Co-exposure to polystyrene nanoplastics and triclosan induces synergistic cytotoxicity in human KGN granulosa cells by promoting reactive oxygen species accumulation.

In recent years, nanoplastics (NPs) and triclosan (TCS, a pharmaceutical and personal care product) have emerged as environmental pollution issues, and their combined presence has raised widespread concern regarding potential risks to organisms. However, the combined toxicity and mechanisms of NPs and TCS remain unclear. In this study, we investigated the toxic effects of polystyrene NPs and TCS and their mechanisms on KGN cells, a human ovarian granulosa cell line. We exposed KGN cells to NPs (150 µg/mL) and TCS (15 µM) alone or together for 24 hours. Co-exposure significantly reduced cell viability. Compared with exposure to NPs or TCS alone, co-exposure increased reactive oxygen species (ROS) production. Interestingly, co-exposure to NPs and TCS produced synergistic effects. We examined the activity of superoxide dismutase (SOD) and catalase (CAT), two antioxidant enzymes; it was significantly decreased after co-exposure. We also noted an increase in the lipid oxidation product malondialdehyde (MDA) after co-exposure. Furthermore, co-exposure to NPs and TCS had a more detrimental effect on mitochondrial function than the individual treatments. Co-exposure activated the NRF2-KEAP1-HO-1 antioxidant stress pathway. Surprisingly, the expression of SESTRIN2, an antioxidant protein, was inhibited by co-exposure treatments. Co-exposure to NPs and TCS significantly increased the autophagy-related proteins LC3B-II and LC3B-Ⅰ and decreased P62. Moreover, co-exposure enhanced CASPASE-3 expression and inhibited the BCL-2/BAX ratio. In summary, our study revealed the synergistic toxic effects of NPs and TCS in vitro exposure. Our findings provide insight into the toxic mechanisms associated with co-exposure to NPs and TCS to KGN cells by inducing oxidative stress, activations of the NRF2-KEAP1-HO-1 pathway, autophagy, and apoptosis.

Oral exposure to polystyrene nanoplastics reduced male fertility and even caused male infertility by inducing testicular and sperm toxicities in mice.

Nanoplastics (NPs) are the novel hazardous materials and ubiquitous in environment with different sizes. Although recent studies showed testicular toxicity of PS-NPs, whether and how NPs affect male fertility and whether they have the size-dependent effect remain ambiguous in mammals. In this study, the male mice were orally exposed to 25-, 50-, and 100-nm polystyrene NPs (PS-NPs) for 56 days. All three sized PS-NPs reduced male fertility and even caused male infertility. They accumulated in the testes, induced oxidative stress, affected the expression of apoptosis- and inflammation-related genes, and compromised energy metabolism, resulting in damaged testicular microstructure and functions. PS-NPs caused more severe testicular toxicity in infertile mice than in fertile mice. In addition, PS-NPs inhibited sperm capacitation and capacitation-dependent processes in infertile mice but not in fertile mice. In infertile mice, PS-NPs reduced the sperm levels of two Rho GTPases (RAC1 and CDC42) via increasing their ubiquitination levels and diminished sperm filamentous actin polymerization, thus inhibiting sperm capacitation. However, these testicular and sperm toxicities showed no size-dependent effect among three sized PS-NPs. In conclusion, PS-NPs inhibit male fertility by their multifaceted toxicity on testes and sperm in mice, providing novel insights into reproductive risks of NPs to mammals.

The ovarian-related effects of polystyrene nanoplastics on human ovarian granulosa cells and female mice.

Nanoplastics (NPs) have recently emerged in the context of global plastic pollution. They may be more toxic than macroplastics litter and microplastic fragments due to its abundances, tiny sizes, and cellular accessibility. The female reproductive toxicity of NPs has been widely documented for aquatic animals, but their effects and underlying mechanisms remain poorly understood in mammals. This study aimed to explore the effects of NPs on female reproduction using human ovarian granulosa cells (GCs) and female mice. The accumulation of polystyrene NPs (PS-NPs) in human granulosa-like tumor cells (KGN cells) and the ovaries of female Balb/c mice were evaluated by exposure to fluorescent PS-NPs. Proliferation and apoptosis, reactive oxygen species (ROS), and Hippo signaling pathway-related factors were analyzed in KGN cells. In addition, fertility rate, litter size, ovarian weight and microstructure, follicle development, serum level of anti-Mullerian hormone, and apoptosis in ovaries were examined in female mice. Here, the PS-NPs can penetrate the KGN cells and accumulate in the ovaries. In vitro, 100 µg/ml PS-NPs inhibited proliferation, induced apoptosis, accumulated ROS, activated three key regulators of the Hippo signaling pathway (MST1, LATS1, and YAP1), and downregulated the mRNA levels of CTGF and Cyr61 in KGN cells. Furthermore, salidroside, an antioxidative compound extracted from Rhodiola rosea, alleviated the damage of PS-NPs to KGN and inhibited the activation of the Hippo signal pathway. In vivo, exposure to 1 mg/day PS-NPs resulted in decreased fertility, abnormal ovarian function, and increased ovarian apoptosis in female mice. Overall, our data suggest that PS-NPs cause granulosa cell apoptosis and affect ovarian functions, leading to reduced fertility in female mice, by inducing oxidative stress and dysregulating the Hippo pathway.

[Role of histone posttranslational modifications in the regulation of ovarian function].

The ovary is the reproductive organ of female mammals, which is responsible for producing mature eggs and secreting sex hormones. The regulation of ovarian function involves the ordered activation and repression of genes related to cell growth and differentiation. In recent years, it has been found that histone posttranslational modification can affect DNA replication, damage repair and gene transcriptional activity. Some regulatory enzymes mediating histone modification are co-activators or co-inhibitors associated with transcription factors, which play important roles in the regulation of ovarian function and the development of ovary-related diseases. Therefore, this review outlines the dynamic patterns of common histone modifications (mainly acetylation and methylation) during the reproductive cycle and their regulation of gene expression for important molecular events, focusing on the mechanisms of follicle development and sex hormone secretion and function. For example, the specific dynamics of histone acetylation are important for the arrest and resumption of meiosis in oocytes, while histone (especially H3K4) methylation affects the maturation of oocytes by regulating their chromatin transcriptional activity and meiotic progression. Besides, histone acetylation or methylation can also promote the synthesis and secretion of steroid hormones before ovulation. Finally, the abnormal histone posttranslational modifications in the development of two common ovarian diseases (premature ovarian insufficiency and polycystic ovary syndrome) are briefly described. It will provide a reference basis for understanding the complex regulation mechanism of ovarian function and further exploring the potential therapeutic targets of related diseases.

A Protein Co-Conservation Network Model Characterizes Mutation Effects on SARS-CoV-2 Spike Protein.

The emergence of numerous variants of SARS-CoV-2 has presented challenges to the global efforts to control the COVID-19 pandemic. The major mutation is in the SARS-CoV-2 viral envelope spike protein that is responsible for virus attachment to the host, and is the main target for host antibodies. It is critically important to study the biological effects of the mutations to understand the mechanisms of how mutations alter viral functions. Here, we propose a protein co-conservation weighted network (PCCN) model only based on the protein sequence to characterize the mutation sites by topological features and to investigate the mutation effects on the spike protein from a network view. Frist, we found that the mutation sites on the spike protein had significantly larger centrality than the non-mutation sites. Second, the stability changes and binding free energy changes in the mutation sites were positively significantly correlated with their neighbors' degree and the shortest path length separately. The results indicate that our PCCN model provides new insights into mutations on spike proteins and reflects the mutation effects on protein function alternations.

ProTstab2 for Prediction of Protein Thermal Stabilities.

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

PON-Sol2: Prediction of Effects of Variants on Protein Solubility.

Genetic variations have a multitude of effects on proteins. A substantial number of variations affect protein-solvent interactions, either aggregation or solubility. Aggregation is often related to structural alterations, whereas solubilizable proteins in the solid phase can be made again soluble by dilution. Solubility is a central protein property and when reduced can lead to diseases. We developed a prediction method, PON-Sol2, to identify amino acid substitutions that increase, decrease, or have no effect on the protein solubility. The method is a machine learning tool utilizing gradient boosting algorithm and was trained on a large dataset of variants with different outcomes after the selection of features among a large number of tested properties. The method is fast and has high performance. The normalized correct prediction rate for three states is 0.656, and the normalized GC2 score is 0.312 in 10-fold cross-validation. The corresponding numbers in the blind test were 0.545 and 0.157. The performance was superior in comparison to previous methods. The PON-Sol2 predictor is freely available. It can be used to predict the solubility effects of variants for any organism, even in large-scale projects.