The Experts below are selected from a list of 215961 Experts worldwide ranked by ideXlab platform
Fenyong Liu - One of the best experts on this subject based on the ideXlab platform.
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effective inhibition of human immunodeficiency virus 1 replication by engineered RNAse p ribozyme
PLOS ONE, 2012Co-Authors: Wenbo Zeng, Fenyong Liu, Phong Trang, Yuanchuan Chen, Yong BaiAbstract:Using an in vitro selection procedure, we have previously isolated RNAse P ribozyme variants that efficiently cleave an mRNA Sequence in vitro. In this study, a variant was used to target the HIV RNA Sequence in the tat region. The variant cleaved the tat RNA Sequence in vitro about 20 times more efficiently than the wild type ribozyme. Our results provide the first direct evidence that combined mutations at nucleotide 83 and 340 of RNAse P catalytic RNA from Escherichia coli (G83 -> U83 and G340 -> A340) increase the overall efficiency of the ribozyme in cleaving an HIV RNA Sequence. Moreover, the variant is more effective in reducing HIV-1 p24 expression and intracellular viral RNA level in cells than the wild type ribozyme. A reduction of about 90% in viral RNA level and a reduction of 150 fold in viral growth were observed in cells that expressed the variant, while a reduction of less than 10% was observed in cells that either did not express the ribozyme or produced a catalytically inactive ribozyme mutant. Thus, engineered ribozyme variants are effective in inhibiting HIV infection. These results also demonstrate the potential of engineering RNAse P ribozymes for anti-HIV application.
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a liver specific microRNA binds to a highly conserved RNA Sequence of hepatitis b virus and negatively regulates viral gene expression and replication
The FASEB Journal, 2011Co-Authors: Yanni Chen, Fenyong Liu, Ao Shen, Paul Rider, Qian Hao, Yingle Liu, Hao Gong, Ying ZhuAbstract:Regulated gene expression and progeny production are essential for persistent and chronic infection by human pathogens, such as hepatitis B virus (HBV), which affects >400 million people worldwide and is a major cause of liver disease. In this study, we provide the first direct evidence that a liver-specific microRNA, miR-122, binds to a highly conserved HBV pregenomic RNA Sequence via base-pairing interactions and inhibits HBV gene expression and replication. The miR-122 target Sequence is located at the coding region of the mRNA for the viral polymerase and the 3′ untranslated region of the mRNA for the core protein. In cultured cells, HBV gene expression and replication reduces with increased expression of miR-122, and the expression of miR-122 decreases in the presence of HBV infection and replication. Furthermore, analyses of clinical samples demonstrated an inverse linear correlation in vivo between the miR-122 level and the viral loads in the peripheral blood mononuclear cells of HBV-positive patients. Our results suggest that miR-122 may down-regulate HBV replication by binding to the viral target Sequence, contributing to the persistent/chronic infection of HBV, and that HBV-induced modulation of miR-122 expression may represent a mechanism to facilitate viral pathogenesis.—Chen, Y., Shen, A., Rider, P. J., Yu, Y., Wu, K., Mu, Y., Hao, Q, Liu, Y., Gong, H., Zhu, Y., Liu, F., Wu, J. A liver-specific microRNA binds to a highly conserved RNA Sequence of hepatitis B virus and negatively regulates viral gene expression and replication.
Qianqian Yang - One of the best experts on this subject based on the ideXlab platform.
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single cell RNA Sequence analysis of mouse glomerular mesangial cells uncovers mesangial cell essential genes
Kidney International, 2017Co-Authors: Yuqiu Lu, Yuting Ye, Qianqian YangAbstract:Mesangial cells are essential for the structure and function of glomeruli, but the mechanisms underlying these roles are not well understood. Here, we performed a single-cell RNA-Sequence (RNA-seq) analysis of mouse mesangial cells using the Fluidigm C1 platform. We found that gene expression in individual mesangial cells was tremendously heterogeneous, with mean correlation coefficients of 0.20, and most mesangial genes were actually expressed in only a portion of mesangial cells and are therefore presumably dispensable. In contrast, 1,045 genes were expressed in every single mesangial cell and were considered mesangial cell essential genes. A gene ontology analysis revealed a significant enrichment of genes associated with the endothelium, supporting the inference that mesangial cells function as pericytes. Among 58 endothelium-associated genes, 18 encode proteins that are secreted and may be directly involved in endothelial homeostasis. Importantly, 11 (Angpt2, Anxa5, Axl, Ecm1, Eng, Fn1, Mfge8, Msn, Nrp1, Serpine2, and Sparc) were upregulated, while 2 (Apoe and Fgf1) were downregulated in various glomerulopathies. The enrichment of genes associated with other reported functions of mesangial cells was also found. Furthermore, we identified 173 genes specifically expressed in every mesangial cell in glomeruli from the mesangial cell essential gene list. Finally, based on single mesangial cell RNA-seq results, we found that commonly used glomerular cell type markers, including Fhl2, Igfbp5, Wt1, Tek/Tie2, Kdr/Flk1, Flt1/Vegfr1, and Cd34, are actually not specific. Thus, single mesangial cell RNA-seq analysis has provided insights into the functions and underlying mechanisms of mesangial cells.
Eric P Nawrocki - One of the best experts on this subject based on the ideXlab platform.
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ribovore ribosomal RNA Sequence analysis for genbank submissions and database curation
BMC Bioinformatics, 2021Co-Authors: Alejandro A Schaffer, Richard Mcveigh, Barbara Robbertse, Conrad L Schoch, Anjanette Johnston, Beverly A Underwood, Ilene Karschmizrachi, Eric P NawrockiAbstract:BACKGROUND The DNA Sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is typically used to identify bacterial and archaeal species. The nuclear 18S SSU rRNA gene, and 28S large subunit (LSU) rRNA gene have been used as DNA barcodes and for phylogenetic studies in different eukaryote taxonomic groups. Because of their popularity, the National Center for Biotechnology Information (NCBI) receives a disproportionate number of rRNA Sequence submissions and BLAST queries. These Sequences vary in quality, length, origin (nuclear, mitochondria, plastid), and organism source and can represent any region of the ribosomal cistron. RESULTS To improve the timely verification of quality, origin and loci boundaries, we developed Ribovore, a software package for Sequence analysis of rRNA Sequences. The ribotyper and ribosensor programs are used to validate incoming Sequences of bacterial and archaeal SSU rRNA. The ribodbmaker program is used to create high-quality datasets of rRNAs from different taxonomic groups. Key algorithmic steps include comparing candidate Sequences against rRNA Sequence profile hidden Markov models (HMMs) and covariance models of rRNA Sequence and secondary-structure conservation, as well as other tests. Nine freely available blastn rRNA databases created and maintained with Ribovore are used for checking incoming GenBank submissions and used by the blastn browser interface at NCBI. Since 2018, Ribovore has been used to analyze more than 50 million prokaryotic SSU rRNA Sequences submitted to GenBank, and to select at least 10,435 fungal rRNA RefSeq records from type material of 8350 taxa. CONCLUSION Ribovore combines single-Sequence and profile-based methods to improve GenBank processing and analysis of rRNA Sequences. It is a standalone, portable, and extensible software package for the alignment, classification and validation of rRNA Sequences. Researchers planning on submitting SSU rRNA Sequences to GenBank are encouraged to download and use Ribovore to analyze their Sequences prior to submission to determine which Sequences are likely to be automatically accepted into GenBank.
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ribovore ribosomal RNA Sequence analysis for genbank submissions and database curation
bioRxiv, 2021Co-Authors: Alejandro A Schaffer, Richard Mcveigh, Barbara Robbertse, Conrad L Schoch, Anjanette Johnston, Beverly A Underwood, Ilene Karschmizrachi, Eric P NawrockiAbstract:Background: The DNA Sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is typically used to identify bacterial and archaeal species. The nuclear 18S SSU rRNA gene, and 28S large subunit (LSU) rRNA gene have been used as DNA barcodes and for phylogenetic studies in different eukaryote taxonomic groups. Because of their popularity, the National Center for Biotechnology Information (NCBI) receives a disproportionate number of rRNA Sequence submissions and BLAST queries. These Sequences vary in quality, length, origin (nuclear, mitochondria, plastid), and organism source and can represent any region of the ribosomal cistron. Results: To improve the timely verification of quality, origin and loci boundaries, we developed Ribovore, a software package for Sequence analysis of rRNA Sequences. The ribotyper and ribosensor programs are used to validate incoming Sequences of bacterial and archaeal SSU rRNA. The ribodbmaker program is used to create high-quality datasets of rRNAs from different taxonomic groups. Key algorithmic steps include comparing candidate Sequences against rRNA Sequence profile hidden Markov models (HMMs) and covariance models of rRNA Sequence and secondary-structure conservation, as well as other tests. At least nine freely available blastn rRNA databases created and maintained with Ribovore are used either for checking incoming GenBank submissions or by the blastn browser interface at NCBI or both. Since 2018, Ribovore has been used to analyze more than 50 million prokaryotic SSU rRNA Sequences submitted to GenBank, and to select at least 10,435 fungal rRNA RefSeq records from type material of 8,350 taxa. Conclusion: Ribovore combines single-Sequence and profile-based methods to improve GenBank processing and analysis of rRNA Sequences. It is a standalone, portable, and extensible software package for the alignment, classification and validation of rRNA Sequences. Researchers planning on submitting SSU rRNA Sequences to GenBank are encouraged to download and use Ribovore to analyze their Sequences prior to submission to determine which Sequences are likely to be automatically accepted into GenBank.
Max L Nibert - One of the best experts on this subject based on the ideXlab platform.
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RNA Sequence determinants of a coupled termination reinitiation strategy for downstream open reading frame translation in helminthosporium victoriae virus 190s and other victoriviruses family totiviridae
Journal of Virology, 2011Co-Authors: Hua Li, Wendy M Havens, Max L NibertAbstract:The genome-length, dicistronic mRNA of the double-stranded RNA fungal virus Helminthosporium victoriae virus 190S (genus Victorivirus, family Totiviridae) contains two long open reading frames (ORFs) that overlap in the tetranucleotide AUGA. Translation of the downstream ORF, which encodes the RNA-dependent RNA polymerase (RdRp), has been proposed to depend on ribosomal reinitiation following termination of the upstream ORF, which encodes the capsid protein. In the current study, we examined the RNA Sequence determinants for RdRp translation in this virus and demonstrated that a coupled termination-reinitiation (stop-restart) strategy is indeed used. Signals for termination-reinitiation are found within a 32-nucleotide stretch of RNA immediately upstream of the AUGA motif, including a predicted pseudoknot structure. The close proximity in which this predicted structure is followed by the upstream ORF's stop codon appears to be especially important for promoting translation of the downstream ORF. The normal strong preferences for an AUG start codon and the canonical Sequence context to favor translation initiation appear somewhat relaxed for the downstream ORF. Similar Sequence motifs and predicted RNA structures in other victoriviruses suggest that they all share a related stop-restart strategy for RdRp translation. Members of the genus Victorivirus thus provide new and unique opportunities for exploring the molecular mechanisms of translational coupling, which remain only partly understood in this and other systems.
Yi Xiong - One of the best experts on this subject based on the ideXlab platform.
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pseui pseudouridine sites identification based on RNA Sequence information
BMC Bioinformatics, 2018Co-Authors: Ting Fang, Zizheng Zhang, Bei Huang, Xiaolei Zhu, Yi XiongAbstract:Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA Sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI , and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA Sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites.
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PseUI: Pseudouridine sites identification based on RNA Sequence information
BMC, 2018Co-Authors: Ting Fang, Zizheng Zhang, Bei Huang, Xiaolei Zhu, Yi XiongAbstract:Abstract Background Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA Sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. Results In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. Conclusion In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA Sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites
