RNA 5-methylcytosine(m^5C)sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular.The enzymatic mechanism and biological functions of m^5C sites in DNA remain the...RNA 5-methylcytosine(m^5C)sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular.The enzymatic mechanism and biological functions of m^5C sites in DNA remain the focusing area of researchers for last few decades.Likewise,the investigators also targeted m^5C sites in RNA due to its cellular functions,positioning and formation mechanism.Currently,several rudimentary roles of the m^5C in RNA have been explored,but a lot of improvements are still under consideration.Initially,the identification of RNA methylcytosine sites was carried out via experimental methods,which were very hard,erroneous and time consuming owing to partial availability of recognized structures.Looking at the significance of m^5C role in RNA,scientists have diverted their attention from structure to sequence-based prediction.In this regards,an intelligent computational model is proposed in order to identify m^5C sites in RNA with high precision.Three RNA sequences formulation methods namely:pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features.In a sequel,the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other.Various learning hypotheses are examined to select the best operational engine,which can truly identify the pattern of the target class.The strength and generalization of the proposed model are measured using two different cross validation tests.The reported outcomes reveal that the proposed model achieved 3%better accuracy than that of the highest present approach in the literature so far.展开更多
The methylcytosine dioxygenases TET proteins （TET1, TET2, and TET3） play important regulatory roles in neural function. In this study, we investigated the role of TET proteins in neuronal differentiation using Neuro...The methylcytosine dioxygenases TET proteins （TET1, TET2, and TET3） play important regulatory roles in neural function. In this study, we investigated the role of TET proteins in neuronal differentiation using Neuro2a cells as a model. We observed that knockdown of TET1, TET2 or TET3 promoted neuronal differentiation of Neuro2a cells, and their overexpression inhibited VPA （valproic acid）-induced neuronal differentiation, suggesting all three TET proteins negatively regulate neu- ronal differentiation of Neuro2a cells. Interestingly, the inducing activity of TET protein is independent of its enzymatic activity. Our previous studies have demon- strated that srGAP3 can negatively regulate neuronal differentiation of Neuro2a cells. Furthermore, we revealed that TET1 could positively regulate srGAP3 expression independent of its catalytic activity, and srGAP3 is required for TET-mediated neuronal differentiation of Neuro2a cells. The results presented here may facilitate better understanding of the role of TET proteins in neuronal differentiation, and provide a possible therapy target for neuroblastoma.展开更多
文摘RNA 5-methylcytosine(m^5C)sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular.The enzymatic mechanism and biological functions of m^5C sites in DNA remain the focusing area of researchers for last few decades.Likewise,the investigators also targeted m^5C sites in RNA due to its cellular functions,positioning and formation mechanism.Currently,several rudimentary roles of the m^5C in RNA have been explored,but a lot of improvements are still under consideration.Initially,the identification of RNA methylcytosine sites was carried out via experimental methods,which were very hard,erroneous and time consuming owing to partial availability of recognized structures.Looking at the significance of m^5C role in RNA,scientists have diverted their attention from structure to sequence-based prediction.In this regards,an intelligent computational model is proposed in order to identify m^5C sites in RNA with high precision.Three RNA sequences formulation methods namely:pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features.In a sequel,the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other.Various learning hypotheses are examined to select the best operational engine,which can truly identify the pattern of the target class.The strength and generalization of the proposed model are measured using two different cross validation tests.The reported outcomes reveal that the proposed model achieved 3%better accuracy than that of the highest present approach in the literature so far.
文摘The methylcytosine dioxygenases TET proteins （TET1, TET2, and TET3） play important regulatory roles in neural function. In this study, we investigated the role of TET proteins in neuronal differentiation using Neuro2a cells as a model. We observed that knockdown of TET1, TET2 or TET3 promoted neuronal differentiation of Neuro2a cells, and their overexpression inhibited VPA （valproic acid）-induced neuronal differentiation, suggesting all three TET proteins negatively regulate neu- ronal differentiation of Neuro2a cells. Interestingly, the inducing activity of TET protein is independent of its enzymatic activity. Our previous studies have demon- strated that srGAP3 can negatively regulate neuronal differentiation of Neuro2a cells. Furthermore, we revealed that TET1 could positively regulate srGAP3 expression independent of its catalytic activity, and srGAP3 is required for TET-mediated neuronal differentiation of Neuro2a cells. The results presented here may facilitate better understanding of the role of TET proteins in neuronal differentiation, and provide a possible therapy target for neuroblastoma.
