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Cancer cells frequently exhibit aerobic glycolysis, known as the Warburg effect. An et al. identify a novel RNA regulatory activity of BAG3, a protein implicated in various cancers. BAG3 enhances posttranscriptionally the expression of Hexokinase 2, the first enzyme involved in glycolysis, and it reprograms glucose metabolism by pancreatic cancer cells., Aerobic glycolysis, a phenomenon known historically as the Warburg effect, is one of the hallmarks of cancer cells. In this study, we characterized the role of BAG3 in aerobic glycolysis of pancreatic ductal adenocarcinoma (PDAC) and its molecular mechanisms. Our data show that aberrant expression of BAG3 significantly contributes to the reprogramming of glucose metabolism in PDAC cells. Mechanistically, BAG3 increased Hexokinase 2 (HK2) expression, the first key enzyme involved in glycolysis, at the posttranscriptional level. BAG3 interacted with HK2 mRNA, and the degree of BAG3 expression altered recruitment of the RNA-binding proteins Roquin and IMP3 to the HK2 mRNA. BAG3 knockdown destabilized HK2 mRNA via promotion of Roquin recruitment, whereas BAG3 overexpression stabilized HK2 mRNA via promotion of IMP3 recruitment. Collectively, our results show that BAG3 promotes reprogramming of glucose metabolism via interaction with HK2 mRNA in PDAC cells, suggesting that BAG3 may be a potential target in the aerobic glycolysis pathway for developing novel anticancer agents.

The paper first analyzed the property of sample confidence measure function applied by noise reduction algorithm, explained the reason of this function being not suitable for multi-class problems. Then a more targeted confidence measure function was designed, and based on this function, an enhanced de-noise algorithm of ensemble parameters learning was proposed. Thus the discriminative learning algorithm not only effectively restrain the noise, but also avoid the overfitting of the classifier. Finally, the experimental results and statistical analysis for hypothesis testing verified that the current ensemble parameters learning algorithms of Bayesian network was improved obviously in the performance.

Based on the problems that traditional image de-noising algorithms easily to lose details features and always has low signal-to-noise ratio, inspired by the cross breeding method and the genetic algorithm, this paper proposes an image hybrid wavelet transform image de-noising algorithm based on Bayesian estimation. The proposed algorithm uses the Bayesian wavelet de-noised image as the male parent, and the Wiener filtering image as the female parent, the picked individual was prepared for the crossover and mutation operations. The optimal offspring will be choosing as the final solution algorithm and the decoding reduction as the de-noised image. The peak signal to noise ratio of the algorithm is much higher than that of the traditional algorithm, and the visual effect is better. The experimental results show that this method can not only eliminate the image noise effectively, but also can preserve the image edges and other details.

// Zhi-Hong Zong 1, 2 , Zhen-Xian Du 3 , Hai-Yan Zhang 4 , Chao Li 1 , Ming-Xin An 1 , Si Li 1 , Han-Bing Yao 1 , Hua-Qin Wang 1, 2 1 Department of Biochemistry & Molecular Biology, China Medical University, Shenyang 110001, China 2 Key Laboratory of Cell Biology, Ministry of Public Health, and Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, Shenyang 110001, China 3 Department of Endocrinology & Metabolism, The 1st Affiliated Hospital, China Medical University, Shenyang 110001, China 4 Department of Geriatrics, The 1st Affiliated Hospital, China Medical University, Shenyang 110001, China Correspondence to: Hua-Qin Wang, e-mail: wanghq_doctor@hotmail.com Keywords: proteasome inhibitor, Nrf2, ORP150, thyroid cancer Received: July 05, 2015 Accepted: November 21, 2015 Published: December 18, 2015 ABSTRACT Oxygen-regulated protein 150 (ORP150) is an inducible ER chaperone by numerous cellular insults and sustains cellular viability. We have previously reported that ORP150 is differentially induced in a panel thyroid cancer cells and represents as an unwanted molecular consequence during exposure to proteasome inhibition. However, the molecular basis for induction of ORP150 by proteasome inhibitors in thyroid cancer cells remains unclear. In the current study, we found that -421/-307 and -243/+53 regions at the ORP150 gene were responsible for its transactivation by MG132 in thyroid cancer cells. Nrf2 directly transactivated the ORP150 gene by direct binding with the -421/-307 region. Nrf2 also indirectly activated OPR150 transcription via facilitating recruitment of ATF4 to the -243/+53 region. Collectively, this study highlights the molecular mechanism by which proteasome inhibition stimulates ORP150 expression via Nrf2 in thyroid cancer cells.