Integrated transcriptomic and endogenous hormones analyses revealed the molecular mechanism of light and auxin for the regeneration of callus tissue in seashore paspalum

doi:10.21203/rs.3.rs-4615496/v1

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Integrated transcriptomic and endogenous hormones analyses revealed the molecular mechanism of light and auxin for the regeneration of callus tissue in seashore paspalum

https://doi.org/10.21203/rs.3.rs-4615496/v1

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Background: Seashore paspalum (Paspalum vaginatum O. Swartz) is a halophyte known for its exceptional salt tolerance and ecological adaptability. It is an excellent candidate for studying salt tolerance mechanisms and screening salt tolerance genes. However, the difficulties with callus tissue regeneration and the influence of genotype during cultivation provide a significant obstacle to the process of molecular breeding employing genetic transformation and gene editing techniques in seashore paspalum.

Results:

To elucidate the molecular mechanism of callus regeneration in seashore paspalum, this study analyzed the content of endogenous hormones and investigated the effects of light, KT, and genotype on callus regeneration; Through transcriptome analysis between different treatments, the molecular mechanisms were explored. Under light conditions, almost all callus tissues of genotype I could produce regenerated green buds, but genotype II could not regenerate. A total of 106.2 Gb clean readings were obtained from 12 cDNA sample libraries in four regeneration states (CK, KT-D, KT-L, and KT-L-NR). The Pearson correlation coefficients, principal component analysis, and DEG hierarchical clustering heatmap analysis results indicated good intra-group repeatability and reliable data. The specific expression genes induced by different genotypes (KT-L-NR vs KT-L) (3083) were significantly higher than those in other groups by Venn plot analysis. A total of 73 endogenous hormone substances were quantitatively detected in all samples. KEGG enrichment analysis showed that all comparison groups significantly enriched differentially changed hormones (DCHs) in diterpenoid biosynthesis and plant hormone signal transduction pathways. In KT-L, GA₅ and GA₅₁ were significantly higher than those in other groups, while GA₂₀ and GA₂₉ were significantly lower. KT-L-NR showed noticeably higher levels of GA₃, GA₂₀, and GA₂₉, which could be a contributing cause to the incapacity of callus regeneration. The expression level of GA2ox (Pavag03G280900. v3.1) was very high, significantly negatively regulating GA₅₁. In KT-L, the content of ABA and JA were the lowest and significantly lower than that in KT-L-NR. The content of indole-3-acetic acid (IAA) in KT-L and KT-L-NR were significantly higher than that in CK and KT-D, indicating that light played an important role in synthesizing of IAA, which was beneficial for the regeneration of callus tissue.

Conclusions:

This is the first report on callus regeneration mechanisms of seashore paspalum by combined transcriptome and endogenous hormone profiling. The results will improve the understanding of molecular mechanisms and the effects of endogenous hormones, and provide new insights to address the issue of genotype dependence in callus regeneration.

seashore paspalum

regeneration

light

genotype

endogenous hormones

Seashore paspalum (Paspalum vaginatum O. Swartz, 2n = 2x = 20) is a halophytic warm-season grass, which is commonly used on landscape areas, golf courses and athletic fields[1, 2]. Seashore paspalum grows in saline regions of landscapes and exhibits tremendous potential for being used under stressful and harsh environmental conditions. The plant is favored owing to its high tolerance to salt, water logging, and adaptation to conditions of weak shade and low irradiance. Seashore paspalum has been found to have greater salt resistance when compared to other grass species[3, 4]. Some varieties are used in areas with limited access to fresh water, with high soil salinity, or turf is irrigated using wastewater[5]. Agrobacterium-mediated transformation of seashore paspalum has been established[6]. The genome assembly for the genotype of seashore paspalum (PI 509022) has been completed[5], and a transcriptome profile for salt resistance has been provided[7]. The selection of transgenic plants expressing CdNF-YC genes has improved drought and salt tolerance[8], and more gene functions related to salt tolerance have been identified[9]. Genetic transformation is a pivotal method for investigating gene function and advancing molecular breeding. The success of genetic transformation in monocotyledonous plants hinges on in vitro tissue culture and regeneration, which also represents a significant bottleneck in plant transformation[10]. The problem of difficult regeneration of callus tissue seriously restricts the process of molecular breeding of seashore paspalum using genetic transformation and gene editing techniques. To date, there have been no reports on the molecular mechanisms underlying regeneration in seashore paspalum.

Genetic transformation and genome editing technology can drastically reduce plant breeding times and endow breeding lines with inheritable excellent target traits, which will breed new varieties with higher commercial promotion value[11]. Agrobacterium-mediated transformation and subsequent whole-plant regeneration from individual cells are optimal methods for generating target traits in transgenic plants. However, plant regeneration and transformation through somatic embryogenesis have significant technological bottlenecks that severely limit the efficiency of obtaining new germplasm[12]. The plant embryogenic callus (EC) is an irregular embryogenic cell mass with robust regenerative capabilities that can be used for propagation and genetic transformation[13]. Callus regeneration is an important process in the genetic transformation system of seashore paspalum. The efficiency of callus proliferation and regeneration directly affects establishing an efficient and stable genetic transformation system. The process of plant regeneration is regulated by intracellular genetic factors and extracellular environment-inducing factors, resulting from an interaction between genotype and environmental conditions. Plant hormones, genotype, and environment are the most important factors involved in the initiation of regeneration. Light plays a decisive role in the dedifferentiation and regeneration of callus tissue[14]. Callus regeneration is closely related to the types and concentrations of hormones. Kinetin (KT) plays an important role in plant callus regeneration [15]. Seeds of the seashore paspalum hybrid 'Sea Spray' (Hyb.7 x Q36313) were used to establish a genetic transformation system, and each seed represents a genotype. Partial genotype has a high regeneration ability, the others are scarce, even recalcitrant to regenerate. Genotype dependency hinders transgenic development utilization in seashore paspalum[6]. It is crucial to have an insight into the molecular mechanisms and influencing factors of regeneration.

The differentiation and regeneration of calli are complex processes, regulated by gene expression. In some plants such as Arabidopsis, the molecular mechanisms during plant regeneration have been revealed[16]. The WOX11-LBD16 pathway facilitates the acquisition of totipotency in callus cells, establishing a root primordium-like identity within the newly formed callus in Arabidopsis[17]. WOX genes which have a role in the regeneration were specifically upregulated in high-regeneration lines of maize[14]. Overexpression of PtWOX11 promoted the formation of calli and regulated the regeneration of new shoots in poplar[18]. Overexpression of plant transcription factors such as WUSCHEL[19], WIND1[20], and BOOM AP2/ERF[21] have been shown to enhance regeneration.

With the rapid development of high-throughput sequencing and mass spectrometry technology, an abundance of data from transcriptomics and metabolomics has been produced, offering valuable insights into the mechanisms of callus regeneration. Transcriptome studies make it possible to efficiently and quickly analyze differentially expressed genes and functionally annotate regulatory pathways[22]. Integrated transcriptomic and endogenous hormone content analysis insighted uncovering regeneration mechanism in Osmanthus fragrans calli[23]. Some potential genes linked to somatic embryogenesis were discovered by examining the transcriptome profile and gene expression regulation alterations of embryogenic callus and redifferentiation in hybrid sweetgum[24]. The molecular response to exogenous auxin signaling during in vitro cotton embryonic redifferentiation has been revealed by dynamic transcriptome analysis[25]. The auxin and cytokinin signaling pathways have been proven critical in redifferentiation[26]. Based on functional annotations in maize, a few closely related genes for callus regeneration have been found, and the functions of hormones and light in regeneration have been examined[14]. The results of multi-omics joint analysis can be beneficial for enriching the molecular regulatory network during regeneration and provide a new perspective for improving the efficiency of genetic transformation.

In this study, we conducted a comprehensive analysis of the transcriptome and endogenous hormones to elucidate the roles and molecular mechanisms of light, KT, and genotypes in the regeneration process of seashore paspalum. This provides a foundation for clarifying the regeneration mechanism of seashore paspalum.

Callus induction and regeneration

The callus tissue induced by genotype Ⅰ and genotype Ⅱ has an excellent growth state on induction medium, and the callus tissue presented a yellow and compact granular shape (Figure. 1A, 1B, 1C). When the callus tissue was transferred to MG medium containing KT, it almost did not regenerate under dark conditions, and the callus tissue still maintained a yellow and compact granular shape. Callus produced a small number of regenerated buds, but the buds appeared white due to lack of light (Figure. 1D, 1E, 1F). Under light conditions, the callus of genotype Ⅰ turned green after 7 days and the shoots regenerated after 4 weeks with 100% regeneration rate (Figure. 1G, 1H,1I). In contrast, genotype Ⅱ calli were comparatively soft, granular, dark yellow, merely proliferating, and exhibited nearly no regeneration under light conditions (Figure. 1J, 1K, 1L).

Sequencing annotation analysis statistics of different transcriptome

To analyze the transcriptome and gene expression profiles of different influencing factors (KT, light, and genotype) during the regeneration process, 12 cDNA samples were extracted from the four states of calli (CK, KT-D, KT-L, and KT-L-NR) and sequenced using the Illumina NovaSeq 6000 system. All sample transcriptome data underwent low-quality data filtering, and the basic quality data was subjected to statistical analysis. A total of 12 samples obtained clean readings of 106.2 Gb, with each sample being sequential with more than 57 million raw reads and more than 55 million clean reads. After removing the adaptor sequences and low-quality reads, the Q20 quality of each sample was stable above 97.4%, the Q30 percentage was over 93.0%, and the GC content was between 52.7 and 54.4%, and the base error rate was low at 0.03%. More than 94.3% of paired-end reads were mapped to the seashore paspalum reference genome, per sample with an average unique mapping reads rate of 89.3%. The rate of reads mapped to the sense strand and antisense strand ranged from 44.0–44.9% (Table 1).

Table 1

Statistics of the base quality from four libraries
Sample	Raw Reads	Clean Reads	Clean Base (G)	Q20 (%)	Q30 (%)	GC Content (%)	Mapped reads	Unique mapped reads	Multi mapped reads	Reads map to sense strand	Reads map to antisense strand
CK-1	62838690	60778320	9.12	97.74	93.71	53.16	57846778 (95.18%)	54505544 (89.68%)	3341234 (5.50%)	27240261 (44.82%)	27265283 (44.86%)
CK-2	60110900	58468030	8.77	97.6	93.36	52.88	55450905 (94.84%)	52494073 (89.78%)	2956832 (5.06%)	26232039 (44.87%)	26262034 (44.92%)
CK-3	58963316	57490556	8.62	97.73	93.66	53.26	54659223 (95.08%)	51564828 (89.69%)	3094395 (5.38%)	25771856 (44.83%)	25792972 (44.86%)
KT-D-1	58759546	57179762	8.58	97.65	93.51	54.09	54280304 (94.93%)	51361559 (89.82%)	2918745 (5.10%)	25672434 (44.90%)	25689125 (44.93%)
KT-D-2	58984434	57249856	8.59	97.69	93.6	54.42	54494833 (95.19%)	50814011 (88.76%)	3680822 (6.43%)	25389745 (44.35%)	25424266 (44.41%)
KT-D-3	60507436	58787680	8.82	97.81	93.86	53.15	55797903 (94.91%)	52441572 (89.21%)	3356331 (5.71%)	26199692 (44.57%)	26241880 (44.64%)
KT-L-1	59094690	56927426	8.54	97.71	93.66	53.94	53978807 (94.82%)	50750242 (89.15%)	3228565 (5.67%)	25350320 (44.53%)	25399922 (44.62%)
KT-L-2	57880802	55926246	8.39	97.56	93.44	53.58	52886504 (94.56%)	50113586 (89.61%)	2772918 (4.96%)	25021366 (44.74%)	25092220 (44.87%)
KT-L-3	64528494	62513252	9.38	97.46	93.15	54.11	58985934 (94.36%)	56021556 (89.62%)	2964378 (4.74%)	27985322 (44.77%)	28036234 (44.85%)
KT-L-NR-1	64769556	63021878	9.45	97.55	93.21	52.75	59659302 (94.66%)	56048800 (88.94%)	3610502 (5.73%)	28010634 (44.45%)	28038166 (44.49%)
KT-L-NR-2	60130944	58593118	8.79	97.56	93.25	53.05	55458519 (94.65%)	52301388 (89.26%)	3157131 (5.39%)	26132637 (44.60%)	26168751 (44.66%)
KT-L-NR-3	64120544	61378058	9.21	97.45	93.09	53.31	57979807 (94.46%)	54139319 (88.21%)	3840488 (6.26%)	27046786 (44.07%)	27092533 (44.14%)
Raw Reads: Total number of original reads; Clean Reads: The total number of high-quality data bases after filtering the original reads; Clean Base(G): Total number of high-quality reads; Q20 (%)/Q30 (%): Percentage of bases with a Phred value above Q20/Q30 in clean data; GC content: Percentage of G and C bases out of the total bases in clean data; Mapped reads: the number of reads that can be matched to the reference genome (proportion of matched reads); Unique/ Multi mapped reads: the number of reads that match to a unique/multiple multiple position in the reference genome sequence (proportion of mapped reads); Reads map to sense/antisense strand: the number of reads comparison to the sense/antisense strand of the genome after UMI deduplication, (proportion of mapped reads).

DEGs identification and enrichment analyses

To reflect the correlation of gene expression in any two samples, we calculated the Pearson correlation coefficients for all gene expression levels between each two samples and reflected these coefficients in the form of a heat map. The average Pearson correlation coefficients between all samples within a group were greater than 0.83, with the highest being 0.99, indicating three repeated samples within the group had good repeatability (Figure. 2A). The significant differentially expressed genes (DEGs) were selected by setting |log2fold change| ≥ 1 and p value ≤ 0.05 as criterions. In this study, a total of 15771 significant DEGs were observed in the four comparison groups related to regeneration. Genotypes have a significant impact on gene expression levels, the total number of DEGs was the highest. The group with the largest number of DEGs was the (KT-L-NR vs KT-L) group, with 1350 downregulated genes and 3550 upregulated genes. While under the same KT conditions, the lowest number of significant DEGs (196 downregulated and 338 upregulated) was detected between the KT-L and KT-D groups (Figure. 2B).

Before doing the unsupervised PCA (principal component analysis), data was unit variance-scaled. Unsupervised PCA was carried out using the statistics function ‘prcomp’ in R1, which was useful for utilizing a small number of primary components to analyze the internal structure of several variables. The transcriptome data of callus regeneration were subject to PCA analysis. In the PCA score plot, the first principal component (PC1) and the second principal component (PC2) represented 25.8% and 19.0% of the total variance, respectively and the cumulative contribution rate of PC1× PC2 reached 44.8%. The callus statuses CK, KT-D, KT-L, and KT-L-NR could be almost completely separated, indicating that the genes expressed differently; the compact cluster formed by the three biological repeats of each status showed that the materials were sufficiently reproducible and appropriate for further qualitative and quantitative analyses. (Figure. 2C).

Venn diagram analysis showed 212 DEGs were detected in common among all three comparison groups, and 197, 2588, and 3083 specific DEGs in comparison groups (KT-L vs KT-D, KT-D vs CK, and KT-L-NR vs KT-L), respectively. In the stage of embryonic regeneration, the specific expression genes induced by different genotypes (KT-L-NR vs KT-L) (3083) were much higher than those induced by KT or light conditions during the regeneration process. The number of specific expression genes induced by KT (KT-D vs CK) was 2588, which was higher than the 197 specific expression genes induced by light (KT-L vs KT-D) (Figure. 2D).

Blue and red on the heat map represent low and high DEG expression levels, respectively. The hierarchical clustering of DEGs in all the biological replicates was classified into the same cluster, which indicated a robust correlation between replicates and the high reliability of our data. The heat map also revealed that some DEGs were specifically accumulated in the KT-L, but some only existed in the KT-L-NR (Figure. 2E).

There were 2484 transcription factors associated with 91 gene families among the induced genes. Of these genes, 149 genes are transcription factors of the AP2/ERF-ERF family, followed by BASIC/HELIX–LOOP–HELIX (bHLH) transcription factors with 147 members that exhibited changes in expression after induction. Following induction, there were noticeable alterations in the expression of 136 NACs, 133 MYBs, 115 C₂H₂s, and 113 WRKYs, among other transcription factors (Figure. 2F).

GO and KEGG enrichment analysis of DEGs in different callus

To further elucidate the effects of KT, light, and genotype on the transcription levels of key genes in callus regeneration, we separately investigated GO and KEGG enrichment analyses of the DEGs in the comparison groups (KT-D vs CK, KT-L vs KT-D, and KT-L-NR vs KT-L). All comparison groups were associated with three ontologies in the GO database enrichment analysis: ‘biological process’, ‘cellular component’, and ‘molecular function’. In the enrichment analysis results of three comparison groups, the 50 GO terms with the lowest FDR were selected, and constructed a column chart to visualize the enrichment items. Three comparison groups have different levels of enriched Go entries in three ontologies. The GO analysis revealed that the DEGs were mostly enriched in biological processes. In the cellular component, KT-D vs CK had the lowest enriched GO terms; in the molecular function, KT-L vs KT-D had the lowest enriched GO terms. Within the biological process, the enriched GO terms in both KT-L vs KT-D and KT-L-NR vs KT-L were 'photosynthesis' and 'generation of precursor metabolites and energy' and the different term enrichment for KT-D vs CK included ‘cellular polysaccharide metabolic process’ and ‘response to oxidative stress’. Within the cellular component, the enriched GO terms in both KT-L vs KT-D and KT-L-NR vs KT-L were 'photosynthetic membrane’ and ‘thylakoid membrane’ and also the different term enrichment for KT-D vs CK included ‘anchored component of membrane’ and ‘intrinsic component of plasma membrane’. Within the molecular function the enriched GO terms in both KT-D vs CK and KT-L-NR vs KT-L were 'hydrolase activity, hydrolyzing O-glycosyl compounds’ (Figure. 3A; 3B and 3C).

To elucidate the functions of the DEGs, KEGG functional enrichment analysis was performed. We mapped the top 20 paths with the lowest Q-value. The pathway was shown to be more significantly enriched when the Q-value value was smaller; the number of enriched DEGs was indicated by a larger point; and the ratio of enriched DEGs to annotated genes was greater when the Rich factor was larger. Based on the KEGG functional enrichment analysis, the number of DEGs involved in metabolic pathways was the largest in all comparison groups (Figure. 3D; 3E and 3F). In KT-L vs KT-D, the significantly and specific enriched pathways were involved in photosynthesis -antenna proteins and porphyrin metabolism. Photosynthesis, metabolic pathways, carbon fixation in photosynthetic organisms, porphyrin metabolism, and carbon metabolism pathways play a critical role in the seashore paspalum regeneration (Figure. 3E). Hierarchical clustering analysis suggested that the overview of the relative changes of DEGs among the comparison groups (KT-D vs CK, KT-L vs KT-D and KT-L-NR vs KT-L). The heatmap displayed tight clustering, indicating strong repeatability in biological replicates and a close correlation of the DEGs between groups (Figure. 3G; 3H and 3I).

Plant hormones analysis based on KEGG enrichment

To explore the role of plant hormones in the regeneration process of callus tissue, this study utilized plant hormones analysis platform based on LC-MS/MS to determine hormone levels in four groups of samples. In this study, a total of 73 substances belonging to 8 major categories of plant hormones were quantitatively detected in the 4 samples, including 27 cytokinins (CK), 17 auxins, 11 gibberellin (GA), 9 jasmonite acids (JA), 5 salicylic acids (SA), 2 abscisic acids (ABA), 1 ethylene (ETH) and 1 melatonin (MLT) (Figure. 4A). To identify the plant hormones that play an important regulatory role in the regeneration process of callus tissue, we analyzed the differential hormones using thresholds for fold change ≥ 2 and fold change ≤ 0.5 to screen differentially changed hormones (DCHs). In KT-D vs CK, KT-L vs KT-D, and KT-L-NR vs KT-L, the upregulated DCHs were 19, 22, and 20, respectively, while the downregulated DCHs were 12, 21, and 18 (Figure. 4B). KEGG enrichment analysis revealed that DCHs were substantially enriched in the diterpenoid biosynthesis and plant hormone signal transduction pathways across all comparison groups (Figure. 4C; 4D and 4E).

Endogenous gibberellin content and metabolic pathways analysis

GA quantification results showed that GA₄ was only detected in CK (Fig. 5A), and GA₅₃ was only detected in KT-D (Fig. 5B). In the treatment groups (KT-L, KT-D, and KT-L-NR) following the addition of KT, no GA₄ was found; nevertheless, the level of GA₁₉ was significantly higher than that of CK. Concurrently, in the non-regenerable genotype KT-L-NR, the content of GA₁₉ was significantly lower than in KT-L and KT-D (Fig. 5C). In KT-L, GA₅ and GA₅₁ were significantly higher than those in other groups (Fig. 5D; 5E), while GA₂₀ and GA₂₉ were significantly lower than those in other groups (Fig. 5F; 5G). GA₃ was only detected in KT-L-NR (Fig. 5H). It is evident that the addition of KT greatly enhanced the content of GA₁₉ while drastically decreasing the content of GA₄; while the content of GA₅ and GA₅₁ increased dramatically in KT-L under light conditions. Changes in the contents of these substances may be the main contributors to the regeneration of callus tissue in seashore paspalum. In contrast to KT-L, non-regenerable genotypes showed noticeably higher levels of GA₃, GA₂₀, and GA₂₉ under the same conditions, which could be a contributing cause to the incapacity of callus regeneration.

Through joint analysis of endogenous hormone and transcriptome data, DEGs identified in the diterpenoid biosynthesis metabolic pathway included 1 gibberellin 13-oxidase (CYP714B) gene, 3 gibberellin-44 dioxygenase (GA20ox) genes, and 7 gibberellin 2beta-dioxygenas (GA2ox) genes. The CYP714B gene and GA20ox genes were significantly upregulated in KT-D vs CK, positively regulating the synthesis of GA₅₃ and GA₁₉. There was one GA2ox gene significantly downregulated in KT-L vs KT-D and KT-D vs CK, negatively regulating the synthesis of GA₅₁ and GA₂₉. The expression level of GA2ox (Pavag03G280900. v3.1) was very high, significantly negatively regulating GA₅₁. In the plant hormone signal transduction metabolic pathway, DEGs included 9 gibberellin receptor GID1 genes, 15 DELLA genes, and 16 phytochrome-interacting factor 3 (PIF3) genes, which were involved in regulating biological processes such as germination in the GA₄ signaling pathway (Fig. 5I).

Endogenous hormone content and signal transduction pathways analysis

The ABA content was the lowest in KT-L which significantly lower than that in KT-L-NR (Fig. 6A), and no ABA-GE was detected (Fig. 6B). The JA content in KT-L was also lower than that in other groups, and significantly lower than KT-L-NR (Fig. 6C). The JA-ILE contents in CK and KT-L-NR were significantly higher than that in KT-L (Fig. 6D). The low contents of ABA and JA in the regeneration group indicated that they may have a negative impact on the regeneration capacity. Through joint analysis of endogenous hormone and transcriptome data, DEGs identified in ABA signal transduction pathway included 4 protein phosphatase 2C (PP2C) genes, 2 SNRK2 genes, and 5 ABA-responsive element binding factor (ABF) genes; and in JA signal transduction included 1 jasmonic acid-amino synthetase (JAR1) gene, 9 jasmonate ZIM domain-containing protein (JAZ) genes, and 10 (MYC2) genes (Fig. 6M).

The contents of indole-3-acetic acid (IAA) in KT-L and KT-L-NR were significantly higher than that in CK and KT-D, suggesting that light plays a crucial role in stimulating the synthesis of IAA, which was beneficial for the regeneration of callus tissue (Fig. 6E). Furthermore, the contents of indole-3-acetyl-L-tryptophan (IAA-Trp), indole-3-acetyl-L-valine methyl ester (IAA-Val-Me), and tryptamine (TRA) were the highest in KT-L, all significantly higher than those in CK (Fig. 6F; 6G and 6H). These findings suggest that IAA and its related compounds like IAA-Trp, IAA-Val-Me, and TRA all contribute to promoting effect on the regeneration of seashore paspalum. Both KT and light factors have important promoting effects on callus regeneration.

Both KT and kinetin riboside (KR) levels were significantly higher in KT-D, KT-L, and KT-L-NR than in CK (Fig. 6I; 6J). Trans-zeatin riboside (tZR) was only detected in KT-L, with N6-isopentenyl adenine (IP) content being the lowest, significantly lower than KT-L-NR (Fig. 6K; 6L). These results indicate that the addition of exogenous KT can substantially increase the content of endogenous KT and KR, which are essential for the regeneration of seashore paspalum.

Validation of transcriptomic data using qRT-qPCR

To confirm the expression patterns of a subset of DEGs identified by Illumina sequencing, eight genes were randomly selected that related to the diterpenoid biosynthesis metabolic pathway and plant hormone signal transduction for qRT-PCR verification. The RT-PCR results showed the expression levels of these genes obtained by qRT-PCR analysis were consistent with the FPKM value trends obtained by the RNA-seq data, which indicated the reliability and accurately of the gene expression data measured by RNA-seq (Fig. 7).

As a halophyte with extremely high salt tolerance and ecological adaptability, seashore paspalum has enormous potential for application in saline-alkali land improvement and soil remediation. The molecular mechanism research on the extremely high salt tolerance of seashore paspalum can provide a theoretical basis and genetic resources for the molecular breeding of turfgrass and crops. At present, a genetic transformation system has been established for seashore paspalum. Research on salt tolerance mechanisms and molecular design breeding processes of seashore paspalum has been limited due to issues such as genotype dependence and regeneration difficulties that have arisen during the establishment of CRISPR/Cas9 genome editing systems and the screening of genetically modified materials. This is the first report on callus regeneration mechanisms of seashore paspalum by combined transcriptome and endogenous hormone profiling. This study analyzed the content of endogenous hormones and investigated the effects of light, KT, and genotype on callus regeneration; the molecular mechanisms were explored by combining transcriptome analysis. The findings will advance knowledge of endogenous hormone effects and molecular mechanisms, as well as offer fresh perspectives on the problem of genotype dependence in callus regeneration.

Genotype dependency analysis based on hormone differences

The universal genotype dependence of tissue culture protocols has impeded the improvement and breeding of crop varieties through genetic transformation or genome editing [27, 28]. Usually, genetic transformation systems have been established only in a limited number of species and optimized for specific genotypes [29]. Moreover, most commercially valuable varieties exhibit recalcitrance or only marginal transformability [30]. The utilization of gene editing technologies for the precise fine-tuning and introduction of desirable traits in newly released elite commercial varieties has been hindered by the restricted range of receptive genotypes with less genetic background and agronomic values. Genotype dependence remains insurmountable and universal in crops including barley [27], wheat [31], sorghum [32], and cotton [33]. To eliminate genotype reliance in plant transformation, several strategies have been tried recently. The genotype-independent transformation has been established in maize by transforming the maize's major transcription factors, Baby Boom (Bbm) and Wuschel2 (Wus2) into immature maize embryos of some previously nontransformable maize inbred lines and high transformation frequencies have been obtained. Some monocots, including (Sorghum bicolor) immature embryos and indica rice (Oryza sativa ssp indica) callus, have improved transformation by expressing maize Bbm and Wus2 genes[34]. In sorghum, Wus2-enabled transformation raises the frequency of CRISPR/Cas-targeted genome editing in addition to the transformation efficiency. Wus2-induced direct somatic embryogenesis and regeneration reduces the duration of tissue culture cycles dramatically and avoids genotype-dependent callus development [32]. Pollen transfected with DNA-coated magnetic nanoparticles was efficiently delivered into maize inbred lines that are resistant to tissue culture-mediated transformation by a genotype-independent pollen transfection technology [35]. A shoot apical meristem cell-mediated transformation (SAMT) has been developed, which enables an efficient transformation and CRISPR/Cas9-mediated genome editing system for various recalcitrant cotton genotypes [33].

However, research on the relationship between genotype dependence and endogenous hormones in plants has not been documented. This study provides new ideas for addressing genotype dependence by analyzing the differences in endogenous hormones and key regulatory genes between regenerative and non-regenerative genotypes. During the regeneration process, genotypes have a significant impact on gene expression levels, the total number of DEGs in the KT-L vs KT-L-NR group was the highest, with 1350 downregulated genes and 3550 upregulated genes; while the specific expression genes were 3083 which were much higher than those induced by KT or light conditions. The hierarchical clustering of DEGs was also clearly different in KT-L vs KT-L-NR.

The role of endogenous hormones in callus regeneration

The process of callus regeneration is closely related to the types, concentrations, and dynamic balance of endogenous hormones in callus that were also controlled by exogenous hormones[23]. The beginning of proliferation centers in explants and the plant architecture are impacted by the amounts of endogenous hormones[36]. Callus tissue can only differentiate into adventitious buds under appropriate external conditions, and the composition and content of endogenous hormones are also different in different states. Previous studies have mainly focused on the effects of external conditions or the supplementation of media with nutrients and phytohormones in culture media on callus induction. Some studies have identified the involvement of endogenous hormones in the morphological reactions of callus tissue, and their concentrations are temporally regulated in response to external culture conditions[37]. The effects of endogenous hormone content and types on callus induction were evaluated in explants such as wheat[38], maize[39], and Medicago truncatula Gaertn[40]. However, there have been no reports on the changes and effects of endogenous hormones during the regeneration stage of callus tissue. We used the LC-MS/MS hormone analysis platform to measure and analyze the content and differences of endogenous hormones during the regeneration process of callus tissue in seaside paspalum. A total of 73 substances were detected in 8 categories of plant hormones. In our study, the gibberellins (GA3, 4, 5, 19, 20, 29, 51 and 53), abscisic acid (ABA, ABA-GE), jasmonic (JA, JA-ILE), auxins (IAA, IAA-Trp, IAA-Val-Me and TRA) and cytokinins (CK, KR, tZR and IP) were presented in the process of callus regeneration and influenced by genotype or external conditions.

GAs regulate a multitude of developmental processes throughout the entire life cycle of plants[41]. The researchs related to the effect of GAs on somatic embryogenesis (SE) and callus regeneration mainly comes from studies on adding exogenous GAs to the culture medium; and there are differences in the role of GA on SE[42]. The research on endogenous hormones mainly focuses on the induction stage of callus tissue, but there have been no reports on the role of endogenous GA in callus regeneration. Compared to the non-embryogenic callus of maize, the contents of endogenous GAs (GA₁, GA₃, GA₂₀) were significantly higher in the embryogenic callus[39], which was consistent with the results in Medicago truncatula Gaertn[43]. Endogenous gibberellins are required for embryo production and embryogenic callus growth in Medicago. All the active gibberellins (GA₁, GA₃, GA₆, and GA₄, GA₇) were presented in the SE progress from the leaf explants of non-embryogenic (M9) and embryogenic (M9-10a). The levels of GA₃ and GA₆ were found to elevate in the M9-10a genotype as the induction phase advanced. Among all the bioactive gibberellins detected, only GA₃ appeared to be correlated with the SE initiation[40]. The GA₃ content in Golden Promise varieties of barley (Hordeum vulgar L.) was much higher than that in local varieties, that may indicated a direct correlation between the amount of GA₃ hormone and the percentage of regeneration[42]. However, endogenous GAs need to be downregulated to promote SE in studies on Arabidopsis[44] and carrots[45].In our study, GA₅ and GA₅₁ were considerably higher and GA₂₀ and GA₂₉ were not detected in regenerable genotypes (KT-L). Non-regenerable genotypes exhibited notably greater levels of GA₂₀ and GA₂₉, and GA₃ only detected in KT-L-NR which may be a factor in the incapacity of callus regeneration.

Genes within the GA2ox family are pivotal in the GAs metabolic pathway, primarily facilitating the inactivation of GAs[46]. Overexpression of GA2ox genes can lead to the degradation of bioactive GAs in plants, culminating in a dwarf phenotype. In rice, the overexpression of the genes OsGA2ox1, OsGA2ox6, and OsGA2ox9 results in diminished plant stature. In wheat, the ectopic expression of the soybean PcGA2ox1 gene significantly diminishes GA levels, thereby reducing plant height[47]. GA2ox was stimulated to inactivate certain bioactive GAs to maintain appropriate ABA to GA ratios, thereby facilitating the SE response in Medicago[48]. Similarly, GA2ox (Pavag03G280900. v3.1) significantly negatively regulated GA₅₁, which expressed very high. Therefore, we speculated that GA2ox may be a key gene involved in regulating callus regeneration. Endogenous hormones in plants form a complex signaling network that affects cell growth and development by regulating key genes involved in cell proliferation[49]. Many genes have been identified from plant hormone signaling pathways, including candidate genes that regulate seashore paspalum callus regeneration such as PvMYC2 in JA plant hormone signal transduction metabolism pathways that may be involved in gene network regulation during callus regeneration. MYC transcription factors serve as regulators of plant growth and development, acting either as activators or repressors of JA-related gene expression. The repression of MYC could plausibly lead to diminished JA levels in Pinellia ternata[50]. Our findings align with the conclusions drawn from the aforementioned studies. These results will provide molecular strategies for overcoming the genotype dependence problem in seashore paspalum regeneration difficulties.

ABA played an important role in the induction of callus tissue in some plant species[50]. In carrot tissue culture systems, high endogenous ABA content may play an important role in embryonic development ability[51], and the endogenous ABA level in embryogenic carrot cells was greater than that in non-embryogenic cells[39, 52]. But the amount of endogenous ABA and percentage of regeneration were inversely related in barley. High regeneration rate barley varieties (Golden Promise cultivar) have significantly lower ABA content than low regeneration rate varieties (Iranian cultivars)[42]. Compared with non-embryogenic (M9), the callus quality of embryogenic (M9-10a) was significantly higher, which may be related to the lower ABA content in this callus tissue of Medicago [40]. Our results were consistent with the above finding in barley and medicago. The ABA content in the regenerative genotype (KT-L) was significantly lower than that in the non-regenerative genotype (KT-L-NR); and lower than that in the differentiation stage. It can be seen that low ABA content was one of the key factors for callus regeneration in seashore paspalum. ABA production from ABA-GE that can regulate the local ABA concentration, mediated by β-glucosidase present in vacuoles[53]. ABA-GE detected in initial explants of Medicago were higher in non-embryogenic (M9) than in the embryogenic (M9-10a)[43]. Unlike this, we did not detect ABA-GE in the regenerative genotype (KT-L).

JA facilitates the activation and regeneration of stem cells in the growth and developmental processes of plant tissues[54]. However, some researches have reported inconsistent conclusions. JA has an inhibitory effect on the formation of callus tissue[55], combating plant organ differentiation by inhibiting cell proliferation and expansion[56]. Low concentrations of endogenous JA have been shown to stimulate callus proliferation and development in both garlic[36] and Pinellia ternata[50], a finding that is consistent with our studies on callus regeneration of seashore paspalum. Meanwhile, JA-ILE was also the lowest in regenerated callus tissue.

Previous research has established that IAA is crucial for callus differentiation in tissue culture systems[57, 58]. Endogenous IAA levels were higher in embryogenic cells compared to non-embryogenic cells in carrot tissue[59]. Higher endogenous levels of IAA within explants are essential for the initiation and proliferation of garlic callus[36]. Tryptophan (TRP) represents a rate-limiting factor in the auxin biosynthesis and its derivatives, with its catabolism product, tryptamine (TRA), potentially exerting a positive influence on callus formation in Pinellia ternata[50]. Consistent with the aforementioned findings, IAA and related substances (IAA-Trp, IAA-Val-Me, and TRA) were detected at the highest concentrations in the regeneration genotype (KT-L), significantly higher than that in the control (CK).

Natural CKs, a class of adenine derivatives, are categorized into two distinct states: free-state CKs and bound-state CKs. Both the type and concentration of CKs in culture medium significantly influence callus proliferation and plant regeneration[60]. when CKs were introduced into the regeneration medium, not only initiate callogenesis and promote de novo shoot formation but also exert impacts on the endogenous CKs balance[61]. In hydroponic cuttings of Pinellia ternata, endogenous CKs serve as the central hormone regulating the formation of calli[50]. There were few reports on the relationship between endogenous CK content and callus regeneration. In our study, the supplementation of exogenous KT to the culture medium resulted in a significant increase in the levels of endogenous KT and KR within callus tissue. Additionally, tZR was exclusively detected in the regenerated callus tissue (KT-L). These observations suggest that the modulation of hormone levels, particularly cytokinins, can significantly influence callus tissue regeneration, thereby offering a potential strategy to enhance regeneration efficiency.

Seashore paspalum, a halophyte with remarkable salt tolerance and ecological adaptability. However, callus tissue regeneration challenges and genotype effects have significantly hindered molecular breeding efforts through genetic transformation and gene editing. Our study presents the first integrated analysis of the transcriptome and endogenous hormone profiles in seashore paspalum to delineate the callus regeneration mechanisms. By examining the interplay of light, KT, and genotypes, our research has uncovered key molecular and hormonal factors that govern the regeneration process. This work provides critical insights into genotype dependence in callus regeneration and lays a foundational framework for developing strategies to improve regeneration efficiency.

Plant growth and treatments

Embryogenic callus inducted from seeds of the seashore paspalum (Paspalum vaginatum O. Swartz) cultivar ‘Sea Spray’ in the dark at 25°С. All materials has been deposited in a publicly Grass Science Laboratory oof Qingdao Agricultural University. Embryogenic calli derived from the embryo of genotype Ⅰ and genotype Ⅱ were cultured in one dish as an individual clone on an induction medium (MS2.5) for proliferation and subcultured every 4 weeks[6]. After subculturing for 5 months, high-quality compact embryogenic calli of genotype Ⅰ and genotype Ⅱ were transferred to a regeneration medium (MG) containing MS basal medium supplemented with 0.2 mg L^− 1 kinetin for regeneration under 16 h photoperiod (200 µmol m^− 2 s^− 1)[6].

Callus tissues with the same status were cultured under different conditions for 4 weeks; Four statuses of callus development were defined: CK, calli under dark on MS2.5; KT-D, calli under dark on MG; KT-L, calli of genotype Ⅰ under light on MG; KT-L-NR, calli of genotype Ⅱ under light on MG. Collect samples of callus tissues from four distinct states and promptly observe and photograph them under a microscope. Callus tissue was sampled using liquid nitrogen and stored at − 80°C for measuring separately RNA isolation and endogenous hormone determination. All experimental treatments were set with three replicates.

Tissue Paraffin Section Preparation

To compare the cytological characteristics of callus, four statuses of callus (CK, KT-D, KT-L and KT-L-NR) were promptly fixed in FAA (75% ethanol: acetic acid: formaldehyde, 90:5:5, v/v/v). Fixation at room temperature for longer than 24 hours, and twice aspirating for 15 minutes each time. Subsequently, fixed samples were dehydrated with a graded series of ethanol (75%, 85%, 90%, 95%, and 100%—each step for 25 min twice, v/v)). Next, the samples were immersed in the same volume of tert-butanol and melted paraffin. Following that, the samples were cooled in the carton while embedded in pure paraffin. Sections (5 µm in thickness) were cut using a microtome (Leica Instrument RM2016, Shanghai, China) and stained with toluidine blue dye (Servicebio G1032, Wuhan, China) for 5 min. Cell proliferation was observed using a microscope (Nikon Eclipse E100, Nikon DS-U3, Nikon Instruments (Shanghai) Co., Ltd., Shanghai, China) in bright field mode.

RNA Extraction and Illumina Sequencing

Total RNA was extracted from calli of four statuses using the Plant Total RNA Extraction Kit (TIANGEN Biotech, Beijing, China) according to the manufacturer's instructions. cDNA was synthesized using the PrimeScript RT reagent kit with a gDNA eraser (Takara, Dalian, China). The quality and integrity of the RNA samples were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), NanoDrop2000 (Thermo Fisher Scientific, MA, USA), and agarose gel electrophoresis. Following the manufacturer's instructions, sequencing libraries were generated using NEBNext Ultra™ RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA). Four libraries (CK, KT-D, KT-L, and KT-L-NR) were constructed for the RNA-seq analysis using Illumina NovaSeq 6000 system (Illumina, San Diego, CA, USA) by Biomarker Technologies (Beijing, China). For the construction of a single cDNA library, it is required that the total RNA per sample ≥ 1 µg, OD_260/280 ≥1.8, and RIN value ≥ 6.5. The RNA-seq analysis was performed after the sample quality validation.

Sequencing data analysis

To obtain clean data, low-quality reads and reads containing adapters were discarded. Before data analysis, stringent quality control was applied to ensure that these reads possessed sufficient quality for the accuracy of subsequent analysis. Using the DESeq2 program (v1.6.3, open source, http://www.bioconductor.org/), differential expression analysis across samples was conducted to obtain differentially expressed genes (DEGs). The parameters were FDR ≤ 0.05 and |log2FC| (FC, fold change) ≥ 1. The seashore paspalum reference genome (https://phytozome-next.jgi.doe.gov/info/Pvaginatum_v3_1) was used as the reference genome. Three replicates of standardized sequencing data with repeatability were applied for analysis. Gene function was annotated using commonly utilized databases. GO-Term Finder (v0.86, http://search.cpan.org/dist/GO-TermFinder/) provided descriptions for the GO terms of molecular function, biological process, and cellular component. GO terms with a p-value < 0.05 were considered significant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (http://en.wikipedia.org/wiki/KEGG) were used to determine which pathways were enriched. Significant metabolic pathways and functional categories were identified within differentially expressed genes, with FDR ≤ 0.05.

Quantitative Real-Time PCR analysis

Quantitative Real-Time PCR (RT-qPCR) was used to validate six genes that may have roles in callus regeneration in four stages that matched transcriptome sequencing. RT-qPCR was performed using the ACTIN gene as a reference. The forward and reverse primers listed in Table 2 were designed using Primer 5.0. The amplification program was as follows: 10 min at 95°C, and then 10 s at 95°C, 10 s at 60°C, and 20 s at 72°C for 40 cycles. To ensure accurate and consistent results, three technical duplicates were carried out for both the test and reference genes in every sample. Relative transcript levels for each gene were calculated using the 2^−ΔΔCt method. Duncan’s multiple range test and variance (ANOVA) analysis were conducted to determine the significant difference (p-values < 0.05) using SPSS 22.0 software (SPSS Inc., Chicago, IL, USA). Results were shown as mean ± standard error of biological replications.

Table 2

Primers used for qRT-PCR
Gene name	Primer	Sequence (5′–3′)
Pv-Actin	Forward	CTTCTCTCAGCACTTTCCAACA
(Pavag03G414200.1)	Reverse	AAACATAACCTGCAATCTCTCC
PvGA2ox	Forward	GCAGATCATCTCCGTGCTCA
(PavagK145700.v3.1)	Reverse	CAGTACACCTGAGCCACCTG
PvGID1	Forward	GTGATGTCCGTGGACTACCG
(Pavag01G411900.v3.1)	Reverse	GTCTCCGAGATGCACACCAG
PvDELLA	Forward	ATCCTGGAGTCGTTCCTCGA
(Pavag02G332900.v3.1)	Reverse	TCCTCCAGCGAGTCCATGTA
PvKAO	Forward	ACATGATGGACCGGCTGATC
(Pavag10G007100.v3.1)	Reverse	GACGGAGATCTCGAGCTTGG
PvCYP714B	Forward	TGAGAGCACAGCAGTCACAG
(Pavag01G365100.v3.1)	Reverse	GCTCTGGCTCCACAATGAGT
PvTF	Forward	CACCATGTCGCCGATGACTA
(Pavag03G251100.v3.1)	Reverse	AAGTACTCGTCGGTTGCCTG
PvSNRK2	Forward	ACAAGTACGAGCCAGTTCGG
(Pavag09G156200.v3.1)	Reverse	GGGTAAGCTCCCACAAGCAT

Endogenous hormone level identification

HPLC grade acetonitrile (ACN) and methanol (MeOH) were acquired from Merck (Darmstadt, Germany). All experiments were conducted using MilliQ water (Millipore, Bradford, USA). All of the standards were purchased from isoReag (Shanghai, China) and Olchemim Ltd. (Olomouc, Czech Republic). The stock solutions of standards were prepared at the concentration of 1 mg/mL in MeOH and stored at -20°C. Before analysis, the stock solutions were diluted with MeOH to create working solutions.

Samples of callus tissues from four distinct states (CK, KT-D, KT-L, and KT-L-NR) were ground into powder (50 Hz, 60 s) in liquid nitrogen and then stored at -80°C. 50 mg of the material was weighed and then dissolved in 1 mL of methanol/water/formic acid (15:4:1, V/V/V). To serve as internal standards (IS) for the quantitation, 10 µL of an internal standard mixed solution (100 ng/mL) was added to the extract. The supernatant was transferred to plastic microtubules and evaporation to dryness, it was dissolved in 100 µ in 80% methanol (V/V); and filtered through a 0.22 µm membrane filter for LC-MS/MS analysis.

A UPLC-ESI-MS/MS system (UPLC, ExionLC™ AD, https://sciex.com.cn/; MS, QTRAP® 6500+, https://sciex.com.cn/) was utilized to analyze the sample extracts. The following were the analytical conditions: LC: column, Waters ACQUITY UPLC HSS T3 C18 (100 mm×2.1 mm i.d., 1.8 µm); solvent system, water with 0.04% acetic acid (A), acetonitrile with 0.04% acetic acid (B); gradient program, started at 5% B (0–1 min), increased to 95% B (1–8 min), 95% B (8–9 min), and finally ramping back to 5% B (9.1–12 min); flow rate, 0.35 mL/min; temperature, 40°C; injection volume: 2 µL.

Linear ion trap (LIT) and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (QTRAP), QTRAP® 6500 + LC-MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in both positive and negative ion mode and controlled by Analyst 1.6.3 software (Sciex).

Data from three replicates were analyzed by using one-way ANOVA. All statistical analysis was performed by Statistical Package for the Social Sciences (SPSS 17.0). Results are shown as mean ± standard error of biological replications. The means were separated using Duncan’s multiple range test (p < 0.05).

KT: Kinetin; DEGs: differentially expressed genes; FDR: false discovery rate; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GA: gibberellin; CK: cytokinin; JA: jasmonic acid; SA: salicylic acid; ABA: abscisic acid, ETH: ethylene; MLT: melatonin; DCHs: differentially changed hormones; CYP714B: gibberellin 13-oxidase, GA20ox: gibberellin-44 dioxygenase; GA2ox: gibberellin 2beta-dioxygenas; PIF3: phytochrome-interacting factor 3; PP2C: protein phosphatase 2C; ABF: ABA responsive element binding factor; JAR1: jasmonic acid-amino synthetase; JAZ: jasmonate ZIM domain-containing protein; IAA: indole-3-acetic acid; IAA-Trp: indole-3-acetyl-L-tryptophan, IAA-Val-Me: indole-3-acetyl-L-valine methyl ester, TRA: tryptamine; KR: kinetin riboside; IP: N6-isopentenyladenine; tZR: trans-zeatin riboside; SE: somatic embryogenesis;

Ethics approval and consent to participate

The use of plant parts in the present study complies with international, national, and/or institutional guidelines. Our research team is affiliated with the Key Laboratory of the Yellow River Delta Grassland Resources and Ecology of the Chinese Forestry and Grassland Administration. We have obtained the permission to collect Seashore Paspalum.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the NCBI repository, [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1128878].

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by the National Natural Science Foundation of China (32101423), the Foundation Project of Shandong Natural Science Foundation (ZR2021MC066), and Fundamental Research Funds for the Universities (6631120002). the Foundation Project of Shandong Natural Science Foundation

Authors’ contributions

X. W. and K. J. conducted experiments; X.H., X. W., Z. Y., Q. S., W. W., and G. Y. gave advice and assistance in this research; Z.W. revised the manuscript; X. W. designed experiments and wrote the manuscript.

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Integrated transcriptomic and endogenous hormones analyses revealed the molecular mechanism of light and auxin for the regeneration of callus tissue in seashore paspalum

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Version 1

Abstract

Figures

Background

Results

Callus induction and regeneration

Sequencing annotation analysis statistics of different transcriptome

DEGs identification and enrichment analyses

GO and KEGG enrichment analysis of DEGs in different callus

Plant hormones analysis based on KEGG enrichment

Endogenous gibberellin content and metabolic pathways analysis

Endogenous hormone content and signal transduction pathways analysis

Validation of transcriptomic data using qRT-qPCR

Discussion

Genotype dependency analysis based on hormone differences

The role of endogenous hormones in callus regeneration

Conclusions

Methods

Plant growth and treatments

Tissue Paraffin Section Preparation

RNA Extraction and Illumina Sequencing

Sequencing data analysis

Quantitative Real-Time PCR analysis

Endogenous hormone level identification

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1