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61.
Haiyan Miao Jingjing Lu Yibing Guo Hongquan Qiu Yu Zhang Xihao Yao Xiaohong Li Yuhua Lu 《Journal of cellular and molecular medicine》2021,25(7):3654-3664
Pancreatic ductal adenocarcinoma (PDAC) is an invasive and aggressive cancer that remains a major threat to human health across the globe. Despite advances in cancer treatments and diagnosis, the prognosis of PDAC patients remains poor. New and more effective PDAC therapies are therefore urgently required. In this study, we identified a novel host factor, namely the LncRNA TP73-AS1, as overexpressed in PDAC tissues compared to adjacent healthy tissue samples. The overexpression of TP-73-AS1 was found to correlate with both PDAC stage and lymph node metastasis. To reveal its role in PDCA, we targeted TP73-AS1 using LnRNA inhibitors in a range of pancreatic cancer (PC) cell lines. We found that the inhibition of TP73-AS1 led to a loss of MMP14 expression in PC cells and significantly inhibited their migratory and invasive capacity. No effects of TP73-AS1 on cell survival or proliferation were observed. Mechanistically, we found that TP73-AS1 suppressed the expression of the known oncogenic miR-200a. Taken together, these data highlight the prognostic potential of TP73-AS1 for PC patients and highlight it as a potential anti-PDAC therapeutic target. 相似文献
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Genome‐wide detection of CNVs associated with beak deformity in chickens using high‐density 600K SNP arrays
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H. Bai Y. Sun N. Liu Y. Liu F. Xue Y. Li S. Xu A. Ni J. Ye Y. Chen J. Chen 《Animal genetics》2018,49(3):226-236
Beak deformity (crossed beaks) is found in several indigenous chicken breeds including Beijing‐You studied here. Birds with deformed beaks have reduced feed intake and poor production performance. Recently, copy number variation (CNV) has been examined in many species and is recognized as a source of genetic variation, especially for disease phenotypes. In this study, to unravel the genetic mechanisms underlying beak deformity, we performed genome‐wide CNV detection using Affymetrix chicken high‐density 600K data on 48 deformed‐beak and 48 normal birds using penncnv . As a result, two and eight CNV regions (CNVRs) covering 0.32 and 2.45 Mb respectively on autosomes were identified in deformed‐beak and normal birds respectively. Further RT‐qPCR studies validated nine of the 10 CNVRs. The ratios of six CNVRs were significantly different between deformed‐beak and normal birds (P < 0.01). Within these six regions, three and 21 known genes were identified in deformed‐beak and normal birds respectively. Bioinformatics analysis showed that these genes were enriched in six GO terms and one KEGG pathway. Five candidate genes in the CNVRs were further validated using RT‐qPCR. The expression of LRIG2 (leucine rich repeats and immunoglobulin like domains 2) was lower in birds with deformed beaks (P < 0.01). Therefore, the LRIG2 gene could be considered a key factor in view of its known functions and its potential roles in beak deformity. Overall, our results will be helpful for future investigations of the genomic structural variations underlying beak deformity in chickens. 相似文献
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This paper studies a Non-convex State-dependent Linear Quadratic Regulator (NSLQR) problem, in which the control penalty weighting matrix in the performance index is state-dependent. A necessary and sufficient condition for the optimal solution is established with a rigorous proof by Euler-Lagrange Equation. It is found that the optimal solution of the NSLQR problem can be obtained by solving a Pseudo-Differential-Riccati-Equation (PDRE) simultaneously with the closed-loop system equation. A Comparison Theorem for the PDRE is given to facilitate solution methods for the PDRE. A linear time-variant system is employed as an example in simulation to verify the proposed optimal solution. As a non-trivial application, a goal pursuit process in psychology is modeled as a NSLQR problem and two typical goal pursuit behaviors found in human and animals are reproduced using different control weighting . It is found that these two behaviors save control energy and cause less stress over Conventional Control Behavior typified by the LQR control with a constant control weighting , in situations where only the goal discrepancy at the terminal time is of concern, such as in Marathon races and target hitting missions. 相似文献
66.
Hyaluronidases are a family of enzymes that degrade hyaluronic acid (hyaluronan, HA) and widely used in many fields. A hyaluronidase producing bacteria strain was screened from the air. 16S ribosomal DNA (16S rDNA) analysis indicated that the strain belonged to the genus Bacillus, and the strain was named as Bacillus sp. A50. This is the first report of a hyaluronidase from Bacillus, which yields unsaturated oligosaccharides as product like other microbial hyaluronate lyases. Under optimized conditions, the yield of hyaluronidase from Bacillus sp. A50 could reach up to 1.5×104 U/mL, suggesting that strain A50 is a good producer of hyaluronidase. The hyaluronidase (HAase-B) was isolated and purified from the bacterial culture, with a specific activity of 1.02×106 U/mg protein and a yield of 25.38%. The optimal temperature and pH of HAase-B were 44°C and pH 6.5, respectively. It was stable at pH 5–6 and at a temperature lower than 45°C. The enzymatic activity could be enhanced by Ca2+, Mg2+, or Ni2+, and inhibited by Zn2+, Cu2+, EDTA, ethylene glycol tetraacetic acid (EGTA), deferoxamine mesylate salt (DFO), triton X-100, Tween 80, or SDS at different levels. Kinetic measurements of HAase-B towards HA gave a Michaelis constant (K
m) of 0.02 mg/mL, and a maximum velocity (V
max) of 0.27 A
232/min. HAase-B also showed activity towards chondroitin sulfate A (CSA) with the kinetic parameters, K
m and V
max, 12.30 mg/mL and 0.20 A
232/min respectively. Meanwhile, according to the sequences of genomic DNA and HAase-B’s part peptides, a 3,324-bp gene encoding HAase-B was obtained. 相似文献
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Xu Zhang Wei Zhang Santosh L. Saraf Mehdi Nouraie Jin Han Michel Gowhari Johara Hassan Galina Miasnikova Adelina Sergueeva Sergei Nekhai Rick Kittles Roberto F. Machado Joe G. N. Garcia Mark T. Gladwin Martin H. Steinberg Paola Sebastiani Donald A. McClain Victor R. Gordeuk 《Human genetics》2015,134(8):895-904