The skin secretions of amphibians are a rich source of bioactive peptides. We isolated chensirin-1 and chensirin-2 from the skin secretion of the Chinese frog Rana chensinensis. Sephadex-G-50 and RP-HPLC were employed to purify these peptides. The amino acid sequences of these peptides were VLPLVGNLLNDLLGE and IIPLPLGYFAKKT, respectively, as determined by Edman degradation. The molecular weights were 1578.7 and 1460.8 Da, respectively, as analyzed by HPLC-ESI-MS. The chensirin cDNA was cloned by 5′ and 3′ amplification of cDNA ends, synthesized and purified. The antibacterial activities of the chensirins were tested using minimum inhibitory concentration, the results indicated that chensirins inhibit the growth of gram-negative and gram-positive bacteria. Among them, chensirin-1 is a novel peptide with a higher antibacterial activity compared to other similar antimicrobial peptides. These low molecular weight peptides with good antimicrobial efficacy are considered potential sources for developing new antimicrobial agents to improve traditional drug resistance.
Biomechanics and Modeling in Mechanobiology - Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many... 相似文献
Nosema ceranae, a newly emergent parasite invading western honey bees (Apis mellifera L.), is indicated to threaten honey bee health at both individual and colony levels. However, the efficient and environmentally-friendly treatments are quite limited at present. To find alternative medicine to control Nosema diseases, the effect of 8 types of herbal extracts against N. ceranae infection were screened under laboratory condition. Of which, 1% Andrographis paniculata (A. paniculata) decoction was found to significantly decrease N. ceranae spore numbers on 7 days post infection (dpi) and 13 dpi. Then, our results further revealed that A. paniculata decoction at doses ranging from 1% to 7% displayed significant efficient inhibition of Nosema spore proliferation and improved the infected bees' survival rates in a dose-dependent manner. A. paniculata decoction was found to protect the gut tissues of infected workers from damage cause by N. ceranae, which might be due to the regulation of the expression of certain genes in Wnt and JNK pathways, including armadillo, basket, frizzled2 and groucho. Additionally, our study suggested that A. paniculata decoction performed this Nosema spore-reducing potential over its two monomers, andrographolide and dehydrographolide. Taken together, this work enables us to better understand A. paniculata decoction's potential to inhibit N. ceranae infection, thus providing a new guidance for developing applicable drugs to control Nosema diseases. 相似文献
Bladder cancer is one of the most common malignant tumors in the urinary system. The development and improvement of treatment efficiency require the deepening of the understanding of its molecular mechanism. This study investigated the role of ALPK2, which is rarely studied in malignant tumors, in the development of bladder cancer. Our results showed the upregulation of ALPK2 in bladder cancer, and data mining of TCGA database showed the association between ALPK2 and pathological parameters of patients with bladder cancer. In vitro and in vivo experiments demonstrated that knockdown of ALPK2 could inhibit bladder cancer development through regulating cell proliferation, cell apoptosis, and cell migration. Additionally, DEPDC1A is identified as a potential downstream of ALPK2 with direct interaction, whose overexpression/downregulation can inhibit/promote the malignant behavioral of bladder cancer cells. Moreover, the overexpression of DEPDC1A can rescue the inhibitory effects of ALPK2 knockdown on bladder cancer. In conclusion, ALPK2 exerts a cancer-promoting role in the development of bladder cancer by regulating DEPDC1A, which may become a promising target to improve the treatment strategy of bladder cancer.Subject terms: Cancer models, Bladder cancer相似文献
Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature. 相似文献