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Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars

BACKGROUND: Cold is one of the main abiotic stresses that severely affect plant growth and development, and crop productivity as well. Transcriptional changes during cold stress have already been intensively studied in various plant species. However, the gene networks involved in the regulation of d...

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Autores principales: Luo, Zhenyu, Zhou, Zhicheng, Li, Yangyang, Tao, Shentong, Hu, Zheng-Rong, Yang, Jia-Shuo, Cheng, Xuejiao, Hu, Risheng, Zhang, Wenli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316383/
https://www.ncbi.nlm.nih.gov/pubmed/35879667
http://dx.doi.org/10.1186/s12870-022-03767-7
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author Luo, Zhenyu
Zhou, Zhicheng
Li, Yangyang
Tao, Shentong
Hu, Zheng-Rong
Yang, Jia-Shuo
Cheng, Xuejiao
Hu, Risheng
Zhang, Wenli
author_facet Luo, Zhenyu
Zhou, Zhicheng
Li, Yangyang
Tao, Shentong
Hu, Zheng-Rong
Yang, Jia-Shuo
Cheng, Xuejiao
Hu, Risheng
Zhang, Wenli
author_sort Luo, Zhenyu
collection PubMed
description BACKGROUND: Cold is one of the main abiotic stresses that severely affect plant growth and development, and crop productivity as well. Transcriptional changes during cold stress have already been intensively studied in various plant species. However, the gene networks involved in the regulation of differential cold tolerance between tobacco varieties with contrasting cold resistance are quite limited. RESULTS: Here, we conducted multiple time-point transcriptomic analyses using Tai tobacco (TT, cold susceptibility) and Yan tobacco (YT, cold resistance) with contrasting cold responses. We identified similar DEGs in both cultivars after comparing with the corresponding control (without cold treatment), which were mainly involved in response to abiotic stimuli, metabolic processes, kinase activities. Through comparison of the two cultivars at each time point, in contrast to TT, YT had higher expression levels of the genes responsible for environmental stresses. By applying Weighted Gene Co-Expression Network Analysis (WGCNA), we identified two main modules: the pink module was similar while the brown module was distinct between the two cultivars. Moreover, we obtained 100 hub genes, including 11 important transcription factors (TFs) potentially involved in cold stress, 3 key TFs in the brown module and 8 key TFs in the pink module. More importantly, according to the genetic regulatory networks (GRNs) between TFs and other genes or TFs by using GENIE3, we identified 3 TFs (ABI3/VP1, ARR-B and WRKY) mainly functioning in differential cold responses between two cultivars, and 3 key TFs (GRAS, AP2-EREBP and C2H2) primarily involved in cold responses. CONCLUSION: Collectively, our study provides valuable resources for transcriptome- based gene network studies of cold responses in tobacco. It helps to reveal how key cold responsive TFs or other genes are regulated through network. It also helps to identify the potential key cold responsive genes for the genetic manipulation of tobacco cultivars with enhanced cold tolerance in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03767-7.
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spelling pubmed-93163832022-07-27 Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars Luo, Zhenyu Zhou, Zhicheng Li, Yangyang Tao, Shentong Hu, Zheng-Rong Yang, Jia-Shuo Cheng, Xuejiao Hu, Risheng Zhang, Wenli BMC Plant Biol Research BACKGROUND: Cold is one of the main abiotic stresses that severely affect plant growth and development, and crop productivity as well. Transcriptional changes during cold stress have already been intensively studied in various plant species. However, the gene networks involved in the regulation of differential cold tolerance between tobacco varieties with contrasting cold resistance are quite limited. RESULTS: Here, we conducted multiple time-point transcriptomic analyses using Tai tobacco (TT, cold susceptibility) and Yan tobacco (YT, cold resistance) with contrasting cold responses. We identified similar DEGs in both cultivars after comparing with the corresponding control (without cold treatment), which were mainly involved in response to abiotic stimuli, metabolic processes, kinase activities. Through comparison of the two cultivars at each time point, in contrast to TT, YT had higher expression levels of the genes responsible for environmental stresses. By applying Weighted Gene Co-Expression Network Analysis (WGCNA), we identified two main modules: the pink module was similar while the brown module was distinct between the two cultivars. Moreover, we obtained 100 hub genes, including 11 important transcription factors (TFs) potentially involved in cold stress, 3 key TFs in the brown module and 8 key TFs in the pink module. More importantly, according to the genetic regulatory networks (GRNs) between TFs and other genes or TFs by using GENIE3, we identified 3 TFs (ABI3/VP1, ARR-B and WRKY) mainly functioning in differential cold responses between two cultivars, and 3 key TFs (GRAS, AP2-EREBP and C2H2) primarily involved in cold responses. CONCLUSION: Collectively, our study provides valuable resources for transcriptome- based gene network studies of cold responses in tobacco. It helps to reveal how key cold responsive TFs or other genes are regulated through network. It also helps to identify the potential key cold responsive genes for the genetic manipulation of tobacco cultivars with enhanced cold tolerance in the future. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-022-03767-7. BioMed Central 2022-07-26 /pmc/articles/PMC9316383/ /pubmed/35879667 http://dx.doi.org/10.1186/s12870-022-03767-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Luo, Zhenyu
Zhou, Zhicheng
Li, Yangyang
Tao, Shentong
Hu, Zheng-Rong
Yang, Jia-Shuo
Cheng, Xuejiao
Hu, Risheng
Zhang, Wenli
Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title_full Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title_fullStr Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title_full_unstemmed Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title_short Transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
title_sort transcriptome-based gene regulatory network analyses of differential cold tolerance of two tobacco cultivars
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316383/
https://www.ncbi.nlm.nih.gov/pubmed/35879667
http://dx.doi.org/10.1186/s12870-022-03767-7
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