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Article

SlbHLH22-Induced Hypertrophy Development Is Related to the Salt Stress Response of the GTgamma Gene in Tomatoes

by
Baolu Cui
1,2,
Min Yu
1,
Jiaojiao Bai
1,* and
Zhiguo Zhu
1,*
1
College of Pharmacy and Life Sciences, Jiujiang University, Jiujiang 332005, China
2
College of Biological Sciences and Agriculture, Qiannan Normal University for Nationalities, Duyun 558000, China
*
Authors to whom correspondence should be addressed.
Metabolites 2023, 13(12), 1195; https://doi.org/10.3390/metabo13121195
Submission received: 31 October 2023 / Revised: 7 December 2023 / Accepted: 7 December 2023 / Published: 11 December 2023
(This article belongs to the Special Issue Plant Metabolic Genetic Engineering)
Browse Figures
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Abstract

:
Hypertrophy development induced by the overexpression of SlbHLH22 (also called SlUPA-like) was susceptible to Xanthomonas in tomatoes. Transcriptome and metabolome analyses were performed on the hypertrophy leaves of a SlbHLH22-overexpressed line (OE) and wild type (WT) to investigate the molecular mechanism. Metabolome analysis revealed that six key metabolites were over-accumulated in the OE, including Acetylserine/O-Acetyl-L-serine, Glucono-1,5-lactone, Gluconate, 2-Oxoglutarate, and Loganate, implying that the OE plants increased salt or oxidant resistance under normal growth conditions. The RNA-seq analysis showed the changed expressions of downstream genes involved in high-energy consumption, photosynthesis, and transcription regulation in OE lines, and we hypothesized that these biological processes were related to the GTgamma subfamily of trihelix factors. The RT-PCR results showed that the expressions of the GTgamma genes in tomatoes, i.e., SlGT-7 and SlGT-36, were suppressed in the hypertrophy development. The expression of the GTgamma gene was downregulated by salinity, indicating a coordinated role of GTgamma in hypertrophy development and salt stress. Further research showed that both SlGT-7 and SlGT-36 were highly expressed in leaves and could be significantly induced by abscisic acid (ABA). The GTgamma protein had a putative phosphorylation site at S96. These results suggested GTgamma’s role in hypertrophy development by increasing the salt resistance.

Graphical Abstract

1. Introduction

Xanthomonas causes a broad disease in crop cultivars, such as spot disease. To overcome plant defense, Xanthomonas delivers transcription activator-like effectors (TALes) into host cells to suppress immune responses [1]. AvrBs3, one of the TALe families, induces cell enlargement in the host leaf by directly activating a master regulator of cell size, i.e., UPA20, a bHLH family gene [2,3]. We also found that SlUPA-like (the orthology of UPA20, also called SlbHLH22) overexpression caused severe hypertrophy and facilitated the infection of Xanthomonas in tomato leaves. The experimental evidence proved that the Gibberellin (GA) response was upregulated and that the jasmonic acid (JA) response was downregulated in SlUPA-like overexpressed lines (OEs) [4]. Additionally, the mature leaves of OEs curled upward and wilted under normal conditions, and the total chlorophyll decreased remarkably [4]. These phenotypes implied that other factors might be involved in the developmental malformation of OE plants.
Previous reports proved that altering plant development with trihelix factors contributes to pathogen susceptibility or resistance. GhGT-3b was strongly induced by Verticillium dahlia and the heterologous expression of GhGT-3b in Arabidopsis enhanced resistance to Verticillium dahlia but inhibited the growth of rosette leaves [5]. ARABIDOPSIS SH4-RELATED 3 (ASR3) overexpressed plants were smaller than the control but enhanced susceptibility to infections of Pseudomonas syringae pv tomato DC3000 and Pseudomonas syringae pv maculicola ES4326 [6]. Meanwhile, a similar mechanism was also found in the over-accumulation of the ASR3-interacting transcriptional factor 1 (AITF1), which negatively regulated Pseudomonas syringae resistance in Arabidopsis [7]. In maize, the seedlings of ZmGT-3b knockdown showed reduced photosynthesis activity but were resistant to the Fusarium graminearum challenge [8]. However, few data verified the role of the trihelix gene in hypertrophy developments.
Most studies focus on trihelix factor functions in abiotic stress. The overexpression of ShCIGT (GT-1) improved cold and drought tolerance in tomatoes [9]. In cotton, GhGT26 (GT-1)-overexpressed lines had higher salt tolerance than the control via the ABA independent pathway, which was partially similar to the SIP1subfamily gene GhGT23 [10]. In rice, the experimental data proved that OsGTgamma-1 and OsGTgamma-2 have specific roles in promoting salt tolerance when directly regulating salinity transporter genes [11,12]. Interestingly, SlbHLH22 enhanced plant tolerance to salinity in MicroTom (one dwarf cultivar of tomato) [13]. It was a hypothesis that perhaps SlbHLH22 regulates abiotic stress-related genes via the trihelix family.
Aside from regulation by the transcription level, trihelix factor functions are often affected by post-transcription modification. Calcium/calmodulin kinase II (CaMKII) can phosphorylate GT-1 at T133 [14]. ShCIGT (SlGT-24) regulated abiotic tolerance by interacting with Snf1-related kinase 1 (SnRK1) [9]. NMR titration experiments suggested the phosphorylation site of GT-1 is located at the N-terminus of the third helix [15]. The N-terminal of PTL, a GT-2 factor, can be phosphorylated by SnRK1α1(AKIN10), an α-subunit of SnRK1 [16]. Meanwhile, ASR3 can be phosphorylated by MAMP-activated MPK4 [6]. Therefore, we speculated that trihelix factors might fulfill the necessary functions via phosphorylation.
In our experiment, transcriptome and metabolome analysis was used to reveal the molecular mechanism of a developmental malformation in OE, suggesting that the susceptibility of OE plants to Xanthomonas was related to increasing salt or oxidant tolerance. Extensive analysis indicated that GTgamma was suppressed downstream of SlbHLH22 protein, which was similar to that inhibited expression in salt stress. Deep analysis forecasted that the GTgamma protein might be phosphorylated at the post-transcription level. Therefore, our research provided a good foundation for studying the pathogenic mechanism of hypertrophy development and GTgamma’s role in biotic and abiotic stress.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions

Solanum lycopersicum Mill. var. Ailsa Craig (AC++, WT) and SlbHLH22 (Solyc03g097820, also called SlUPA-like) OE lines [3] were grown in a glasshouse under controlled conditions with 16-h-light/8-h-dark cycles, 25 °C-day/18 °C-night temperatures, 80% relative humidity, and 250 μmol m−2 s−1 luminous intensity. Flowers were tagged at the anthesis stage, immature green fruit was defined as 20 DPA (days past anthesis), mature green fruit as 35 DPA, and breaker fruit as 38 DPA with the color starting to generate a slight yellow shade. Other fruits from the 4th (B+4) and 7th (B+7) days after the breaker were harvested. Fruits at different ripening stages were collected, frozen immediately in liquid nitrogen, and stored at −80 °C until use [17].

2.2. Transcriptome and Metabolome Analysis

Total RNA was extracted from OE and WT leaves by using Trizol reagent (Invitrogen, Carlsbad, CA, USA), and the concentration and purity of RNA were measured by Nanodrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). The RNA integrity was measured by Agient 2100, LabChip GX (Santa Clara, CA, USA). Three biological replicates were sampled for each group (WT, OE). RNA and then transcriptomic experiments were conducted by BMKcloud, Beijing, China (http://www.biomarker.com.cn, accessed on 19 May 2023). Clean reads were obtained by removing adapters. Reads were then mapped to the Solanaceae genome (https://solgenomics.net/, accessed on 19 May 2023) using HISAT2 and gene expression levels were quantified with HTseq (BMKcloud, Beijing, China) [18].
Samples were ground to powder using a grinder (MM 400, Retsch, Shanghai, China) and dissolved into an extraction solution to remove by ultrasonic extraction. The extracted metabolites were analyzed by LC-MS/MS with Waters Xevo G2-XS QTOF (Milford, CT, USA). The metabolomics experiments and conjoint analyses of transcriptome and metabolome sequencing were conducted by BMKcloud, Beijing, China (http://www.biomarker.com.cn/, accessed on 19 May 2023) [18].

2.3. Hormonal and Salt Treatments

A 35-day-old tomato seedling of AC++ planted in green house of Jiujiang University (Jiujiang, China) was used for hormonal and abiotic treatments with three biological replicates.
For hormonal treatment, all the potted tomato seedlings were sprayed with different hormonal (50 μM 3-Indoleacetic Acid IAA, 50 μM Gibberellin GA, 100 μM 1-Aminocyclopropane-1-Carboxylicacid, ACC, 100 μM Abscisic Acid ABA, 50 μM Methyl Jasmonic Acid MeJA; 50 μM Epibrassinolide EBR; 50 μM Uniconazole NA) (Coolaber, Beijing, Chia) and distilled water (the control). Plants were enclosed in plastic immediately and left for 0, 1, 4, 8, 12, 24 h; the leaves of the tomato seedlings were taken and stored at −80 °C until use [19,20,21].
Salinity treatments were operated by submerging the roots of the tomato seedlings in distilled water with 200 mM NaCl for 0, 1, 4, 8, 12, 24, 48 and 72 h; Roots and leaves from the treated seedlings were collected and stored at −80 °C until use [22].

2.4. RT-PCR

The total RNA was reverse-transcribed to cDNA. RT-PCR was performed using SYBR ® Premix Ex Taq TM (TaKaRa, Dalian, China). RT-PCR primers were designed with Primer 5 (Supplementary Table S1). The tomato SlCAC and SlEF1a gene were used as an internal control of expression patterns and treatments. All the selected genes were calculated with three technical replicates.

2.5. Statistic Analysis

All data are means ± standard deviation of at least three independent experiments. Significance in a difference between the two groups was assessed by a Student’s t-test (*, p < 0.05 or **, p < 0.01). The different letters above the column in the figures indicate that significant differences of p < 0.05 were assessed by ANOVA. These statistical programs were performed using DPS v2.1.3 software (Ruifeng, Hangzhou, China).

2.6. Computational Modeling

The structure of the peptides was drawn using SWISS-MODEL. The peptide was sent to the GRAMMX protein–protein docking server (Version 12.0). Conformation models were obtained. These docking conformations were sent to the Rosetta FlexPepDock 4.0 server to be refined from a complex between a protein receptor and an estimated conformation for a peptide, allowing full flexibility to the peptide and sidechain of the receptor. FlexPepDock 4.0 gave an output of predicted energies for the complex. Peptides were added to the CHARMM36 force field to correct any resulting mischarges [23].

3. Results

3.1. Metabolome Analysis of OE vs. WT

After metabolome analysis, different expressed genes (DEGs) encoding metabolic processes in OE were primarily clustered in “alanine, aspartate and glutamate metabolism”, “carbon metabolism”, “monoterpenoid biosynthesis”, “taurine and hypotaurine metabolism”, “tyrosine metabolism” and “zeatin biosynthesis” compared to those in WT. The different metabolic processes were most enriched in “ascorbate and aldarate metabolism”, “carbon metabolism”, “pentose and glucuronate interconversions”, and “vitamin B6 metabolism”. They were further enriched in “arginine biosynthesis”, “unsaturated fatty acids biosynthesis”, “monoterpenoid biosynthesis”, “phosphatidylinositol signaling system”, “sulfur metabolism”, “taurine and hypotaurine metabolism”, “terpenoid backbone biosynthesis”, and “zeatin biosynthesis” (Figure 1). The consistent results between metabolic processes and their DEGs were “carbon metabolism”, “monoterpenoid biosynthesis”, “taurine and hypotaurine metabolism”, and “zeatin biosynthesis”. Within these processes, six key metabolites were abundant, including Acetylserine/O-Acetyl-L-serine (OAS), Glucono-1,5-lactone, Gluconate, 2-Oxoglutarate (2-OG) and Loganate (Figure S1). Previous studies confirmed that these metabolites were helpful to salt or oxidant resistance [24,25,26,27,28].

3.2. Transcriptome Analysis of OE vs. WT

To better understand the molecular mechanism of malformation developments in OE leaves, we performed transcriptome analysis in the mature leaves of OE vs. WT. Through RNA-seq analysis, we obtained 6 RNA-seq libraries and 24 to 27 million clean reads. After alignment with reference sequences, the alignment efficiency of clean reads ranged from 94.47% to 96.22% (Supplementary Table S2). Clearly, 2815 DEGs were identified, including 1299 upregulated and 1516 downregulated DEGs (Figure 2).
Gene ontology (GO) analysis clarified that upregulated DEGs remarkably converged on “amino acid” and the “sulfate transmembrane transport process” in the biological process (Figure 3A). In cellular component ontology, “integral component of membrane” and “plasma membrane” were the most abundant categories (Figure 3B). Genes involved in “amino acid transmembrane transporter activity”, “sequence-specific DNA binding”, “transcription factor activity” and “secondary sulfate transmembrane transporter activity” were enriched in the molecular function category (Figure 3C). Downregulated DEGs markedly gathered in “photosynthesis”, “light harvesting in PSI”, “protein-chromophore linkage”, “responses to light stimulus”, “flavonoid glucuronidation”, “flavonoid synthesis”, “DNA replication initiation” and “cell wall biogenesis” in biological processes (Figure 3D). In cellular component ontology, “photosystem”, “plastoglobule”, “MCM complex”, “chloroplast”, “cell wall”, “nucleosome”, “intracellular membrane-bounded organelle”, and ”THO complex” were the most abundant categories (Figure 3E). Genes involved in “chlorophyll” and “pigments binding” were enriched in the molecular function category (Figure 3F). These data suggested that the strongly repressed photosynthesis increased the substance transmembrane transport and transcription factor activities in OE.
Our KEGG enrichment analysis is shown in Figure 4A. The pathway “galactose metabolism”, “fatty acid degradation”, “amino acids (valine, leucine and isoleucine) degradation”, “tyrosine metabolism” and “α-linolenic acid metabolism” were primarily clustered. From a wider range of KEGG enrichment results, “protein processing in endoplasmic reticulum”, “ubiquitin mediated proteolysis”, “plant hormone signal transduction”, and the “phosphatidylinositol signaling process” were also enriched (Figure S2). Downregulated DEGs clustered in “antenna proteins”, “DNA replication”, “ribosome”, “glutathione metabolism”, “steroid biosynthesis” and “ribosome biogenesis” (Figure 4B). These results point to accelerated energy consumption, decreased growth, and development processes in OE.

3.3. Analysis of the Transcription Factor among DEGs

GO analysis indicated that DEGs encoding transcription factors were significantly enriched in downstream genes. Through an amino acid blast in the NCBI and SGN databases (plantTFDB), 206 DEGs and 46 TF (transcription factors) families were obtained in OE (Table 1). Trihelix factors always take part in plant photosynthesis, growth, and development [29,30]. Four genes of the trihelix family in OE were clearly regulated, including upregulated SlGT-31 (GT-2) and SlGT-32 (SIP1) and downregulated SlGT-34 (GT-2) and SlGT-36 (GTgamma) (Figure 5). Recently, the role of the GTgamma subfamily in salt stress has been emphasized [12], but GTgamma gene responses in hypertrophy development have rarely been reported.

3.4. Expression Patterns of GTgamma Genes in AC++ and Their Responses to External Stimuli

Given that GTgamma is a downstream gene of SlbHLH22 protein and has a positive function in salt tolerance in rice [12], GTgamma responses to salt treatments and expression patterns were investigated in tomatoes. We tested the expression profiles of 11 different organs of the tomato cultivar AC++. Two GTgamma genes (SlGT-7 and SlGT-36) were expressed in the leaves of AC++, especially SlGT-36. SlGT-7 displayed significantly higher expressions in B+4 and B+7 (Figure 6A). SlGT-36 transcripts accumulated the lowest in the B stage (Figure 6B). Thus, the expression patterns of two GTgamma genes exhibited tissue specificity.
To examine the endogenous response of GTgamma genes to salinity, 35-day-old tomato seedlings were watered with salinity (Figure 7). Both SlGT-7 and SlGT-36 were gradually induced to 2~2.5 fold at 12 h and then suddenly suppressed to less than 50% at 24 h in leaves. In the next two days, they remained at a low level (Figure 7A,B). In seedling roots, SlGT-7 was gradually upregulated to about 4.5-fold within 48 h and then downregulated (Figure 7C,D). The experimental results suggested that both GTgamma genes were repressed in leaves due to salinity stress.
To find the putative signaling pathway, SlGT-7 and SlGT-36 were treated with seven hormones. The expression levels of both GTgamma genes were higher in all hormonal treatments than in water spraying after 8 h (Figure 8A−D). Within 24 h, SlGT-7 and SlGT-36 were maintaining higher levels than controls under ABA treatments (Figure 8A,C). In addition, both GTgamma genes showed sensitivity to other hormonal stimuli (Figure 8B,D). These results suggested that GTgamma genes might participate in the ABA signaling pathway.

3.5. Three-Dimensional Structures of SlGT-7 and Its Potential Phosphorylation Site

Transcription factors have a critical role in plant physiology and development, and most of these events are commonly mediated by protein phosphorylation [15,16]. To anticipate the posttranscriptional modification of GTgamma factors, a three-dimensional model of SlGT-7 was built. Using SWISS-MODEL, the lowest energy structure of SlGT-7 is shown as ribbon models in Figure 9A. In this model, two classical domains were found including triple-helix (Helix 1, Helix 2 and Helix 3) and the fourth helix at the C-terminal. SlGT-7 looked like an ellipse with a hole on one side (Figure 9B). ATP molecules putatively entered into the hole and interacted with SlGT-7 at the lowest energy (−6.35 kcal/mol) (Figure 9C–E). Further analysis showed that five amino acids (S96, Y196, Q199, N200 and R201) inside the hole interacted with the ATP molecules via hydrogen bonds (Figure 9F). The distance estimation of γ-phosphate to five amino acids implied that S96 in Helix 1 was the potential phosphorylation site.

4. Discussion

Xanthomonas delivers TALes into plant cells to overcome a plant’s defense [1]. Like a transcription factor, AvrBS3, one TALe targets UPA20 to induce hypertrophy development in pepper leaves, which promotes the infection of Xanthomonas [2,3] and SlUPA-like (SlbHLH22) functions in tomato leaves [2,4]. To reveal the malformation development of OE leaves in more depth, transcriptome and metabolome analyses were carried out in WT vs. OE. The metabolome results showed that the following metabolites were over-accumulated: Acetylserine, O-Acetyl-L-serine (OAS), Glucono-1,5-lactone, Gluconate, 2-Oxoglutarate (2-OG), and Loganate (Figure 1). OAS accumulations are related to resistance to salt stress [24,25], which was analogous to the biological function of the GTgamma factor in rice [12]. Gluconate induces increased abiotic stress resistance in plants [28]. 2-OG is linked to the metal toxicity alleviatory of tomato and hormonal synthesis in the sulfate-dependent or independent pathway [26,31], which was similar to our results in the GO analysis (Figure 3). Through RNA-seq analysis, 1299 and 1516 DEGs were, respectively, up- and downregulated (Figure 2). The transcriptome enrichment results indicated that weak photosynthesis, high-energy consumption, increased transcription factor activity, and sulfate transmembrane transport occurred in OE (Figure 3 and Figure 4). Loganate has the capability of scavenging against superoxide radicals [25]. In addition, SlbHLH22 (also called SlUPA-like) enhances plant salinity [13,32]. Therefore, both transcriptome and metabolome analyses suggested that the hypertrophy phenotypes of OE lines might be connected with promoting salt or oxidative resistance.
Further research showed that the GTgamma gene was not only suppressed in hypertrophy leaves, but also inhibited by salt stress. The GO analysis showed that these biological processes, e.g., “light harvesting”, “photosynthesis”, “responses to light stimulus”, “flavonoid synthesis”, etc., were prominently restrained in OE, which always took place in the trihelix factor [30,33]. Fortunately, four trihelix genes exhibited remarkable regulation: increased SlGT-31(GT-2) and SlGT-32 (SIP1) and decreased SlGT-34 (GT-2) and SlGT-36 (GTgamma) (Figure 5). Furthermore, six metabolites (Acetylserine, OAS, Glucono-1,5-lactone, Gluconate, 2-OG and Loganate) had a possible role in promoting salt or oxidant tolerance [24,25,26,27,28]. It was reported that GTgamma played the role of a positive regulator in salt stress in rice and that SlbHLH22 boosted salt resistance in tomatoes [11,12,13]. These results implied that GTgamma, as downstream genes of SlbHLH22 protein, might perform a salt-resistant function in tomatoes. Figure 7 shows that both GTgamma genes were prominently inhibited by salt stress, implying a consistent role in malformation development of the OE line and salt stress.
Through an extensive analysis of the GTgamma genes, we found that two GTgamma genes were expressed in AC++ leaves, especially SlGT-36, indicating the reason why only one GTgamma gene was repressed by SlbHLH22 in hypertrophy. Tissue-specific expression patterns were present when SlGT-7 transcripts were specifically expressed in B+4 and B+7 stages fruit and SlGT-36 in all tissues except B stage fruit (Figure 6), which was slightly different from Yu et al. [34], indicating the following different varieties: AC++ and LA1777. In addition, SlGT-7 was remarkably upregulated by ABA, which was very similar to OsGTgamma-1 [11]. Both SlGT-7 and SlGT-36 responded to all selected phytohormone, indicating their versatile role in plant growth and development (Figure 8). Moreover, we also found that water inhibited SlGT-7 and SlGT-36 expressions by over 60% in the leaves of AC++ seedlings (Figure 8). Whether SlGT-7 was involved in the regulation of water stress needs more evidence.
Protein posttranslational modification is a fine-tuned mechanism in abiotic or biotic resistance [6,9,15,16,17]. Therefore, we hypothesized that GTgamma performed this function via phosphorylation but required further experimental evidence support. We constructed a three-dimensional model of SlGT-7 as a candidate. We discovered the interactions between ATP and SlGT-7 in a putative hole (Figure 9). We also predicted that S96 was the most likely phosphorylation site. It was commonly believed that protein kinases transfer γ-phosphate from ATP to Ser (S), Thr (T), or Tyr (Y) during protein modification [35]. Our model implied that S96 got closer to the γ-phosphate of ATP than others, suggesting the phosphorylation site of S96 (Figure 9F). In short, our present findings about the posttranslational modification model of the GTgamma protein provide the foundation for an in-depth study of the hypertrophy development of OE lines and the regulatory role of downstream genes in tomatoes.

5. Conclusions

Xanthomonas injects TALes into the host cells to suppress plant immune defense. One TALe, AvrBS3, activates the plant target gene: pepper upa20. The overexpression of SlbHLH22 (also called SlUPA-like), i.e., the orthology of upa20, causes the hypertrophy and susceptibility of Xanthomonas in tomatoes. The metabolome analysis showed that specific metabolites were over-accumulated in OE with a potential role in promoting salt resistance. The transcriptome analysis verified that OE plants suffered from high energy consumption, weak photosynthesis, and increased transcription factors activity. GTgamma gene expression was suppressed by SlbHLH22. Furthermore, it was simultaneously inhibited by salt stress, indicating GTgamma’s role in the formation of hypertrophy development via the salt stress response. Extensive analysis proved that both GTgamma genes expressed in leaves were induced by ABA. Moreover, the GTgamma protein had a putative phosphorylation site at S96. Our results provide the basis for disclosing the pathogenic mechanism of hypertrophy development medicated by the GTgamma subfamily.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/metabo13121195/s1, Table S1: The primers used for qRT-PCR analysis; Table S2: Quality control of RNA-seq reads in different samples; Figure S1: Key metabolites in four metabolic processes; Figure S2: Comprehensive analysis of up—(A) and down—(B) regulated DEGs by KEGG.

Author Contributions

B.C.: design, funding acquisition, laboratory experiments, data analysis, and manuscript drafting. M.Y.: laboratory experiments. J.B.: performed laboratory experiments. Z.Z.: manuscript drafting. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (No. 31960605 and No. 32160711).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available within the article and the Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Pathways annotated with differential metabolic process and genes by KEGG analysis. Gene: related to metabolic process; Meta: metabolic process.
Figure 1. Pathways annotated with differential metabolic process and genes by KEGG analysis. Gene: related to metabolic process; Meta: metabolic process.
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Figure 2. Comparing DEGs by volcano (A) and heatmap (B) pictures.
Figure 2. Comparing DEGs by volcano (A) and heatmap (B) pictures.
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Figure 3. Go enrichment analysis of up—(A–C) and down—(D–F) regulated DEGs.
Figure 3. Go enrichment analysis of up—(A–C) and down—(D–F) regulated DEGs.
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Figure 4. KEGG analysis of up—(A) and down—(B) regulated DEGs.
Figure 4. KEGG analysis of up—(A) and down—(B) regulated DEGs.
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Figure 5. qRT-PCR validation of four differentially expressed trihelix genes in WT vs OE. All data are means ± standard deviation of at least three independent experiments. Significance in difference between the two groups was assessed by a Student’s t-test using DPS software (*, p < 0.05; **, p < 0.01).
Figure 5. qRT-PCR validation of four differentially expressed trihelix genes in WT vs OE. All data are means ± standard deviation of at least three independent experiments. Significance in difference between the two groups was assessed by a Student’s t-test using DPS software (*, p < 0.05; **, p < 0.01).
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Figure 6. Expressions patterns of GTgamma genes, SlGT-7 (A) and SlGT-36 (B) in AC++. R: roots; ST: stem; YL: young leaves; ML: mature leaves; SL: senescent leaves; F: flowers; IMG: immature green fruit; mature green fruit; breaker fruit; B+4: 4 days after breaker fruit; B+7: 7 days after breaker fruit; All data are means ± standard deviation of at least three independent experiments. The different letters above the column indicated that significant expressions of GTgamma genes among diverse organs were assessed by ANOVA (p < 0.05) using DPS software.
Figure 6. Expressions patterns of GTgamma genes, SlGT-7 (A) and SlGT-36 (B) in AC++. R: roots; ST: stem; YL: young leaves; ML: mature leaves; SL: senescent leaves; F: flowers; IMG: immature green fruit; mature green fruit; breaker fruit; B+4: 4 days after breaker fruit; B+7: 7 days after breaker fruit; All data are means ± standard deviation of at least three independent experiments. The different letters above the column indicated that significant expressions of GTgamma genes among diverse organs were assessed by ANOVA (p < 0.05) using DPS software.
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Figure 7. Expressions of GTgamma genes, SlGT-7 (A,C) and SlGT-36 (B,D) in salt stress. The leaves and roots of a 35-day-old AC++ seedling were used. All data are means ± standard deviation of at least three independent experiments. The different letters above the column indicate that significant expressions of GTgamma genes among diverse time points were assessed by ANOVA (p < 0.05) using DPS software.
Figure 7. Expressions of GTgamma genes, SlGT-7 (A,C) and SlGT-36 (B,D) in salt stress. The leaves and roots of a 35-day-old AC++ seedling were used. All data are means ± standard deviation of at least three independent experiments. The different letters above the column indicate that significant expressions of GTgamma genes among diverse time points were assessed by ANOVA (p < 0.05) using DPS software.
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Figure 8. Expressions of two GTgamma genes, SlGT-7 (A,B) and SlGT-36 (C,D), in hormonal treatments. IAA: 3-Indoleacetic Acid; GA: Gibberellin; ACC: 1-Aminocyclopropane-1-Carboxylicacid; ABA: Abscisic Acid; MeJA: Methyl Jasmonic Acid; EBR: Epibrassinolide; NA: Uniconazole. The leaves of 35-day-old AC++ seedlings were used. All data are means ± standard deviation of at least three independent experiments. Significance in different expressions of GTgamma genes between hormonal treatments and control were assessed by a Student’s t-test using DPS software (*, p < 0.05; **, p < 0.01).
Figure 8. Expressions of two GTgamma genes, SlGT-7 (A,B) and SlGT-36 (C,D), in hormonal treatments. IAA: 3-Indoleacetic Acid; GA: Gibberellin; ACC: 1-Aminocyclopropane-1-Carboxylicacid; ABA: Abscisic Acid; MeJA: Methyl Jasmonic Acid; EBR: Epibrassinolide; NA: Uniconazole. The leaves of 35-day-old AC++ seedlings were used. All data are means ± standard deviation of at least three independent experiments. Significance in different expressions of GTgamma genes between hormonal treatments and control were assessed by a Student’s t-test using DPS software (*, p < 0.05; **, p < 0.01).
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Figure 9. Construction of SlGT-7 model and interaction between SlGT-7 and ATP molecular by autodock. (A): The ribbon models of SlGT-7; (B): Three-dimensional model of SlGT-7 protein. Electrostatic potential: Positive (blue), negative (red) and hydrophobic (green); (C): Interactions of SlGT-7 and ATP molecular as ribbon models; (D): Ramachandran plot showing the lowest energy of all the amino acids interacting with the ATP molecular. Phi and Psi represent the rotation angle of the C-N and C-C bonds of α carbon in every peptide unit, respectively. Blue curves indicate the low energy and red the high energy. The dot represents amino acid; (E): The putative action site of the SlGT-7 model and ATP molecular; (F): The binding of SlGT-7 and ATP by hydrogen bonds.
Figure 9. Construction of SlGT-7 model and interaction between SlGT-7 and ATP molecular by autodock. (A): The ribbon models of SlGT-7; (B): Three-dimensional model of SlGT-7 protein. Electrostatic potential: Positive (blue), negative (red) and hydrophobic (green); (C): Interactions of SlGT-7 and ATP molecular as ribbon models; (D): Ramachandran plot showing the lowest energy of all the amino acids interacting with the ATP molecular. Phi and Psi represent the rotation angle of the C-N and C-C bonds of α carbon in every peptide unit, respectively. Blue curves indicate the low energy and red the high energy. The dot represents amino acid; (E): The putative action site of the SlGT-7 model and ATP molecular; (F): The binding of SlGT-7 and ATP by hydrogen bonds.
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Table 1. Statistical analysis of all differentially expressed transcription factor genes.
Table 1. Statistical analysis of all differentially expressed transcription factor genes.
Serial
Number		TF
Family		DEGs
Numbers		Serial
Number		TF
Family		DEGs
Numbers		Serial
Number		TF
Family		DEGs
Numbers
1AP2/ERF-AP2217E2F-DP133MYB-related4
2AP2/ERF-ERF1918EIL134NAC19
3B3519GARP-ARR-B135NF-YA4
4B3-ARF220GARP-G2-like236NF-YB1
5BBR-BPC121GeBP137NF-YC1
6bHLH1522GRAS438OFP1
7bZIP1023HB-BELL239PLATZ2
8C2C2-CO-like224HB-HD-ZIP1540RWP-RK1
9C2C2-Dof525HB-KNOX241SRS1
10C2C2-GATA326HB-other442TCP6
11C2C2-YABBY227HMG243Tify2
12C2H21128HSF644Trihelix4
13C3H229LOB145WRKY8
14CPP130MADS-MIKC846zf-HD1
15DBB131MADS-M-type3
16DBP132MYB16
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MDPI and ACS Style

Cui, B.; Yu, M.; Bai, J.; Zhu, Z. SlbHLH22-Induced Hypertrophy Development Is Related to the Salt Stress Response of the GTgamma Gene in Tomatoes. Metabolites 2023, 13, 1195. https://doi.org/10.3390/metabo13121195

AMA Style

Cui B, Yu M, Bai J, Zhu Z. SlbHLH22-Induced Hypertrophy Development Is Related to the Salt Stress Response of the GTgamma Gene in Tomatoes. Metabolites. 2023; 13(12):1195. https://doi.org/10.3390/metabo13121195

Chicago/Turabian Style

Cui, Baolu, Min Yu, Jiaojiao Bai, and Zhiguo Zhu. 2023. "SlbHLH22-Induced Hypertrophy Development Is Related to the Salt Stress Response of the GTgamma Gene in Tomatoes" Metabolites 13, no. 12: 1195. https://doi.org/10.3390/metabo13121195

APA Style

Cui, B., Yu, M., Bai, J., & Zhu, Z. (2023). SlbHLH22-Induced Hypertrophy Development Is Related to the Salt Stress Response of the GTgamma Gene in Tomatoes. Metabolites, 13(12), 1195. https://doi.org/10.3390/metabo13121195

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