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Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method
BACKGROUND: Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788926/ https://www.ncbi.nlm.nih.gov/pubmed/26969675 http://dx.doi.org/10.1186/s12864-016-2525-5 |
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author | Grassi, Angela Di Camillo, Barbara Ciccarese, Francesco Agnusdei, Valentina Zanovello, Paola Amadori, Alberto Finesso, Lorenzo Indraccolo, Stefano Toffolo, Gianna Maria |
author_facet | Grassi, Angela Di Camillo, Barbara Ciccarese, Francesco Agnusdei, Valentina Zanovello, Paola Amadori, Alberto Finesso, Lorenzo Indraccolo, Stefano Toffolo, Gianna Maria |
author_sort | Grassi, Angela |
collection | PubMed |
description | BACKGROUND: Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. RESULTS: Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. CONCLUSIONS: Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2525-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4788926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47889262016-03-13 Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method Grassi, Angela Di Camillo, Barbara Ciccarese, Francesco Agnusdei, Valentina Zanovello, Paola Amadori, Alberto Finesso, Lorenzo Indraccolo, Stefano Toffolo, Gianna Maria BMC Genomics Research Article BACKGROUND: Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. A challenging task for many researchers working in the field of systems biology is to build up an experiment with a limited budget and produce a dataset suitable to reconstruct putative regulatory modules worth of biological validation. RESULTS: Here, we focus on small-scale gene expression screens and we introduce a novel experimental set-up and a customized method of analysis to make inference on regulatory modules starting from genetic perturbation data, e.g. knockdown and overexpression data. To illustrate the utility of our strategy, it was applied to produce and analyze a dataset of quantitative real-time RT-PCR data, in which interferon-α (IFN-α) transcriptional response in endothelial cells is investigated by RNA silencing of two candidate IFN-α modulators, STAT1 and IFIH1. A putative regulatory module was reconstructed by our method, revealing an intriguing feed-forward loop, in which STAT1 regulates IFIH1 and they both negatively regulate IFNAR1. STAT1 regulation on IFNAR1 was object of experimental validation at the protein level. CONCLUSIONS: Detailed description of the experimental set-up and of the analysis procedure is reported, with the intent to be of inspiration for other scientists who want to realize similar experiments to reconstruct gene regulatory modules starting from perturbations of possible regulators. Application of our approach to the study of IFN-α transcriptional response modulators in endothelial cells has led to many interesting novel findings and new biological hypotheses worth of validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2525-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-03-12 /pmc/articles/PMC4788926/ /pubmed/26969675 http://dx.doi.org/10.1186/s12864-016-2525-5 Text en © Grassi et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Grassi, Angela Di Camillo, Barbara Ciccarese, Francesco Agnusdei, Valentina Zanovello, Paola Amadori, Alberto Finesso, Lorenzo Indraccolo, Stefano Toffolo, Gianna Maria Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title | Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title_full | Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title_fullStr | Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title_full_unstemmed | Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title_short | Reconstruction of gene regulatory modules from RNA silencing of IFN-α modulators: experimental set-up and inference method |
title_sort | reconstruction of gene regulatory modules from rna silencing of ifn-α modulators: experimental set-up and inference method |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788926/ https://www.ncbi.nlm.nih.gov/pubmed/26969675 http://dx.doi.org/10.1186/s12864-016-2525-5 |
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