Biosynthesis of rare 20(R)-protopanaxadiol/protopanaxatriol type ginsenosides through Escherichia coli engineered with uridine diphosphate glycosyltransferase genes

Lu Yu,Yuan Chen,Jie Shi,Ru-Feng Wang,Ying-Bo Yang,Li Yang,Shujuan Zhao,Zheng-Tao Wang 고려인삼학회 2019 Journal of Ginseng Research Vol.43 No.1

Background: Ginsenosides are known as the principal pharmacological active constituents in Panax medicinal plants such as Asian ginseng, American ginseng, and Notoginseng. Some ginsenosides, especially the 20(R) isomers, are found in trace amounts in natural sources and are difficult to chemically synthesize. The present study provides an approach to produce such trace ginsenosides applying biotransformation through Escherichia coli modified with relevant genes. Methods: Seven uridine diphosphate glycosyltransferase (UGT) genes originating from Panax notoginseng, Medicago sativa, and Bacillus subtilis were synthesized or cloned and constructed into pETM6, an ePathBrick vector, which were then introduced into E. coli BL21star (DE3) separately. 20(R)-Protopanaxadiol (PPD), 20(R)-protopanaxatriol (PPT), and 20(R)-type ginsenosides were used as substrates for biotransformation with recombinant E. coli modified with those UGT genes. Results: E. coli engineered with GT95syn selectively transfers a glucose moiety to the C20 hydroxyl of 20(R)-PPD and 20(R)-PPT to produce 20(R)-CK and 20(R)-F1, respectively. GTK1- and GTC1-modified E. coli glycosylated the C3eOH of 20(R)-PPD to form 20(R)-Rh2. Moreover, E. coli containing p2GT95synK1, a recreated two-step glycosylation pathway via the ePathBrich, implemented the successive glycosylation at C20eOH and C3eOH of 20(R)-PPD and yielded 20(R)-F2 in the biotransformation broth. Conclusion: This study demonstrates that rare 20(R)-ginsenosides can be produced through E. coli engineered with UTG genes.

NMR Study of Magnetic Properties of Eu1−xSrxMnO3 (0 ≤ x ≤ 0.5)

Kenji Shimizu,Masanori Yamaguchi,Shujuan Yuan,Shixun Cao 한국물리학회 2013 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.63 No.3

The perovskite manganite EuMnO3 is known to be a compound with a narrow eg-band width due to small ionic radius of Eu3+ compared to La3+. The Eu1−xSrxMnO3 compounds show complex magnetic properties, depending on the Sr concentration x. We have carried out 55Mn spin-echo nuclear magnetic resonance (NMR) measurements on polycrystalline Eu1−xSrxMnO3 (0 ≤ x≤ 0.5) compounds at 4.2 or 1.4 K in order to clarify the microscopic local magnetic state of the Mn ions. NMR spectra have been observed in the frequency range from 250 MHz to 400 MHz. For the A-type antiferromagnetic EuMnO3, the resonance frequency is about 260 MHz, which is much lower than that observed for Mn3+ in La1−xSrxMnO3. For 0.1 ≤ x ≤ 0.4, the resonance lines corresponding to Mn4+ and Mn3+ have been observed at about 315 MHz and 390 MHz, respectively. Furthermore, the spectra originating from Mn2+ have been observed around 590 MHz for 0.1 ≤ x ≤ 0.4. For Eu0.5Sr0.5MnO3, the NMR spectrum spreads widely with several distinct peaks in the frequency range from 250 MHz to 410 MHz, which shows that the compound is in an inhomogeneous state due to competition between the antiferromagnetic and the ferromagnetic states.

Biosynthesis of rare 20(R)-protopanaxadiol/protopanaxatriol type ginsenosides through Escherichia coli engineered with uridine diphosphate glycosyltransferase genes

Yu, Lu,Chen, Yuan,Shi, Jie,Wang, Rufeng,Yang, Yingbo,Yang, Li,Zhao, Shujuan,Wang, Zhengtao The Korean Society of Ginseng 2019 Journal of Ginseng Research Vol.43 No.1

Background: Ginsenosides are known as the principal pharmacological active constituents in Panax medicinal plants such as Asian ginseng, American ginseng, and Notoginseng. Some ginsenosides, especially the 20(R) isomers, are found in trace amounts in natural sources and are difficult to chemically synthesize. The present study provides an approach to produce such trace ginsenosides applying biotransformation through Escherichia coli modified with relevant genes. Methods: Seven uridine diphosphate glycosyltransferase (UGT) genes originating from Panax notoginseng, Medicago sativa, and Bacillus subtilis were synthesized or cloned and constructed into pETM6, an ePathBrick vector, which were then introduced into E. coli BL21star (DE3) separately. 20(R)-Protopanaxadiol (PPD), 20(R)-protopanaxatriol (PPT), and 20(R)-type ginsenosides were used as substrates for biotransformation with recombinant E. coli modified with those UGT genes. Results: E. coli engineered with $GT95^{syn}$ selectively transfers a glucose moiety to the C20 hydroxyl of 20(R)-PPD and 20(R)-PPT to produce 20(R)-CK and 20(R)-F1, respectively. GTK1- and GTC1-modified E. coli glycosylated the C3-OH of 20(R)-PPD to form 20(R)-Rh2. Moreover, E. coli containing $p2GT95^{syn}K1$, a recreated two-step glycosylation pathway via the ePathBrich, implemented the successive glycosylation at C20-OH and C3-OH of 20(R)-PPD and yielded 20(R)-F2 in the biotransformation broth. Conclusion: This study demonstrates that rare 20(R)-ginsenosides can be produced through E. coli engineered with UTG genes.

Exploitation of Low Dimensional Systems for Advanced Photovoltaics

Gavin Conibeer,Ivan Perez-Wurfl,Santosh Shrestha,Shujuan Huang,Long Hu,Supriya Pillai,Robert Patterson,Zhilong Zhang,Lin Yuan 한국신재생에너지학회 2018 한국신재생에너지학회 학술대회논문집 Vol.2018 No.4

An Intelligent Fault Diagnosis Method for Imbalanced Nuclear Power Plant Data Based on Generative Adversarial Networks

Dai Yuntao,Peng Lizhang,Juan Zhaobo,Liang Yuan,Shen Jihong,Wang Shujuan,Tan Sichao,Yu Hongyan,Sun Mingze 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4

In the fault diagnosis problem, where sample data of fault cases are imbalanced, data generation and expansion are performed based on a generative adversarial network to obtain balanced data for training. Combining a gated recurrent neural network and an autoencoder model, the GRU-BEGAN model for generating multiple time series data is proposed for the intelligent fault diagnosis of imbalanced nuclear power plant data. To guarantee the consistency of the probability distribution between the generated data and real data, the K-L losses are included as a part of the loss function of the generator. At the same time, the potential feature vector of the real data obtained by the discriminator encoder is introduced as a hidden variable in the generator, and the similarity between the generated data and the real data is controlled by introducing the hidden variables according to the probability to make the generated data diverse. For the imbalanced fault dataset of the nuclear power plant thermal–hydraulic systems, the proposed GRU-BEGAN model is used to expand the original data to obtain a balanced state. Then, a 1D-CNN fault diagnosis model is established based on a convolutional neural network. The experimental results show that the fault diagnosis accuracy of the total test data is improved by 1.45% after data expansion, and the fault diagnosis accuracy of the minority sample is improved by 6.8% after data expansion.

Metastatic pattern of ovarian cancer delineated by tracing the evolution of mitochondrial DNA mutations

Xu Zhiyang,Zhou Kaixiang,Wang Zhenni,Liu Yang,Wang Xingguo,Gao Tian,Xie Fanfan,Yuan Qing,Gu Xiwen,Liu Shujuan,Xing Jinliang 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-

Ovarian cancer (OC) is the most lethal gynecologic tumor and is characterized by a high rate of metastasis. Challenges in accurately delineating the metastatic pattern have greatly restricted the improvement of treatment in OC patients. An increasing number of studies have leveraged mitochondrial DNA (mtDNA) mutations as efficient lineage-tracing markers of tumor clonality. We applied multiregional sampling and high-depth mtDNA sequencing to determine the metastatic patterns in advanced-stage OC patients. Somatic mtDNA mutations were profiled from a total of 195 primary and 200 metastatic tumor tissue samples from 35 OC patients. Our results revealed remarkable sample-level and patient-level heterogeneity. In addition, distinct mtDNA mutational patterns were observed between primary and metastatic OC tissues. Further analysis identified the different mutational spectra between shared and private mutations among primary and metastatic OC tissues. Analysis of the clonality index calculated based on mtDNA mutations supported a monoclonal tumor origin in 14 of 16 patients with bilateral ovarian cancers. Notably, mtDNA-based spatial phylogenetic analysis revealed distinct patterns of OC metastasis, in which a linear metastatic pattern exhibited a low degree of mtDNA mutation heterogeneity and a short evolutionary distance, whereas a parallel metastatic pattern showed the opposite trend. Moreover, a mtDNA-based tumor evolutionary score (MTEs) related to different metastatic patterns was defined. Our data showed that patients with different MTESs responded differently to combined debulking surgery and chemotherapy. Finally, we observed that tumor-derived mtDNA mutations were more likely to be detected in ascitic fluid than in plasma samples. Our study presents an explicit view of the OC metastatic pattern, which sheds light on efficient treatment for OC patients.