Cancer cells, including in chronic myeloid leukemia (CML), depend on the hypoxic response to persist in hosts and evade therapy. Accordingly, there is significant interest in drugging cancer-specific hypoxic responses. However, a major challenge in leukemia is identifying differential and druggable hypoxic responses between leukemic and normal cells. Previously, we found that arginase 2 (ARG2), an enzyme of the urea cycle, is overexpressed in CML but not normal progenitors. ARG2 is a target of the hypoxia inducible factors (HIF1−α and HIF2−α), and is required for the generation of polyamines which are required for cell growth. We therefore explored if the clinically-tested arginase inhibitor Nω−hydroxy−nor−arginine (nor−NOHA) would be effective against leukemic cells under hypoxic conditions. Remarkably, nor−NOHA effectively induced apoptosis in ARG2-expressing cells under hypoxia but not normoxia. Co-treatment with nor−NOHA overcame hypoxia-mediated resistance towards BCR−ABL1 kinase inhibitors. While nor−NOHA itself is promising in targeting the leukemia hypoxic response, we unexpectedly found that its anti-leukemic activity was independent of ARG2 inhibition. Genetic ablation of ARG2 using CRISPR/Cas9 had no effect on the viability of leukemic cells and their sensitivity towards nor−NOHA. This discrepancy was further evidenced by the distinct effects of ARG2 knockouts and nor−NOHA on cellular respiration. In conclusion, we show that nor−NOHA has significant but off-target anti-leukemic activity among ARG2-expressing hypoxic cells. Since nor−NOHA has been employed in clinical trials, and is widely used in studies on endothelial dysfunction, immunosuppression and metabolism, the diverse biological effects of nor−NOHA must be cautiously evaluated before attributing its activity to ARG inhibition.
Under the background of green low-carbon economy, it is of great significance to accurately estimate the future CO2 emissions of countries with large CO2 emissions for the development of the world green economy. A new Nonlinear Grey Bernoulli and BP neural network combined model (BP-ONGBM (1,1) model) has been proposed to study the CO2 emissions of China, the United States, the European Union, India and Japan. Firstly, the Particle Swarm Optimization (PSO) algorithm is optimized by using the idea of Artificial Fish Swarm Algorithm (AFSA), and then the background value of ONGBM (1,1) model is dynamically optimized. Based on the linearization of the model, the time response function is derived. Then, the ONGBM (1,1) model is combined with the BP neural network model. The combination weight and the background value coefficient are determined by improved PSO algorithm. Finally, according to the observation data from 2010 to 2021 in the Emissions Database for Global Atmospheric Research 2022, the model is established to calculate the CO2 emissions of the selected countries from 2022 to 2026, and compared with the prediction results provided by multiple competitive models. The empirical application shows that the newly proposed BP-ONGBM (1,1) model is significantly better than other competitive models.
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