Proteome-wide prediction and analysis of the Cryptosporidium parvum protein-protein interaction network through integrative methods

Understanding the Cryptosporidium species and their challenges to animal health and livestock species for informed development of new, specific treatment strategies

Cryptosporidium species are parasitic organisms of vertebrates with a worldwide distribution. They have an important impact globally upon human and animal health, and livestock productivity. The life cycle of these species is complex and difficult to disrupt to improve human health, animal health, food security and economic growth. This may contribute to the fact that no new treatment strategy has been widely accepted or applied in livestock for years. Here we consider the natural history of these parasites, their biochemistry and economic impact. Using recent developments in understanding these parasites we then consider viable and affordable approaches to enhancing control of their effects on livestock. These are based on advances in drug discovery, omics research and artificial intelligence applications to human and veterinary medicine that indicate putative new therapeutic approaches.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Notoginsenoside R1 can inhibit the interaction between FGF1 and VEGFA to retard podocyte apoptosis

BACKGROUND

Diabetic nephropathy (DN) is a chronic condition resulting from microangiopathy in a high-glucose environment. The evaluation of vascular injury in DN has primarily focused on active molecules of VEGF, namely VEGFA and VEGF2(F2R). Notoginsenoside R1 (NGR1), a traditional anti-inflammatory medication, exhibits vascular activity. Therefore, identifying classical drugs with vascular inflammatory protection for the treatment of DN is a valuable pursuit.

METHODS

The "Limma" method was employed to analyze the glomerular transcriptome data, while the Spearman algorithm for Swiss target prediction was utilized to analyze the drug targets of NGR1. The molecular docking technique was employed to investigate the relationship between vascular active drug targets, and the COIP experiment was conducted to verify the interaction between fibroblast growth factor 1 (FGF1) and VEGFA in relation to NGR1 and drug targets.

RESULTS

According to the Swiss target prediction, the LEU32(b) site of the Vascular Endothelial Growth Factor A (VEGFA) protein, as well as the Lys112(a), SER116(a), and HIS102(b) sites of the Fibroblast Growth Factor 1 (FGF1) protein, are potential binding sites for NGR1 through hydrogen bonding. Additionally, the Co-immunoprecipitation (COIP) results suggest that VEGFA and FGF1 proteins can interact with each other, and NGR1 can impede this interaction. Furthermore, NGR1 can suppress the expression of VEGFA and FGF1 in a high-glucose environment, thereby decelerating podocyte apoptosis.

CONCLUSION

The inhibition of the interaction between FGF1 and VEGFA by NGR1 has been observed to decelerate podocyte apoptosis.