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Article

Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng

by
Jaewook Kim
1,†,
Jung-Woo Lee
2,† and
Ick-Hyun Jo
3,*
1
Department of Biology Education, Korea National University of Education, Cheongju 28173, Republic of Korea
2
Department of Herbal Crop Research, Rural Development Administration, Eumseong 27709, Republic of Korea
3
Department of Crop Science and Biotechnology, Dankook University, Cheonan 31116, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(5), 1244; https://doi.org/10.3390/agronomy15051244
Submission received: 29 April 2025 / Revised: 12 May 2025 / Accepted: 14 May 2025 / Published: 20 May 2025
(This article belongs to the Special Issue Application of In Vitro Technology to Improving the Yield and Quality of Common and Alternative Crops)
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Abstract

Callus induction is one of the most important techniques in plant-based industries. Important features in the use of callus induction are the maintenance of pluripotency and the proliferation of cells. Although the importance of callus induction is also understood in ginseng, there are no studies on the genetic modules associated with callus induction and growth regulation. Panax ginseng embryo tissue was wounded and cultured in callus-inducing media, and its time-course physiology was observed. Time-course callus samples were collected for total RNA extraction and RNA-Seq analysis using the Illumina HiSeq X Ten platform. P. ginseng embryo tissue was wounded and treated with varying amounts of gamma radiation in callus-inducing media, and samples were also collected for total RNA extraction and RNA-Seq analysis. A combinatory analysis of various network analyses was used to reveal the regulatory network underlying callus development. We were able to determine the time-course physiology of callus development and the dose-dependent effect of gamma radiation on callus development. Network analysis revealed two networks correlated with callus induction and two networks correlated with callus growth. Our research provides a regulatory network illustrating how callus is induced and growth is regulated in P. ginseng. This result would be helpful in the development of a cell culture system or clonal propagation protocol in P. ginseng.

1. Introduction

Panax ginseng (ginseng) is a medicinal plant commonly used in East Asia. Ginseng is rich in triterpenoids, called ginsenosides, and antioxidants, such as anthocyanins. The pharmaceutical properties of ginseng include anti-cancer, anti-aging, and renoprotective activities. Since 1964, many efforts have been made to provide an efficient protocol for cell culture methods in P. ginseng [1]. Previous efforts include the comparison of ginsenoside productivity in terms of different cell culture systems and cell biomass [1]. Of these, solid callus was the second most efficient culture system, but the ginsenoside content per biomass was inversely proportional to the cell biomass [1].
Callus is the name given to plant cells that normally form in wounded tissue with pluripotency. However, plant callus cells can also be induced using a callus induction medium (CIM) [2]. In nature, the wounding of most tissues randomly induces callus cells [2]. Callus induction is known to be associated with lob domain binding transcription factors (LDBs), Arabidopsis response regulators (ARRs), and ethylene response factors (ERFs) [2]. These regulators have been shown to regulate downstream auxin- and cytokinin-responsive genes [3,4,5]. On the other hand, cell wall biogenesis and several epigenetic regulators have been shown to inhibit callus formation by repressing cytokinin pathways and cell fate regulatory modules [2].
The aim of callus induction in P. ginseng was to develop an in vitro system for pharmaceutical components or clonal propagation [6,7,8,9]. Previous studies have attempted to induce and grow callus in P. ginseng using a root culture system [10]. Other tissues or cells, such as embryonic cells in anthers or seed embryos and protoplasts, were also studied with a focus on inducing callus [9,11]. During the development of the cell culture system for pharmaceutical production, the effects of auxins or cytokinin derivatives were investigated in terms of callus induction and saponin production [12]. Gamma-rays were briefly shown to have a stimulatory effect at a lower dosage and an inhibitory effect at a higher dosage in the callus growth of P. ginseng [13]. Yet, most studies on the gamma-ray effect on callus have focused on either mutagenic effects or altered ginsenoside biosynthesis activity [14,15,16].
Transcriptome analysis has been used as a major tool for revealing the genetic relevance underlying the developmental process of ginseng. Transcriptome analysis in combination with metabolomic analysis revealed that gibberellic acid and arginine are associated with the dormancy release of ginseng seeds [17]. Photomorphogenic growth in the seedling stage was analyzed using transcriptome analysis, which revealed the modulation of phytochrome-PIF (phytochrome interacting factor) modules or CRY1/2 (cryptochrome 1/2)-COP1 (constitutive photomorphogenesis protein 1)-HY5 (elongated hypocotyl 5) modules [18]. The secondary growth of storage roots was assessed using transcriptome analysis, which revealed several important genes correlated with secondary growth [19,20]. As ginseng is a perennating plant, its buds undergo dormancy, which was also assessed using transcriptome analysis, revealing key signaling networks [21]. Even the aging process of ginseng was briefly analyzed, and a few aging-related transcription factors were revealed [22]. Both natural development and callus formation were analyzed using transcriptome analysis, revealing totipotency-related genes [23].
Building on previous efforts, we can efficiently induce callus formation and produce pharmaceuticals by culturing it. However, genetic explanations for callus induction in P. ginseng have not been revealed. Thus, we carefully analyzed the physiology of callus development and the formation of ginseng and performed a transcriptome analysis throughout the developmental stages to reveal highly associated gene networks. We also wanted to elucidate the genetic modules associated with gamma-irradiated callus growth, as gamma irradiation of callus has been shown to enhance pharmaceutical accumulation in P. ginseng [24]. To reveal this, we used gamma-ray treatment just before callus formation and observed the resulting physiology. Then, we performed additional transcriptome analysis to reveal highly correlated gene networks. Our study includes a comprehensive and intensive bioinformatics approach and reveals four transcriptomic networks associated with callus formation and growth.

2. Materials and Methods

2.1. Plant Materials, Gamma Irradiation, and Embryo Induction

Seeds of the cultivar “Cheonryang” were collected at the end of July 2022 from the experimental fields of the National Institute of Horticultural and Herbal Science, Rural Development Administration (Eumseong, Republic of Korea; 36°94′28.4″ N, 127°74′86.6″ E). Prior to the experiment, the seeds were dehisced and cold-treated according to previously described methods to ensure sufficient maturation of the zygotic embryos. Briefly, the indehiscent seeds were mixed with sand at a ratio of 3:1 by volume and stacked in gravel and sand for 90 days. The dehisced seeds were additionally cold-treated in a storage chamber maintained at 2 °C for 90 days [25].
To evaluate changes in somatic embryogenesis efficiency according to gamma irradiation dose, the dehisced seeds were irradiated with gamma-rays at doses of 50 Gy and 100 Gy for 24 h using a Co-60 gamma source (3000 Ci; Nordion Inc., Ottawa, ON, Canada) at the Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute (KAERI), Republic of Korea. Each treatment involved 10 seeds, and the entire procedure was repeated three times.
Matured zygotic embryos (embryo length of ≥4500 µm or greater), including cotyledons, were isolated according to a previously described procedure [26], and somatic embryogenesis was induced according to an established protocol [27]. Briefly, zygotic embryos were dissected with a scalpel to prepare explants, which were cultured in an induction medium consisting of Murashige and Skoog (MS) basal salts [28] supplemented with 5% (w/v) sucrose and 0.8% (w/v) agar. Somatic embryo induction was performed over a period of 60 days without subculturing.

2.2. Total RNA Isolation and RNA-Seq Data Production

Samples were collected for RNA extraction at the time of zygotic embryo isolation and at 15, 30, and 60 days after explant placement. For gamma irradiation treatments, samples were collected according to the dose of gamma irradiation at 60 days after explant inoculation.
All samples were immediately frozen in liquid nitrogen and stored until total RNA extraction. Total RNA was extracted from ground ginseng fruits using the Easy Spin RNA Extraction Kit (iNtRON Biotechnology, Seongnam, Republic of Korea) according to the manufacturer’s protocol.
RNA-seq libraries were prepared from total RNA using a TruSeq Stranded mRNA Library Prep Kit (Illumina Inc., San Diego, CA, USA), with three biological replicates per treatment. cDNA synthesis, end repair, single ‘A’ addition, and adapter ligation were performed sequentially. The libraries were then purified and enriched by PCR amplification before being sequenced using the Illumina HiSeq X ten platform with 151 bp paired-end (PE) reads.

2.3. DEG Identification and Functional Annotation

The raw reads were cleaned using prinseq-lite version 0.20.4 with the following parameters: min_len 50, min_qual_score 5, min_qual_mean 20, derep 14, trim_qual_left 20, and trim_qual_right 20 [29]. The cleaned paired-end reads from each sample were aligned to the ginseng reference transcriptome using Bowtie2 [30]. RSEM 1.3.0 was then used to count the reads and perform normalization using the TMM normalization method per transcript [31]. EdgeR version 3.16.5 was used to compute the negative binomial dispersion across conditions for differential gene expression analysis [32]. Differentially expressed genes (DEGs) were identified using a false discovery rate (FDR) adjusted for p < 0.05; fold-change > 4 [33]. T2T (Telomere-to-telomere) level genome was applied for the reference sequences (https://figshare.com/articles/dataset/annotated_file/25477741/2, accessed on 5 April 2025) [34]. For the functional annotation of differentially expressed genes, the BLASTp program was used with an e-value threshold of 10 × −5 against the Arabidopsis thaliana protein database (TAIR10). Gene Ontology (GO) term enrichment analyses were performed using DAVID. Enriched GO terms were determined using the modified Fisher’s exact test (EASE score, p < 0.05) [35,36]. The enriched GO terms were further analyzed using Gene Set Enrichment Analysis (GSEA), as described in previous studies [22,37]. In detail, GSEA was performed by comparing the sample of interest and the others in time-course transcriptome analysis and by comparing the 0 Gy to the treated samples. A heatmap of the gene expression and GO analysis was generated with the Z-score and log [p-value] for each sample, calculated manually. GSEA was visualized using a combination of a p-value heatmap and a net enrichment score (NES) heatmap. Venn diagrams and heat maps were visualized using the TBtools (version 1.130) or ggplot2 package in R (version 4.1.2) [38,39].

2.4. Network Analysis

Through DEG analysis combined with functional annotation, we revealed the gene most highly correlated with each physiological status. Using these genes, we performed GeneMania, a Cytoscape plugin (version 3.8.2), to reveal co-expressed genes using the A. thaliana database [40,41]. We then performed GO analysis to identify functionally correlated genes from the most correlated genes and subjected these genes to the GENIE3 package (version 1.20.0) to reveal the regulatory network in each physiological condition [42]. Specifically, the TMM-normalized TPM values were used as the input, and the weight matrix was calculated using the default settings [32]. Then, the regulator genes were defined by manual annotation against the Arabidopsis thaliana protein database (https://www.arabidopsis.org/search/protein, accessed on 5 May 2025). The top 20 regulatory links were extracted from the identified regulatory links, and only those with a weight greater than 0.2 were selected and visualized as the final regulatory links [32]. To reveal the co-expression network across the entire transcriptome, we performed a WGCNA analysis with the following details [43]. First, we imported the TPM values processed with Bowtie2-RSEM-edgeR into the project R [19,20,21]. Then, DESeq2 was applied to normalize the expression values. From the entire dataset, the 75th quantile data were extracted to reduce the dataset after applying the get Variance Stabilized Data function [33]. To select the soft-thresholding powers, the power vector was tested up to 30, with 27 being applied [44]. The co-expression module was then revealed using the blockwise Modules function with a power vector of 27, the network type signed, a minimum module size of 30, and a maximum module size of 4000 [34]. The modules were visualized using the plot Dendro and Colors function [34]. The module Eigengenes function was used to extract eigengenes with the default settings, and the modules were then reordered by eigengene [34]. The module of interest was selected, and its network relationships were extracted and visualized using Cytoscape [31,34].

3. Results and Discussion

3.1. Time-Course Transcriptome Analysis of the Callus Induction Process

To investigate the time-course physiology of ginseng callus development, we induced callus from the embryonic tissue of seeds on CIM media (Figure 1A,B). Callus induction was established within 30 days, and the callus cells were grown for a further 30 days under our conditions (Figure 1A,B). As no obvious cell growth was observed in the 15-day sample, we hypothesized that the regulatory cue for callus development would occur around the 15-day mark (Figure 1A,B). Thus, we performed RNA sequencing analysis of the time-course samples in triplicate and revealed four distinct transcriptomic stages (Figure 1C).
To reveal the regulatory cues in our transcriptome, we performed a clustering analysis based on the identification of DEGs across all the comparison pairs, with a 4-fold change and an FDR value lower than 0.5. We identified both a 15-day-specific up-regulated cluster (856 genes) and a 15-day-specific down-regulated cluster (247 genes). We then performed GO analysis to reveal functional enrichment (Figure 2A,C). Several terms, including light-responsive and gibberellin-, lipid-, and ROS-correlated genes, were enriched in the 15-day-up-regulated cluster, while ABA-, auxin-, gibberellin-, and brassinosteroid-correlated genes were enriched in the 15-day-down-regulated cluster (Figure 2B,D).
We then performed a GSEA analysis to verify which GO-term-correlated genes were associated with callus growth phenotypes by comparing each sample with the others (Figure 3A–D). Our analysis revealed that the genes associated with “unsaturated fatty acid biosynthetic process”, “blue light signaling pathway”, “starch biosynthetic process”, and “hydrogen peroxide catabolic process” were positively correlated with the 15-day samples (Figure 3A–D). Indeed, most of these genes exhibited a 15-day up-regulated pattern (Figure 3E).

3.2. ARR12-Correlated Gene Networks Were Identified to Be Associated with Callus Induction

Among the functionally associated genes that showed a 15-day up-regulated pattern, we identified ARR12, which is recognized as a regulator of callus induction [4] (Figure 3E). To investigate the functional significance of pg_11001685 (ARR12) gene expression, we conducted a GeneMania analysis to identify ARR12-associated genes (Figure 4A). Many callus-inducing regulators, such as ARR genes and atML1 (MERISTEM LAYER1), were identified among the closely associated genes (Figure 4A). To reveal how this network is associated, we performed GENIE3 analysis using the expression patterns of these genes (Figure 4A and Figure S1). This analysis revealed RR1-, ML1-, and NPH4-associated modules (Figure 4A and Figure S1). This was further observed in the expression patterns, with most genes not being highly expressed in the 15-day sample (Figure 4B).
In summary, our data revealed that the gene networks associated with ARR12 are correlated with transcriptional cues for callus induction. A previous study indicated that RR1 is a potent repressor of callus formation, whereas ARR12 is a weak inducer of callus formation [4]. Furthermore, ARR12 was shown to be genetically epistatic to RR1 [4]. Our results showed that the ARR12 network was expected to negatively regulate callus development and that the RR1 network was negatively correlated with callus development (Figure 3E and Figure 4A). Specifically, CKI1 was a regulatory target of ARR12 (Figure 4A). CKI1 is a histidine kinase homologue involved in the signaling of the hormone auxin and has positive and negative functions in callus formation [45]. ETR2 has been shown to be an ethylene receptor and is, therefore, associated with the early developmental stage of seedlings [46]. Therefore, at least for the ARR12-CKI1 module, a solid negative regulatory module in callus formation, particularly in ginseng callus formation, could be suggested (Figure 4A). CLASP genes encode microtubule-associated proteins involved in cell expansion and division [47]. In the RR1-related network, the auto-regulatory network itself can be considered a negative regulatory network in ginseng callus induction, involving CLASP or ETR2 as downstream developmental target genes (Figure 4A).
NPH4 encodes an auxin-regulated transcription factor that is particularly important for the specialization of the hypocotyl–root axis during the early developmental stage [48]. RPT2 was found to be regulated and present in the early developmental process of the Pinus plant, specifically in somatic embryo gellan gum-dependent maturation [49]. AGO1 was shown to accumulate in the callus induction process [50]. ARR24 is a type-A response regulator whose function is unknown. The NPH4 homologous gene was suggested to regulate the homologous genes of RPT2, AGO1, ETR2, and ARR24, all of which are associated with callus development or the early developmental process [48,49,50]. Thus, the NPH4-related network may be positively associated with callus induction (Figure 4A).
AtML1 has been shown to be a key regulator of epidermal cell differentiation [51]. Another function of the AtML1-SRDX overexpressed line is to develop a large number of callus-like cells [51,52]. AFB2 (auxin signaling F-box 2) has been shown to be highly expressed in plants that are easily regenerated and has been shown to play a positive role in callus formation [53]. Therefore, the AtML1-related network would also be positively associated with callus induction (Figure 4A).

3.3. Gamma Radiation Treatment Modulates Cell Growth of Panax ginseng Callus

We then investigated whether gamma radiation treatment modulated callus size. We observed an increase in the callus cell size following treatment with 50 Gy of gamma radiation, whereas growth arrest was observed following treatment with 100 Gy of gamma radiation (Figure 5A,B). We therefore performed additional RNA sequencing to reveal the regulatory network associated with this dose-dependent phenotype (Figure 5C).
Using a new set of RNA-seq data, we identified the genes associated with the phenotypes. Thus, based on DEGs with the same criteria as in Figure 2, we sought 50 Gy up-regulated and 100 Gy down-regulated clusters (growth-positive), as well as 50 Gy down-regulated and 100 Gy up-regulated clusters (growth-negative), from the cluster analysis result. However, we could only find a growth-negative cluster, so we performed a GO analysis on these genes to reveal functional enrichment (Figure 6A,B). GO analysis revealed an enrichment of genes related to the response to both red light and the presence of the plant hormone auxin, as well as cell wall organization and cell differentiation (Figure 6B). Among these genes, those associated with the “cytokinin-activated signaling pathway” exhibited expression patterns similar to those of the growth-negative cluster identified by GSEA (Figure 6C,D). Several other functional categories were also found to be associated with the gamma-ray dose-dependent phenotype (Figure 6C,D).

3.4. The WIND1-Associated Gene Network Was Associated with a Dose-Dependent Growth Pattern of Gamma-Ray Irradiation

Among these genes, WIND1 (WOUND-INDUCED DEDIFFERENTIATION1) was shown to be negatively correlated with dose-dependent gamma-irradiated callus growth in a dose-dependent manner (Figure 6E). We therefore performed a GeneMANIA analysis on the WIND1 gene, which identified that many ERF genes were closely associated. We then analyzed the WIND1-associated network genes using GENIE3 to reveal the regulatory modules among them. The GENIE3 analysis revealed that the ERF3- and ERF4-associated modules, as well as the JAI1-associated module, were correlated within the WIND1 network (Figure 7A).
WIND1 has been shown to be positively correlated with callus formation and growth by regulating many genes, including ERF subfamily genes [54,55]. The WIND1-associated network includes ERF3 and ERF4 homologous genes, which are somewhat correlated with our previous results (Figure S1). WIND1 was down-regulated by 50 Gy of gamma irradiation, whereas it was up-regulated by 100 Gy of gamma irradiation (Figure 6E). Two of the ERF3 homologue genes were up-regulated by 50 Gy gamma irradiation, suggesting that WIND1 may negatively regulate ERF3 gene expression to promote callus cell growth (Figure 7B).
ERF3 has been shown to be up-regulated in callus tissues, which suggests a potential role in callus formation or growth [56,57]. ERF4 was shown to modulate the growth rate of seedlings, yet no evidence regarding its role in callus formation or relevant developmental processes has been found [58]. A loss-of-function mutation, FREE1, was shown to be lethal in the early stages since it mediates pleiotropic functions as an ESCRT (endosomal sorting complex required for transport) machinery [59]. These previous results suggest an uncertain role for the FREE1-associated network in ginseng callus growth (Figure 7A). However, the ERF3 self-regulatory network may play a role in callus growth (Figure 7A).
Within the WIND1 submodule, we identified the JAI1 (JASMONIC ACID INSENSITIVE1) -CRF6 (CYTOKININ RESPONSE FACTOR6) module (Figure 7A). Currently, there is no evidence that JAI1 or jasmonate is involved in callus development. JAI1 encodes MYC2, which is a positive regulator of jasmonate signaling [60]. We also found no evidence for the contribution of CRF6 to the callus development process. Two of the JAI1 homologue genes were up-regulated at higher doses of gamma irradiation (Figure 6E). As jasmonate has been shown to enhance the ginsenoside biosynthesis process in ginseng species, our analysis coincidentally revealed a regulatory module for ginsenoside accumulation in gamma irradiation [24,61,62].

3.5. Global Co-Expression Network Analysis Reveals the RR1- and NPH4-Associated Networks to Be Associated with Callus Development

To further elucidate the gene networks associated with either callus induction or growth, we performed WGCNA analysis using the full transcriptome dataset. This analysis revealed sixteen co-expressed networks (Figure 8A,B). Of these, yellow, pink, green-yellow, black, brown, and green networks were shown to be associated with the callus size phenotype (Figure 8B). From these networks, we identified three genes that were previously associated with either the callus-inducing or callus-growth networks (Figure S2). Pg_14001135 (RR1), pg_11012163 (NPH4), and pg_23008678 (RR1) were identified in the brown, yellow, and green networks, respectively (Figure S2). Of these three genes, the pg_23008678-associated network member contained only one other member and could therefore not be analyzed for functional annotation. Consequently, we analyzed the pg_14001135- and pg_11012163-associated networks to determine their association with callus development (Figure 9).
The Pg_14001135-associated network consisted of 60 members, all of which exhibited positive correlations with callus growth (Figure 9A–C). These members were functionally enriched for abscisic acid (ABA), salicylic acid (SA), and primary root development (Figure 9B). The Pg_11012163-associated network comprised 43 members that exhibited a down-regulated expression pattern that correlated with the callus cell size (Figure 9D–F). These members were functionally enriched for cell wall modification (Figure 9E). Thus, these two networks are highly likely to contribute to the callus development process. The WGCNA analysis revealed that the Pg_14001135 (RR1)-associated network did not contain two of the GENIE3-identified regulatory members, including ETR2 and CLASP (Figure 5A and Figure 8A). This suggests that the RR1-associated network identified by WGCNA is merely a co-expression network. Meanwhile, the RR1-associated network identified in Figure 9C contained RR1 and RAPTOR1. Though there is no direct functional evidence linking RAPTOR1 (regulatory associated protein of TOR 1) to callus formation, it has been shown to be up-regulated in callus tissue [63]. The GO analysis also indicated the presence of ICU2 (DNA-directed DNA polymerase), TK1a (Thimidine kinase 1a), and TK1b (Thimidine kinase 1b) in this network (Figure 9C). These are functionally correlated with the DNA biosynthetic process, which has been shown to be positively correlated with callus formation [64]. This information further confirms that the RR1 co-expression network would play a role in callus growth (Figure 10).
The Pg_11012163 (NPH4)-associated network from the WGCNA network did not contain any members of the GENIE3 network (Figure 5A and Figure 9D). Instead, it contained PME44 (pectin methylesterase 44), PMEPCRF (pectin methylesterase PCR fragment F), and receptor-like protein 4 (RLP4) (Figure 9F). PME44 and PMEPCRF showed some indirect evidence of an association with callus formation. For example, the expression of PME44 was found to be up-regulated during callogenesis, and STM (SHOOT MERISTEMLESS, a master regulator of meristem activity) was found to directly target PMEPCRF [65,66]. We also identified FPS2 (Farnesyl diphosphate synthase 2) in this network, the double mutant of which with FPS1 is embryo-lethal in Arabidopsis [67]. These enzyme homologs also have the interesting function of catalyzing the precursor steps of squalene, a sterol precursor that can be synthesized into terpenoids, including ginsenoside species [67]. Thus, FPS2 may function not only in callus development but also in enhancing ginsenoside accumulation upon weak gamma-ray irradiation [14,67].
In summary, our results revealed potent molecular mechanisms underlying callus formation and development under CIM. Our results also covered the putative mechanisms underlying callus growth at different gamma-ray irradiation doses. Furthermore, our analysis suggested how gamma-ray treatment modulates the biosynthesis of ginsenosides, which, in turn, modulates the relative accumulation of the ginsenoside species. We believe that the functional validation of the key regulators identified in callus formation, such as RR1, ARR12, and WIND1, is required in the future.

4. Conclusions

Our study involves a comparative analysis of the transcriptome of callus development and gamma-irradiated callus growth over time. A comprehensive network analysis identified four regulatory modules that would contribute to ginseng callus induction and cell growth. Specifically, we found that the ARR12- and RR1-associated networks were negatively correlated with callus induction and callus cell growth, respectively, while the WIND1-associated network was positively associated with callus cell growth. A submodule of the WIND1-associated network contained a JAI1-associated module, which explains the accumulation of ginsenosides correlated with gamma-irradiation treatment. In conclusion, our study provides the first insight into the regulatory modules involved in callus development in ginseng.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15051244/s1: Supplementary Figure S1. Detailed network identified from GENIE3 analysis. White boxes indicate the regulator genes, such as transcription factors or histone modification enzymes, while eggshell-colored boxes indicate the other genes. (A–H). Callus formation-associated networks identified by analyzing the callus developmental process-associated transcriptome. A. Module 1. B. Module 2. C. Module 3. D. Module 4. E. Module 5. F. Module 6. G. Module 7. H. Module 8. (I,J). Callus growth-associated networks identified by analyzing the gamma-ray treatment-associated transcriptome. I. Module 9. J. Module 10. Supplementary Figure S2. The whole network identified by WGCNA analysis. Only six phenotype-associated networks are visualized. A. Network view of the yellow gene module. B. Network view of the pink gene module. C. Network view of the green gene module. D. Network view of the brown gene module. E. Network view of the black gene module. F. Network view of the green gene module. Supplementary Table S1. Full list of DEGs comparing 0d vs. 15d. Each DEG was selected by a 4-fold change, FDR < 0.05. ExtendedDEG was selected by a 2-fold change, FDR < 0.05. Supplementary Table S2. Full list of DEGs comparing 0d vs. 30d. Each DEG was selected by a 4-fold change, FDR < 0.05. Each ExtendedDEG was selected by a 2-fold change, FDR < 0.05. Supplementary Table S3. Full list of DEGs comparing 0d vs. 60d. Each DEG was selected by a 4-fold change, FDR < 0.05. Each ExtendedDEG was selected by a 2-fold change, FDR < 0.05.; Supplementary Table S4. Full list of DEGs comparing 0Gy vs. 50Gy. Each DEG was selected by a 4-fold change, FDR < 0.05. Each ExtendedDEG was selected by a 2-fold change, FDR < 0.05.; Supplementary Table S5. Full list of DEGs comparing 0Gy vs. 100Gy. Each DEG was selected by a 4-fold change, FDR < 0.05. Each ExtendedDEG was selected by a 2-fold change, FDR < 0.05.

Author Contributions

All authors contributed to the conception and design of this study. Conceptualization, J.-W.L. and I.-H.J.; methodology, J.K.; software, J.K.; validation, J.K., and I.-H.J.; data curation, J.K. and J.-W.L.; writing—original draft preparation, J.K. and I.-H.J.; writing—review and editing, J.K. and I.-H.J.; supervision, I.-H.J.; project administration, I.-H.J.; funding acquisition, I.-H.J. All authors have read and agreed to the published version of the manuscript.

Funding

The present research was supported by the research fund of Dankook University in 2023 (Grant Number: Dankook2023).

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Time-course transcriptome analysis of callus developmental progress. (A) Representative photographs of the callus developmental process over time. The scale bar indicates 100 µm. White circles indicate callus cells. (B) Heatmap representation of the phenotypes observed in callus formation. Darker colors indicate severe phenotypes, such as bigger cell sizes, more callus formation, or more anthocyanin accumulation. (C) Principal Component Analysis (PCA) illustrating the overall gene expression profiles throughout the experiment.
Figure 1. Time-course transcriptome analysis of callus developmental progress. (A) Representative photographs of the callus developmental process over time. The scale bar indicates 100 µm. White circles indicate callus cells. (B) Heatmap representation of the phenotypes observed in callus formation. Darker colors indicate severe phenotypes, such as bigger cell sizes, more callus formation, or more anthocyanin accumulation. (C) Principal Component Analysis (PCA) illustrating the overall gene expression profiles throughout the experiment.
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Figure 2. Functional annotation of callus trigger-associated clusters. (A) Expression pattern of callus-inducing trigger gene clusters. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (B) Bubble plot showing the results of functional annotation using GO analysis of callus-inducing trigger gene clusters. Larger, darker blue bubbles indicate greater significance. (C) Expression pattern of callus-inhibiting trigger gene clusters. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (D) Bubble plot of functional annotation analysis of callus-inhibiting trigger gene clusters, with larger, darker blue bubbles indicating more significant enrichment.
Figure 2. Functional annotation of callus trigger-associated clusters. (A) Expression pattern of callus-inducing trigger gene clusters. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (B) Bubble plot showing the results of functional annotation using GO analysis of callus-inducing trigger gene clusters. Larger, darker blue bubbles indicate greater significance. (C) Expression pattern of callus-inhibiting trigger gene clusters. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (D) Bubble plot of functional annotation analysis of callus-inhibiting trigger gene clusters, with larger, darker blue bubbles indicating more significant enrichment.
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Figure 3. GSEA analysis of GO terms correlated with callus formation. Gene set enrichment analysis (GSEA) was conducted by comparing each time point with the rest. The X-axis indicates NES values, of which positive values indicate up-regulation in each time point, while negative values indicate up-regulation in the other time points. A larger bubble indicates a more significant enrichment. (A) GSEA analysis of the 0-day sample. (B) GSEA analysis of the 15-day sample. (C) GSEA analysis of the 30-day sample. (D) GSEA analysis of the 60-day sample. (E) Expression patterns of callus trigger-correlated genes. The color key indicates the expression level as the Z-score.
Figure 3. GSEA analysis of GO terms correlated with callus formation. Gene set enrichment analysis (GSEA) was conducted by comparing each time point with the rest. The X-axis indicates NES values, of which positive values indicate up-regulation in each time point, while negative values indicate up-regulation in the other time points. A larger bubble indicates a more significant enrichment. (A) GSEA analysis of the 0-day sample. (B) GSEA analysis of the 15-day sample. (C) GSEA analysis of the 30-day sample. (D) GSEA analysis of the 60-day sample. (E) Expression patterns of callus trigger-correlated genes. The color key indicates the expression level as the Z-score.
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Figure 4. Identification of the regulatory network of callus formation. (A) GeneMania-derived gene network associated with ARR12. Hexagons show each gene, where the red-colored hexagon indicates the 15d up-regulated gene, while the others are not. Pale dotted lines indicate co-expression relationships, solid red lines indicate direct interactions, dotted red lines indicate predicted interactions, the solid green line indicates a genetic interaction, and black arrows indicate the GENIE3-identified regulatory network from ARR12-correlated genes with correlated direction. (B) Expression patterns of ARR12-associated regulatory network genes. The color key indicates the expression level based on the Z-score.
Figure 4. Identification of the regulatory network of callus formation. (A) GeneMania-derived gene network associated with ARR12. Hexagons show each gene, where the red-colored hexagon indicates the 15d up-regulated gene, while the others are not. Pale dotted lines indicate co-expression relationships, solid red lines indicate direct interactions, dotted red lines indicate predicted interactions, the solid green line indicates a genetic interaction, and black arrows indicate the GENIE3-identified regulatory network from ARR12-correlated genes with correlated direction. (B) Expression patterns of ARR12-associated regulatory network genes. The color key indicates the expression level based on the Z-score.
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Figure 5. Gamma-ray-treated callus phenotype and transcriptome analysis. (A) Representative photographs of callus cells treated with different degrees of gamma-ray treatment over 60 days. Scale bars indicate 100 µm. White circles indicate the callus cells. (B) Heatmap representation of the phenotypes observed in callus formation. Darker colors indicate severe phenotypes, such as a bigger cell size, more callus formation, or more anthocyanin accumulation. (C) Principal Component Analysis (PCA) illustrating the overall gene expression profiles throughout the experiment. g0Gy is the same sample as 0d in the first experiment.
Figure 5. Gamma-ray-treated callus phenotype and transcriptome analysis. (A) Representative photographs of callus cells treated with different degrees of gamma-ray treatment over 60 days. Scale bars indicate 100 µm. White circles indicate the callus cells. (B) Heatmap representation of the phenotypes observed in callus formation. Darker colors indicate severe phenotypes, such as a bigger cell size, more callus formation, or more anthocyanin accumulation. (C) Principal Component Analysis (PCA) illustrating the overall gene expression profiles throughout the experiment. g0Gy is the same sample as 0d in the first experiment.
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Figure 6. Functional annotation of gamma-ray-treatment-correlated genes. Gamma-ray-correlated genes were investigated and found to be only negatively correlated genes. (A) Expression pattern of the gamma-ray negatively correlated gene cluster. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (B) Bubble plot showing the results of functional annotation using GO analysis. Larger, darker blue bubbles indicate greater significance. (C,D) GSEA analysis of GO terms correlated with gamma-ray-correlated callus growth. Gene set enrichment analysis (GSEA) was conducted by comparing 0 Gy with the target gamma-ray treatment. The X-axis indicates NES values, of which positive values indicate up-regulation in gamma-ray treatment, while negative values indicate up-regulation in the 0 Gy samples. Larger bubbles indicate a more significant enrichment. (C) GSEA results in 50 Gy. (D) GSEA results in 100 Gy. (E) Expression patterns of gamma-ray-correlated callus cell growth. The color key indicates the expression level based on the Z-score.
Figure 6. Functional annotation of gamma-ray-treatment-correlated genes. Gamma-ray-correlated genes were investigated and found to be only negatively correlated genes. (A) Expression pattern of the gamma-ray negatively correlated gene cluster. The black line shows the average expression value of each point, while the grey hue indicates the range of the expression values. (B) Bubble plot showing the results of functional annotation using GO analysis. Larger, darker blue bubbles indicate greater significance. (C,D) GSEA analysis of GO terms correlated with gamma-ray-correlated callus growth. Gene set enrichment analysis (GSEA) was conducted by comparing 0 Gy with the target gamma-ray treatment. The X-axis indicates NES values, of which positive values indicate up-regulation in gamma-ray treatment, while negative values indicate up-regulation in the 0 Gy samples. Larger bubbles indicate a more significant enrichment. (C) GSEA results in 50 Gy. (D) GSEA results in 100 Gy. (E) Expression patterns of gamma-ray-correlated callus cell growth. The color key indicates the expression level based on the Z-score.
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Figure 7. Identification of the regulatory network associated with callus growth in gamma-ray treatment conditions. (A) GeneMania-derived gene network associated with WIND1. Hexagons show each gene, where the blue hexagon indicates 50 Gy down-regulated genes and grey hexagons indicate the others. Pale dotted lines indicate co-expression relationships, the solid red line indicates a direct interaction, dotted red lines indicate predicted interactions, and black arrows indicate the GENIE3-identified regulatory network. (B) Expression patterns of WIND1-associated regulatory network genes. The color key indicates the expression level based on the Z-score.
Figure 7. Identification of the regulatory network associated with callus growth in gamma-ray treatment conditions. (A) GeneMania-derived gene network associated with WIND1. Hexagons show each gene, where the blue hexagon indicates 50 Gy down-regulated genes and grey hexagons indicate the others. Pale dotted lines indicate co-expression relationships, the solid red line indicates a direct interaction, dotted red lines indicate predicted interactions, and black arrows indicate the GENIE3-identified regulatory network. (B) Expression patterns of WIND1-associated regulatory network genes. The color key indicates the expression level based on the Z-score.
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Figure 8. Identification of the co-expression network associated with callus growth in whole transcriptome samples. The co-expression network was identified with WGCNA analysis. (A) Cluster dendrogram of WGCNA-identified gene modules. The modules are shown with different colors under the dendrogram. (B) The module–trait relationship visualized as a heatmap representation. Positive correlations are visualized with red colors, while negative correlations are visualized with blue colors. Black arrows indicate the callus growth-correlated gene modules in any direction.
Figure 8. Identification of the co-expression network associated with callus growth in whole transcriptome samples. The co-expression network was identified with WGCNA analysis. (A) Cluster dendrogram of WGCNA-identified gene modules. The modules are shown with different colors under the dendrogram. (B) The module–trait relationship visualized as a heatmap representation. Positive correlations are visualized with red colors, while negative correlations are visualized with blue colors. Black arrows indicate the callus growth-correlated gene modules in any direction.
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Figure 9. Two callus formation correlation networks identified by WGCNA analysis. In detail, a brown module and a yellow module were identified. In those modules, we further limited the modules by selecting two callus-formation-correlated genes, RR1 (pg_14001135) and NPH4 (pg_11012163). (A) Visualization of the pg_14001135-associated gene network. (B) Expression pattern of the pg_14001135-associated gene network. The color key indicates the expression level as the Z-score. (C) GO analysis of the pg_14001135-associated gene network. Larger, darker blue bubbles indicate greater significance. (D) Visualization of the pg_11012163-associated gene network. (E) Expression pattern of the pg_11012163-associated gene network. The color key indicates the expression level based on the Z-score. (F) GO analysis of the pg_11012163-associated gene network. Larger, darker blue bubbles indicate greater significance.
Figure 9. Two callus formation correlation networks identified by WGCNA analysis. In detail, a brown module and a yellow module were identified. In those modules, we further limited the modules by selecting two callus-formation-correlated genes, RR1 (pg_14001135) and NPH4 (pg_11012163). (A) Visualization of the pg_14001135-associated gene network. (B) Expression pattern of the pg_14001135-associated gene network. The color key indicates the expression level as the Z-score. (C) GO analysis of the pg_14001135-associated gene network. Larger, darker blue bubbles indicate greater significance. (D) Visualization of the pg_11012163-associated gene network. (E) Expression pattern of the pg_11012163-associated gene network. The color key indicates the expression level based on the Z-score. (F) GO analysis of the pg_11012163-associated gene network. Larger, darker blue bubbles indicate greater significance.
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Figure 10. Illustration of the suggested model in this study. (A) Illustration of the time-course and gamma-ray-treated callus phenotype and the expression patterns of ARR12 and WIND1 network genes. Ivory, orange, and purple objects indicate either embryo or embryonic segments, while white circles indicate callus cells. The callus cell size is visualized as the size of the white circles. The expression level of ARR12-associated network genes is visualized with yellow bars in a continuous manner; thus, the lower yellow bar indicates a lower expression level of ARR12 network genes. The expression level of WIND1-associated network genes is visualized with blue bars in a continuous manner; thus, the lower blue bar indicates a lower expression level of WIND1 network genes. (B) Visualization of the suggested model in wounding-induced callus formation and the contribution of the ARR12- and pg_11012163-associated gene networks. (C) Visualization of the suggested model in the gamma-ray treatment modulation of callus growth and the contribution of the WIND1- and pg_14001135-associated gene networks.
Figure 10. Illustration of the suggested model in this study. (A) Illustration of the time-course and gamma-ray-treated callus phenotype and the expression patterns of ARR12 and WIND1 network genes. Ivory, orange, and purple objects indicate either embryo or embryonic segments, while white circles indicate callus cells. The callus cell size is visualized as the size of the white circles. The expression level of ARR12-associated network genes is visualized with yellow bars in a continuous manner; thus, the lower yellow bar indicates a lower expression level of ARR12 network genes. The expression level of WIND1-associated network genes is visualized with blue bars in a continuous manner; thus, the lower blue bar indicates a lower expression level of WIND1 network genes. (B) Visualization of the suggested model in wounding-induced callus formation and the contribution of the ARR12- and pg_11012163-associated gene networks. (C) Visualization of the suggested model in the gamma-ray treatment modulation of callus growth and the contribution of the WIND1- and pg_14001135-associated gene networks.
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MDPI and ACS Style

Kim, J.; Lee, J.-W.; Jo, I.-H. Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng. Agronomy 2025, 15, 1244. https://doi.org/10.3390/agronomy15051244

AMA Style

Kim J, Lee J-W, Jo I-H. Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng. Agronomy. 2025; 15(5):1244. https://doi.org/10.3390/agronomy15051244

Chicago/Turabian Style

Kim, Jaewook, Jung-Woo Lee, and Ick-Hyun Jo. 2025. "Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng" Agronomy 15, no. 5: 1244. https://doi.org/10.3390/agronomy15051244

APA Style

Kim, J., Lee, J.-W., & Jo, I.-H. (2025). Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng. Agronomy, 15(5), 1244. https://doi.org/10.3390/agronomy15051244

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