Mol. Cells 2021; 44(10): 746-757
Published online October 20, 2021
https://doi.org/10.14348/molcells.2021.0160
© The Korean Society for Molecular and Cellular Biology
Correspondence to : pjseo1@snu.ac.kr
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
Plant somatic cells can be reprogrammed into a pluripotent cell mass, called callus, which can be subsequently used for de novo shoot regeneration through a two-step in vitro tissue culture method. MET1-dependent CG methylation has been implicated in plant regeneration in Arabidopsis, because the met1-3 mutant exhibits increased shoot regeneration compared with the wild-type. To understand the role of MET1 in de novo shoot regeneration, we compared the genome-wide DNA methylomes and transcriptomes of wild-type and met1-3 callus and leaf. The CG methylation patterns were largely unchanged during leaf-to-callus transition, suggesting that the altered regeneration phenotype of met1-3 was caused by the constitutively hypomethylated genes, independent of the tissue type. In particular, MET1-dependent CG methylation was observed at the blue light receptor genes, CRYPTOCHROME 1 (CRY1) and CRY2, which reduced their expression. Coexpression network analysis revealed that the CRY1 gene was closely linked to cytokinin signaling genes. Consistently, functional enrichment analysis of differentially expressed genes in met1-3 showed that gene ontology terms related to light and hormone signaling were overrepresented. Overall, our findings indicate that MET1-dependent repression of light and cytokinin signaling influences plant regeneration capacity and shoot identity establishment.
Keywords Arabidopsis, callus, cryptochrome 1, cytokinin, DNA methylation, MET1, shoot regeneration
Plant somatic cells can be reprogrammed to form an unorganized pluripotent cell mass, called callus. Incubation on callus-inducing medium (CIM) activates cell proliferation, facilitating callus formation. Accumulating evidence shows that callus tissue resembles root primordium, regardless of the origin of tissue explants (Atta et al., 2009; Sugimoto et al., 2010). Consistently, callus formation is initiated from pericycle-like cells (Sugimoto et al., 2010). The founder cell undergoes asymmetric cell division and then enables the acquisition of root primordium identity (Dubrovsky et al., 2000; Sugimoto et al., 2010), with the activation of genes including
Chemical modifications of DNA or core histone proteins alter chromatin structure, contributing to gene expression regulation, independent of the changes in DNA sequence. Methylation of the fifth carbon of cytosine residue is the most extensively studied epigenetic modification in both plants and mammals (Kim et al., 2021; Yoo et al., 2021). DNA methylation usually represses gene transcription (Chen et al., 2008; Jeddeloh et al., 1998; Zilberman et al., 2007), although increasing evidence shows that DNA methylation can also activate gene expression (Baubec et al., 2013; Brackertz et al., 2002; Fujita et al., 2003; Fukushige et al., 2006; Harris et al., 2018; Lang et al., 2017; Waterfield et al., 2014; Zemach and Grafi, 2003). The
DNA methylation is closely associated with plant regeneration. For example, MET1-dependent DNA methylation negatively controls the expression of core shoot regeneration regulator genes, including
In this study, we conducted whole-genome bisulfite sequencing (BS-seq) of the
BS-seq libraries were constructed as described previously (Shim et al., 2021). Callus samples are heterogeneous and exhibit significant variation in gene expression; therefore, a large amount of sample (>1 g) was used to perform high-depth BS-seq (>70× coverage) for single biological replicate. The third and fourth leaves of
Raw BS-seq reads were analyzed as described previously (Smallwood et al., 2014), with slight modifications. Briefly, according to the Bismark Bisulfite Mapper guidelines (https://rawgit.com/FelixKrueger/Bismark/master/Docs/Bismark_User_Guide.html), the first 8 bp of raw BS-seq reads were trimmed using TrimGalore (parameters: --gzip --paired --clip_R1 8 --clip_R2 8) to prevent adaptor contamination (Krueger and Andrews, 2011). The trimmed reads were initially aligned to the TAIR10 version of the
To identify DMRs between
To determine whether DMRs overlapped with genic regions (defined as the region encompassing the gene body and 1 kb sequence upstream of the transcription start site) and transposable element (TE) regions, the BS-seq data were investigated using BEDTools (Quinlan and Hall, 2010), based on the TAIR10 reference genome annotation. Genome-wide patterns of DNA methylation were visualized using Pandas, NumPy, SciPy, and pyplot libraries of Python. The cytosine conversion rate in genic regions was depicted using deepTools (Ramírez et al., 2014).
To analyze the enriched biological functions of differentially methylated genes, the MapMan annotation was used for comparing the observed ratio of genes of interest (GOIs) in a selected gene group with the expected ratio of genes in the reference genome for a specific pathway through the hypergeometric test using a homemade Python script (Usadel et al., 2009). Moreover, to construct a coexpression network, the GOIs were inputted as queries into the NetworkDrawer implemented in ATTED-II (https://atted.jp) (Obayashi et al., 2018).
To analyze the impact of DNA methylation on gene expression in the wild-type and
Total RNA was extracted from
Whole genome BS-seq and RNA-seq data are available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) data under the BioProject accession number PRJNA601842.
MET1 is involved in plant regeneration from root and pistil tissues (Li et al., 2011; Liu et al., 2018). To further analyze the functional impact of MET1 in plant regeneration, we performed the two-step
Plant regeneration capacity was determined by monitoring
To understand the molecular basis of enhanced
Notably, the CG methylation level showed a negligible change during leaf-to-callus transition both in the wild-type and
Given that most of the hypomethylated CG-DMRs were found in genic regions (Supplementary Fig. S2), we collected genes containing hypomethylated CG regions in
To further understand the biological processes regulated by MET1, we conducted RNA-seq analysis and identified genes differentially expressed between
GO enrichment analysis of the DEGs showed that GO terms related to light signaling and responses were significantly enriched for light signaling and responses (external stimuli response for UV-A/blue light [
The biological impact of light signaling on plant regeneration remains unknown; therefore, we decided to investigate how light signaling regulates
Next, we investigated whether shoot regeneration efficiency of
To estimate the extent of the biological impact of MET1 during plant regeneration, we identified genes both hypomethylated and up-regulated in
In agreement with the finding that blue light receptor genes were included in the coexpression network of genes regulated by MET1-dependent CG methylation (Fig. 4A), the
Considering that enhanced cytokinin signaling promotes
Taken together, our findings demonstrate that MET1-dependent CG methylation represses
Plant regeneration involves substantial changes in the epigenetic landscape. Histone H3 modifications, including trimethylation of lysine 4 and lysine 36 residues (H3K4me3 and H3K36me3, respectively) and acetylation, exhibit dynamic changes during leaf-to-callus transition (Kim et al., 2018; Lee et al., 2017; Li et al., 2011). Various chromatin modifiers and remodelers have been identified as key regulators of cell fate transition (He et al., 2012; Ishihara et al., 2019). DNA methylation is also altered during the process of plant regeneration. In particular, genome-wide CHG and CHH methylation levels change during callus formation (Shim et al., 2021). By contrast, in this study, CG methylation was largely insensitive to cell fate transition, and CG methylation landscapes were mostly maintained during leaf-to-callus transition. It is plausible that CG methylation evolved to ensure genome integrity rather than to regulate gene expression.
Given the stable nature of CG methylation during leaf-to-callus transition, the altered
Each step of the plant regeneration process requires a balance between auxin and cytokinin signaling. A high auxin-to-cytokinin ratio promotes callus formation (Atta et al., 2009; Skoog and Miller, 1957; Sugimoto et al., 2010), whereas a low auxin-to-cytokinin ratio promotes
This work was supported by the Basic Science Research (NRF-2019R1I1A1A01061376 to S.S.; NRF-2019R1A2C2006915 to P.J.S.) and Basic Research Laboratory (NRF-2020R1A4A2002901 to P.J.S.) programs provided by the National Research Foundation of Korea, by the National Research Foundation of Korea, and by the Creative-Pioneering Researchers Program through Seoul National University (0409-20200281 to P.J.S.).
P.J.S. conceived the project. S.S. conducted bioinformatics analyses. H.G.L. performed experiments. S.S. wrote the first draft of manuscript. P.J.S. revised the manuscript. All authors read and approved the final manuscript.
The authors have no potential conflicts of interest to disclose.
Functional enrichment analysis of DEGs in
Functional categorya | No. of query genes per category | Total No. of query genes | Observed value (%) | No. of reference genes per category | Total No. of reference genes | Expected value (%) | |
---|---|---|---|---|---|---|---|
RNA biosynthesis (transcriptional regulation) | 442 | 3,936 | 11.2 | 1,962 | 27,206 | 7.2 | 1.87E-23 |
Enzyme classification (EC_2 transferases) | 100 | 3,936 | 2.5 | 382 | 27,206 | 1.4 | 1.22E-09 |
Enzyme classification (EC_1 oxidoreductases) | 87 | 3,936 | 2.2 | 420 | 27,206 | 1.5 | 2.94E-04 |
Solute transport (carrier-mediated transport) | 148 | 3,936 | 3.8 | 784 | 27,206 | 2.9 | 3.42E-04 |
External stimuli response (pathogen) | 53 | 3,936 | 1.3 | 233 | 27,206 | 0.9 | 4.55E-04 |
Vesicle trafficking (clathrin-independent machinery) | 3 | 3,936 | 0.1 | 3 | 27,206 | 0.0 | 3.03E-03 |
Carbohydrate metabolism (fermentation) | 4 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 5.14E-03 |
Carbohydrate metabolism (oligosaccharide metabolism) | 6 | 3,936 | 0.2 | 13 | 27,206 | 0.0 | 6.27E-03 |
Phytohormone action (ethylene-biosynthesis) | 7 | 3,936 | 0.2 | 17 | 27,206 | 0.1 | 6.75E-03 |
External stimuli response (light-UV-A/blue light) | 10 | 3,936 | 0.3 | 30 | 27,206 | 0.1 | 7.46E-03 |
Protein modification (phosphorylation) | 195 | 3,936 | 5.0 | 1,148 | 27,206 | 4.2 | 8.34E-03 |
Lipid metabolism (lipid bodies-associated activities) | 12 | 3,936 | 0.3 | 40 | 27,206 | 0.1 | 8.99E-03 |
Cell wall organization (pectin) | 39 | 3,936 | 1.0 | 185 | 27,206 | 0.7 | 9.17E-03 |
Phytohormone action (cytokinin-perception and signal transduction) | 10 | 3,936 | 0.3 | 31 | 27,206 | 0.1 | 9.61E-03 |
Protein modification (S-glutathionylation) | 19 | 3,936 | 0.5 | 76 | 27,206 | 0.3 | 1.06E-02 |
Phytohormone action (abscisic acid-conjugation and degradation) | 5 | 3,936 | 0.1 | 11 | 27,206 | 0.0 | 1.36E-02 |
Phytohormone action (auxin-biosynthesis) | 6 | 3,936 | 0.2 | 15 | 27,206 | 0.1 | 1.41E-02 |
External stimuli response (gravity) | 6 | 3,936 | 0.2 | 15 | 27,206 | 0.1 | 1.41E-02 |
Multi-process regulation (programmed cell death [PCD] system) | 9 | 3,936 | 0.2 | 29 | 27,206 | 0.1 | 1.79E-02 |
DNA damage response (photoreactivation) | 2 | 3,936 | 0.1 | 2 | 27,206 | 0.0 | 2.09E-02 |
Phytohormone action (cytokinin-transport) | 2 | 3,936 | 0.1 | 2 | 27,206 | 0.0 | 2.09E-02 |
Phytohormone action (signaling peptides) | 43 | 3,936 | 1.1 | 219 | 27,206 | 0.8 | 2.16E-02 |
Phytohormone action (abscisic acid-transport) | 3 | 3,936 | 0.1 | 5 | 27,206 | 0.0 | 2.41E-02 |
External stimuli response (light-UV-B light) | 3 | 3,936 | 0.1 | 5 | 27,206 | 0.0 | 2.41E-02 |
Solute transport (channels) | 35 | 3,936 | 0.9 | 180 | 27,206 | 0.7 | 4.00E-02 |
Phytohormone action (gibberellin-biosynthesis) | 5 | 3,936 | 0.1 | 14 | 27,206 | 0.1 | 4.07E-02 |
Multi-process regulation (phosphatidylethanolamine-binding [PEB] protein-dependent signaling) | 3 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 4.30E-02 |
Phytohormone action (salicylic acid-conjugation and degradation) | 3 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 4.30E-02 |
Phytohormone action (gibberellin-perception and signal transduction) | 4 | 3,936 | 0.1 | 10 | 27,206 | 0.0 | 4.44E-02 |
Multi-process regulation (SnRK1-kinase regulatory system) | 8 | 3,936 | 0.2 | 29 | 27,206 | 0.1 | 4.88E-02 |
aSignificantly overrepresented terms (
Functional enrichment analysis of all genes contained in the coexpression subnetwork shown in Fig.4A
Functional categorya | No. of query genes per category | Total No. of query genes | Observed value (%) | No. of reference genes per category | Total No. of reference genes | Expected value (%) | |
---|---|---|---|---|---|---|---|
External stimuli response (pathogen) | 66 | 3,391 | 1.9 | 233 | 27,206 | 0.9 | 6.60E-11 |
Nutrient uptake (iron uptake) | 26 | 3,391 | 0.8 | 61 | 27,206 | 0.2 | 4.10E-09 |
Protein modification (phosphorylation) | 207 | 3,391 | 6.1 | 1,148 | 27,206 | 4.2 | 1.85E-08 |
Enzyme classification (EC_2 transferases) | 80 | 3,391 | 2.4 | 382 | 27,206 | 1.4 | 1.88E-06 |
Carbohydrate metabolism (starch metabolism) | 19 | 3,391 | 0.6 | 52 | 27,206 | 0.2 | 7.85E-06 |
Phytohormone action (cytokinin-perception and signal transduction) | 13 | 3,391 | 0.4 | 31 | 27,206 | 0.1 | 3.95E-05 |
Amino acid metabolism (degradation) | 19 | 3,391 | 0.6 | 58 | 27,206 | 0.2 | 4.64E-05 |
Protein homeostasis (autophagy) | 17 | 3,391 | 0.5 | 50 | 27,206 | 0.2 | 6.78E-05 |
Protein modification (S-glutathionylation) | 20 | 3,391 | 0.6 | 76 | 27,206 | 0.3 | 8.02E-04 |
Cytoskeleton organization (microtubular network) | 29 | 3,391 | 0.9 | 132 | 27,206 | 0.5 | 1.56E-03 |
Nutrient uptake (copper uptake) | 9 | 3,391 | 0.3 | 24 | 27,206 | 0.1 | 1.61E-03 |
Phytohormone action (jasmonic acid-biosynthesis) | 8 | 3,391 | 0.2 | 21 | 27,206 | 0.1 | 2.61E-03 |
Cell cycle organization (cytokinesis) | 18 | 3,391 | 0.5 | 74 | 27,206 | 0.3 | 3.66E-03 |
Phytohormone action (salicylic acid-perception and signal transduction) | 3 | 3,391 | 0.1 | 4 | 27,206 | 0.0 | 7.02E-03 |
Solute transport (channels) | 34 | 3,391 | 1.0 | 180 | 27,206 | 0.7 | 8.53E-03 |
Solute transport (carrier-mediated transport) | 120 | 3,391 | 3.5 | 784 | 27,206 | 2.9 | 9.78E-03 |
Multi-process regulation (target of rapamycin [TOR] signaling) | 5 | 3,391 | 0.1 | 12 | 27,206 | 0.0 | 1.11E-02 |
Phytohormone action (salicylic acid-biosynthesis) | 2 | 3,391 | 0.1 | 2 | 27,206 | 0.0 | 1.55E-02 |
Nutrient uptake (sulfur assimilation) | 5 | 3,391 | 0.1 | 13 | 27,206 | 0.0 | 1.63E-02 |
Enzyme classification (EC_1 oxidoreductases) | 67 | 3,391 | 2.0 | 420 | 27,206 | 1.5 | 2.01E-02 |
Cell cycle organization (DNA replication) | 16 | 3,391 | 0.5 | 76 | 27,206 | 0.3 | 2.36E-02 |
Vesicle trafficking (target membrane tethering) | 17 | 3,391 | 0.5 | 84 | 27,206 | 0.3 | 2.87E-02 |
Phytohormone action (salicylic acid-conjugation and degradation) | 3 | 3,391 | 0.1 | 6 | 27,206 | 0.0 | 2.89E-02 |
Enzyme classification (EC_3 hydrolases) | 41 | 3,391 | 1.2 | 251 | 27,206 | 0.9 | 4.22E-02 |
Vesicle trafficking (clathrin-independent machinery) | 2 | 3,391 | 0.1 | 3 | 27,206 | 0.0 | 4.27E-02 |
Chromatin organization (chromatin remodeling complexes) | 13 | 3,391 | 0.4 | 63 | 27,206 | 0.2 | 4.48E-02 |
aSignificantly overrepresented terms (
Mol. Cells 2021; 44(10): 746-757
Published online October 31, 2021 https://doi.org/10.14348/molcells.2021.0160
Copyright © The Korean Society for Molecular and Cellular Biology.
Sangrea Shim1,2 , Hong Gil Lee2, and Pil Joon Seo1,2,3,*
1Department of Chemistry, Seoul National University, Seoul 08826, Korea, 2Plant Genomics and Breeding Institute, Seoul National University, Seoul 08826, Korea, 3Research Institute of Basic Sciences, Seoul National University, Seoul 08826, Korea
Correspondence to:pjseo1@snu.ac.kr
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
Plant somatic cells can be reprogrammed into a pluripotent cell mass, called callus, which can be subsequently used for de novo shoot regeneration through a two-step in vitro tissue culture method. MET1-dependent CG methylation has been implicated in plant regeneration in Arabidopsis, because the met1-3 mutant exhibits increased shoot regeneration compared with the wild-type. To understand the role of MET1 in de novo shoot regeneration, we compared the genome-wide DNA methylomes and transcriptomes of wild-type and met1-3 callus and leaf. The CG methylation patterns were largely unchanged during leaf-to-callus transition, suggesting that the altered regeneration phenotype of met1-3 was caused by the constitutively hypomethylated genes, independent of the tissue type. In particular, MET1-dependent CG methylation was observed at the blue light receptor genes, CRYPTOCHROME 1 (CRY1) and CRY2, which reduced their expression. Coexpression network analysis revealed that the CRY1 gene was closely linked to cytokinin signaling genes. Consistently, functional enrichment analysis of differentially expressed genes in met1-3 showed that gene ontology terms related to light and hormone signaling were overrepresented. Overall, our findings indicate that MET1-dependent repression of light and cytokinin signaling influences plant regeneration capacity and shoot identity establishment.
Keywords: Arabidopsis, callus, cryptochrome 1, cytokinin, DNA methylation, MET1, shoot regeneration
Plant somatic cells can be reprogrammed to form an unorganized pluripotent cell mass, called callus. Incubation on callus-inducing medium (CIM) activates cell proliferation, facilitating callus formation. Accumulating evidence shows that callus tissue resembles root primordium, regardless of the origin of tissue explants (Atta et al., 2009; Sugimoto et al., 2010). Consistently, callus formation is initiated from pericycle-like cells (Sugimoto et al., 2010). The founder cell undergoes asymmetric cell division and then enables the acquisition of root primordium identity (Dubrovsky et al., 2000; Sugimoto et al., 2010), with the activation of genes including
Chemical modifications of DNA or core histone proteins alter chromatin structure, contributing to gene expression regulation, independent of the changes in DNA sequence. Methylation of the fifth carbon of cytosine residue is the most extensively studied epigenetic modification in both plants and mammals (Kim et al., 2021; Yoo et al., 2021). DNA methylation usually represses gene transcription (Chen et al., 2008; Jeddeloh et al., 1998; Zilberman et al., 2007), although increasing evidence shows that DNA methylation can also activate gene expression (Baubec et al., 2013; Brackertz et al., 2002; Fujita et al., 2003; Fukushige et al., 2006; Harris et al., 2018; Lang et al., 2017; Waterfield et al., 2014; Zemach and Grafi, 2003). The
DNA methylation is closely associated with plant regeneration. For example, MET1-dependent DNA methylation negatively controls the expression of core shoot regeneration regulator genes, including
In this study, we conducted whole-genome bisulfite sequencing (BS-seq) of the
BS-seq libraries were constructed as described previously (Shim et al., 2021). Callus samples are heterogeneous and exhibit significant variation in gene expression; therefore, a large amount of sample (>1 g) was used to perform high-depth BS-seq (>70× coverage) for single biological replicate. The third and fourth leaves of
Raw BS-seq reads were analyzed as described previously (Smallwood et al., 2014), with slight modifications. Briefly, according to the Bismark Bisulfite Mapper guidelines (https://rawgit.com/FelixKrueger/Bismark/master/Docs/Bismark_User_Guide.html), the first 8 bp of raw BS-seq reads were trimmed using TrimGalore (parameters: --gzip --paired --clip_R1 8 --clip_R2 8) to prevent adaptor contamination (Krueger and Andrews, 2011). The trimmed reads were initially aligned to the TAIR10 version of the
To identify DMRs between
To determine whether DMRs overlapped with genic regions (defined as the region encompassing the gene body and 1 kb sequence upstream of the transcription start site) and transposable element (TE) regions, the BS-seq data were investigated using BEDTools (Quinlan and Hall, 2010), based on the TAIR10 reference genome annotation. Genome-wide patterns of DNA methylation were visualized using Pandas, NumPy, SciPy, and pyplot libraries of Python. The cytosine conversion rate in genic regions was depicted using deepTools (Ramírez et al., 2014).
To analyze the enriched biological functions of differentially methylated genes, the MapMan annotation was used for comparing the observed ratio of genes of interest (GOIs) in a selected gene group with the expected ratio of genes in the reference genome for a specific pathway through the hypergeometric test using a homemade Python script (Usadel et al., 2009). Moreover, to construct a coexpression network, the GOIs were inputted as queries into the NetworkDrawer implemented in ATTED-II (https://atted.jp) (Obayashi et al., 2018).
To analyze the impact of DNA methylation on gene expression in the wild-type and
Total RNA was extracted from
Whole genome BS-seq and RNA-seq data are available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) data under the BioProject accession number PRJNA601842.
MET1 is involved in plant regeneration from root and pistil tissues (Li et al., 2011; Liu et al., 2018). To further analyze the functional impact of MET1 in plant regeneration, we performed the two-step
Plant regeneration capacity was determined by monitoring
To understand the molecular basis of enhanced
Notably, the CG methylation level showed a negligible change during leaf-to-callus transition both in the wild-type and
Given that most of the hypomethylated CG-DMRs were found in genic regions (Supplementary Fig. S2), we collected genes containing hypomethylated CG regions in
To further understand the biological processes regulated by MET1, we conducted RNA-seq analysis and identified genes differentially expressed between
GO enrichment analysis of the DEGs showed that GO terms related to light signaling and responses were significantly enriched for light signaling and responses (external stimuli response for UV-A/blue light [
The biological impact of light signaling on plant regeneration remains unknown; therefore, we decided to investigate how light signaling regulates
Next, we investigated whether shoot regeneration efficiency of
To estimate the extent of the biological impact of MET1 during plant regeneration, we identified genes both hypomethylated and up-regulated in
In agreement with the finding that blue light receptor genes were included in the coexpression network of genes regulated by MET1-dependent CG methylation (Fig. 4A), the
Considering that enhanced cytokinin signaling promotes
Taken together, our findings demonstrate that MET1-dependent CG methylation represses
Plant regeneration involves substantial changes in the epigenetic landscape. Histone H3 modifications, including trimethylation of lysine 4 and lysine 36 residues (H3K4me3 and H3K36me3, respectively) and acetylation, exhibit dynamic changes during leaf-to-callus transition (Kim et al., 2018; Lee et al., 2017; Li et al., 2011). Various chromatin modifiers and remodelers have been identified as key regulators of cell fate transition (He et al., 2012; Ishihara et al., 2019). DNA methylation is also altered during the process of plant regeneration. In particular, genome-wide CHG and CHH methylation levels change during callus formation (Shim et al., 2021). By contrast, in this study, CG methylation was largely insensitive to cell fate transition, and CG methylation landscapes were mostly maintained during leaf-to-callus transition. It is plausible that CG methylation evolved to ensure genome integrity rather than to regulate gene expression.
Given the stable nature of CG methylation during leaf-to-callus transition, the altered
Each step of the plant regeneration process requires a balance between auxin and cytokinin signaling. A high auxin-to-cytokinin ratio promotes callus formation (Atta et al., 2009; Skoog and Miller, 1957; Sugimoto et al., 2010), whereas a low auxin-to-cytokinin ratio promotes
This work was supported by the Basic Science Research (NRF-2019R1I1A1A01061376 to S.S.; NRF-2019R1A2C2006915 to P.J.S.) and Basic Research Laboratory (NRF-2020R1A4A2002901 to P.J.S.) programs provided by the National Research Foundation of Korea, by the National Research Foundation of Korea, and by the Creative-Pioneering Researchers Program through Seoul National University (0409-20200281 to P.J.S.).
P.J.S. conceived the project. S.S. conducted bioinformatics analyses. H.G.L. performed experiments. S.S. wrote the first draft of manuscript. P.J.S. revised the manuscript. All authors read and approved the final manuscript.
The authors have no potential conflicts of interest to disclose.
. Functional enrichment analysis of DEGs in
Functional categorya | No. of query genes per category | Total No. of query genes | Observed value (%) | No. of reference genes per category | Total No. of reference genes | Expected value (%) | |
---|---|---|---|---|---|---|---|
RNA biosynthesis (transcriptional regulation) | 442 | 3,936 | 11.2 | 1,962 | 27,206 | 7.2 | 1.87E-23 |
Enzyme classification (EC_2 transferases) | 100 | 3,936 | 2.5 | 382 | 27,206 | 1.4 | 1.22E-09 |
Enzyme classification (EC_1 oxidoreductases) | 87 | 3,936 | 2.2 | 420 | 27,206 | 1.5 | 2.94E-04 |
Solute transport (carrier-mediated transport) | 148 | 3,936 | 3.8 | 784 | 27,206 | 2.9 | 3.42E-04 |
External stimuli response (pathogen) | 53 | 3,936 | 1.3 | 233 | 27,206 | 0.9 | 4.55E-04 |
Vesicle trafficking (clathrin-independent machinery) | 3 | 3,936 | 0.1 | 3 | 27,206 | 0.0 | 3.03E-03 |
Carbohydrate metabolism (fermentation) | 4 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 5.14E-03 |
Carbohydrate metabolism (oligosaccharide metabolism) | 6 | 3,936 | 0.2 | 13 | 27,206 | 0.0 | 6.27E-03 |
Phytohormone action (ethylene-biosynthesis) | 7 | 3,936 | 0.2 | 17 | 27,206 | 0.1 | 6.75E-03 |
External stimuli response (light-UV-A/blue light) | 10 | 3,936 | 0.3 | 30 | 27,206 | 0.1 | 7.46E-03 |
Protein modification (phosphorylation) | 195 | 3,936 | 5.0 | 1,148 | 27,206 | 4.2 | 8.34E-03 |
Lipid metabolism (lipid bodies-associated activities) | 12 | 3,936 | 0.3 | 40 | 27,206 | 0.1 | 8.99E-03 |
Cell wall organization (pectin) | 39 | 3,936 | 1.0 | 185 | 27,206 | 0.7 | 9.17E-03 |
Phytohormone action (cytokinin-perception and signal transduction) | 10 | 3,936 | 0.3 | 31 | 27,206 | 0.1 | 9.61E-03 |
Protein modification (S-glutathionylation) | 19 | 3,936 | 0.5 | 76 | 27,206 | 0.3 | 1.06E-02 |
Phytohormone action (abscisic acid-conjugation and degradation) | 5 | 3,936 | 0.1 | 11 | 27,206 | 0.0 | 1.36E-02 |
Phytohormone action (auxin-biosynthesis) | 6 | 3,936 | 0.2 | 15 | 27,206 | 0.1 | 1.41E-02 |
External stimuli response (gravity) | 6 | 3,936 | 0.2 | 15 | 27,206 | 0.1 | 1.41E-02 |
Multi-process regulation (programmed cell death [PCD] system) | 9 | 3,936 | 0.2 | 29 | 27,206 | 0.1 | 1.79E-02 |
DNA damage response (photoreactivation) | 2 | 3,936 | 0.1 | 2 | 27,206 | 0.0 | 2.09E-02 |
Phytohormone action (cytokinin-transport) | 2 | 3,936 | 0.1 | 2 | 27,206 | 0.0 | 2.09E-02 |
Phytohormone action (signaling peptides) | 43 | 3,936 | 1.1 | 219 | 27,206 | 0.8 | 2.16E-02 |
Phytohormone action (abscisic acid-transport) | 3 | 3,936 | 0.1 | 5 | 27,206 | 0.0 | 2.41E-02 |
External stimuli response (light-UV-B light) | 3 | 3,936 | 0.1 | 5 | 27,206 | 0.0 | 2.41E-02 |
Solute transport (channels) | 35 | 3,936 | 0.9 | 180 | 27,206 | 0.7 | 4.00E-02 |
Phytohormone action (gibberellin-biosynthesis) | 5 | 3,936 | 0.1 | 14 | 27,206 | 0.1 | 4.07E-02 |
Multi-process regulation (phosphatidylethanolamine-binding [PEB] protein-dependent signaling) | 3 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 4.30E-02 |
Phytohormone action (salicylic acid-conjugation and degradation) | 3 | 3,936 | 0.1 | 6 | 27,206 | 0.0 | 4.30E-02 |
Phytohormone action (gibberellin-perception and signal transduction) | 4 | 3,936 | 0.1 | 10 | 27,206 | 0.0 | 4.44E-02 |
Multi-process regulation (SnRK1-kinase regulatory system) | 8 | 3,936 | 0.2 | 29 | 27,206 | 0.1 | 4.88E-02 |
aSignificantly overrepresented terms (
. Functional enrichment analysis of all genes contained in the coexpression subnetwork shown in Fig.4A.
Functional categorya | No. of query genes per category | Total No. of query genes | Observed value (%) | No. of reference genes per category | Total No. of reference genes | Expected value (%) | |
---|---|---|---|---|---|---|---|
External stimuli response (pathogen) | 66 | 3,391 | 1.9 | 233 | 27,206 | 0.9 | 6.60E-11 |
Nutrient uptake (iron uptake) | 26 | 3,391 | 0.8 | 61 | 27,206 | 0.2 | 4.10E-09 |
Protein modification (phosphorylation) | 207 | 3,391 | 6.1 | 1,148 | 27,206 | 4.2 | 1.85E-08 |
Enzyme classification (EC_2 transferases) | 80 | 3,391 | 2.4 | 382 | 27,206 | 1.4 | 1.88E-06 |
Carbohydrate metabolism (starch metabolism) | 19 | 3,391 | 0.6 | 52 | 27,206 | 0.2 | 7.85E-06 |
Phytohormone action (cytokinin-perception and signal transduction) | 13 | 3,391 | 0.4 | 31 | 27,206 | 0.1 | 3.95E-05 |
Amino acid metabolism (degradation) | 19 | 3,391 | 0.6 | 58 | 27,206 | 0.2 | 4.64E-05 |
Protein homeostasis (autophagy) | 17 | 3,391 | 0.5 | 50 | 27,206 | 0.2 | 6.78E-05 |
Protein modification (S-glutathionylation) | 20 | 3,391 | 0.6 | 76 | 27,206 | 0.3 | 8.02E-04 |
Cytoskeleton organization (microtubular network) | 29 | 3,391 | 0.9 | 132 | 27,206 | 0.5 | 1.56E-03 |
Nutrient uptake (copper uptake) | 9 | 3,391 | 0.3 | 24 | 27,206 | 0.1 | 1.61E-03 |
Phytohormone action (jasmonic acid-biosynthesis) | 8 | 3,391 | 0.2 | 21 | 27,206 | 0.1 | 2.61E-03 |
Cell cycle organization (cytokinesis) | 18 | 3,391 | 0.5 | 74 | 27,206 | 0.3 | 3.66E-03 |
Phytohormone action (salicylic acid-perception and signal transduction) | 3 | 3,391 | 0.1 | 4 | 27,206 | 0.0 | 7.02E-03 |
Solute transport (channels) | 34 | 3,391 | 1.0 | 180 | 27,206 | 0.7 | 8.53E-03 |
Solute transport (carrier-mediated transport) | 120 | 3,391 | 3.5 | 784 | 27,206 | 2.9 | 9.78E-03 |
Multi-process regulation (target of rapamycin [TOR] signaling) | 5 | 3,391 | 0.1 | 12 | 27,206 | 0.0 | 1.11E-02 |
Phytohormone action (salicylic acid-biosynthesis) | 2 | 3,391 | 0.1 | 2 | 27,206 | 0.0 | 1.55E-02 |
Nutrient uptake (sulfur assimilation) | 5 | 3,391 | 0.1 | 13 | 27,206 | 0.0 | 1.63E-02 |
Enzyme classification (EC_1 oxidoreductases) | 67 | 3,391 | 2.0 | 420 | 27,206 | 1.5 | 2.01E-02 |
Cell cycle organization (DNA replication) | 16 | 3,391 | 0.5 | 76 | 27,206 | 0.3 | 2.36E-02 |
Vesicle trafficking (target membrane tethering) | 17 | 3,391 | 0.5 | 84 | 27,206 | 0.3 | 2.87E-02 |
Phytohormone action (salicylic acid-conjugation and degradation) | 3 | 3,391 | 0.1 | 6 | 27,206 | 0.0 | 2.89E-02 |
Enzyme classification (EC_3 hydrolases) | 41 | 3,391 | 1.2 | 251 | 27,206 | 0.9 | 4.22E-02 |
Vesicle trafficking (clathrin-independent machinery) | 2 | 3,391 | 0.1 | 3 | 27,206 | 0.0 | 4.27E-02 |
Chromatin organization (chromatin remodeling complexes) | 13 | 3,391 | 0.4 | 63 | 27,206 | 0.2 | 4.48E-02 |
aSignificantly overrepresented terms (
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