Diallyl Trisulfide Causes Male Infertility with Oligoasthenoteratospermia in Sitotroga cerealella through the Ubiquitin-Proteasome Pathway.

Essential oils extracted from plant sources along with their biologically active components may have negative effects on insects. Diallyl trisulfide (DAT) is an active component of garlic essential oil, and it exhibits multi-targeted activity against many organisms. Previously we reported that DAT induces male infertility and leads to apyrene and eupyrene sperm dysfunction in Sitotroga cerealella. In this study, we conducted an analysis of testis-specific RNA-Seq data and identified 449 downregulated genes and 60 upregulated genes in the DAT group compared to the control group. The downregulated genes were significantly enriched in the ubiquitin-proteasome pathway. Furthermore, DAT caused a significant reduction in mRNA expression of proteasome regulatory subunit particles required for ATP-dependent degradation of ubiquitinated proteins as well as decreased the expression profile of proteasome core particles, including ß1, ß2, and ß5. Sperm physiological analysis showed that DAT decreased the chymotrypsin-like activity of the 20S proteasome and formed aggresomes in spermatozoa. Overall, our findings suggest that DAT impairs the testis proteasome, ultimately causing male infertility characterized by oligoasthenoteratospermia due to disruption in sperm proteasome assembly in S. cerealella.

A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class.

Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.

Diallyl Trisulfide, a Biologically Active Component of Garlic Essential Oil, Decreases Male Fertility in Sitotroga cerealella by Impairing Dimorphic Spermatogenesis, Sperm Motility and Lipid Homeostasis.

Diallyl trisulfide (DAT) is a biologically active component of garlic essential oil and exhibits multi-targeted activity against many organisms. The current study tested the capacity of DAT to decrease the male fertility of Sitotroga cerealella. The effects on testis morphology, sperm number, motility, and lipid homeostasis were observed in adult males fumigated with DAT at a dose of 0.01 µL/L in air. The results indicated that the DAT significantly decreased the dimorphic sperm number. Meanwhile, the ultrastructural analysis of the sperm showed that the DAT caused malformed and aberrant structures of mitochondrial derivatives of dimorphic sperm. Additionally, the lipid homeostasis and ATP contents in the male adults were significantly decreased after treatment. Moreover, the total sperm motility was reduced, while the wave-propagation velocity, amplitude, frequency, and wavelength were significantly decreased compared with the controls. Overall, this study reported, for the first time, that DAT impairs energy metabolism, inhibits dimorphic spermatogenesis, and decreases sperm motility, while these abnormalities in sperm lead to adult-male infertility.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.