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Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19
Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct tempo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Clinical Investigation
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9057597/ https://www.ncbi.nlm.nih.gov/pubmed/35175937 http://dx.doi.org/10.1172/jci.insight.157255 |
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author | Wang, Xinge Sanborn, Mark A. Dai, Yang Rehman, Jalees |
author_facet | Wang, Xinge Sanborn, Mark A. Dai, Yang Rehman, Jalees |
author_sort | Wang, Xinge |
collection | PubMed |
description | Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets. |
format | Online Article Text |
id | pubmed-9057597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Society for Clinical Investigation |
record_format | MEDLINE/PubMed |
spelling | pubmed-90575972022-05-04 Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 Wang, Xinge Sanborn, Mark A. Dai, Yang Rehman, Jalees JCI Insight Resource and Technical Advance Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets. American Society for Clinical Investigation 2022-04-08 /pmc/articles/PMC9057597/ /pubmed/35175937 http://dx.doi.org/10.1172/jci.insight.157255 Text en © 2022 Wang et al. https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Resource and Technical Advance Wang, Xinge Sanborn, Mark A. Dai, Yang Rehman, Jalees Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title | Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title_full | Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title_fullStr | Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title_full_unstemmed | Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title_short | Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19 |
title_sort | temporal transcriptomic analysis using trendcatcher identifies early and persistent neutrophil activation in severe covid-19 |
topic | Resource and Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9057597/ https://www.ncbi.nlm.nih.gov/pubmed/35175937 http://dx.doi.org/10.1172/jci.insight.157255 |
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