Shang, Chao | Engineering Papers

DOE OSTI2021

Ad-apoptin inhibits glycolysis, migration and invasion in lung cancer cells targeting AMPK/mTOR signaling pathway

Ad-apoptin is a recombinant oncolytic adenovirus constructed by our laboratory that can express apoptin. It can selectively kill tumor cells without damaging normal cells. This study investigated the effects of Ad-apoptin on glycolysis, migration and invasion of non-small cell lung cancer. Cell viability and apoptosis were detected by CCK-8 and flow cytometry, respectively. Glycolysis was investigated by glucose consumption, lactic acid production and glycolytic key enzyme protein levels. Migration and invasion were evaluated via wound healing, transwell assays and epithelial-mesenchymal transition (EMT) protein levels. The interaction between apoptin and AMPK was detected by Co-IP. A nude mice tumor model was established to investigate the anti-cancer role of Ad-apoptin in vivo. The results showed that Ad-apoptin inhibits cell viability and induces apoptosis of A549 and NCI–H23 cells. Ad-apoptin can reduce the glucose uptake and lactic production in lung cancer cells, and reduce the expression of related glycolysis-limiting enzymes. At the same time, Ad-apoptin inhibited the migration and invasion of lung cancer. Immunoprecipitation showed that apoptin and AMPK could interact directly. Moreover, knockdown of AMPK significantly attenuated the inhibitory effect of Ad-apoptin on glycolysis, migration and invasion of A549 and NCI–H23 cells. Ad-apoptin can inhibit the growth of tumors in nude mice. Compared with the control group, Ad-apoptin had a significant inhibitory effect on AMPK knockdown tumors. The immunohistochemical results of tumor tissues were consistent with those in vitro. Collectively, Ad-apoptin targets AMPK and inhibits glycolysis, migration and invasion of lung cancer cells through the AMPK/mTOR signaling pathway. This suggests that Ad-apoptin may have therapeutic potential for lung cancer by targeting AMPK activation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Discrete Graph Structure Learning for Forecasting Multiple Time Series

Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based.

Shang, Chao↗