A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.

microRNAs (miRNAs) are short non-coding RNAs with regulatory functions in various biological processes including cell differentiation, development and oncogenic transformation. They can bind to mRNA transcripts of protein-coding genes and repress their translation or lead to mRNA degradation. Conver...

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Autores principales:	Julia C Engelmann, Rainer Spang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a2441c68f164d59b96693bfaee39c19
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spelling	oai:doaj.org-article:7a2441c68f164d59b96693bfaee39c192021-11-18T07:12:16ZA least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.1932-620310.1371/journal.pone.0040634https://doaj.org/article/7a2441c68f164d59b96693bfaee39c192012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22815777/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203microRNAs (miRNAs) are short non-coding RNAs with regulatory functions in various biological processes including cell differentiation, development and oncogenic transformation. They can bind to mRNA transcripts of protein-coding genes and repress their translation or lead to mRNA degradation. Conversely, the transcription of miRNAs is regulated by proteins including transcription factors, co-factors, and messenger molecules in signaling pathways, yielding a bidirectional regulatory network of gene and miRNA expression. We describe here a least angle regression approach for uncovering the functional interplay of gene and miRNA regulation based on paired gene and miRNA expression profiles. First, we show that gene expression profiles can indeed be reconstructed from the expression profiles of miRNAs predicted to be regulating the specific gene. Second, we propose a two-step model where in the first step, sequence information is used to constrain the possible set of regulating miRNAs and in the second step, this constraint is relaxed to find regulating miRNAs that do not rely on perfect seed binding. Finally, a bidirectional network comprised of miRNAs regulating genes and genes regulating miRNAs is built from our previous regulatory predictions. After applying the method to a human cancer cell line data set, an analysis of the underlying network reveals miRNAs known to be associated with cancer when dysregulated are predictors of genes with functions in apoptosis. Among the predicted and newly identified targets that lack a classical miRNA seed binding site of a specific oncomir, miR-19b-1, we found an over-representation of genes with functions in apoptosis, which is in accordance with the previous finding that this miRNA is the key oncogenic factor in the mir-17-92 cluster. In addition, we found genes involved in DNA recombination and repair that underline its importance in maintaining the integrity of the cell.Julia C EngelmannRainer SpangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e40634 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Julia C Engelmann Rainer Spang A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
description	microRNAs (miRNAs) are short non-coding RNAs with regulatory functions in various biological processes including cell differentiation, development and oncogenic transformation. They can bind to mRNA transcripts of protein-coding genes and repress their translation or lead to mRNA degradation. Conversely, the transcription of miRNAs is regulated by proteins including transcription factors, co-factors, and messenger molecules in signaling pathways, yielding a bidirectional regulatory network of gene and miRNA expression. We describe here a least angle regression approach for uncovering the functional interplay of gene and miRNA regulation based on paired gene and miRNA expression profiles. First, we show that gene expression profiles can indeed be reconstructed from the expression profiles of miRNAs predicted to be regulating the specific gene. Second, we propose a two-step model where in the first step, sequence information is used to constrain the possible set of regulating miRNAs and in the second step, this constraint is relaxed to find regulating miRNAs that do not rely on perfect seed binding. Finally, a bidirectional network comprised of miRNAs regulating genes and genes regulating miRNAs is built from our previous regulatory predictions. After applying the method to a human cancer cell line data set, an analysis of the underlying network reveals miRNAs known to be associated with cancer when dysregulated are predictors of genes with functions in apoptosis. Among the predicted and newly identified targets that lack a classical miRNA seed binding site of a specific oncomir, miR-19b-1, we found an over-representation of genes with functions in apoptosis, which is in accordance with the previous finding that this miRNA is the key oncogenic factor in the mir-17-92 cluster. In addition, we found genes involved in DNA recombination and repair that underline its importance in maintaining the integrity of the cell.
format	article
author	Julia C Engelmann Rainer Spang
author_facet	Julia C Engelmann Rainer Spang
author_sort	Julia C Engelmann
title	A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
title_short	A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
title_full	A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
title_fullStr	A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
title_full_unstemmed	A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.
title_sort	least angle regression model for the prediction of canonical and non-canonical mirna-mrna interactions.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/7a2441c68f164d59b96693bfaee39c19
work_keys_str_mv	AT juliacengelmann aleastangleregressionmodelforthepredictionofcanonicalandnoncanonicalmirnamrnainteractions AT rainerspang aleastangleregressionmodelforthepredictionofcanonicalandnoncanonicalmirnamrnainteractions AT juliacengelmann leastangleregressionmodelforthepredictionofcanonicalandnoncanonicalmirnamrnainteractions AT rainerspang leastangleregressionmodelforthepredictionofcanonicalandnoncanonicalmirnamrnainteractions
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A least angle regression model for the prediction of canonical and non-canonical miRNA-mRNA interactions.

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