The product of the H19 gene may function as an RNA

1990 ◽  
Vol 10 (1) ◽  
pp. 28-36
Author(s):  
C I Brannan ◽  
E C Dees ◽  
R S Ingram ◽  
S M Tilghman
Keyword(s):  
Rna Polymerase Ii ◽  
Transcriptional Control ◽  
Translation Termination ◽  
Sedimentation Coefficient ◽  
Open Reading Frames ◽  
Reading Frame ◽  
Translational Machinery ◽  
H19 Gene ◽  
Reading Frames ◽  
Small Open Reading Frames

The mouse H19 gene was identified as an abundant hepatic fetal-specific mRNA under the transcriptional control of a trans-acting locus termed raf. The protein this gene encoded was not apparent from an analysis of its nucleotide sequence, since the mRNA contained multiple translation termination signals in all three reading frames. As a means of assessing which of the 35 small open reading frames might be important to the function of the gene, the human H19 gene was cloned and sequenced. Comparison of the two homologs revealed no conserved open reading frame. Cellular fractionation showed that H19 RNA is cytoplasmic but not associated with the translational machinery. Instead, it is located in a particle with a sedimentation coefficient of approximately 28S. Despite the fact that it is transcribed by RNA polymerase II and is spliced and polyadenylated, we suggest that the H19 RNA is not a classical mRNA. Instead, the product of this unusual gene may be an RNA molecule.

scholarly journals The product of the H19 gene may function as an RNA.

1990 ◽  
Vol 10 (1) ◽  
pp. 28-36 ◽  
Author(s):  
C I Brannan ◽  
E C Dees ◽  
R S Ingram ◽  
S M Tilghman
Keyword(s):  
Rna Polymerase Ii ◽  
Transcriptional Control ◽  
Translation Termination ◽  
Sedimentation Coefficient ◽  
Open Reading Frames ◽  
Reading Frame ◽  
Translational Machinery ◽  
H19 Gene ◽  
Reading Frames ◽  
Small Open Reading Frames

The mouse H19 gene was identified as an abundant hepatic fetal-specific mRNA under the transcriptional control of a trans-acting locus termed raf. The protein this gene encoded was not apparent from an analysis of its nucleotide sequence, since the mRNA contained multiple translation termination signals in all three reading frames. As a means of assessing which of the 35 small open reading frames might be important to the function of the gene, the human H19 gene was cloned and sequenced. Comparison of the two homologs revealed no conserved open reading frame. Cellular fractionation showed that H19 RNA is cytoplasmic but not associated with the translational machinery. Instead, it is located in a particle with a sedimentation coefficient of approximately 28S. Despite the fact that it is transcribed by RNA polymerase II and is spliced and polyadenylated, we suggest that the H19 RNA is not a classical mRNA. Instead, the product of this unusual gene may be an RNA molecule.

scholarly journals Translation Termination Reinitiation between Open Reading Frame 1 (ORF1) and ORF2 Enables Capsid Expression in a Bovine Norovirus without the Need for Production of Viral Subgenomic RNA

2008 ◽  
Vol 82 (17) ◽  
pp. 8917-8921 ◽  
Author(s):  
Christopher J. McCormick ◽  
Omar Salim ◽  
Paul R. Lambden ◽  
Ian N. Clarke
Keyword(s):  
Hepg2 Cells ◽  
Nonstructural Protein ◽  
Translation Termination ◽  
Surrogate Marker ◽  
Open Reading Frames ◽  
Open Reading Frame ◽  
Subgenomic Rna ◽  
Reading Frame ◽  
Reading Frames ◽  
Open Reading Frame 1

ABSTRACT A generally accepted view of norovirus replication is that capsid expression requires production of a subgenomic transcript, the presence of capsid often being used as a surrogate marker to indicate the occurrence of viral replication. Using a polymerase II-based baculovirus delivery system, we observed capsid expression following introduction of a full-length genogroup 3 norovirus genome into HepG2 cells. However, capsid expression occurred as a result of a novel translation termination/reinitiation event between the nonstructural-protein and capsid open reading frames, a feature that may be unique to genogroup 3 noroviruses.

scholarly journals attI1-Located Small Open Reading Frames ORF-17 and ORF-11 in a Class 1 Integron Affect Expression of a Gene Cassette Possessing a Canonical Shine-Dalgarno Sequence

2016 ◽  
Vol 61 (3) ◽  
Author(s):  
Costas C. Papagiannitsis ◽  
Leonidas S. Tzouvelekis ◽  
Eva Tzelepi ◽  
Vivi Miriagou
Keyword(s):  
Single Gene ◽  
Gene Cassette ◽  
Open Reading Frames ◽  
Translation Initiation Region ◽  
Class 1 Integron ◽  
Reading Frame ◽  
Class 1 ◽  
Shine Dalgarno Sequence ◽  
Reading Frames ◽  
Small Open Reading Frames

ABSTRACT By searching the Integrall integron and GenBank databases, a novel open reading frame (ORF) of 51 nucleotides (nts) (ORF-17) overlapping the previously described ORF-11 was identified within the attI1 site in virtually all class 1 integrons. Using a set of isogenic plasmid constructs carrying a single gene cassette (bla GES-1) and possessing a canonical translation initiation region, we found that ORF-17 contributes to GES-1 expression.

scholarly journals Small open reading frames and cellular stress responses

2019 ◽  
Vol 15 (2) ◽  
pp. 108-116 ◽  
Author(s):  
Alexandra Khitun ◽  
Travis J. Ness ◽  
Sarah A. Slavoff
Keyword(s):  
Stress Responses ◽  
Cellular Stress ◽  
Open Reading Frames ◽  
Open Reading Frame ◽  
Reading Frame ◽  
Cellular Stress Responses ◽  
Small Open Reading Frame ◽  
Reading Frames ◽  
Small Open Reading Frames

Increasing evidence suggests that some small open reading frame-encoded polypeptides (SEPs) function in prokaryotic and eukaryotic cellular stress responses.

scholarly journals smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

2020 ◽  
Author(s):  
Xiangwen Ji ◽  
Chunmei Cui ◽  
Qinghua Cui
Keyword(s):  
Open Reading Frames ◽  
Open Reading Frame ◽  
Biological Processes ◽  
Reading Frame ◽  
Functional Annotations ◽  
Uniprot Database ◽  
Muscle Formation ◽  
Small Open Reading Frame ◽  
Reading Frames ◽  
Small Open Reading Frames

Abstract Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at http://www.cuilab.cn/smorfunction.

scholarly journals smORFunction: a tool for predicting functions of small open reading frames and microproteins

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiangwen Ji ◽  
Chunmei Cui ◽  
Qinghua Cui
Keyword(s):  
Open Reading Frames ◽  
Open Reading Frame ◽  
Biological Processes ◽  
Reading Frame ◽  
Functional Annotations ◽  
Uniprot Database ◽  
Muscle Formation ◽  
Small Open Reading Frame ◽  
Reading Frames ◽  
Small Open Reading Frames

Abstract Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction.

scholarly journals smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

2020 ◽  
Author(s):  
Xiangwen Ji ◽  
Chunmei Cui ◽  
Qinghua Cui
Keyword(s):  
Open Reading Frames ◽  
Open Reading Frame ◽  
Biological Processes ◽  
Reading Frame ◽  
Functional Annotations ◽  
Uniprot Database ◽  
Muscle Formation ◽  
Small Open Reading Frame ◽  
Reading Frames ◽  
Small Open Reading Frames

Abstract Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at http://www.cuilab.cn/smorfunction.

scholarly journals smORFunction: A Tool for Predicting Functions of Small Open Reading Frames and Microproteins

2020 ◽  
Author(s):  
Xiangwen Ji ◽  
Chunmei Cui ◽  
Qinghua Cui
Keyword(s):  
Open Reading Frames ◽  
Open Reading Frame ◽  
Biological Processes ◽  
Reading Frame ◽  
Functional Annotations ◽  
Uniprot Database ◽  
Muscle Formation ◽  
Small Open Reading Frame ◽  
Reading Frames ◽  
Small Open Reading Frames

Abstract Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at http://www.cuilab.cn/smorfunction.

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