Melatonin inhibiting the survival of human gastric cancer cells under ER stress involving autophagy and Ras-Raf-MAPK signalling.

Melatonin exhibits antitumour activities in the treatment of many human cancers. In the present study, we aimed to improve the therapeutic potential of melatonin in gastric cancer. Our results confirmed that melatonin dose-dependently suppressed the proliferation and necrosis, and increased G0/G1 phase arrest, apoptosis, autophagy and endoplasmic reticulum (ER) stress. The Ras-Raf-MAPK signalling pathway was activated in cells after melatonin treatment. RNA-seq was performed and GSEA analysis further confirmed that many down-regulated genes in melatonin-treated cells were associated with proliferation. However, GSEA analysis also indicated that many pathways related to metastasis were increased after melatonin treatment. Subsequently, combinatorial treatment was conducted to further investigate the therapeutic outcomes of melatonin. A combination of melatonin and thapsigargin increased the apoptotic rate and G0/G1 cell cycle arrest when compared to treatment with melatonin alone. Melatonin in combination with thapsigargin triggered the increased expression of Bip, LC3-II, phospho-Erk1/2 and phospho-p38 MAPK. In addition, STF-083010, an IRE1a inhibitor, further exacerbated the decrease in survival rate induced by combinatorial treatment with melatonin and thapsigargin. Collectively, melatonin was effective in gastric cancer treatment by modifying ER stress.

Computational methods for in situ structural studies with cryogenic electron tomography.

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.