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Transcriptome dynamics of the microRNA inhibition response
We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line—the first such dynamic study of the microRNA inhibition response—revealing both general and specific aspects of the physiological response. We show m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513874/ https://www.ncbi.nlm.nih.gov/pubmed/26089393 http://dx.doi.org/10.1093/nar/gkv603 |
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author | Wen, Jiayu Leucci, Elenora Vendramin, Roberto Kauppinen, Sakari Lund, Anders H. Krogh, Anders Parker, Brian J. |
author_facet | Wen, Jiayu Leucci, Elenora Vendramin, Roberto Kauppinen, Sakari Lund, Anders H. Krogh, Anders Parker, Brian J. |
author_sort | Wen, Jiayu |
collection | PubMed |
description | We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line—the first such dynamic study of the microRNA inhibition response—revealing both general and specific aspects of the physiological response. We show miR-9 inhibition inducing a multiphasic transcriptome response, with a direct target perturbation before 4 h, earlier than previously reported, amplified by a downstream peak at ∼32 h consistent with an indirect response due to secondary coherent regulation. Predictive modelling indicates a major role for miR-9 in post-transcriptional control of RNA processing and RNA binding protein regulation. Cluster analysis identifies multiple co-regulated gene regulatory modules. Functionally, we observe a shift over time from mRNA processing at early time points to translation at later time points. We validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies. |
format | Online Article Text |
id | pubmed-4513874 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-45138742015-07-27 Transcriptome dynamics of the microRNA inhibition response Wen, Jiayu Leucci, Elenora Vendramin, Roberto Kauppinen, Sakari Lund, Anders H. Krogh, Anders Parker, Brian J. Nucleic Acids Res Computational Biology We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line—the first such dynamic study of the microRNA inhibition response—revealing both general and specific aspects of the physiological response. We show miR-9 inhibition inducing a multiphasic transcriptome response, with a direct target perturbation before 4 h, earlier than previously reported, amplified by a downstream peak at ∼32 h consistent with an indirect response due to secondary coherent regulation. Predictive modelling indicates a major role for miR-9 in post-transcriptional control of RNA processing and RNA binding protein regulation. Cluster analysis identifies multiple co-regulated gene regulatory modules. Functionally, we observe a shift over time from mRNA processing at early time points to translation at later time points. We validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies. Oxford University Press 2015-07-27 2015-06-18 /pmc/articles/PMC4513874/ /pubmed/26089393 http://dx.doi.org/10.1093/nar/gkv603 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Wen, Jiayu Leucci, Elenora Vendramin, Roberto Kauppinen, Sakari Lund, Anders H. Krogh, Anders Parker, Brian J. Transcriptome dynamics of the microRNA inhibition response |
title | Transcriptome dynamics of the microRNA inhibition response |
title_full | Transcriptome dynamics of the microRNA inhibition response |
title_fullStr | Transcriptome dynamics of the microRNA inhibition response |
title_full_unstemmed | Transcriptome dynamics of the microRNA inhibition response |
title_short | Transcriptome dynamics of the microRNA inhibition response |
title_sort | transcriptome dynamics of the microrna inhibition response |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513874/ https://www.ncbi.nlm.nih.gov/pubmed/26089393 http://dx.doi.org/10.1093/nar/gkv603 |
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