Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica

Quercus mongolica, a common tree species for building and landscaping in northern China, has great commercial and ecological value. The seedlings of Q. mongolica grow poorly and develop chlorosis when introduced from high-altitude mountains to low-altitude plains. Effective cultivation measures are...

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Main Authors: Min Jiang, Xinman Li, Yangchen Yuan, Guowei Zhang, Jiushuai Pang, Junjie Ren, Jinmao Wang, Minsheng Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Plant Science
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2022.947696/full
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author Min Jiang
Xinman Li
Yangchen Yuan
Guowei Zhang
Jiushuai Pang
Junjie Ren
Jinmao Wang
Minsheng Yang
author_facet Min Jiang
Xinman Li
Yangchen Yuan
Guowei Zhang
Jiushuai Pang
Junjie Ren
Jinmao Wang
Minsheng Yang
author_sort Min Jiang
collection DOAJ
description Quercus mongolica, a common tree species for building and landscaping in northern China, has great commercial and ecological value. The seedlings of Q. mongolica grow poorly and develop chlorosis when introduced from high-altitude mountains to low-altitude plains. Effective cultivation measures are key to improving the quality of seedlings. To investigate the complex responses of Q. mongolica to different cultivation measures, we compared the adaptability of 3-year-old Q. mongolica seedlings to pruning (P), irrigation (W), and fertilization [F (nitro compound fertilizer with 16N-16P-16K)]. Physiological measurements and transcriptome sequencing were performed on leaves collected under the P treatments (control, cutting, removal of all lateral branches, and removal of base branches to one-third of seedling height), the W treatments (0, 1, 2, 3, 4, or 5 times in sequence), and the F treatments (0, 2, 4, and 6 g/plant). Analyses of the physiological data showed that P was more effective than W or F for activating intracellular antioxidant systems. By contrast, W and F were more beneficial than P for inducing the accumulation of soluble sugar. OPLS-DA identified superoxide dismutase, malondialdehyde, and peroxidase as critical physiological indices for the three cultivation measures. Transcriptome analyses revealed 1,012 differentially expressed genes (DEGs) in the P treatment, 1,035 DEGs in the W treatment, and 1,175 DEGs in the F treatment; these DEGs were mainly enriched in Gene Ontology terms related to the stress response and signal transduction. Weighted gene coexpression network analyses indicated that specific gene modules were significantly correlated with MDA (one module) and soluble sugar (four modules). Functional annotation of the hub genes differentially expressed in MDA and soluble sugar-related modules revealed that Q. mongolica responded and adapted to different cultivation measures by altering signal transduction, hormone levels, reactive oxygen species, metabolism, and transcription factors. The hub genes HOP3, CIPK11, WRKY22, and BHLH35 in the coexpression networks may played a central role in responses to the cultivation practices. These results reveal the mechanism behind the response of Q. mongolica to different cultivation measures at the physiological and molecular levels and provide insight into the response of plants to cultivation measures.
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spelling doaj.art-6cb01b7aed424528b75e373973b95dda2022-12-22T02:49:02ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-08-011310.3389/fpls.2022.947696947696Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolicaMin Jiang0Xinman Li1Yangchen Yuan2Guowei Zhang3Jiushuai Pang4Junjie Ren5Jinmao Wang6Minsheng Yang7College of Forestry, Hebei Agricultural University, Baoding, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, ChinaHongyashan State-Owned Forest Farm, Baoding, ChinaHongyashan State-Owned Forest Farm, Baoding, ChinaHongyashan State-Owned Forest Farm, Baoding, ChinaHongyashan State-Owned Forest Farm, Baoding, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, ChinaCollege of Forestry, Hebei Agricultural University, Baoding, ChinaQuercus mongolica, a common tree species for building and landscaping in northern China, has great commercial and ecological value. The seedlings of Q. mongolica grow poorly and develop chlorosis when introduced from high-altitude mountains to low-altitude plains. Effective cultivation measures are key to improving the quality of seedlings. To investigate the complex responses of Q. mongolica to different cultivation measures, we compared the adaptability of 3-year-old Q. mongolica seedlings to pruning (P), irrigation (W), and fertilization [F (nitro compound fertilizer with 16N-16P-16K)]. Physiological measurements and transcriptome sequencing were performed on leaves collected under the P treatments (control, cutting, removal of all lateral branches, and removal of base branches to one-third of seedling height), the W treatments (0, 1, 2, 3, 4, or 5 times in sequence), and the F treatments (0, 2, 4, and 6 g/plant). Analyses of the physiological data showed that P was more effective than W or F for activating intracellular antioxidant systems. By contrast, W and F were more beneficial than P for inducing the accumulation of soluble sugar. OPLS-DA identified superoxide dismutase, malondialdehyde, and peroxidase as critical physiological indices for the three cultivation measures. Transcriptome analyses revealed 1,012 differentially expressed genes (DEGs) in the P treatment, 1,035 DEGs in the W treatment, and 1,175 DEGs in the F treatment; these DEGs were mainly enriched in Gene Ontology terms related to the stress response and signal transduction. Weighted gene coexpression network analyses indicated that specific gene modules were significantly correlated with MDA (one module) and soluble sugar (four modules). Functional annotation of the hub genes differentially expressed in MDA and soluble sugar-related modules revealed that Q. mongolica responded and adapted to different cultivation measures by altering signal transduction, hormone levels, reactive oxygen species, metabolism, and transcription factors. The hub genes HOP3, CIPK11, WRKY22, and BHLH35 in the coexpression networks may played a central role in responses to the cultivation practices. These results reveal the mechanism behind the response of Q. mongolica to different cultivation measures at the physiological and molecular levels and provide insight into the response of plants to cultivation measures.https://www.frontiersin.org/articles/10.3389/fpls.2022.947696/fullQuercus mongolicacultivation measuresphysiologytranscriptome analysisWGCNA
spellingShingle Min Jiang
Xinman Li
Yangchen Yuan
Guowei Zhang
Jiushuai Pang
Junjie Ren
Jinmao Wang
Minsheng Yang
Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
Frontiers in Plant Science
Quercus mongolica
cultivation measures
physiology
transcriptome analysis
WGCNA
title Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
title_full Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
title_fullStr Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
title_full_unstemmed Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
title_short Integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in Quercus mongolica
title_sort integrated physiological and transcriptomic analyses reveal the molecular mechanism behind the response to cultivation in quercus mongolica
topic Quercus mongolica
cultivation measures
physiology
transcriptome analysis
WGCNA
url https://www.frontiersin.org/articles/10.3389/fpls.2022.947696/full
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