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. 2022 Aug 28;28(8):1501–1513. doi: 10.1007/s12298-022-01223-2

Transcriptome analysis of gene expression in the tomato leaf premature senescence mutant

Fengmei Jin 1,✉, Mingyan Hua 1, Lanfang Song 1, Shaojie Cui 1, Haibo Sun 1, Weidong Kong 1, Zhiyu Hao 1
PMCID: PMC9530104  PMID: 36389094

Abstract

Premature senescence of leaves can critically influence tomato yield and quality. In this study, the leaf premature senescence mutant MT318 was a spontaneous mutant and was controlled by a single recessive nuclear gene. The maximum photochemical efficiency (Fv/Fm), superoxide dismutase (SOD), and chlorophyll content in the leaves of mutant MT318 gradually decreased, while malondialdehyde (MDA) content significantly increased. Under the level 2 category, Gene Ontology (GO) enrichment analysis indicated that 45 terms were enriched, comprising 22 in biological process, 12 in cellular component, and 11 in molecular function. Genes are mainly involved in the metabolic processes (696 differentially expressed genes, DEGs), cellular processes (573 DEGs), single-organism processes (503 DEGs), and catalytic activity (675 DEGs). Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the 4 pathways with the largest number of genes were biosynthesis of secondary metabolites, plant-pathogen interaction, plant hormone signal transduction, and MAPK signaling pathway-plant. The ‘plant hormone signal transduction’ pathway was the most significantly enriched at the T2 stage. Pearson correlation analysis showed that the auxin regulatory pathway and SA signal transduction pathway may play important roles. These results not only lay the foundation for the further cloning and functional analysis of the MT318 premature senescence gene but also provide a reference for the study of tomato leaf senescence.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01223-2.

Keywords: Solanum lycopersicum, Mutant, Senescence, RNA-Seq

Introduction

In the process of plant growth and development, leaf senescence is necessary. During senescence, a series of physiological, morphological, cellular, and molecular events take place (Lim et al. 2007; Zhao et al. 2018). In agriculture, if senescence occurs early, the ability of plants to absorb CO2 is reduced (Astrid et al. 2006), influencing the accumulation of sugars during seed formation, which in turn impacts crop yield (Xu et al. 2017). The early maturation and senescence of crops caused by abiotic stress can reduce the average yield of the major grain-producing areas in the world by half (Zentgraf et al. 2010). Therefore, the timing of leaf senescence is crucial to crop yield.

The process of leaf senescence is regulated by both internal developmental endogenous signaling molecules, such as plant hormones, and the external environments, such as salinity, drought, pathogen infection, and temperature stress(Chen et al. 2022). Each factor and the developmental signal form a complex signaling network to regulate and control the senescence process.

Leaf senescence is a process involving programmed cell death that is actively regulated by nuclear genes. Liu et al. (2008) identified 533 differentially expressed genes (DEGs) associated with senescence by suppressing subtractive hybridization, including functions related to protein biosynthesis regulation, macromolecule metabolism, gene expression regulation, energy metabolism, pathogenicity and stress, detoxification, flower development, and cytoskeleton organization.

Thus far, many senescence-related genes have been identified in rice, Arabidopsis, soybean, tomato, and other plants. These genes include transcription factors, such as AP2/ERF (Chen et al. 2022), NAC (Ma et al. 2018; Moschen et al. 2012), OsDOS (Kong et al. 2006), AtWRKY (Balazadeh et al. 2008), SUB1A (Fukao and Bailey-Serres 2012), and ARF (Guan et al. 2018); protease or kinase genes, such as PP2C (Jiang 2020), OsABC1-2 (Gao et al. 2012), SlSnRK2 (Yang et al. 2014), and Osh69; and metabolic processing degradation genes, such as SGR (Jiang et al. 2007; Park et al. 2007), SGRL (Ys et al. 2014), nyc1 (Kusaba et al. 2007), NYC (Yamatani et al. 2013) and PAO (Wang et al. 2011). Therefore, senescence is a complex network regulatory process involving many genes.

Plant hormones are essential in the many factors affecting leaf senescence. Jasmonate (JA), ethylene, abscisic acid (ABA), salicylic acid (SA), and brassinosteroids (BRs) can accelerate senescence, whereas cytokinin, gibberellic acid (GA), and auxin can repress senescence (Khan et al. 2013; Jibran et al. 2013). However, the regulatory mechanisms of leaf senescence and the role of hormones therein have not been completely elucidated in tomato.

Tomato (Solanum lycopersicum L.) is a popular vegetable crop because of its high yield and rich nutrient profile, resulting in it being one of the most widely cultivated vegetable crops in the world. Tomato leaves are important photosynthetic organs for the synthesis of essential substances, such as carbohydrates, lipids, and amino acids. During tomato cultivation, premature leaf senescence occurs frequently, resulting in a decline in plant photosynthetic capacity and slow growth and development, which seriously affects yield and quality and may even lead to premature death. Therefore, it is of great significance to elucidate the functional genes controlling senescence in tomato.

In this study, RNA sequencing (RNA-Seq) analysis was used to explore the key genes that may affect tomato senescence, providing a foundation for elucidating the molecular mechanism of leaf senescence in tomato.

Materials and methods

Tomato materials and genetic analysis

The tomato leaf premature senescence mutant MT318 is a spontaneous mutant derived from ‘aomei-318’. After many generations of self-breeding and observation, the agronomic characteristics remained stable. The lower leaves of mutant MT318 turned yellow when the first cluster of flowers opened, and the plant became shorter and thinner compared to the wild-type (WT). The WT and MT318 plants were crossed to obtain the F1 generation, F1 generation seeds were planted in the field to obtain F2 generation seeds, and 300 F2 generation seeds were planted in the field. The segregation ratio was counted at the flowering stage using a chi-square (χ2) test.

Investigation of agronomic traits

The experiment was conducted at the experimental base of the Tianjin Academy of Agricultural Sciences (Tianjin, China). In the experiment, the seeds of the WT and MT318 were homozygous for phenotype and were sown each year (2019, 2020, 2021) on February 9 and planted on March 19, at a 9 plants/m2 density. The plant diameter, plant height, weight of fruit, and longitudinal and transverse diameter of the fruit were measured.

Measurement of Fv/Fm, chlorophyll, malondialdehyde (MDA), and superoxide dismutase (SOD)

The Fv/Fm, chlorophyll, MDA, and SOD quantification of the leaves of the WT and MT318 were performed at different stages. The leaves of the same position as mutant MT318 and the WT were selected (single stem pruning). The first leaf from the first cluster of flowers was selected.

The measurement of Fv/Fm was carried out from 9:00 to 11:00 in the morning. Fv/Fm was measured using a fluorescence analyzer (PAM-2500, WALZ, Germany). Before measurement, the selected leaves were treated with a dark adaptation clip for 40 min, and the measurement was performed according to the instructions. Each sample was repeated 3 times.

The leaf silk extraction method was used to extract chlorophyll (Liu et al. 2020; Shu et al. 2010). After shredding, samples were mixed well, and 0.2 g was weighed. The weighed samples were placed in a 50 mL tube, and 20 mL of 80% acetone was added. Samples were placed in the dark to extract for 12 h until the leaf tissue turned white. The supernatant was retained, and the debris was removed by centrifugation at 12,000g for 10 min. The absorbance of the solution was measured at 470, 649, and 665 nm with a spectrometer, and the concentrations of chlorophyll were calculated(Lichtenthaler 1987).

The MDA content was measured using the thiobarbituric acid (TBA) method (Li 2000). The leaves (0.5 g) were weighed and placed in an ice-bath mortar. Two milliliters of 5% trichloroacetic acid (TCA) and a small amount of quartz sand were added, ground to a homogenate, and centrifuged at 3000g for 10 min. Retained the supernatant, and added 3 mL of 0.5% TBA solution. The mixture was reacted in a boiling water bath for 30 min, cooled quickly, and then centrifuged. Retained the supernatant and measured the absorbance at 450, 532, and 600 nm. SOD was measured using a total SOD assay Kit (Nanjing Jian Cheng Bioengineering Institute) according to the instructions. Each sample was repeated 3 times.

RNA extraction and library construction

The leaves of MT318 and the WT were collected at the T1 (April 29), T2 (May 15), and T3 (May 30) stages and the WT was used as a control. Three replicates were used per stage. The TRIzol reagent kit (Invitrogen, Carlsbad, CA, USA) was used for the isolation of total RNA. The quality of RNA was checked on an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). The mRNA was enriched by oligo(dT) beads and then fragmented using fragmentation buffer. The fragments were reverse transcribed into cDNA using random primers and further cDNA fragments were purified by a QiaQuick PCR extraction kit (Qiagen, Venlo, The Netherlands). The cDNA fragments were then end-repaired, poly(A) added, and ligated to Illumina sequencing adapters. The ligation products were size-selected with agarose gel electrophoresis, amplified by PCR and sequenced using Illumina HiSeq2500 by Gene Denovo Biotechnology Co. (Guangzhou, China). To obtain high-quality clean reads, reads were filtered by fastp (Chen et al. 2018) (version 0.18.0). Sequencing reads were mapped to the ribosome RNA (rRNA) database by Bowtie2 (Langmead and Salzberg 2012) (version 2.2.8) and all the mapped reads were removed. The clean reads were used in the assembly and for gene abundance calculations. The reference genome index was built and mapped to the reference genome “GCF_000188115.4_SL3.0.” by HISAT2. 2.4 (Kim et al. 2015). The mapped reads of each sample were assembled with StringTie v1.3.1 (Pertea et al. 2015, 2016). The FPKM (fragment per kilobase of transcript per million mapped reads) value was calculated by StringTie software to quantify the expression abundance and variations. RNA differential expression analysis was carried out using DESeq2 software (Love et al. 2014). The genes with a false discovery rate (FDR) below 0.05 and |log2FoldChange| ≥ 1 were considered DEGs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to perform GO functional enrichment analysis and KEGG metabolic pathway enrichment analysis (p < 0.05). The network regulatory relationship between genes was determined using the Pearson correlation coefficient.

Quantitative RT-PCR

The validity of the RNA-Seq data was verified by Quantitative RT-PCR (qRT-PCR). The RNA samples were the same as those used for RNA-Seq. We randomly selected 15 genes related to leaf senescence. The specific genes and their qRT-PCR primer sequences are shown in Supplementary Table 1. The glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and Actin genes of tomato were used as internal controls to normalize expression (Han et al. 2015; Jiang et al. 2017). Primer 3 (https://primer3.ut.ee/) was used to design the primers. qRT-PCR analysis was conducted with a Roche LightCycler 480 Real-Time System (Roche, Switzerland). The reaction system was 15 µL and included 7.5 µL of 2 × SYBR Green PCR Mastermix (Toyobo, Japan), 2 µL of primers (2 µmol L−1), 1 µL of cDNA, and 4.5 µL of ddH2O. The qRT-PCR program was as follows: 95 °C for 3 min and 40 cycles of 95 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s to collect the fluorescence signals. The expression levels of the genes were estimated using 2−△△Ct method. All results are the mean of three independent replicates.

Results

Agronomic traits and genetic analysis

In 2018, a natural leaf premature senescence mutant was found in the tomato high-generation progeny line ‘aomei-318’. The mutant looked the same as the wild-type at the seedling stage (Fig. 1a). Approximately 40 days after planting, when the first cluster of flowers opened, leaf senescence accelerated, the lower leaves began to yellow, and the plant grew slowly. The tip of the first leaf under the first flower showed yellowing, and this period was called the T1 stage (Fig. 1c, T1). Approximately 55 days after planting, in the first leaf under the first cluster of flowers, half of the leaves showed yellowing, and this period was called the T2 stage (Fig. 1c, T2). Approximately 70 days after planting, the first leaf under the first cluster of flowers was completely yellowed, and this period was called the T3 stage (Fig. 1c, T3), while the leaves of the control WT did not exhibit yellowing during these 3 stages. At the adult stage, the plant height, stem diameter, and fruit size of the mutant were significantly different from those of the wild-type (Table 1).

Fig. 1.

Fig. 1

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The phenotypes of the WT and MT318. a The wild-type (WT) phenotype (left) and the MT318 mutant phenotype (right) at the seedling stage, their leaves grew normally. b Leaf phenotypes of the WT (left) and MT318 mutant (right) at the flowering stage, the leaves of mutant MT318 turned yellow while WT grew normally. c Leaves of the WT (down) and MT 318 (up) at T1, T2, and T3

Table 1.

The agronomic traits of wild-type and MT318

Material Year Plant height (cm) Diameter of plant (cm) Weight of fruit (g) Longitudinal diameter of fruit (cm) Transverse diameter of fruit (cm)
WT 2019 160.87 ± 0.47a 1.51 ± 0.02a 194.00 ± 3.61a 5.77 ± 0.09a 8.03 ± 0.09a
MT318 150.53 ± 0.43b 1.25 ± 0.03b 125.00 ± 2.89b 5.03 ± 0.09b 7.23 ± 0.07b
WT 2020 161.97 ± 0.5a 1.61 ± 0.01a 197.67 ± 3.71a 6.00 ± 0.06a 8.13 ± 0.15a
MT318 150.97 ± 0.56b 1.31 ± 0.01b 127.33 ± 1.45b 5.03 ± 0.07b 7.07 ± 0.09b
WT 2021 162.13 ± 0.38a 1.51 ± 0.01a 203.00 ± 3.61a 6.10 ± 0.06a 7.97 ± 0.12a
MT318 149.47 ± 0.49b 1.36 ± 0.02b 138.67 ± 2.33b 5.13 ± 0.03b 7.10 ± 0.06b
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Means marked by different lowercase letters indicate a significant difference (p < 0.05)

To study the genetics of this mutant, the F2 generation was planted in the field, and the segregation ratio was calculated. Among the 300 F2 plants, 69 showed the phenotype of MT318, whereas the others showed the phenotype of WT. The segregation ratio was 3.35. After the chi-square (χ2) test, the segregation ratio of the F2 population was 1:3, which proved that the premature aging trait was controlled by a single recessive gene.

Measurement of physiological traits

The first leaf under the tomato flower was crucial for tomato yield. At T1, T2, and T3, we measured the Fv/Fm, MDA, SOD, and chlorophyll content. With an increase in senescence, the Fv/Fm, SOD, and chlorophyll of the leaves of MT318 gradually decreased (Fig. 2a, c, d), while MDA increased significantly (Fig. 2b). The WT did not change significantly during the three stages.

Fig. 2.

Fig. 2

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Phenotypic and physiological index measurements of sample leaves of MT318 and the WT. a Measurements of Fv/Fm performed on MT 318 and the WT at different stages. b The MDA contents of MT 318 and the WT at different stages. c The SOD contents of MT 318 and the WT at different stages. d The chlorophyll contents of the WT and MT 318 at different stages. Error bars indicate the mean ± SD (n = 3). **P < 0.01

RNA-Seq analysis

The transcriptomes of wild-type WT-T1, WT-T2, and WT-T3 and mutant MT318-T1, MT318-T2, and MT318-T3 leaves were sequenced. In this study, we obtained 44,764,480–61,915,928 raw data and 44,715,810–61,842,598 clean data (Supplementary Table 2). After aligning with the tomato genome, a total of 47,707,639–51,829,960 high-quality sequences (average of three repeats) were obtained for each group. The Q30 values were all above 93%, and the GC content was between 44.41 and 45.14%, indicating that the data quality met the requirements for further bioinformatics analysis (Table 2).

Table 2.

The statistics of the sequencing data

Sample ID Total Clean data (G) Q30 (%) GC (%) Total mapped (%) Unique mapped (%) Multiple mapped (%)
WT-T1 50,850,550 8.19 93.65 44.96 97.43 95.46 1.97
WT-T2 47,707,639 7.68 93.76 44.78 97.62 95.78 1.83
WT-T3 52,676,254 8.36 93.66 45.14 97.58 95.33 1.91
MT318-T1 51,829,960 8.26 93.54 44.85 97.25 95.22 1.92
MT318-T2 49,500,274 8.22 93.50 44.41 97.12 95.31 1.80
MT318-T3 51,210,496 8.14 94.04 44.70 97.15 95.31 1.84
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Differentially expressed genes

In order to investigate the genes that may be related to leaf senescence, we identified DEGs between MT318 and the WT at different stages (Supplementary Table 3). At the T1 stage (WT-T1 vs. MT318-T1), when the leaf tips of MT318 showed senescence, 2068 DEGs were identified, of which 1265 were upregulated and 803 were downregulated (Fig. 3a). At the T2 stage (WT-T2 vs. MT318-T2), half of the leaves of MT318 had yellowed, and 2990 DEGs were identified, including 2041 upregulated genes and 949 downregulated genes (Fig. 3b). At the T3 stage (WT-T3 vs. MT318-T3), the leaves of MT318 had completely yellowed, and 1603 DEGs were identified, including 1164 upregulated genes and 439 downregulated genes (Fig. 3c). DEGs were the most numerous at the T2 stage. In addition, we examined specific DEGs at different stages. Venn diagrams showed that 475, 1028, and 313 genes were upregulated, and 504, 566, and 127 genes were downregulated at T1, T2, and T3, respectively. There were 400 and 141 genes that were simultaneously upregulated and downregulated in all stages (Fig. 4a, b). There were many genes showing differential expression patterns during tomato leaf senescence, indicating that tomato leaf senescence is a complex process involving multiple genes acting in combination.

Fig. 3.

Fig. 3

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Volcano plots of differentially expressed genes. a WT-T1 versus MT318-T1; b WT-T2 versus MT318-T2; and c WT-T3 versus MT318-T3. The x-axis indicates the fold-change difference in the expression of genes in the different comparison groups. The y-axis represents the adjusted p values for the differences in expression. The red dots indicate upregulated genes in the mutant, and the blue dots indicate downregulated genes in the mutant

Fig. 4.

Fig. 4

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Venn diagrams of the differentially expressed genes. a Venn diagram indicating the overlap in upregulated genes between the mutant and WT at different stages. b Venn diagram indicating the overlap in downregulated genes between the mutant and WT at different stages. The numbers in each circle represent the downregulated or upregulated genes at different stages

GO enrichment analysis

To explore the functions of the DEGs, we mapped all DEGs to the GO database. Under the level 2 category, 45 GO terms were enriched, comprising 22 in the biological process, 12 in the cellular component, and 11 in molecular function (Fig. 5; Supplementary Table 4). Genes related to biological processes mainly included metabolic process (696 DEGs), cellular process (573 DEGs), single-organism process (503 DEGs), biological regulation (191 DEGs), response to stimulus (186 DEGs), regulation of biological process (173 DEGs), and localization (123 DEGs). Genes in the molecular functions were enriched for binding (445 DEGs) and catalytic activity (675 DEGs). Genes in the cellular component were enriched for cell (270 DEGs), cell part (270 DEGs), membrane (212 DEGs), organelle (198 DEGs), and membrane part (168 DEGs). From this, differences in metabolic process, biological regulation, and single-organism process between the WT and MT318 were identified, further reflecting the differences in growth and development between the WT and MT318. To a certain extent, this explains the observed phenotypes, such as leaf yellowing and stunted plant growth.

Fig. 5.

Fig. 5

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GO enrichment analyses of DEGs between the WT and MT318 at different stages. The y-axis represents the number of genes belonging to the GO terms below. The x-axis indicates the GO terms, including biological process (green), cellular component (red), and molecular function (blue)

The top 15 GO terms with the smallest p values were selected for bar graph analysis (Fig. 6; Supplementary Table 5). The number of genes enriched in the T2 stage was the largest, and there were more GO terms related to plant hormone signal transduction, such as GO: 009725 response to hormone, GO: 0009755 hormone-mediated signaling pathway, and GO: 0032870 cellular response to hormone stimulus. This indicates that plant hormones may a key factor in the process of leaf senescence in tomato.

Fig. 6.

Fig. 6

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GO enrichment analyses of DEGs between the WT and MT318. a T1 stage; b T2 stage; and c T3 stage. The x-axis indicates the number of genes belonging to the GO terms. The y-axis represents GO terms

KEGG pathway analysis of DEGs

KEGG analysis of the DEGs was conducted at different stages to further inquire into the leaf senescence pathway in MT318. The DEGs were significantly enriched in 29 KEGG pathways (Supplementary Fig. 1; Supplementary Table 6). The 4 pathways with the largest number of genes were ‘biosynthesis of secondary metabolites’, ‘plant–pathogen interaction’, ‘plant hormone signal transduction’, and ‘MAPK signaling pathway-plant’. Among these pathways, the ‘plant hormone signal transduction’ pathway was the most significantly enriched (Rich factor = 0.24; p value = 2.1 × 10−8) at the T2 stage (Fig. 7), and it was also within the top 20 at the T1 and T3 stages (Supplementary Fig. 2; Supplementary Fig. 3; Supplementary Table 7).

Fig. 7.

Fig. 7

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Bubble chart of the top 20 pathways in KEGG analysis at the T2 stage

At T2, half of the leaves of MT318 showed senescence, many DEGs were significantly enriched in plant hormone signal transduction (Table 3). Therefore, we selected and analyzed the genes belonging to the plant hormone signal transduction pathway.

Table 3.

The DEGs of plant hormones

Gene ID Log2(Fc) p value Description Symbol Participation in signal transduction pathway
entrzID_101055548 3.59 2.36 × 10−11 Protein IAA8 IAA8 Auxin signaling
entrzID_100527977 1.01 8.28 × 10−4 Auxin response factor 17 ARF17 Auxin signaling
entrzID_100498663 − 1.82 6.30 × 10−7 Auxin response factor 19 ARF19 Auxin signaling
entrzID_100191131 − 1.08 7.60 × 10−5 Auxin response factor 7 LOC100191131 Auxin signaling
entrzID_101258495 − 1.66 2.15 × 10−5 Indole-3-acetic acid-amido synthetase GH3.10 LOC101258495 Auxin signaling
entrzID_101265243 4.50 4.47 × 10−12 Auxin-responsive protein SAUR71 LOC101265243 Auxin signaling
entrzID_101251725 − 5.28 1.26 × 10−11 Auxin-responsive protein SAUR50 LOC101251725 Auxin signaling
MSTRG.2550 − 2.22 4.71 × 10−6 PREDICTED: auxin-responsive protein SAUR67-like [Capsicum annuum] SAUR21 Auxin signaling
entrzID_101250847 − 3.96 2.60 × 10−5 Auxin-responsive protein SAUR50-like LOC101250847 Auxin signaling
entrzID_101244440 − 9.30 1.57 × 10−4 Auxin-responsive protein SAUR21-like LOC101244440 Auxin signaling
entrzID_101247499 − 1.27 2.46 × 10−4 Auxin-responsive protein SAUR50 LOC101247499 Auxin signaling
entrzID_109118704 − 2.33 2.85 × 10−4 Auxin-induced protein 15A-like LOC109118704 Auxin signaling
entrzID_101248621 − 2.11 3.61 × 10−4 Auxin-induced protein 15A-like LOC101248621 Auxin signaling
entrzID_101255313 − 1.01 4.01 × 10−4 Auxin-responsive protein SAUR71 LOC101255313 Auxin signaling
entrzID_101246226 − 1.48 5.17 × 10−4 Auxin-responsive protein SAUR32 LOC101246226 Auxin signaling
entrzID_101245895 − 1.32 1.48 × 10−4 Auxin-induced protein 6B LOC101245895 Auxin signaling
entrzID_104644302 − 1.32 3.50 × 10−3 Auxin-induced protein 15A-like LOC104644302 Auxin signaling
entrzID_101249503 − 1.13 6.85 × 10−3 Auxin-responsive protein SAUR50-like LOC101249503 Auxin signaling
entrzID_101249791 − 2.98 6.25 × 10−5 Histidine-containing phosphotransfer protein 4 LOC101249791 Cytokinin signaling
entrzID_101253938 − 1.37 3.73 × 10−3 Histidine-containing phosphotransfer protein 1 LOC101253938 Cytokinin signaling
entrzID_101252689 − 1.05 1.75 × 10−3 Two-component response regulator ORR26 LOC101252689 Cytokinin signaling
entrzID_101253384 5.77 9.31 × 10−10 Two-component response regulator ARR17 LOC101253384 Cytokinin signaling
entrzID_101258963 1.40 1.68 × 10−3 Abscisic acid receptor PYL4 LOC101258963 ABA signaling
entrzID_101248921 1.21 7.67 × 10−5 Serine/threonine-protein kinase SRK2B LOC101248921 ABA signaling
entrzID_100736493 3.40 1.52 × 10−10 GID1 GID1 Gibberellin signaling
entrzID_101253192 1.22 6.56 × 10−4 LOC101253192 LOC101253192 Gibberellin signaling
entrzID_543588 1.90 1.01 × 10−4 Ethylene receptor homolog precursor ETR4 Ethylene signal
entrzID_544185 6.17 1.11 × 10−20 Pathogenesis-related leaf protein 4 precursor P4 SA signaling
entrzID_544123 1.62 1.30 × 10−4 Pathogenesis-related leaf protein 6 precursor PR1b1 SA signaling
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A total of 18 genes were enriched in the auxin signal transduction pathway. Among them, encoding auxin response factor 17 was upregulated (entrzID_100527977), and ARF19 and ARF7 were downregulated (entrzID_100498663, entrzID_100191131). The transcriptional repressor Aux/IAA (entrzID_101055548) was upregulated. Most of the genes encoding SAUR family proteins were downregulated. This indicates that the signal transduction of auxin in MT318 was severely inhibited.

There were 4 DEGs enriched in the cytokinin signal transduction pathway. Among them, the genes encoding AHP (entrzID_101249791, entrzID_101253938), and B-type ARRs (entrzID_101252689) were downregulated, while an A-type ARR (entrzID_101253384) was upregulated.

There were 2 DEGs enriched in the ABA signal transduction pathway. The genes of the ABA receptor PYL/PYR (entrzID_101258963) and the serine/threonine protein kinase SnRK2 (entrzID_101248921) were upregulated, indicating that the ABA signal transduction pathway was promoted in MT318. ETR4 (entrzID_543588), which is enriched in the ethylene signal transduction pathway, was upregulated. PR (entrzID_544185 and entrzID_544123), which encodes disease process-related proteins and are enriched in the SA signal transduction pathway, were upregulated. This may indicate that the poor growth state of the plant renders it sensitive to stimuli from the environment, thereby activating the plant’s defense mechanisms.

Quantitative RT-PCR validation

We selected 15 genes that may be involved in the regulation of leaf senescence, comprising 5 genes belonged to the auxin signal transduction pathway, 4 genes belonged to the cytokinin signal transduction pathway, 2 genes belonged to the gibberellin signal transduction pathway, 1 gene belonged to the ethylene signal transduction pathway, 2 genes belonged to the SA signal transduction pathway, and 1 gene belonged to the ABA signal transduction pathway (Supplementary Table 1). The results showed that the qRT-PCR gene expression was consistent with the RNA-Seq gene expression, demonstrating the reliability of the RNA-Seq results (Fig. 8).

Fig. 8.

Fig. 8

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Plant hormone signaling transduction pathway-related gene expression levels analyzed using qRT-PCR. Columns indicate the value of log2 (Fold Change) (x-axis). The right columns indicate the expression levels of upregulated genes, and the left columns indicate the expression levels of downregulated genes. The blue columns indicate the value of qRT-PCR, and the yellow columns indicate the value of RNA-Seq. Error bars indicate the SD from two independent experiments using Actin and GAPDH as internal reference genes

Network regulatory relationships between target genes

To better understand the interaction between target genes, we performed a Pearson correlation analysis on the expression levels of the predicted key genes associated with the plant hormone signal transduction pathway in each sample. Relationship pairs whose absolute value of cor was greater than 0.9 were selected to draw the network regulatory relationship diagram (Fig. 9; Supplementary Table 8). In the figure, entrzID_544185, entrzID_544123, entrzID_109118704, entrzID_104644302, and entrzID_100527977 had regulatory relationships with most other genes, played an important role in gene regulation, and belonged to the main central nodes in the network. Genes entrzID_109118704, entrzID_104644302, and entrzID_100527977 regulated the auxin pathway in tryptophan metabolism, of which entrzID_100527977 encoded the auxin response factor ARF17 and entrzID_109118704 and entrzID_104644302 encoded SAUR family proteins. entrzID_544185 and entrzID_544123 encoded disease process-related proteins that regulate the SA signal transduction pathway in phenylalanine metabolism. Therefore, in the process of leaf senescence in MT318 mutants, genes related to the auxin regulatory pathway and SA signal transduction pathway may play important roles.

Fig. 9.

Fig. 9

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Network regulatory relationship between target genes. The red line indicates a strong positive correlation, and the green line indicates a strong negative correlation

Discussion

Most leaf senescence mutations are controlled by a recessive nuclear gene, such as in rice (Huang et al. 2007), barley (Wang and Tang 1996), wheat (Cha et al. 2002; Luo and Ren 2006), eggplant (Liu et al. 2020), tomato (Cui et al. 2017), and carrot (Nothnagel and Straka 2003; Shen et al. 2006). However, some are also controlled by two genes and exhibit incomplete dominant nuclear gene control (Cao et al. 2008; Hansson et al. 1999). The MT318 leaf premature senescence mutant in this study was a spontaneous mutation, and the genetic analysis showed that the mutation was controlled by a nuclear recessive gene, which is consistent with the inheritance of most leaf mutations.

Although most premature senescence mutations are controlled by a single recessive gene, many mutants exhibit changes in other agronomic traits, such as dwarfism and reduced yield (Cang et al. 2007; Yang et al. 2018; Zhang et al. 2017, 2018). In this study, MT318, which exhibited a normal phenotype at the seedling stage, showed premature senescence at the flowering stage. Compared with MT, mutant MT318 also showed dwarfism and produced smaller fruit (Table 1). Chlorophyll plays an important role in light absorption. The chlorophyll of MT318 was significantly degraded (Fig. 2d), and thus the growth, development, and agronomic traits of MT318 were greatly affected.

If the balance between the production and clearing of reactive oxygen species (ROS) in plants is disrupted, the accumulation of ROS will lead to increased membrane lipid peroxidation, resulting in increased MDA accumulation, chlorophyll degradation, and even plant death (Chinnusamy et al. 2005). The results of this study demonstrated that following the leaf senescence of MT318, SOD content gradually decreased (Fig. 2c), and MDA significantly increased (Fig. 2b). Fv/Fm (Fig. 2a), which affects leaf photosynthesis and photosynthetic accumulation, was significantly reduced; therefore, leaf senescence accelerated.

Leaf senescence is affected by the environment and endogenous signals. In plants, most of the cloned leaf senescence-related genes are involved in peroxidation (Lin et al. 2011), chlorophyll degradation (Fan et al. 2015; Sakuraba et al. 2013), stress environments (Lee et al. 2012; Miao et al. 2018), and hormone signaling (Cheng et al. 2014). In this study, many genes of the plant hormone signal transduction pathway were differentially expressed (Table 3).

In the plant hormone signal transduction pathway, auxin has an inhibitory effect on senescence (Teale et al. 2006). Auxin response factor (ARF) and Aux/IAA are two important transcription factors that regulate auxin signal transduction (Woodward and Bartel 2005). Different transcription factors have different effects on auxin. Studies have shown that ARF7 (entrzID_100191131) and ARF19 (entrzID_100498663) are transcriptional activators of auxin (Okushima et al. 2007; Tiwari 2003; Wang et al. 2005), and that Aux/IAA and ARF17 are transcriptional repressors of auxin (Bouzayen 2012; Mallory 2005). In this study, ARF7 and ARF19 of MT318 were downregulated, while Aux/IAA (entrzID_101055548) and ARF17 (entrzID_100527977) were upregulated (Table 3), indicating that the auxin signal transduction pathway was inhibited. It was speculated that the mutant did not synthesize sufficient auxin, resulting in stunted and weakened plant growth.

Cytokinins are widely involved in plant growth and stress resistance (Sakakibara 2006). In addition to significantly delaying leaf senescence, cytokinins can also affect crop yield (Bartrina et al. 2011; Zhang et al. 2014). B-type ARRs and A-type ARRs are positive and negative regulators of cytokinins, respectively (Mason et al. 2005; To et al. 2004). In this study, the expression of type B-ARRs (entrzID_101252689) was downregulated, while the gene encoding a type A-ARR (entrzID_101253384) was upregulated. This indicated that the cytokinin signal transduction pathway in MT318 was severely inhibited, which alleviated the impacts associated with leaf senescence and yield.

ABA inhibits growth and promotes senescence (Jibran et al. 2013). The PYL protein negatively regulates PP2C, and PP2C negatively regulates SnRK2. Together, these three core components form a dual negative regulatory system in ABA signal transduction (Gonzalez et al. 2012; Nishimura et al. 2010; Yang 2013). In this study, PYL4 (entrzID_101258963) and SRK2B (entrzID_101248921) were upregulated, indicating that the ABA signal transduction pathway was promoted in MT318, thereby inhibiting plant growth.

Through the analysis of the network regulation relationship between target genes, it can be seen that the auxin regulatory pathway (entrzID_109118704, entrzID_104644302, entrzID_100527977) and the SA signal transduction pathway (entrzID_544185 and entrzID_544123) may play key roles in the process of leaf senescence in MT318 mutants. The auxin synthesis pathway of the MT318 mutant was significantly inhibited; thus, the mutant plants and fruit became smaller, the plants did not acquire enough nutrients, and the leaves turned yellow. Plants in a poor growth state are more sensitive to any stimuli from the external environment, thereby activating the plant’s defense mechanism and promoting SA signal transduction.

In summary, the growth of MT318 was impacted by both its weakened photosynthetic ability and the levels of various hormones. A complex regulatory network exists in plants, with the various components interacting and coordinately regulating growth and development. In this study, we analyzed the phenotypes associated with dwarfism, weak growth, and yellow leaves in MT318 at the molecular level, thus providing a foundation for further exploration of the metabolic regulation mode in tomato in the future.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 91 kb) (91.1KB, doc)
Supplementary file2 (XLS 23 kb) (23.5KB, xls)
Supplementary file3 (XLS 1652 kb) (1.6MB, xls)
Supplementary file4 (XLS 23 kb) (23.5KB, xls)
Supplementary file5 (XLS 29 kb) (29.5KB, xls)
Supplementary file6 (XLS 22 kb) (22KB, xls)
Supplementary file7 (XLS 31 kb) (31.5KB, xls)
Supplementary file8 (XLS 35 kb) (35.5KB, xls)
Supplementary file9 (PNG 61 kb) (61.9KB, png)
Supplementary file10 (PNG 183 kb) (183.1KB, png)
Supplementary file11 (PNG 176 kb) (176.5KB, png)

Acknowledgements

This study was supported by theTianjin Science and Technology Planning Project (No. 21ZYCGN00420). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Author contributions

JF designed the experiments and wrote this paper. HM,SL, CS,SH, KW, HZ conducted the experiments. All authors read and approved the final manuscript.

Data availability

The transcriptome data used in this study have been submitted to the NCBI database under accession number PRJNA813642.

Declarations

Conflict of interest

The authors declare no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOC 91 kb) (91.1KB, doc)
Supplementary file2 (XLS 23 kb) (23.5KB, xls)
Supplementary file3 (XLS 1652 kb) (1.6MB, xls)
Supplementary file4 (XLS 23 kb) (23.5KB, xls)
Supplementary file5 (XLS 29 kb) (29.5KB, xls)
Supplementary file6 (XLS 22 kb) (22KB, xls)
Supplementary file7 (XLS 31 kb) (31.5KB, xls)
Supplementary file8 (XLS 35 kb) (35.5KB, xls)
Supplementary file9 (PNG 61 kb) (61.9KB, png)
Supplementary file10 (PNG 183 kb) (183.1KB, png)
Supplementary file11 (PNG 176 kb) (176.5KB, png)

Data Availability Statement

The transcriptome data used in this study have been submitted to the NCBI database under accession number PRJNA813642.

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