Abstract

Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

Abstract

Ectopic ATP synthase on the plasma membrane (eATP synthase) has been found in various cancer types and is a potential target for cancer therapy. However, whether it provides a functional role in tumor progression remains unclear. Here, quantitative proteomics reveals that cancer cells under starvation stress express higher eATP synthase and enhance the production of extracellular vesicles (EVs), which are vital regulators within the tumor microenvironment. Further results show that eATP synthase generates extracellular ATP to stimulate EV secretion by enhancing P2X7 receptor–triggered Ca2+ influx. Surprisingly, eATP synthase is also located on the surface of tumor-secreted EVs. The EVs-surface eATP synthase increases the uptake of tumor-secreted EVs in Jurkat T-cells via association with Fyn, a plasma membrane protein found in immune cells. The eATP synthase-coated EVs uptake subsequently represses the proliferation and cytokine secretion of Jurkat T-cells. This study clarifies the role of eATP synthase on EV secretion and its influence on immune cells.

Biomedicine & Pharmacotherapy  volume 166, 115429 (2023)

https://doi.org/10.1016/j.biopha.2023.115429 

Abstract

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2023

https://embc.embs.org/2023/   

Abstract

Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models’ performance in DTA prediction.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023

https://2023.ieeeicassp.org/   

Abstract

One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset.