Search

EPAS1 involves in the hypoxic response and is suggested to be responsible for the genetic adaptation of high-altitude hypoxia in Tibetans. However, the detailed molecular mechanism remains unknown. In this study, a single nucleotide polymorphism rs56721780:G>C and an insertion/deletion (indel) polymorphism —742 indel in the promoter region showed divergence between Tibetans and non-Tibetan lowlanders. rs56721780:G>C regulated the transcription of EPAS1 by IKAROS family zinc finger 1 (IKZF1), which was identified as a new transcriptional repressor for EPAS1 gene. It demonstrated that the C allele of rs56721780:G>C decreased the binding of IKZF1, leading to the attenuated transcriptional repression of EPAS1 gene. The insertion at —742 indel provided a new binding site for Sp1 and was related to the activation of EPAS1 promoter. Further functional analysis revealed that lysyl oxidase (LOX) gene, which was reported to be responsible for extracellular matrix protein cross-linking of amnion previously, was a direct target of EPAS1. The CC genotype at rs56721780:G>C and the insertion genotype at—742 indel were found associated with higher EPAS1 andLOX expression levels in amnion, as well as higher birth weight of Tibetan newborns, suggesting that EPAS1 gene might play important roles in the development of amnion, fetus growth and high-altitude adaptation of Tibetans. [ABSTRACT FROM AUTHOR]

The Snail family member snail encodes a zinc finger-containing transcriptional factor that is involved in heart formation. Yet, little is known about how Snail regulates heart development. Here, we identified that one of the duplicated snail genes, snai1b, was expressed in the heart region of zebrafish embryos. Depletion of Snai1b function dramatically reduced expression of α5 integrin, disrupted Fibronectin layer in the heart region, especially at the midline, and prevented migration of cardiac precursors, resulting in defects in cardiac morphology and function in zebrafish embryos. Injection of α5β1 protein rescued the Fibronectin layer and then the myocardial precursor migration in snai1b knockdown embryos. The results provide the molecular mechanism how Snail controls the morphogenesis of heart during embryonic development. [ABSTRACT FROM AUTHOR]

An investigation was carried out to assess the effect of the P content on the microstructure and mechanical properties of ZCuPb20Sn5 alloy. Alloys of various compositions, (0.05, 0.1, 0.2, 0.3, 0.5% wt.% P) were melted in a melting furnace under 1200 °C and cast into metal mould, the hardness, strength and elongation of alloy castings which adding P or not in melting process were tested and the casting mircostructure was analyzed. The results show that the second phase appeared and gradually increased in amount with the content of P elements increased. Also, the microstructure of ZCuPb20Sn5 alloy was refined, and the average size of lead inclusions was reduced and formed a dispersed network of eutectoid inclusions.The addition of P had a beneficial effect on the microstructure and properties of ZCuPb20Sn5 alloy. The hardness and tensile strength of ZCuPb20Sn5 alloy increased, but the elongation increased at first, then decreased, when the P content increased. When the P content was less than 0.1 wt.%, the functions of phosphorous copper mainly was used as a deoxidizing initial gas, but when exceeded 0.1 wt.%, a second phase reinforcing particle formed with copper or nickel together, which improved the mechanical properties of the alloy. However, the elongation was lowered due to the brittle phosphide phase. [ABSTRACT FROM AUTHOR]

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Deep-RBPPred has three advantages comparing to previous methods. (1) Deep-RBPPred only needs few physicochemical properties based on protein sequences. (2) Deep-RBPPred runs much faster. (3) Deep-RBPPred has a good generalization ability. In the meantime, Deep-RBPPred is still as good as the state-of-the-art method. Testing in A. thaliana, S. cerevisiae and H. sapiens proteomes, MCC values are 0.82 (0.82), 0.65 (0.69) and 0.85 (0.80) for balance model (imbalance model) when the score cutoff is set to 0.5, respectively. In the same testing dataset, different machine learning algorithms (CNN and SVM) are also compared. The results show that CNN-based model can identify more RBPs than SVM-based. In comparing the balance and imbalance model, both CNN-base and SVM-based tend to favor the majority class in the imbalance set. Deep-RBPPred forecasts 280 (balance model) and 265 (imbalance model) of 299 new RBP. The sensitivity of balance model is about 7% higher than the state-of-the-art method. We also apply deep-RBPPred to 30 eukaryotes and 109 bacteria proteomes downloaded from Uniprot to estimate all possible RBPs. The estimating result shows that rates of RBPs in eukaryote proteomes are much higher than bacteria proteomes. [ABSTRACT FROM AUTHOR]