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Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection
To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923009/ https://www.ncbi.nlm.nih.gov/pubmed/35300459 http://dx.doi.org/10.1093/nargab/lqac020 |
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author | de Almeida, Rita M C Thomas, Gilberto L Glazier, James A |
author_facet | de Almeida, Rita M C Thomas, Gilberto L Glazier, James A |
author_sort | de Almeida, Rita M C |
collection | PubMed |
description | To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples. |
format | Online Article Text |
id | pubmed-8923009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-89230092022-03-16 Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection de Almeida, Rita M C Thomas, Gilberto L Glazier, James A NAR Genom Bioinform Standard Article To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 in vitro infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples. Oxford University Press 2022-03-15 /pmc/articles/PMC8923009/ /pubmed/35300459 http://dx.doi.org/10.1093/nargab/lqac020 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Standard Article de Almeida, Rita M C Thomas, Gilberto L Glazier, James A Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title | Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title_full | Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title_fullStr | Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title_full_unstemmed | Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title_short | Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection |
title_sort | transcriptogram analysis reveals relationship between viral titer and gene sets responses during corona-virus infection |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923009/ https://www.ncbi.nlm.nih.gov/pubmed/35300459 http://dx.doi.org/10.1093/nargab/lqac020 |
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