosis is still elusive. Therefore, potential targets of PFOA/PFOS must be explored to ameliorate its adverse consequences. This research aims to investigate the molecular mechanisms of PFOA and PFOS-induced liver steatosis, with emphasis on identifying a potential target that links these PFASs to liver steatosis. The potential target that causes PFOA and PFOS-induced liver steatosis have been explored and determined based on molecular docking, molecular dynamics (MD) simulation, and transcriptomics analysis. In silico results show that PFOA/PFOS can form a stable binding conformation with HNF4A, and PFOA/PFOS may interact with HNF4A to affect the downstream conduction mechanism. Transcriptome data from PFOA/PFOS-induced human stem cell spheres showed that HNF4A was inhibited, suggesting that PFOA/PFOS may constrain its function. PFOS mainly down-regulated genes related to cholesterol synthesis while PFOA mainly up-regulated genes related to fatty acid ß-oxidation. This study explored the toxicological mechanism of liver steatosis caused by PFOA/PFOS. These compounds might inhibit and down-regulate HNF4A, which is the molecular initiation events (MIE) that induces liver steatosis.

Plant pathogen resistance is mediated by recruitment of specific rhizosphere fungi.

Beneficial interactions between plants and rhizosphere microorganisms are key determinants of plant health with the potential to enhance the sustainability of agricultural practices. However, pinpointing the mechanisms that determine plant disease protection is often difficult due to the complexity of microbial and plant-microbe interactions and their links with the plant's own defense systems. Here, we found that the resistance level of different banana varieties was correlated with the plant's ability to stimulate specific fungal taxa in the rhizosphere that are able to inhibit the Foc TR4 pathogen. These fungal taxa included members of the genera Trichoderma and Penicillium, and their growth was stimulated by plant exudates such as shikimic acid, D-(-)-ribofuranose, and propylene glycol. Furthermore, amending soils with these metabolites enhanced the resistance of a susceptible variety to Foc TR4, with no effect observed for the resistant variety. In total, our findings suggest that the ability to recruit pathogen-suppressive fungal taxa may be an important component in determining the level of pathogen resistance exhibited by plant varieties. This perspective opens up new avenues for improving plant health, in which both plant and associated microbial properties are considered.