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Discovering structural cis-regulatory elements by modeling the behaviors of mRNAs.

Foat BC, Stormo GD - Mol. Syst. Biol. (2009)

Bottom Line: In addition, we discovered six putative SCREs in flies and three in humans.We characterized the SCREs based on their condition-specific regulatory influences, the annotation of the transcripts that contain them, and their locations within transcripts.Overall, we show that modeling functional genomics data in terms of combined RNA structure and sequence motifs is an effective method for discovering the specificities and regulatory roles of RNA-binding proteins.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Center for Genome Sciences, Washington University School of Medicine, St Louis, MO 63108, USA.

ABSTRACT
Gene expression is regulated at each step from chromatin remodeling through translation and degradation. Several known RNA-binding regulatory proteins interact with specific RNA secondary structures in addition to specific nucleotides. To provide a more comprehensive understanding of the regulation of gene expression, we developed an integrative computational approach that leverages functional genomics data and nucleotide sequences to discover RNA secondary structure-defined cis-regulatory elements (SCREs). We applied our structural cis-regulatory element detector (StructRED) to microarray and mRNA sequence data from Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We recovered the known specificities of Vts1p in yeast and Smaug in flies. In addition, we discovered six putative SCREs in flies and three in humans. We characterized the SCREs based on their condition-specific regulatory influences, the annotation of the transcripts that contain them, and their locations within transcripts. Overall, we show that modeling functional genomics data in terms of combined RNA structure and sequence motifs is an effective method for discovering the specificities and regulatory roles of RNA-binding proteins.

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Putative human structural cis-regulatory elements. (A) We discovered three putative SCREs when applying the StructRED algorithm to human data (Hs1–3). (B) Hs1 was discovered using data that measured ribosome association in a metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line, SW480 (Provenzani et al, 2006). Hs2 correlated with decreased mRNA levels in U937 cells that have been exposed to 12-myristate 13-acetate (PMA; Kitamura et al, 2004). Hs3 was discovered through a positive correlation with ribosome association in human mammary epithelial cells (with and without overexpressed translation initiation factor 4F, eIF4E; Larsson et al, 2007). (C) We did not discover a Smaug/Vts1p-like specificity from the human data. However, when the Drosophila Smaug specificities were scored against the human data, we observed significant correlations with the RBP pull-down microarray data for poly-pyrimidine tract binding protein (PTB; GEO accession GSE6021; Gama-Carvalho et al, 2006) and Staufen1 and Staufen2 (Furic et al, 2008; GEO accessions GSE8437, GSE8438), suggesting that hSmaug shares many target mRNAs with these RBPs.
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f7: Putative human structural cis-regulatory elements. (A) We discovered three putative SCREs when applying the StructRED algorithm to human data (Hs1–3). (B) Hs1 was discovered using data that measured ribosome association in a metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line, SW480 (Provenzani et al, 2006). Hs2 correlated with decreased mRNA levels in U937 cells that have been exposed to 12-myristate 13-acetate (PMA; Kitamura et al, 2004). Hs3 was discovered through a positive correlation with ribosome association in human mammary epithelial cells (with and without overexpressed translation initiation factor 4F, eIF4E; Larsson et al, 2007). (C) We did not discover a Smaug/Vts1p-like specificity from the human data. However, when the Drosophila Smaug specificities were scored against the human data, we observed significant correlations with the RBP pull-down microarray data for poly-pyrimidine tract binding protein (PTB; GEO accession GSE6021; Gama-Carvalho et al, 2006) and Staufen1 and Staufen2 (Furic et al, 2008; GEO accessions GSE8437, GSE8438), suggesting that hSmaug shares many target mRNAs with these RBPs.

Mentions: We discovered three SCREs with functionally coherent TFAPs (Figure 7A and B). Occurrences of the first SCRE, Hs1, correlated with decreased translation in the metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line from the same patient, SW480, as measured in a polysome association microarray study (Provenzani et al, 2006; GEO accession GSE2509). Transcripts containing Hs2 SCREs are expressed at a lower level in U937 cells that have been exposed to 12-myristate 13-acetate (PMA) and caused to differentiate into a macrophage-like state (Kitamura et al, 2004; GEO accession GSE1783). Finally, occurrences of Hs3 in mRNAs correlate with increased association with ribosomes in human mammary epithelial cells, regardless of whether translation initiation factor 4F is overexpressed (Larsson et al, 2007; GEO accession GSE6043).


Discovering structural cis-regulatory elements by modeling the behaviors of mRNAs.

Foat BC, Stormo GD - Mol. Syst. Biol. (2009)

Putative human structural cis-regulatory elements. (A) We discovered three putative SCREs when applying the StructRED algorithm to human data (Hs1–3). (B) Hs1 was discovered using data that measured ribosome association in a metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line, SW480 (Provenzani et al, 2006). Hs2 correlated with decreased mRNA levels in U937 cells that have been exposed to 12-myristate 13-acetate (PMA; Kitamura et al, 2004). Hs3 was discovered through a positive correlation with ribosome association in human mammary epithelial cells (with and without overexpressed translation initiation factor 4F, eIF4E; Larsson et al, 2007). (C) We did not discover a Smaug/Vts1p-like specificity from the human data. However, when the Drosophila Smaug specificities were scored against the human data, we observed significant correlations with the RBP pull-down microarray data for poly-pyrimidine tract binding protein (PTB; GEO accession GSE6021; Gama-Carvalho et al, 2006) and Staufen1 and Staufen2 (Furic et al, 2008; GEO accessions GSE8437, GSE8438), suggesting that hSmaug shares many target mRNAs with these RBPs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683727&req=5

f7: Putative human structural cis-regulatory elements. (A) We discovered three putative SCREs when applying the StructRED algorithm to human data (Hs1–3). (B) Hs1 was discovered using data that measured ribosome association in a metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line, SW480 (Provenzani et al, 2006). Hs2 correlated with decreased mRNA levels in U937 cells that have been exposed to 12-myristate 13-acetate (PMA; Kitamura et al, 2004). Hs3 was discovered through a positive correlation with ribosome association in human mammary epithelial cells (with and without overexpressed translation initiation factor 4F, eIF4E; Larsson et al, 2007). (C) We did not discover a Smaug/Vts1p-like specificity from the human data. However, when the Drosophila Smaug specificities were scored against the human data, we observed significant correlations with the RBP pull-down microarray data for poly-pyrimidine tract binding protein (PTB; GEO accession GSE6021; Gama-Carvalho et al, 2006) and Staufen1 and Staufen2 (Furic et al, 2008; GEO accessions GSE8437, GSE8438), suggesting that hSmaug shares many target mRNAs with these RBPs.
Mentions: We discovered three SCREs with functionally coherent TFAPs (Figure 7A and B). Occurrences of the first SCRE, Hs1, correlated with decreased translation in the metastatic colorectal cancer cell line, SW620, versus a non-metastatic cell line from the same patient, SW480, as measured in a polysome association microarray study (Provenzani et al, 2006; GEO accession GSE2509). Transcripts containing Hs2 SCREs are expressed at a lower level in U937 cells that have been exposed to 12-myristate 13-acetate (PMA) and caused to differentiate into a macrophage-like state (Kitamura et al, 2004; GEO accession GSE1783). Finally, occurrences of Hs3 in mRNAs correlate with increased association with ribosomes in human mammary epithelial cells, regardless of whether translation initiation factor 4F is overexpressed (Larsson et al, 2007; GEO accession GSE6043).

Bottom Line: In addition, we discovered six putative SCREs in flies and three in humans.We characterized the SCREs based on their condition-specific regulatory influences, the annotation of the transcripts that contain them, and their locations within transcripts.Overall, we show that modeling functional genomics data in terms of combined RNA structure and sequence motifs is an effective method for discovering the specificities and regulatory roles of RNA-binding proteins.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Center for Genome Sciences, Washington University School of Medicine, St Louis, MO 63108, USA.

ABSTRACT
Gene expression is regulated at each step from chromatin remodeling through translation and degradation. Several known RNA-binding regulatory proteins interact with specific RNA secondary structures in addition to specific nucleotides. To provide a more comprehensive understanding of the regulation of gene expression, we developed an integrative computational approach that leverages functional genomics data and nucleotide sequences to discover RNA secondary structure-defined cis-regulatory elements (SCREs). We applied our structural cis-regulatory element detector (StructRED) to microarray and mRNA sequence data from Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We recovered the known specificities of Vts1p in yeast and Smaug in flies. In addition, we discovered six putative SCREs in flies and three in humans. We characterized the SCREs based on their condition-specific regulatory influences, the annotation of the transcripts that contain them, and their locations within transcripts. Overall, we show that modeling functional genomics data in terms of combined RNA structure and sequence motifs is an effective method for discovering the specificities and regulatory roles of RNA-binding proteins.

Show MeSH
Related in: MedlinePlus