Xudong Han

Uveal melanoma (UM) is among the most prevalent intraocular malignant tumors worldwide. Celastrol exhibits broad-spectrum anticancer properties; however, its underlying therapeutic mechanism in UM is yet to be elucidated. In this study, a network pharmacology approach was employed to identify potential common targets of celastrol and UM. These targets were further analyzed in conjunction with transcriptomic data and machine learning algorithms, which led to the identification of CTNNB1 and STAT3 as key molecular targets. The functional roles of these targets were investigated through immune infiltration analysis and single-cell RNA sequencing (scRNA-seq), while the binding stability between celastrol and CTNNB1/STAT3 was assessed using molecular docking (MD) and molecular dynamics simulation (MDS). Subsequently, celastrol was administered to B16-F10 and C918 cell lines, demonstrating that it significantly suppresses cell proliferation and migration by downregulating CTNNB1 and STAT3 expression, while simultaneously inducing apoptosis and cell cycle arrest. Moreover, real-time quantitative PCR (qPCR) and western blot (WB) analyses corroborated the modulation of target expression levels. Therefore, celastrol exerts potent anti-tumor effects in UM by inhibiting the CTNNB1 and STAT3 signaling pathways, thereby suppressing tumor cell proliferation and metastasis, as well as promoting cell cycle arrest and apoptosis.

Objective: Melanoma, with its high degree of malignancy, stands as one of the most dangerous skin cancers and remains the primary cause of death from skin cancer. With studies demonstrating the potential of traditional Chinese medicine to intervene and treat melanoma, we turned our attention to celastrol. Celastrol is a triterpene compound extracted from the traditional Chinese medicine derived from Tripterygium wilfordii. Previous studies have shown that celastrol exerts inhibitory effects on various malignant tumors, including melanoma. Hence, our goal was to clarify the impact of celastrol on cell viability, apoptosis, and cell cycle progression by elucidating its effects on the PI3K/AKT/mTOR pathway. Methods: CCK-8 and wound healing assays were used to determine the effect of celastrol on the viability and migration of B16-F10 cells. Changes in cell apoptosis, cell cycle, reactive oxygen species (ROS), and mitochondrial membrane potential were detected by flow cytometry. PI3K/AKT/mTOR pathway proteins and HIF-α mRNA expression in B16-F10 cells were detected by western blotting and qPCR. Moreover, the addition of a PI3K activator demonstrated that celastrol could inhibit the function of B16-F10 cells via the PI3K/AKT/mTOR pathway. Results: Celastrol inhibited the viability and migration of B16-F10 cells. Through the inhibition of the PI3K/AKT/mTOR pathway down-regulates the expression of HIF-α mRNA, thereby causing an increase of ROS in cells and a decrease in the mitochondrial membrane potential to promote cell apoptosis and cell cycle arrest. The inhibitory effect of celastrol on B16-F10 cells was further demonstrated by co-culturing with a PI3K activator. Conclusion: Celastrol inhibits the function of B16-F10 cells by inhibiting the PI3K/AKT/mTOR cellular pathway and regulating the expression of downstream HIF-α mRNA.

Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry–based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)–based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.